Agenda packet — Ontario City Council, June 16, 2026 (part 17 of 28)

BASELINE AND LAND USE ANALYSIS DEFICIENCIES
CEQA requires that impacts be analyzed against an accurate environmental baseline (CEQA Guidelines §15125; Communities for a Better Environment v. South Coast AQMD (2010) 48 Cal.4th 310).

The project converts land planned for residential and mixed-use development into industrial use.

The DSEIR fails to adequately analyze:
• Loss of planned housing capacity
• Policy implications of this land use conversion
• Differences in environmental impacts between residential and logistics uses This constitutes a failure to evaluate impacts against a meaningful baseline.

SIGNIFICANT AND UNAVOIDABLE IMPACTS
The DSEIR acknowledges significant impacts related to:
• Traffic and truck congestion
• Air quality degradation
• Noise
Under CEQA, mitigation measures must be feasible and enforceable (CEQA Guidelines §15126.4).

Here, mitigation measures are vague, deferred, or dependent on future action, rendering them legally insufficient.

GREENHOUSE GAS ANALYSIS IS DEFICIENT
The DSEIR admits the project:
“would result in an increase in GHG emissions… and would not achieve the carbon neutrality threshold of AB 1279.”
CEQA requires mitigation measures to be enforceable and effective, not speculative (Communities for a Better Environment v. City of Richmond (2010) 184 Cal.App.4th 70).

Deferring mitigation—such as electrification or solar deployment—to future implementation violates CEQA.

FAILURE TO PROVIDE ADEQUATE RESPONSES TO COMMENTS
CEQA requires good-faith, reasoned responses to public comments (CEQA Guidelines §15088).

As documented in submitted comments:
• Key issues are redirected without analysis
• Agency constraints are not addressed
• Substantive objections receive no meaningful response O3-2
O3-3
O3-4
O3-5
3698
Comments: DSEIR State Clearinghouse No. 2025110102
April 20, 2026

Page 3 ends

This failure undermines the integrity of the environmental review process.

INADEQUATE CUMULATIVE IMPACT ANALYSIS
CEQA requires analysis of cumulative impacts (CEQA Guidelines §15130; Kings County Farm Bureau v. City of Hanford (1990) 221 Cal.App.3d 692).

The project is located within a region already saturated with warehouse and logistics development.

The DSEIR fails to adequately evaluate:
• Combined regional truck traffic
• Air quality impacts across multiple projects
• Public health consequences
This omission is legally significant.

PROPOSITION 70: FAILURE TO ADDRESS PUBLIC TRUST OBLIGATIONS The DSEIR fails to analyze legal constraints associated with Proposition 70 lands.

As documented in the Planning and Conservation League’s October 20, 2006 letter:
• San Bernardino County received state bond funds to acquire agricultural land • The County explicitly committed to placing conservation easements on these lands • These easements were required to function as perpetual deed restrictions • These commitments constitute binding public trust obligations Further, the County:
• Accepted approximately $20 million in bond funds
• Generated additional trust funds from agricultural operations • Was required to preserve the land consistent with the original conservation purpose The DSEIR does not analyze:
• Whether these obligations have been satisfied
• Whether conversion to industrial use is legally permissible • Whether required conservation easements have been implemented This is not a question of land configuration or contiguity.

It is a question of compliance with the original purpose and intent of Proposition 70 Fulfillment of binding public trust obligations, and failure to analyze these issues renders the DSEIR incomplete and legally deficient.

FAILURE TO ANALYZE AGRICULTURAL PRESERVATION ALTERNATIVES CEQA requires evaluation of feasible alternatives that reduce environmental impacts (Laurel Heights Improvement Assn. v. Regents of UC (1988) 47 Cal.3d 376).

The DSEIR does not adequately evaluate alternatives that:

O3-5 cont.

O3-6
O3-7
O3-8
3699
Comments: DSEIR State Clearinghouse No. 2025110102
April 20, 2026

Page 4 ends

• Preserve agricultural use
• Maintain compliance with Proposition 70
• Reduce environmental impacts

QUESTIONABLE ECONOMIC BENEFITS
The project’s economic justification is unsupported.

Warehouse development:
• Produces fewer jobs per acre
• Is increasingly automated
• Provides lower-wage employment
The DSEIR fails to substantiate claimed benefits or compare them to alternative land uses.

CONCLUSION
The DSEIR fails to comply with CEQA and does not adequately analyze critical environmental, legal, and policy issues.

Fair Ontario respectfully requests:

1. Recirculation of the DSEIR with an accurate project description

2. Full and enforceable mitigation measures

3. Comprehensive cumulative and GHG analysis

4. Explicit analysis of Proposition 70 public trust obligations

5. Evaluation of agricultural preservation alternatives

6. Meaningful responses to all public comments
Until these deficiencies are corrected, the project should not proceed.

Respectfully submitted,
Chris Robles
President and Executive Director
cc: Board Members, Fair Ontario
O3-8 cont.

O3-9
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Numerous Public Comments cite the necessity for controlling the undeniable harms caused by ever increasing levels of GHG emissions. This is clearly not dealt with adequately as per the admission on page 93, Table ES-3
“Implementation of the proposed project would result in an increase in GHG emissions compared to the approved project (current plan) and would not achieve the carbon neutrality threshold of AB 1279”.

The suggested remedy is to revise and front load the mitigation measures to a future date to bring them back into compliance with the CCAP guidelines. This is not acceptable. The mitigation “suggestions” found in several places in the document need to be changed to mandates for immediate inclusion. Rooftop solar to cover the complete building demand and built out charging stations need to be in 100% operation. Use of electric battery storage on site and preferential treatment for EV delivery vehicles are other means of meeting GHG targets.

The Public Comments (Page 122, Section 1.2.1., Table 1-1) are not specifically addressed.

Rather they are referred to general, non-specific topics in the SEIR.

For example the comment by San Bernardino County Public Works states that they own a parcel included in the Specific Plan and “that development cannot proceed at this parcel”. No where is this addressed in Section 3.11 as promised.

Again the Endangered Habitats League requests assurances that terms of a Settlement and General Use Agreement be observed. They are simply referred to the same Section 3.11.

Another, Jason Alonzo generally objects to the proposed land use changes but his “strong dissatisfaction” gets no reply in Section 3.11.

CCAEJ wondered about the cumulative impacts of all the existing warehouses with the addition of 6 more in this proposal. Specifically what do we learn from reading 3.8 and 3.11? Why is the EBPSP being zoned Business Park? The document here and elsewhere (Zoning Code) specifies this zone encompasses:
“Employee-intensive office uses including corporate offices, technology centers, research and development, “clean” industry, light manufacturing and supporting retail within a business park setting”.

There is no mention of warehousing large or small.

How are you making a legitimate response and consideration of these comments due on April 20 when the SEIR goes before the Planning Commission on April 28? Are we just steam rolling over the existing PACE agreements? Are we being told that the city is just going to arbitrarily cancel these so the EBPSP can just move ahead? Raymond Smith, [email protected]
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3704
From: randi campbell <[email protected]>
Sent: Friday, April 17, 2026 3:35 PM
To: Thomas Grahn <[email protected]>
Subject: Citizen concerns about warehouses in the 2050 Ontario plan Dear Mr. Grahn,
I’m a long time resident of Ontario and an active member of the community. I am a member of the board and donate my time and money to multiple organizations in the City. I attend City Council meetings, Planning Commission meetings and am very active in arts and culture events in Ontario.

I am writing today because I am very concerned about the expansion of mega warehouses in our city. Ontario already is cited as having the worst air quality in the entire United States and the truck traffic needed for warehouses is notorious for contributing to air pollution in addition to general traffic and noise.

We all understand the need for the City to generate revenue and provide economic opportunity. But mega warehouses are not the only option; my neighbors and I want to live in a city that is citizen friendly as well. Smaller, multi-use developments provide a much better alternative; safer streets, cleaner air, reduced noise, improved quality of life.

We deserve to live in a healthy environment. We must reconsider this choice for our city.

Thank you in advance and best regards,
Randi Campbell
(909) 268-9823
R5-1
3705
From: TINA SILVA <[email protected]>
Sent: Monday, April 20, 2026 2:29 PM
To: Thomas Grahn <[email protected]>
Subject: Amendments to Ontario Plan and SEIR
Dear Mr. Grahn,
I am writing to oppose the Ontario Plan amendments. The EIR clearly identifies unavoidable impacts to air quality, traffic, and noise, which will harm residents’ health and quality of life.

I am especially concerned about the proposed use of Prop 70 land, which should be protected and preserved for open space and environmental benefits. This project would result in the permanent loss of land that supports groundwater recharge and helps mitigate flooding and climate impacts.

The plan will also increase truck traffic through local streets, as many of these warehouse and business park sites are not located directly adjacent to freeways. This means diesel trucks will travel longer distances on city roads, worsening congestion, damaging infrastructure, and increasing air pollution in residential neighborhoods.

Additionally, the project proposes roughly 2.9 million square feet of business park/warehouse uses yet is projected to generate only about 1,500 jobs. This is a low return for such a large amount of land, especially when weighed against the environmental and public health costs.

Given these unavoidable harms and limited benefits, I urge the City to reject these amendments and prioritize a healthier, more sustainable future for our community.

I hope the community emails are taken into consideration before the upcoming Planning Committee meeting on April 28th.

Would you kindly acknowledge my email as well?
Thank you,
Tina Silva
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3706
From: Marty McBride <[email protected]>
Sent: Monday, April 20, 2026 4:17 PM
To: Thomas Grahn <[email protected]>
Subject: Draft Supplemental EIR Ontario Plan 2050 - Public Comment Good afternoon -
I am a resident of Ontario and I have some grave concerns regarding the environmental impact of the proposed development. There appears to be significant impact issues with respect to air quality, noise, pollution, and disturbance to native wildlife and plants that are an important part of our local ecosystem. These issues must be addressed by the City prior to greenlighting these projects - and the appropriate agencies and experts must be collaborators to mitigate as much as possible any harmful effects of development.

My greatest concern, however, is that the people of Ontario were not provided with public information sessions to review the reports and understand the significance of these developments in layman's terms. Without such sessions, residents have no opportunity to ask for clarification prior to the end of the comment period.

The city can and must do better to assist its residents to be well informed so they can make informed decisions and recommendations to the city. Resident engagement is a hallmark of good governance, and more residents would feel comfortable providing feedback if they were fully aware of the issues they are being asked to comment on.

Sincerely,
--
Marta McBride
E: [email protected]
C: 626-343-0481
CONFIDENTIALITY NOTICE: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential or legally privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of this original message.

Save our natural resources - Please do not print this email unless absolutely necessary.

R7-1
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Source: T&B 2026 Date: 05/06/2026
0 275 550 Feet
EBPSP Boundary

EXECUTIVE SUMMARY
Figure ES-5
Conceptual EBPSP Land Use Plan
3708
3709
Policy Plan Amendment
Areas of Change
Source: The City of Ontario 2026 Date: 5/7/2026
County of
San Bernardino
County of Riverside
uC cam agno nnahC el
60 60
10 15
15 10

CITY OF

MONTCLAIR

CITY OF
UPLAND

CITY OF

FONTANA

CITY OF CHINO

CITY OF RANCHO

CUCAMONGA

EVA DILCUE S

EVA SUPMAC S

EVA NEVAH N

EVA ANATLUS S

EVA EVORG S

N MILLIKEN AVE

E RIVERSIDE DR
E G ST

W PHILLIPS ST
W D ST

EVA DRAYENIV N

EVA OINOTNA NAS N

EVA OINOTNA NAS S

EVA ANATLUS N

EVA DLABIHCRA S

EVA EVORG N

EVA ADNAWITE S

EVA NIATNUOM S

E PHILADELPHIA ST

W FOURTH ST

EVA SUPMAC N

EVA NIATNUOM N

EVA NOSNEB N
E D ST

W MISSION BLVD

EVA DILCUE N
W G ST

E FOURTH ST

S HAVEN AVE

EVA NEKILLIM S

W PHILADELPHIA ST

E EIGHTH ST

EVA RENRUT S

EVA DRAYENIV S

E JURUPA ST

EVA DLABIHCRA N

E AIRPORT DR

EVA NOSNEB S

ONTARIO RANCH RD

E MISSION BLVD

EVA ADNAWITE N

MERRILL AVE

EVA ELLIVENIW S

EVA EGATNIV S

E EDISON AVE

E INLAND EMPIRE BLVD

E SANTA ANA ST

E HOLT AVE

EVA RENMAH S

E SCHAEFER AVE

E EUCALYPTUS AVE

E CHINO AVE

EASTVALE

JURUPA VALLEY
Figure 2-6a
0 2,500 5,000 10,000
FT
Proposed TOP Areas of Change
Ontario City Boundary
County Boundary
Rail Network
Overlay Zones
Business Park
Industrial
Landfill Impact Area
Residential
RR Rural Residential
LDR Low Density Residential
LMDR Low Medium Density Residential
MDR Medium Density Residential
HDR High Density Residential
Mixed-Use
MU-RV; MU Mixed Use
Commercial
NC Neighborhood Commercial
GC General Commercial
OC Office Commercial
HOS Hospitality
Employment
BP Business Park
IND Industrial
Other
OS-NR Open Space - Non-Rec
OS-R Open Space - Recreation
OS-W Open Space - Water
PF Public Facility
ROW; ROW-bp
PS Public School
ARPT Airport
LF Landfill
Rail
*Areas where no land use
change is proposed are
shown at 40% opacity

2. PROJECT DESCRIPTION
3710
Policy Plan Amendment
Areas of Change
Source: The City of Ontario 2026 Date: 5/7/2026

NOT TO SCALE

EVA EVORG S

E EDISON AVE

E EUCALYPTUS AVE

S EUCLID AVE S EUCLID AVE
105355101
105339101
021632309
021632202
021632301
021632107
021632103
021632106 021632210
021632308
021632102 021632302
021632201
105333104
105334101
105335102
105353102
105352101
105353104
105342102
105342101
105354102 105351106
105358104
105360102 105357102 105360101
105334102
105351105
105357101
105354101
105358103
105359101
105352102
105360103
105353101
105351101
105359102
105360104
021632101
105341101
105351108
105351102
105351103
Figure 2-6b
Proposed TOP Areas of Change
Ontario City Boundary
County Boundary
Rail Network
Overlay Zones
Business Park
Industrial
Landfill Impact Area
Residential
RR Rural Residential
LDR Low Density Residential
LMDR Low Medium Density Residential
MDR Medium Density Residential
HDR High Density Residential
Mixed-Use
MU-RV; MU Mixed Use
Commercial
NC Neighborhood Commercial
GC General Commercial
OC Office Commercial
HOS Hospitality
Employment
BP Business Park
IND Industrial
Other
OS-NR Open Space - Non-Rec
OS-R Open Space - Recreation
OS-W Open Space - Water
PF Public Facility
ROW; ROW-bp
PS Public School
ARPT Airport
LF Landfill
Rail
*Areas where no land use
change is proposed are
shown at 40% opacity

2. PROJECT DESCRIPTION

S CAMPUS AVE

S BON VIEW AVE
104910135
104910130
104913102
104910105
104910112
104910106
104910131
104910110 104910107
104910116
104913108
104910115
104910114
104913109
104910104
104910113
104910118
104913106
104910101
104913101
104910111
104913104
104910137
104913103
104910132
104913118
104913119
104910129
104910102
104910136 104910134 104913105
104910108
104910117
104910133 104910139
104910140 104910138

S GROVE AVE
104917107
104917106
104916124
104916125
104917105

N VINEYARD AVE

E INLAND EMPIRE BLVD

N ARCHIBALD AVE
011031144
011032181 011032177
011032168
011032175
011031143
011032174
011031162
011031164
011031137
011031161
011031165
011032180
011032179 011032176
011031136
011032129
011032182

S TURNER AVE

S ARCHIBALD AVE

ONTARIO RANCH RD

E EUCALYPTUS AVE

E CHINO AVE

S HAVEN AVE
021824143
021824145
021813135
021840232
021871509
021871507

E HOLT BLVD

E EMPORIA ST

E ONTARIO BLVD

QVC WAY
INTERSTATE 10

S SULTANA AVE

S CAMPUS AVE

S BON VIEW AVE

S CUCAMONGA AVE

S WALKER AVE

S SULTANA AVE

S CAMPUS AVE

S BON VIEW AVE

S CUCAMONGA AVE

FLIGHT AVE

VAN VIET AVE

S WALKER AVE FLIGHT AVE

VAN VIET AVE
3711
Policy Plan Amendment
Areas of Change
Source: The City of Ontario 2026 Date: 5/7/2026
lennahC agnomacuC

EVA EVORG S

E RIVERSIDE DR

EVA ANATLUS S

S CAMPUS AVE

EVA DRAYENIV S

E EDISON AVE

E SCHAEFER AVE

E CHINO AVE

S SULTANA AVE

S CAMPUS AVE

S BON VIEW AVE

S CUCAMONGA AVE

S WALKER AVE

ONTARIO AVE

BAKER AVE
105216103
021631205
021621402
021617408
021631203
021810211
021631208
021631204
021621406
021617407
021617401
021631308
021621403
021617416
021621404
021631202
021617402
021621124
021631307
021811150
021621405
021631408
021617406
021631405
021631301
021631210
021811167
021621104
021621407
021631105
021631303
021631104
021631302 021631402
021631206
021811111
021811106
021812106
021812105
021631406 021631404
021621401
021631401
021631409 021631109
021811108
021631108
021631207
021631103
021811168
021811169
021810210
021812107
021812102
021812202 021812208
021812108
021812101
021812203
021812209 021812201
021818116
021812204
021812103
021812104
021812210
021812206
105215102
105215111
021818123
105215109
105332103
021818125
105317104
105317105
105317103
105247102
105319101 105319102 105318101 105318102
021819119
021621123
021818112
021818101
105332102
021819124
021818122
021818121
021818111
021818102
105248102
105309101
105316102
105317101
105249106
105311103
105249103
105249105
105330102 105330101
105315104
105311104
105310101
105311108
105311101
105310102
021818119
105331102 105331101
105215105
021631403
021631209
021631306
021617417
021631304
021621126
021621101
021631407
105320101
021818115
105315105
021818124
011328215
021818126
021631305
021811109
021631309
Figure 2-6c
Proposed TOP Areas of Change
Ontario City Boundary
County Boundary
Rail Network
Overlay Zones
Business Park
Industrial
Landfill Impact Area
Residential
RR Rural Residential
LDR Low Density Residential
LMDR Low Medium Density Residential
MDR Medium Density Residential
HDR High Density Residential
Mixed-Use
MU-RV; MU Mixed Use
Commercial
NC Neighborhood Commercial
GC General Commercial
OC Office Commercial
HOS Hospitality
Employment
BP Business Park
IND Industrial
Other
OS-NR Open Space - Non-Rec
OS-R Open Space - Recreation
OS-W Open Space - Water
PF Public Facility
ROW; ROW-bp
PS Public School
ARPT Airport
LF Landfill
Rail
*Areas where no land use
change is proposed are
shown at 40% opacity

2. PROJECT DESCRIPTION
0 500 1,000 2,000
FT
3712
Source: T&B 2026 Date: 05/06/2026
0 275 550 Feet
EBPSP Boundary
Conceptual EBPSP Land Use Plan
Figure 2-11

2. PROJECT DESCRIPTION
3713
èé èé èé èé
èé èé èé èé CAMPUS AVE BON VIEW AVE

ONTARIO RANCH RD

BON VIEW AVE

COMET AVE

CUCAMONGA AVE

GROVE AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE
Public Street 'A'
PA 1

BUSINESS PARK

7.70 AC.

PA 4

BUSINESS PARK

18.19 AC.

PA 2

BUSINESS PARK

18.33 AC.

PA 5

BUSINESS PARK

37.76 AC.

PA 3

BUSINESS PARK

18.21 AC.

PA 6

BUSINESS PARK

37.71 AC.

PA 9

OPEN SPACE

13.89 AC.

PA 8

OPEN SPACE

13.99 AC.

PA 7

OPEN SPACE

8.00 AC.
0 20 Conceptual Vehicular Circulation and Access Plan
Sourc
26 PARK
0 400 800
Figure 4-1

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
e(s): Esri, Nearmap Imagery (January 2025), Michael Baker (04-01-2026), San Bernardino County (2025) Specific Plan Area
Legend
èé Traffic Signal
èé Potential Signal Location
Full Movement Driveway
Right-In/Right-Out Driveway
Grove Avenue (124' ROW - Principal Arterial)
Eucalyptus Avenue (108' ROW - Collector)
Campus Avenue (108' ROW - Minor Arterial)
Walker Avenue (88' ROW - Collector)
Public Street 'A' (64' ROW - Local Street)
Bon View Avenue (66' ROW - Collector)
4-2
PA 1

BUSIN ESS PARK

7.75 AC.

PA 4

BUSIN ESS PARK
1 8.1 7 AC.

PA 2

BUSIN ESS PARK
1 8.45 AC.

PA 5

BUSIN ESS PARK

37.68 AC.

PA 3

BUSIN ESS PARK
1 8.32 AC.

PA 6

BUSIN ESS PARK

37.64 AC.

PA 9

OPEN SPACE
1 4.1 1 AC.

PA 8

OPEN SPACE
1 4.22 AC.

PA 7

OPEN SPACE

8.1 1 AC.

EDISON AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE

PUBLIC STREET 'A'

BON VIEW AVE

CAMPUS AVE
0 400 800 200 Conceptual Vehicular Circulation and Access Plan Feet
Figure 4-1

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (05-16-2025), San Bernardino County (2025)
Source: T&B 2026 Date: 05/06/20
0 275 550 Feet
EBPSP Boundary
Grove Avenue
(124’ ROW - Principal Arterial)
Eucalyptus Avenue
(108’ ROW - Collector)
Campus Avenue
(108’ ROW - Minor Arterial)
Walker Avenue
(88’ ROW - Collector)
Bon View Avenue
(66’ ROW - Collector)
Public Street ‘A’
(64’ ROW - Local Street)
Traffic Signal
Potential Signal Location
èé èé
CAMPUS

BON VIEW
PA 4

BUSINESS PARK

18.19 AC.

PA 5

BUSINESS

37.76 AC.
0 400 800 200
Feet
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (04-01-2026), San Bernardino Co Specific Plan Area
Legend
èé Traffic Signal
èé Potential Signal Location
Full Movement Driveway
Right-In/Right-Out Driveway
Full Movement Driveway
Right-In/Right-Out Driveway
4-2
PA 1

BUSIN ESS PARK

7.75 AC.

PA 4

BUSIN ESS PARK
1 8.1 7 AC.

PA 2

BUSIN ESS PARK
1 8.45 AC.

PA 5

BUSIN ESS PARK

37.68 AC.

PA 3

BUSIN ESS PARK
1 8.32 AC.

PA 6

BUSIN ESS PARK

37.64 AC.

PA 9

OPEN SPACE
1 4.1 1 AC.

PA 8

OPEN SPACE
1 4.22 AC.

PA 7

OPEN SPACE

8.1 1 AC.

EDISON AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE

PUBLIC STREET 'A'

BON VIEW AVE

CAMPUS AVE
0 400 800 200 Conceptual Vehicular Circulation and Access Plan Feet
Figure 4-1

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (05-16-2025), San Bernardino County (2025)
Conceptual EBPSP Vehicular
Circulation and Access Plan
Figure 2-13

2. PROJECT DESCRIPTION
3714
4-11
kj kj kj kj
kj
kj kj kj CAMPUS AVE BON VIEW AVE

ONTARIO RANCH RD

BON VIEW AVE

COMET AVE

CUCAMONGA AVE

GROVE AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE
Public Street 'A'
PA 1

BUSINESS PARK

7.70 AC.

PA 4

BUSINESS PARK

18.19 AC.

PA 2

BUSINESS PARK

18.33 AC.

PA 5

BUSINESS PARK

37.76 AC.

PA 3

BUSINESS PARK

18.21 AC.

PA 6

BUSINESS PARK

37.71 AC.

PA 9

OPEN SPACE

13.89 AC.

PA 8

OPEN SPACE

13.99 AC.

PA 7

OPEN SPACE

8.00 AC.
0 400 800 200 Conceptual Non-Vehicular Circulation and Mobility Plan Feet
Figure 4-4

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (04-01-2026), San Bernardino County (2025) kj
kj
Specific Plan Area Sidewalk Only
Sidewalk and Multi-Purpose Trail
Sidewalk, Class II Bikeway, and Multi-Purpose Trail
Legend
Pedestrian Crosswalk
Potential Pedestrian Crosswalk
4-2
PA 1

BUSIN ESS PARK

7.75 AC.

PA 4

BUSIN ESS PARK
1 8.1 7 AC.

PA 2

BUSIN ESS PARK
1 8.45 AC.

PA 5

BUSIN ESS PARK

37.68 AC.

PA 3

BUSIN ESS PARK
1 8.32 AC.

PA 6

BUSIN ESS PARK

37.64 AC.

PA 9

OPEN SPACE
1 4.1 1 AC.

PA 8

OPEN SPACE
1 4.22 AC.

PA 7

OPEN SPACE

8.1 1 AC.

EDISON AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE

PUBLIC STREET 'A'

BON VIEW AVE

CAMPUS AVE
0 400 800 200 Conceptual Vehicular Circulation and Access Plan Feet
Figure 4-1

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (05-16-2025), San Bernardino County (2025)
0 275 550 Feet
Source: T&B 2026 Date: 05/06/2026
EBPSP Boundary
Pedestrian Crosswalk
Potential Pedestrian Crosswalk
Sidewalk Only
Sidewalk Only and Multi-Purpose Trail
Sidewalk, Class II Bikeway,
and Multi-Purpose Trail
Conceptual EBPSP Non-Vehicular
Circulation and Mobility Plan
Figure 2-14

2. PROJECT DESCRIPTION
3715
S
4-20
15"
18"
21"
8" 8" 8"
16"
20"
20"
18"
21"
8" 8"

CAMPUS AVE

BON VIEW AVE

ONTARIO RANCH RD

BON VIEW AVE

COMET AVE

CUCAMONGA AVE

GROVE AVE

EUCALYPTUS AVE

GROVE AVE

WALKER AVE
Public Street 'A'
PA 1

BUSINESS PARK

7.70 AC.

PA 4

BUSINESS PARK

18.19 AC.

PA 2

BUSINESS PARK

18.33 AC.

PA 5

BUSINESS PARK

37.76 AC.

PA 3

BUSINESS PARK

18.21 AC.

PA 6

BUSINESS PARK

37.71 AC.

PA 9

OPEN SPACE

13.89 AC.

PA 8

OPEN SPACE

13.99 AC.

PA 7

OPEN SPACE

8.00 AC.
0 400 800 200 Sanitary Sewer Infrastructure Plan
Feet
Figure 4-8

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (04-01-2026), San Bernardino County (2025) Specific Plan Area Existing Sanitary Sewer Line
Proposed Sanitary Sewer Line
Legend
Flow Direction
Point of Connection
Optional Sewer line that may be required if needed
to support development parcels not fronting a master
planned sewer main, at the City's discretion. Tributary Flow Direction Tributaries
Source: T&B 2026 Date: 05/06/2026
0 275 550 Feet
EBPSP Boundary
Point of Connection
Existing Sanitary Sewer Line
Proposed Sanitary Sewer Line
Optional Sewer Line that may be required (at the City’s Discretion) Flow Direction
Tributaries
Tributary Flow Direction
Note: Final sizing of utility mains shall be dependent
on the hydraulic studies done at the time of development.

EBPSP Sanitary Sewer
Infrastructure Plan
Figure 3.19-4

3.19 UTILITIES AND SERVICE SYSTEM
3716
4-23
96"
60"
108"
108"
24" 24" 36" 36" 36"
24" 24"
60"
102"
120"
24" 8'x13' RCB

ONTARIO RANCH RD
18"
#
DUAL 5' X 3' RCB
DUAL 9' X 5' RCB 9.5' X 9.5' RCB 120" 72" 24" 24"
24" 42" 24"
36"

EUCALYPTUS AVE

GROVE AVE

WALKER AVE

PUBLIC STREET 'A'

CAMPUS AVE

BON VIEW AVE

ONTARIO RANCH RD

COMET AVE

CUCAMONGA AVE

MERRILL AVE
PA 1

BUSINESS PARK

7.70 AC.

PA 4

BUSINESS PARK

18.19 AC.

PA 2

BUSINESS PARK

18.33 AC.

PA 5

BUSINESS PARK

37.76 AC.

PA 3

BUSINESS PARK

18.21 AC.

PA 6

BUSINESS PARK

37.71 AC.

PA 9

OPEN SPACE

13.89 AC.

PA 8

OPEN SPACE

13.99 AC.

PA 7

OPEN SPACE

8.00 AC.
0 550 1,100 275 Storm Drain Infrastructure Plan
Feet
Figure 4-9

EUCALYPTUS BUSINESS PARK SPECIFIC PLAN
Source(s): Esri, Nearmap Imagery (January 2025), Michael Baker (04-01-2026), San Bernardino County (2025) Specific Plan Area Existing Storm Drain Line
Proposed Project Specific Storm Drain Line
Legend
Flow Direction
Point of Connection
Proposed Master Planned Storm Drain Line
Note: Final sizing of storm drain infrastructure shall be dependent on City-approved hydraulic studies, master plan updates, and project final designs.

Source: T&B 2026 Date: 05/06/2026
0 275 800 Feet
EBPSP Boundary
Point of Connection
Existing Storm Drain Line
Proposed Master Planned Storm Drain Line
Proposed Project Specific Storm Drain Line
Flow Direction
EBPSP Storm Drain
Infrastructure Plan
Figure 3.19-5

3.19 UTILITIES AND SERVICE SYSTEMS
3717
APPENDIX C:

BUILDOUT METHODOLOGY
.,
3718
3719
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 1 of 2
Appendix C
The Ontario Plan 2050 Policy Plan Amendments
Methodology for 2025–2050 Population, Housing, and Employment Projections
Purpose, Design, and Limitations
The following summarizes the methodology and factors used to calculate projected conditions under both the current Policy Plan (approved project) and the proposed Policy Plan (proposed project) at the planning horizon for purposes of the Supplemental Environmental Impact Report (SEIR). The Policy Plan serves as the General Plan for the City of Ontario, establishing the framework for land use, development intensity, and long-range growth.

All estimates are generated using the best available data and are intended to represent development capacity across the entire Policy Plan area.

Whenever possible, reliable and authoritative data sources were used directly or cited as references. These sources have their own inherent error rates and may present information at varying geographic scales or in different categories. Where more precise data was not available, estimates were compared to aggregated or citywide totals from authoritative sources to confirm general accuracy and ensure that results are within a reasonable range for planning-level analysis.

The buildout analysis is designed as a capacity-based evaluation that applies standardized assumptions to parcellevel land use designations to estimate the amount of development that could reasonably be accommodated under each land use category. The methodology integrates parcel data, land use classifications, and development assumptions related to density, intensity, occupancy, and employment generation. These assumptions are documented in Table LU-03 (Future Buildout) of the Land Use Element, which serves as the primary reference for translating land use designations into quantifiable development capacity. The City maintains and updates Table LU-03 as part of each General Plan Amendment (GPA) to ensure that buildout assumptions remain consistent with the land use plan.

The amount of development permitted under the Policy Plan is determined primarily by the land use plan and the density and intensity standards associated with each designation; however, these standards define a range of allowable development and are not intended to reflect typical outcomes across all sites. Local development patterns, parcel configuration, site constraints, and market conditions all influence how land is developed in practice, and development is therefore unlikely to occur uniformly at the maximum allowable density or intensity.

While the analysis is grounded in the regulatory framework established by the Policy Plan, it does not assume that all parcels will develop to the maximum density or intensity permitted. Instead, the methodology applies adjusted assumptions that reflect how development is likely to occur over time based on site constraints, development patterns, and market conditions. In this way, the analysis is intended to represent a reasonable estimate of buildout capacity rather than a theoretical maximum. This approach reflects how development is expected to occur given current conditions, including the presence of established neighborhoods, the availability of vacant or underutilized land, and the economic context that influences the feasibility of higher-intensity development.

Overestimating horizon-year conditions by assuming full buildout of all parcels at maximum allowable density or intensity can result in unrealistic projections that overstate potential impacts and lead to unnecessary mitigation measures or misallocation of public resources. Accordingly, the projections presented in this analysis are based 3720
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 2 of 3
on assumptions of what is reasonably likely to occur over the planning horizon. This approach is consistent with accepted planning practice and supported by case law, including Sierra Club v. County of Tehama (2012).1

It is important to understand that the planning horizon represents an informed estimate of future conditions rather than a prediction. The timing, location, and mix of future development will vary based on market conditions, infrastructure availability, and individual development decisions. The SEIR provides a programmatic evaluation of potential impacts associated with the proposed Policy Plan and supports tiering for future projects that are consistent with the assumptions analyzed. Future development projects may still require project-level environmental review, as appropriate, under the California Environmental Quality Act (CEQA).

Because this analysis supports an SEIR, it focuses on the incremental difference in development capacity between the approved project and the proposed project. The results are therefore intended to identify the magnitude of change attributable to the proposed Policy Plan, rather than to establish a baseline of existing conditions. This approach ensures that the analysis isolates the effects of the proposed changes and provides a clear basis for evaluating potential environmental impacts.

Current Policy Plan (Approved Project)
For purposes of the SEIR, the approved project represents the baseline condition against which the proposed project is evaluated. The approved project reflects the level of development that could reasonably be accommodated under the City’s existing land use framework, based on the land use designations, density and intensity standards, and associated development assumptions.

Capacity Estimation Methodology
Development capacity refers to the number of dwelling units and the amount of non-residential square footage expected to be developed under the approved project within the planning horizon.

Consistent with the overall methodology, development capacity under the approved project is estimated using a parcel-based approach that applies standardized assumptions to each land use category. Residential development potential is calculated by multiplying the assumed density, expressed as dwelling units per acre (du/ac) by the parcelized acreage, while non-residential development potential is calculated by applying assumed intensity, expressed as floor area ratios (FARs) to parcel area. Assumed densities and intensities are codified as part of the Policy Plan in Table LU-03 (Future Buildout) of the Land Use Element and are discussed in the following section.

Density and Intensity Assumptions
Density and intensity assumptions are calibrated to reflect how development is expected to occur under realworld conditions rather than maximum allowable standards. A key distinction in the application of these assumptions is between areas generally north of Riverside Drive (including Model Colony) and areas to the south (Ontario Ranch), which reflect different development patterns for some land uses.

In Model Colony and other established areas, parcels are largely already subdivided and function as net developable sites, meaning that residential densities more closely reflect realized development conditions. In many of these established single-family residential neighborhoods, the primary drivers for residential growth 1 Sierra Club v. County of Tehama (2012) 2012 Cal.App.Unpub.Lexis 8813, No. C066996. “However, as we have seen, "[t]he level of specificity of an EIR is determined by the nature of the project and the 'rule of reason' . . . ." [citation] "Where a lead agency is using the tiering process in connection with an EIR for a large-scale planning approval, such as a general plan or component thereof (e.g., an area plan or community plan), the development of detailed, sitespecific information may not be feasible but can be deferred, in many instances, until such time as the lead agency prepares a future environmental document in connection with a project of a more limited geographical scale, as long as deferral does not prevent adequate identification of significant effects of the planning approval at hand." (Guidelines, § 15152, subd. (c).) General Plan EIRs may be used in a tiering situation. (Guidelines, § 15152, subd. (h).) The GPU EIR was not required to analyze environmental impacts for speculative hypothetical future development as part of the "project" (i.e., the GPU).” 3721
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 3 of 4
include options that allow existing housing stock to expand above maximum densities, such as accessory dwelling units and lot splits, in addition to incremental intensification over time. Growth in medium- and high-density areas is anticipated to include infill development and incremental intensification.

In contrast, much of Ontario Ranch is undeveloped; growth typically occurs on large parcels that must be subdivided to create individual lots, local streets, and supporting infrastructure. As a result, densities in Ontario Ranch are effectively applied to gross acreage, requiring land to be set aside for internal circulation, access, and other improvements. This distinction results in lower realized densities on a gross-acre basis and supports the use of differentiated assumptions between these areas, particularly for residential uses. Portions of the Ontario Ranch, however, have already begun to develop under specific plans. Each specific plan entitles a specific number of units. In some cases, the number of entitled units exceeds the gross acre buildout estimate largely because the land area has already been subdivided. Custom assumed densities were identified in these specific plan areas to ensure that buildout of the approved project accounted for all entitled units. Approved project buildout estimates for specific plan areas not listed in the table notes either met or exceeded the number of entitled units and/or non-residential square feet entitled in the specific plan. The assumed densities and intensities by land use category are summarized in Table C-1.

The approved project also includes several mixed-use land use designations, where a mix of development types is allowed and expected to occur. These areas are further guided by the vision and intended outcomes for mixeduse place types identified in the community design element, and custom density and intensity assumptions were developed for each mixed-use category based on existing uses that were expected to remain, the allowable density and intensity range, and the vision for each mixed-use area. Assumptions for mixed-use areas within specific plans were further aligned with the specific plans' intent. The buildout assumptions for mixed-use designations are listed in Table C-2.
3722
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 4 of 5
Table C-1: Approved Plan Density and Intensity Assumptions by Land Use Category Land Use Category Allowable
Assumed
(North1)
Assumed
(South1) Notes
Rural (RR) 0-2.0 du/ac 2.0 du/ac n/a Reflects large-lot residential development with limited subdivision potential (All Rural land uses are in northern Ontario) Low Density Residential
(LDR)2 2.1–5.0 du/ac 5.9 du/ac2 4.5 du/ac2
North1. Reflects built conditions with the continued development of ADUs and lot splits within established neighborhoods, exceeding the maximum allowed.

South1: Reflects gross acreage subdivision and infrastructure needs.

See Note 2 for alternative assumptions in certain specific plan areas.

Low-Medium Density
Residential (LMDR) 5.1–11.0 du/ac 8.5 du/ac 8.5 du/ac
Represents typical small-lot and attached housing products achievable in both contexts, as well as mobile home
development primarily within northern Ontario.

Medium Density
Residential (MDR)3

11.1–25.0
du/ac 18 du/ac 22 du/ac3
North1. Reflects infill constraints
South1. Reflects master-planned product types, and required minimum density of 20 du/ac on MDR properties with the
affordable housing overlay;
See Note 3 for alternative assumptions in certain specific plan areas.

High-Density Residential
(HDR)

25.1–45.0
du/ac 25 du/ac 35 du/ac North1. Reflects infill constraintsSouth1. Reflects higher-density multifamily corridors Mixed Use See Table C-2 See Table C-2 See Table C-2 See Table C-2 Neighborhood Commercial
(NC) 0.25–0.35 FAR 0.30 FAR 0.30 FAR Reflects single-story retail formats with parking constraints General Commercial (GC) 0.25–0.50 FAR 0.30 FAR 0.30 FAR Reflects single-story retail formats with parking constraints but allows for a broader category of uses and larger buildings.

Assumed intensity is consistent with regional commercial development patterns.

Office / Commercial (OC) 0.50–1.50 FAR 0.75 FAR 0.75 FAR Reflects moderate-intensity suburban office development Hospitality (HOS) 0.75–2.00 FAR 1.00 FAR 1.00 FAR Reflects hotel and entertainment development patterns Business Park (BP)4 0.40–0.80 FAR 0.50 FAR 0.50 FAR Reflects campus-style development with a mix of small warehouse and light industrial uses with internal circulation.

Industrial (IND) 0.40–0.80 FAR 0.55 FAR 0.55 FAR4 Reflects warehouse and heavy industrial building types Airport (ARPT) Consistent with Airport Plan (per TOP 2050) Public Facilities (PF) /
Public Schools (PS) /
Landfill (LF) / Rail (Rail)
N/A N/A N/A Existing to remain
Open Space (all types) N/A N/A N/A Existing to remain
Right-of-way N/A N/A N/A No vertical construction anticipated in these areas Source: City of Ontario, 2022 (TOP 2050, Policy Plan Tables LU-02 and LU-03) Notes:

1. North refers to areas north of Riverside Drive. South refers to areas south of Riverside Drive.

2. The assumed density for Low Density Residential land uses was estimated at higher rates for the following adopted specific plans to align with entitlements: Countryside Specific Plan: 6.18 du/ac; Creekside Specific Plan: 7.47 du/ac; Subarea 29 Specific Plan: 6.55 du/ac. The densities modeled in these areas exceed the maximum allowable density because the approved plan allows blending of residential densities within the plan.

This flexibility allows for higher densities to be achieved, provided the specific plan as a whole does not exceed the total number of units allowed within its boundaries.

3. The assumed density for Medium Density Residential land uses was estimated at higher rates for the following adopted specific plan to align with entitlements: Creekside Specific Plan: 15.81 du/ac.

4. The intensity assumed for Business Park land uses was estimated at higher rates to align with entitlements for the following adopted specific plan:

Merrill Commerce Center: 0.60 FAR.
3723
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 5 of 6
Table C-2 Mixed Use Density and Intensity Assumptions by Mixed Use Area Land Use Category
Residential
(Percent1,
Density)
NeighborhoodServing
Retail/Service,
(Percent1, FAR)
Community &
Regional
Retail/Service
(Percent1, FAR)
Office
(Percent1, FAR)
Hospitality
(Percent1, FAR)
Industrial
(Percent1, FAR)
Mixed Use -Downtown
(MU-DT) 60%, 35 du/ac – 20%, 0.80 FAR 20%, 0.80 FAR – – Mixed Use - Holt Blvd [East & West]
(MU-H) 75%, 30 du/ac – 25%, 0.80 FAR – – –
Mixed Use - Meredith
(MU-ME) 50%, 40 du/ac – 50%, 0.35 FAR – – –
Mixed Use - Multimodal
(MU-MM) 15%, 60 du/ac – 15%, 1.00 FAR 45%, 1.00 FAR 25%, 0.70 FAR – Mixed Use - Inland Empire Corridor
(MU-IE) 82%, 10 du/ac – 18%, 0.35 FAR – – –
Mixed Use - Guasti
(MU-GU) 30%, 30 du/ac – 15%, 1.00 FAR 30%, 0.70 FAR 25%, 0.70 FAR – Mixed Use - Ontario Center
(MU-OC -- South of Concourse &
East of Haven)
50%, 40 du/ac – 30%, 0.70 FAR 20%, 1.00 FAR – –
Mixed Use - Ontario Center
(MU-OC -- North of Concourse &
West of Haven)
75%, 40 du/ac – 15%, 0.80 FAR 10%, 1.00 FAR – –
Mixed Use - Ontario Mills
(MU-OM) 40%, 40 du/ac – 55%, 0.50 FAR 5%, 0.75 FAR – –
Mixed Use - Rich Haven
(MU-RH) 86%, 18 du/ac – 12%, 0.30 FAR – – –
Mixed Use - Great Park2
(MU-GP) 70%, 35 du/ac – 20%, 0.70 FAR 10%, 0.70 FAR – – Mixed Use - Grove
(MU-GR) 65%, 35 du/ac – 25%, 0.70 FAR 10%, 0.70 FAR – – Mixed Use - Eucalyptus / Chino
Airport Overlay (MU-EU) 40%, 35 du/ac – 10%, 0.70 FAR 20%, 1.00 FAR – 30%, 0.60 FAR Mixed Use - Parkside
(MU-PS) 50%, 35 du/ac 50%, 0.80 FAR – – – –
Neighborhood Activity Hub
Mountain Village (MU-MV) 50%, 35 du/ac 50%, 0.80 FAR – – – – Neighborhood Activity Hub
Mountain & Fourth (MU-MF) 70%, 50 du/ac 30%, 0.80 FAR – – – – Neighborhood Activity Hub
Euclid & Francis (MU-EF) 50%, 30 du/ac 50%, 0.80 FAR – – – – Neighborhood Activity Hub
Euclid & Walnut (MU-EW) 75%, 30 du/ac 25%, 0.80 FAR – – – – Neighborhood Activity Hub
Euclid & Riverside (MU-ER) 75%, 35 du/ac 25%, 0.80 FAR – – – – Source: City of Ontario, 2025 (TOP 2050, Policy Plan Table LU-03) Notes:

1. Percent refers to the percent of the total land area within the corresponding mixed-use area estimated to be developed with the generalized use identified in each column.

2. This table reflects the approved project, so Mixed-Use Great Park is listed as “Great Park,” the name is proposed to be changed to “Grand Park” as part of the proposed project. Table LU-03 in the March 2026 Draft Policy Plan Amendments included an error for the percent breakdown of Mixed Use Great Park / Grand Park estimates. The breakdown within this table is correct, and reflects what was studied and disclosed in the environmental review. Table LU-03 will be corrected consistent with these assumptions. 3724
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 6 of 7
Population
Population estimates are derived by applying person-per-household (PPH) factors to occupied dwelling units. An occupancy rate of 0.96 is applied to residential units to reflect a stabilized housing market condition. This assumption accounts for vacancy rates typical in a healthy housing market (which is anticipated upon buildout of the approved plan) and ensures that population estimates are not overstated. These factors vary by housing type and are based on demographic assumptions informed by the City’s Parks and Open Space Development Impact Fee (DIF) evaluation. Lower-density housing types generally have larger household sizes, while higher-density housing types have smaller household sizes. The occupancy and persons per household assumptions for all land use designations are listed in Table C-3.

Table C-3: Approved Plan Occupancy and Persons Per Household Assumptions by Residential Land Use Category Land Use
Category2
Occupancy
Rate
PPH
(North1)
PPH
(South1) Notes
Rural 96% 3.806 3.806 Reflects single-family home household characteristics Low Density
Residential1 96% 3.806 3.806 Reflects single-family home household characteristics Low-Medium Density
Residential 96% 3.522 3.806
North1. Reflects a blend of single-family and modular household characteristics
South1. Reflects single-family home household characteristics Medium Density
Residential 96% 3.373 3.373 Reflects medium-density household characteristics (attached townhomes, alley-loaded, motor courts, etc.).

High Density
Residential 96% 2.669 2.669 Reflects multifamily household characteristics Mixed Use (all areas) 96% 2.669 2.669 Reflects multifamily household characteristics Source: City of Ontario, 2022 (derived from TOP 2050, Policy Plan, Table LU-03) Notes:

1. North refers to areas north of Riverside Drive. South refers to areas south of Riverside Drive.

2. This table only includes land use categories that allow for residential uses, since those are the only uses that contribute to future population estimates. Land Use categories that are not listed in this table do not allow for residential uses and are not factored into population estimates.

Jobs
Employment capacity is estimated by dividing the non-residential building square footage for each land use category between generalized employment types allowed within the land use, and applying square footage per employee factors associated with the generalized employment type to the project non-residential building square footage. The generalized employment types assumed for each land use category are listed in Table C-4.

Table C-4: Generalized Employment Types by Land Use Category Land Use Category
Neighborhood
-Serving
Retail/Service
Community &
Regional
Retail/Service Office Hospitality Industrial Other
Neighborhood Commercial 80% – 20% – – –
General Commercial – 90% 10% – – –
Office – 30% 70% – – –
Hospitality – 10% 20% 70% – –
Business Park 10% (North1) /
0% (South1)
0% (North1) /
10% (South1)
40% (North1) /
25% (South1) – 50% (North1) / 65% (South1) –
Industrial 5% (North1) /
5% (South1) – 10% (North1) / 5% (South1) – 85% (North1) / 90% (South1) – Airport See Note 2 – See Note 2 See Note 2 See Note 2 See Note 2 Mixed Use See Table C-2 – See Table C-2 See Table C-2 See Table C-2 See Table C-2 Public, Institutional, Open Space (all
types) – – – – – 100%
Source: City of Ontario, 2022 (derived from TOP 2050)
Notes:

1. North refers to areas north of Riverside Drive. South refers to areas south of Riverside Drive.

2. Consistent with Airport Plan
3725
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 7 of 8
Employment generation factors vary by generalized land use type and reflect typical employment densities derived from comparing an assessment of existing building square footage (conducted in 2021) against employment estimates by NAICS code published as part of the US Census LEHD dataset. The most recent dataset evaluated at the time the employment generation rates were calculated for the 2022 TOP update was from 2018.

The generalized employment generation rate for each land use category are listed in Table C-5.

Table C-5: Generalized Employment Generation Rates
Generalized Land Use Type SF per Employee
Neighborhood-Serving Retail/Service 400
Community & Regional Retail/Service 1200
Office 275
Hospitality: Hotel and Entertainment 1300
Industrial 2,000
Airport Consistent with the Airport Plan
Public Facility / Public School / Open Space (all types) / Landfill / Rail Existing to remain
Source: City of Ontario, 2022 (derived from TOP 2050 & 2018 Census (LEHD) data) Approved Project
The assumptions summarized above are the inputs used to generate the buildout results presented in Table LU03 of the approved Policy Plan and are detailed for both the area within the EBPSP boundaries Table C-6 and Citywide (Tables C-7a and C-7b) by land use category below. The approved project provides a baseline for evaluating the incremental change in development potential associated with the proposed project and supports the programmatic analysis of impacts in the SEIR.

Eucalyptus Business Park Specific Plan Area: Approved Project Estimates The Eucalyptus Business Park Specific Plan (EBPSP) Area includes 189 acres. The approved project within this area includes 4 land uses: Medium Density Residential (MDR), Mixed Use - Eucalyptus / Chino Airport Overlay (MUEU), Open Space – Recreation (OS-R), and Right-of-way (ROW). Growth for uses within the EBPSP area was estimated using the same methodology as the approved plan, described above. Estimates specific to the EBPSP area are listed in Table C-6 below.

Table C-6 Approved Project Buildout Estimates within EBPSP Area Land Use Acreage
Residential
(DU / Pop)
Community &
Regional
Retail/Service
(SF / Jobs)
Office
(SF / Jobs)
Industrial
(SF / Jobs)
Other1
(SF / Jobs)
Total nonresidential
(SF / Jobs)
Medium Density Res. 48 1,051 du /
3,403 ppl
- - - - -
Mixed Use - Eucalyptus /
Chino Airport Overlay (MU-EU)
82 1,144 du /
2,931 ppl
249,161 SF/
208 Jobs
711,887 SF /
2,589 Jobs
640,699 SF /
320 Jobs
- 1,601,747 SF /
3,117 Jobs
Open Space – Recreation 57 - - - - 30,396 SF /
10 Jobs
30,396 SF /
10 Jobs
Right-of-way 2 - - - - - -
Total 189 2,195 du /
6,335 ppl
249,161 SF/
208 Jobs
711,887 SF /
2,589 Jobs
640,699 SF /
320 Jobs
30,396 SF /
10 Jobs
1,632,143 SF /
3,127 Jobs
Notes:

1. Employment Generation Rates for “Other” Employment Category:

Open Space – Recreation: The approved project anticipated that an existing 30k sq.ft. structure within the park area would be preserved for park uses and programming. The Approved Project also assumed that 10 existing public jobs identified during the existing conditions analysis conducted for the 2022 TOP 2050 update within this area would be retained and associated with future park operations; these equate to approximately 0.18 jobs per acre.

2. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

3. The estimates for the EBPSP in Table C-6 are included in the citywide estimates in Tables C-7a and C-7b. 3726
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 8 of 9
Policy Plan (Citywide): Approved Project Estimates
Table C-7a Approved Project Citywide Buildout Estimates Land Use Category Acres Units Pop
NRS2
SF
NRS2
Jobs
CRS2
SF
CRS2
Jobs
OFF2
SF
OFF2
Jobs
HOS2
SF
HOS2
Jobs
IND2
SF
IND2
Jobs
OTH2
SF
OTH2
Jobs
Total NonRes
SF
Total
Jobs
Rural 529 1,057 3,863 - - - - - - - - - - - - - -
Low Density Residential 6,274 34,848 127,326 - - - - - - - - - - - - - - Low-Medium Density Residential 889 7,518 26,471 - - - - - - - - - - - - - - Medium Density Residential 2,299 47,206 152,855 - - - - - - - - - - - - - - High-Density Residential 198 5,089 13,038 - - - - - - - - - - - - - - Mixed Use
see Table C-7b for buildout estimates by Mixed Use Area 1,756 34,234 87,715 756,932 1,892 11,656,527 9,714 7,883,478 28,667 1,211,282 932 823,904 412 - - 22,332,123 41,617 Neighborhood Commercial 246 - - 2,570,043 6,425 - - 642,511 2,336 - - - - - - 3,212,554 8,761 General Commercial 385 - - - - 4,530,055 3,775 503,339 1,830 - - - - - - 5,033,394 5,605 Office / Commercial
including areas w/in landfill overlay 309 - - - - 3,030,520 2,525 7,071,213 25,714 - - - - - - 10,101,733 28,239 Hospitality 194 - - - - 845,898 705 1,691,796 6,152 5,921,284 4,555 - - - - 8,458,978 11,412 Business Park 1,056 - - 1,403,224 3,508 934,081 778 7,948,099 28,902 - - 13,087,645 6,544 - - 23,373,049 39,732 Business Park overlay areas
including base designations LDR, LMDR, NC 90 - - 195,140 488 - - 780,559 2,838 - - 975,699 488 - - 1,951,398 3,814 Industrial
including areas w/in landfill overlay 7,591 - - - - 9,093,661 7,578 16,939,486 61,598 - - 155,840,078 77,920 - - 181,873,225 147,096 Industrial overlay areas
including base designations LDR, NC 60 - - - - 72,063 60 144,126 524 - - 1,225,068 613 - - 1,441,257 1,197 Airport 1,423 - - - - - - - - - - 2,401,641 6,400 - - 2,401,641 6,400 Open Space, Recreation
including OS-R and OS-R areas within the Industrial
overlay 1,041 - - - - - - - - - - - - 193,455 82 193,455 82 Open Space, Other
including OS-NR, OS-W, and OS-NR areas w/in landfill
overlay 1,218 - - - - - - - - - - - - 285,160 243 285,160 243 Public Facility 90 - - - - - - - - - - - - 495,688 1,806 495,688 1,806 Public School 603 - - - - - - - - - - - - 2,666,341 3,102 2,666,341 3,102 Landfill 137 - - - - - - - - - - - - 6,528 34 6,528 34
Rail
including areas w/in landfill overlay 250 - - - - - - - - - - - - 1,956 62 1,956 62 Right-of-way 5,384 - - - - - - - - - - - - - - - -
TOTAL 32,022 129,952 411,268 4,925,339 12,313 30,162,805 25,135 43,604,607 158,561 7,132,566 5,487 174,354,035 92,377 3,649,128 5,329 263,828,480 299,202 Source: City of Ontario, 2025 (TOP 2050, extrapolated from Land Use Plan and Policy Plan Table LU-03) Notes:

1. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

2. NRS=Neighborhood-Serving Retail/Service, CRS=Community & Regional Retail/Service, OFF=Office, HOS=Hospitality, IND=Industrial, OTH=Other, Total Non-Res.=Total Non-Residential 3727
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 9 of 10
Table C-7b Approved Project Citywide Buildout Estimates: Mixed-Use Area Details Land Use Category Acres Units Pop
NRS3
SF
NRS3
Jobs
CRS3
SF
CRS3
Jobs
OFF3
SF
OFF3
Jobs
HOS3
SF
HOS3
Jobs
IND3
SF
IND3
Jobs
OTH3
SF
OTH3
Jobs
Total NonRes
SF
Total
Jobs
Mixed Use -Downtown (MU-DT) 132 2,762 7,077 - - 916,646 764 916,646 3,333 - - - - - - 1,833,292 4,097 Mixed Use - Holt Blvd [East & West] (MU-H) 77 1,725 4,420 - - 667,900 557 - - - - - - - - 667,900 557 Mixed Use – Meredith (MU-ME) 91 1,815 4,651 - - 691,939 577 - - - - - - - - 691,939 577 Mixed Use – Multimodal (MU-MM) 73 653 1,673 - - 474,108 395 1,422,323 5,172 553,126 425 - - - - 2,449,557 5,992 Mixed Use - Inland Empire Corridor (MU-IE) 37 300 769 - - 100,455 84 - - - - - - - - 100,455 84 Mixed Use – Guasti (MU-GU) 86 777 1,991 - - 564,134 470 789,787 2,872 658,156 506 - - - - 2,012,077 3,848 Mixed Use - Ontario Center
(MU-OC -- South of Concourse & East of Haven) 186 3,729 9,553 - - 1,705,357 1,421 1,624,150 5,906 - - - - - - 3,329,507 7,327 Mixed Use - Ontario Center
(MU-OC -- North of Concourse & West of Haven) 169 5,076 13,006 - - 884,419 737 737,016 2,680 - - - - - - 1,621,435 3,417 Mixed Use - Ontario Mills (MU-OM) 249 3,990 10,223 - - 2,987,306 2,489 407,360 1,481 - - - - - - 3,394,666 3,970 Mixed Use - Rich Haven (MU-RH) 154 2,380 6,097 - - 241,071 201 46,875 170 - - - - - - 287,946 371 Mixed Use - Great Park2 (MU-GP) 300 7,342 18,813 - - 1,827,582 1,523 913,791 3,323 - - - - - - 2,741,373 4,846 Mixed Use – Grove (MU-GR) 36 821 2,104 - - 275,204 229 110,082 400 - - - - - - 385,286 629 Mixed Use - Eucalyptus / Chino Airport Overlay
(MU-EU) 105 1,471 3,769 - - 320,408 269 915,449 3,329 - - 823,904 412 - - 2,059,761 4,008 Mixed Use – Parkside (MU-PS) 3 52 132 51,440 129 - - - - - - - - - - 51,440 129 Neighborhood Activity Hub
Mountain Village (MU-MV)
8 137 350 136,070 340 - - - - - - - - - - 136,070 340
Neighborhood Activity Hub
Mountain & Fourth (MU-MF)
7 251 643 75,008 188 - - - - - - - - - - 75,008 188
Neighborhood Activity Hub
Euclid & Francis (MU-EF)
13 190 487 220,912 552 - - - - - - - - - - 220,912 552
Neighborhood Activity Hub
Euclid & Walnut (MU-EW)
16 369 945 142,840 357 - - - - - - - - - - 142,840 357
Neighborhood Activity Hub
Euclid & Riverside (MU-ER)
15 394 1,009 130,662 327 - - - - - - - - - - 130,662 327 TOTAL Mixed Use 1,756 34,234 87,715 756,932 1,892 11,656,527 9,714 7,883,478 28,667 1,211,282 932 823,904 412 - - 22,332,123 41,617 Source: City of Ontario, 2025 (TOP 2050, extrapolated from Land Use Plan and Policy Plan Table LU-03) Notes:

1. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

2. The name of Mixed-Use Great Park is proposed to be changed to “Mixed-Use Grand Park” as part of the proposed project. References to this designation in the approved project section use the name “Great Park” while those in the proposed project section use the name “Grand Park.”

3. NRS=Neighborhood-Serving Retail/Service, CRS=Community & Regional Retail/Service, OFF=Office, HOS=Hospitality, IND=Industrial, OTH=Other, Total Non-Res.=Total Non-Residential

3728
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 10 of 11
Proposed Project (Proposed Policy Plan Amendments and Eucalyptus Business Park Specific Plan)
To ensure a meaningful comparison of impacts under each plan, the buildout estimates for the proposed project were prepared using the same methodology as the approved plan described above (with minor adjustments).

Minor adjustments to assumptions and methodology were necessary to account for changes in land-use designations and the inclusion of the EBPSP, as described in the following section.

Because the proposed project also includes changes to 5 land use designations, the buildout factors used to estimate growth in those areas were evaluated and, where necessary, updated to ensure the estimated growth likely to occur under the proposed project reflects the development likely to occur as a result of the proposed land use plan.

The proposed project also includes the EEBPSP. The growth within the specific plan area was estimated as the maximum allowable square footage identified in the specific plan document. Changes in buildout assumptions compared to the approved project are described below.

Adjustments in Buildout Assumptions (compared to the approved project) Policy Plan Amendments
The proposed project includes changes to 5 land use designations. To ensure buildout assumptions and methodology reflect these changes, the buildout factors for each were evaluated and updated if necessary to reflect the estimated growth likely to occur under the proposed project, which reflects the intent and likely buildout of the proposed land use plan. The evaluation and update of assumptions for these land uses are described in Tables C-8 and C-9.
3729
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 11 of 12
Table C-8 Summary of Proposed Changes to Land Use Designations and Impact on Buildout Methodology Land Use Description of land use change Impact on buildout methodology (relative to approved project)
Mixed-Use Meredith: The Proposed Project increases the minimum density from 14 du/ac to 25 du/ac to ensure that future
development in the area aligns with the vision for the
Inland Empire Boulevard Corridor Place Type (Figure
CD-08, page 2 of the Proposed Policy Plan). This
changed the allowed density range from 14 to 125
(approved project) du/ac to 25 to 125 du/ac (proposed
project).

The approved project assumed a buildout density in this area of 40 du/ac, which remains consistent with the
proposed change in minimum density. No changes to the
buildout assumptions were proposed as a result of this
change.

Mixed Use Great Park: The Proposed Project renames this land use as “Mixed Use Grand Park.”
This change only affected the designation name. No
changes to the buildout assumptions were proposed as a
result of this change.

Mixed Use Sports Park: The Proposed Project establishes a new mixed-use designation that applies to mixed-use areas within the
“Sports Empire District” Place Type (Figure CD-07 of
the Proposed Policy Plan).

New buildout factors to estimate capacity were created, as noted in Table C-9. Population and jobs factors were
estimated consistent with the approved project, as follows:
population was estimated using the mixed-use occupancy
and persons per household rate identified in Table C-3, and jobs were estimated by applying the appropriate
generalized employment generation rate in Table C-5 to the square footage for each generalized employment type
identified in Table C-9.

Mixed Use Neighborhood
Activity Hub
(Vineyard & Riverside):

The Proposed Project establishes a new mixed-use
neighborhood activity hub at the southwest corner of
Riverside Drive and Vineyard Avenue, identified as
Neighborhood Center Place Type “8I” (Figure CD-09 of
the Proposed Policy Plan).

Mixed Use Eucalyptus/Chino
Airport Overlay:

The Proposed Project eliminates this designation and
removes it from all tables and maps.

There are no instances of this land use in the proposed plan, so buildout of properties that were previously
designated as such is estimated to be consistent with the proposed land use and the EBPSP. Because this land use
is not included in the proposed project, no methodology is needed.

All other land uses: No changes are proposed. See Table C-1. The methodology and assumptions for all other land use designations are consistent with the approved project.

Table C-9 Changes to Proposed Mixed Use Density and Intensity Assumptions Land Use Category
Residential
(Percent1,
Density)
NeighborhoodServing
Retail/Service
(Percent1, FAR)
Community &
Regional
Retail/Service(P
ercent1, FAR)
Office
(Percent1, FAR)
Hospitality
(Percent1, FAR)
Industrial
(Percent1, FAR)
REMOVE:

Mixed Use - Eucalyptus / Chino
Airport Overlay (MU-EU) 40%, 35 du/ac – 10%, 0.70 FAR 20%, 1.00 FAR – 30%, 0.60 FAR ADD:

Mixed Use - Sports Park
(MU-SP) 30%, 30 du/ac – 30%, 0.70 FAR 15%, 1.00 FAR 25%, 0.70 FAR – Mixed Use - Neighborhood
Activity Hub
(Vineyard & Riverside) (MU-VR)
30%, 30 du/ac – 30%, 0.70 FAR 15%, 1.00 FAR 25%, 0.70 FAR – ALL OTHER MIXED-USES:

No changes are proposed.

See Table C-2.
3730
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 12 of 13
Eucalyptus Business Park Specific Plan Area: Proposed Project Estimates Buildout of the EBPSP was estimated to reflect the square footage identified in Table ES-1 of the Specific Plan, broken down into generalized employment categories anticipated to occur based on the allowable uses within the Specific Plan. Employment generation rates identified in Table C-5 were applied to the estimated square footage to estimate the number of jobs.

These estimates are shown in Table C-10 below.

Table C-10 Proposed Project EBPSP Buildout Estimates
Land Use Acreage
Community &
Regional Retail /
Service (SF / Jobs)
Industrial
(SF / Jobs)
Other1
(SF / Jobs)
Total Non-Residential
(SF / Jobs)
Business Park 138 10,000 SF / 8 Jobs 2,894,000 SF / 1,447 Jobs 35,000 SF / 127 Jobs 2,939,000 SF / 1,583 Jobs Open Space – Recreation 36 - - 0 SF / 7 Jobs 0 SF / 7 Jobs Right-of-way 15 - - - -
Total 189 10,000 SF / 8 Jobs 2,894,000 SF / 1,447 Jobs 35,000 SF / 134 Jobs 2,939,000 SF / 1,590 Jobs Notes:

1. Employment Generation Rates for “Other” Employment Category:

Business Park: The Other use projected within the EBPSP area includes a new public/institutional facility expected to operate similarly to an office.

The office employment generation rate identified in Table C-5 was used to estimate the anticipated jobs resulting from this development type.

Open Space – Recreation: The approved project anticipated that 10 existing public jobs (identified in 2018 LEHD employment data) within this area would be retained and associated with future park operations; these existing jobs equated to approximately 0.18 jobs per acre. The proposed project assumes potential employment related to park operations will remain consistent with the approved project and was projected at the rate of 0.18 jobs per acre.

2. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

3. The estimates for the EBPSP in Table C-10 are included in the citywide estimates in Tables C-11a and C-11b.

Policy Plan (Citywide): Proposed Project Estimates
The assumptions summarized above are the inputs used to generate the buildout results presented in Table LU03 of the proposed Policy Plan and are detailed by land use category in Tables C-11a and C-11b. The proposed project buildout estimates support evaluation of the incremental change relative to the approved project baseline and supports the programmatic analysis of impacts in the SEIR. 3731
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 13 of 14
Table C-11a Proposed Project Citywide Buildout Estimates Land Use Category Acres Units Pop
NRS2
SF
NRS2
Jobs
CRS2
SF
CRS2
Jobs
OFF2
SF
OFF2
Jobs
HOS2
SF
HOS2
Jobs
IND2
SF
IND2
Jobs
OTH2
SF
OTH2
Jobs
Total NonRes
SF
Total
Jobs
Rural 529 1,057 3,863 - - - - - - - - - - - - - -
Low Density Residential 5,810 32,753 119,671 - - - - - - - - - - - - - - Low-Medium Density Residential 929 7,865 27,737 - - - - - - - - - - - - - - Medium Density Residential 2,292 47,010 152,223 - - - - - - - - - - - - - - High-Density Residential 536 16,902 43,308 - - - - - - - - - - - - - - Mixed Use
see Table C-11b for buildout estimates by Mixed Use
Area
1,672 31,589 80,938 756,932 1,892 11,851,777 9,876 7,355,947 26,749 2,030,895 1,562 - - - - 21,995,551 40,080 Neighborhood Commercial 237 - - - - 2,480,415 6,201 620,104 2,255 - - - - - - 3,100,519 8,456 General Commercial 357 - - - - 4,195,173 3,496 466,130 1,695 - - - - - - 4,661,303 5,191 Office / Commercial
including areas w/in landfill overlay
309 - - - - 3,030,520 2,525 7,071,213 25,714 - - - - - - 10,101,733 28,239 Hospitality 167 - - - - 727,323 606 1,454,646 5,290 5,091,262 3,916 - - - - 7,273,232 9,812 Business Park
including EBPSP
1,336 - - 1,366,309 3,416 1,288,620 1,074 8,661,787 31,497 - - 18,036,578 9,018 35,000 127 29,388,295 45,133 Business Park overlay areas
including base designations LDR, LMDR, NC
90 - - 195,140 488 - - 780,559 2,838 - - 975,699 488 - - 1,951,398 3,814 Industrial
including areas w/in landfill overlay
7,606 - - - - 9,111,414 7,593 16,974,991 61,727 - - 156,141,877 78,071 - - 182,228,282 147,391 Industrial overlay areas
including base designations LDR, NC
60 - - - - 72,063 60 144,126 524 - - 1,225,068 613 - - 1,441,256 1,197 Airport 1,408 - - - - - - - - - - 2,401,641 6,400 - - 2,401,641 6,400 Open Space, Recreation
including OS-R and OS-R areas within the Industrial
overlay and OS-R areas within EBPSP
998 - - - - - - - - - - - - 164,012 76 164,012 76
Open Space, Other
including OS-NR, OS-W, and OS-NR areas w/in landfill
overlay
1,218 - - - - - - - - - - - - 285,160 243 285,160 243
Public Facility 90 - - - - - - - - - - - - 495,688 1,806 495,688 1,806 Public School 604 - - - - - - - - - - - - 2,666,341 3,102 2,666,341 3,102 Landfill 137 - - - - - - - - - - - - 6,528 34 6,528 34
Rail
including areas w/in landfill overlay
250 - - - - - - - - - - - - 1,956 62 1,956 62
Right-of-way 5,389 - - - - - - - - - - - - - - - -
TOTAL 32,022 137,177 427,740 2,318,381 5,796 32,757,305 31,431 43,529,503 158,289 7,122,157 5,478 178,780,863 94,590 3,654,685 5,450 268,162,895 301,036 Source: City of Ontario, 2025
Notes:

1. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

2. NRS=Neighborhood-Serving Retail/Service, CRS=Community & Regional Retail/Service, OFF=Office, HOS=Hospitality, IND=Industrial, OTH=Other, Total Non-Res.=Total Non-Residential A
3732
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 14 of 15
Table C-11b Proposed Project Citywide Buildout Estimates: Mixed-Use Area Details Land Use Category Acres Units Pop
NRS3
SF
NRS3
Jobs
CRS3
SF
CRS3
Jobs
OFF3
SF
OFF3
Jobs
HOS3
SF
HOS3
Jobs
IND3
SF
IND3
Jobs
OTH3
SF
OTH3
Jobs
Total NonRes
SF
Total
Jobs
Mixed Use -Downtown (MU-DT) 132 2,783 7,131 - - 916,646 764 916,646 3,333 - - - - - - 1,833,291 4,097 Mixed Use - Holt Blvd [East & West]
(MU-HE & MU- HW)
94 2,106 5,397 - - 815,560 680 - - - - - - - - 815,560 680 Mixed Use – Meredith (MU-ME) 91 1,815 4,651 - - 691,939 577 - - - - - - - - 691,939 577 Mixed Use – Multimodal (MU-MM) 73 653 1,673 - - 474,108 395 1,422,323 5,172 553,126 425 - - - - 2,449,557 5,993 Mixed Use - Inland Empire Corridor
(MU-IE)
37 300 769 - - 100,455 84 - - - - - - - - 100,455 84
Mixed Use – Guasti (MU-GU) 86 777 1,991 - - 564,134 470 789,787 2,872 658,156 506 - - - - 2,012,077 3,848 Mixed Use - Ontario Center
(MU-OC -- South of Concourse & East of Haven)
169 5,076 13,006 - - 884,419 737 737,016 2,680 - - - - - - 1,621,435 3,417 Mixed Use - Ontario Center
(MU-OC -- North of Concourse & West of Haven)
186 3,729 9,553 - - 1,705,357 1,421 1,624,150 5,906 - - - - - - 3,329,507 7,327 Mixed Use - Ontario Mills (MU-OM) 249 3,990 10,223 - - 2,987,306 2,489 407,360 1,481 - - - - - - 3,394,666 3,971 Mixed Use - Rich Haven (MU-RH) 154 2,380 6,097 - - 241,071 201 46,875 170 - - - - - - 287,946 371 Mixed Use - Grand Park (MU-GP)2 188 4,595 11,772 - - 1,143,657 953 571,829 2,079 - - - - - - 1,715,486 3,032 Mixed Use – Grove (MU-GR) 45 1,025 2,627 - - 343,589 286 137,436 500 - - - - - - 481,025 786 Mixed Use – Sports Park (MU-SP) 71 637 1,632 - - 616,668 514 519,092 1,888 513,890 395 - - - - 1,649,650 2,637 Mixed Use – Parkside (MU-PS) 3 52 132 51,440 129 - - - - - - - - - - 51,440 129 Neighborhood Activity Hub
Mountain Village (MU-MV)
8 137 350 136,070 340 - - - - - - - - - - 136,070 340
Neighborhood Activity Hub
Mountain & Fourth (MU-MF)
7 251 643 75,008 188 - - - - - - - - - - 75,008 188
Neighborhood Activity Hub
Euclid & Francis (MU-EF)
13 190 487 220,912 552 - - - - - - - - - - 220,912 552
Neighborhood Activity Hub
Euclid & Walnut (MU-EF)
16 369 945 142,840 357 - - - - - - - - - - 142,840 357
Neighborhood Activity Hub
Euclid & Riverside (MU-ER)
15 394 1,009 130,662 327 - - - - - - - - - - 130,662 327 Neighborhood Activity Hub
Vineyard & Riverside (MU-VR)
37 331 847 - - 366,868 306 183,434 667 305,723 235 - - - - 856,025 1,368 TOTAL Mixed Use 1,672 31,589 80,938 756,932 1,892 11,851,777 9,876 7,355,947 26,749 2,030,895 1,562 - - - - 21,995,551 40,080 Source: City of Ontario, 2025 (TOP 2050, extrapolated from Land Use Plan and Policy Plan Table LU-03) Notes:

1. All estimates are rounded. Some totals may deviate by a small amount due to rounding.

3. NRS=Neighborhood-Serving Retail/Service, CRS=Community & Regional Retail/Service, OFF=Office, HOS=Hospitality, IND=Industrial, OTH=Other, Total Non-Res.=Total Non-Residential 3733
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 15 of 16
No Net Loss Analysis (Housing Capacity)
The proposed TOP Land Use Plan was prepared in part to ensure no net loss of overall housing capacity compared to the approved TOP Land Use Plan, in accordance with the no net loss provisions in state law enacted initially through Senate Bill 330 (Skinner, 2019) and subsequently amended in 2021 through Senate Bill 8 (Skinner, 2021)— see Section 66300 of the California Government Code.

Estimated Residential Capacity
As noted in Table C-7a, the approved plan is estimated to yield 129,952 dwelling units, when the metrics adopted in the Policy Plan through Figure LU-03 are applied to the land use plan. The proposed plan is estimated to yield 137,177 dwelling units using the same metrics as noted in Table C-11a. This reflects an increase of 7,225 dwelling units and is therefore in compliance with the no net loss provisions enacted through Senate Bill 330.

No Net Loss Analysis (Housing Element Sites Inventory)
The proposed TOP Land Use Plan was also prepared to ensure no net loss of housing capacity used to meet its Regional Housing Need Assessment. Pursuant to Government Code Section 65863, as amended by Senate Bill 166 (SB 166), jurisdictions are required to maintain sufficient residential site capacity throughout the Housing Element planning period to accommodate the remaining unmet Regional Housing Needs Allocation (RHNA) by income category. If development approvals, reductions in allowable density or intensity, or other land use actions result in a reduction of residential capacity on sites identified in the Housing Element sites inventory, the jurisdiction must evaluate whether the remaining inventory continues to satisfy Housing Element Law and No Net Loss requirements. Where remaining capacity is insufficient, the jurisdiction must identify and make available additional adequate sites with equivalent capacity and appropriate zoning within 180 days, consistent with Government Code Sections 65583.2 and 65863.

The proposed project includes removing sites where residential capacity was reduced from the current inventory and adding upzoned sites to make up for the shortfall. The City has amended the Housing Element Technical Report to evaluate remaining RHNA obligation and site availability consistent with SB 166 requirements. The City’s updated revised inventory, including capacity assumptions and remaining RHNA calculations by income category, is provided in Section 5.2 of the Housing Element Technical Report Amendments the table summarizing the availability of land to meet the 2021-2029 RHNA is copied in this document as Table C-12, while the map of the sites by Strategy area included in the Draft Amendment to the Housing Element Technical Report is copied in this document as Figure C-1. The parcel-level accounting of the analysis contained in Section 5.2 of the Housing Element Technical Report Amendments is included in this document as Tables C-13a and C-13b. 3734
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 16 of 17
Figure C-1 Housing Element Sites by Strategy Area
Source: City of Ontario, Draft Housing Element Technical Report Amendments, March 2026 Draft: Figure 5-1, Housing Opportunity Areas Emporia II
_ _J

CITY OF
UPLAND

CITY OF CHINO

MERRILL AVE
Housing Opportunity Areas
Strategies - Grand Park
- Downtown - Grove Corridor
- West Holl SCE Easement
- East Holl - Euclid Corridor
- Old Cardenas Market Vineyard Corridor
- Ontario Center Specific Plan - Sports Park
- Ontario Mills
Rail
-+ Rail Network
Boundaries
[_-=. "J TOP 2050 Growth Areas
c:::J Ontario City Boundary
c:::J County Boundary
Lu
;;',=
: l'URUPA ST

CITY OF

FONTANA
~ E PHILADELPHIA ST "' ~,.. ---'---....... ---··- •-
"'I

CITY OF

JURUPA VALLEY
ft ~ Miles
• , 0 0.5 2 r-·
Source: PlaceWorks & City of Ontario 2026
3735
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 17 of 18
Table C-12 Availability of Land to Meet RHNA, 2021–2029 Site Area Lower
Income
Moderate
Income
Above
Moderate
Income
Final
Realistic
Capacity
2021–2029 RHNA 8,926 3,329 8,599 20,854
Project Credits (Per APRs)1 (51) (7) (12,043) (12,102)
Remainder 8,875 3,322 (3,444)2 12,1972
Strategies
#1 Downtown 5 6 - 11
#2 West Holt 159 1598 - 318
East Holt5 4685,8 3715,8 - 8395
#3 Old Cardenas Market 33 32 - 65
#4 Ontario Center Specific Plan 96 97 - 193
#5 Ontario Mills Specific Plan4 [1,564]4 [782]4 [782]4 [3,128]4 #6 Grand Park Corridor6 1,0036 5016 5046 2,008
SCE Easement Area 7248 3618 3628 1,4478
Grove Corridor 2,191 4688 3,2278 5,8868
Euclid Corridor 8648 - 2,107 2,9718
Vineyard Corridor 2,8778 1,4368 1,4398 5,7528
Sports Park7 1,0878 5428 5428 2,1718
#7 Accessory Dwelling Units3 0 0 475 475
Total Capacity4 9,5074 3,9734 8,6562,4 22,1362,4
Surplus / Buffer4 6324 (7.1%) 6514 (19.6%) 12,1004
(140.7%)
13,2894
(63.7%)
RHNA met RHNA met RHNA met RHNA met
Source: City of Ontario, Draft Housing Element Technical Report Amendments Table 5-16, March 2026 as amended per footnotes 5-8 below.

Notes

1. See Table 5-2 of the Draft Housing Element Technical Report Amendments (March 2026) for details.

2. While the City has met the above moderate RHNA obligation, as reported on APRs, it must continue to maintain capacity for each income tier. Due to this, the 3,444 unit surplus reported in the “Remainder” row for above moderate housing units are counted as zero in the total column for the row.

3. The City does not and has not relied on Strategy 7 to fulfill its lower income RHNA obligation for the 6th cycle. Should the City confirm lower income affordability through subsequent surveys, ADU capacity will be revised.

4. The City does not and has not relied on Strategy 5 to fulfill its RHNA obligation for any portion of the 6th cycle. Because the zoning for Strategy 5 is not in place, it is not included in the Total Capacity or Surplus/Buffer row in Table 5-16. Should the rezoning program (program 13) be implemented on Strategy 5 sites, the City will have more capacity and a larger buffer.

5. East Holt corrections to the March 2026 Draft Housing Element Technical Report include: In the March 2026 Draft Housing Element Technical Report Amendments, several East Holt Corridor sites were incorrectly summarized in Table 5-6 under MU-1 and MU-1-AH categories, although the realistic-capacity calculations, density assumptions, and RHNA income allocations for those sites had already been evaluated using the MU-6 methodology. To correct this inconsistency, rows associated with MU-1 and MU-1-AH classifications were removed and the affected sites were reassigned to the appropriate MU-6 and MU-6-AH categories. The evaluation of the proposed land use change was appropriately calculated and evaluated for the Policy Plan Amendments, and this change in how the RHNA was summarized will not impact the environmental analysis for the proposed project.
a. As revised, the MU-6 category was updated to reflect the consolidation of sites previously summarized under MU-1 into the MU-6 category, resulting in a revised total of 13 parcels on 13.3 acres with a realistic capacity of 149 lower-income units and 149 moderate-income units. Moderateincome capacity calculations initially resulted in 148 dwelling units; however, the final total was adjusted to 149 units to reflect rounding at the parcel level rather than the strategy level to ensure consistency with Tables B-1 and B-2 (included in this memorandum as Tables C-13a and C-13b). b. The MU-6-AH category was updated to reflect a total of 12 parcels on 13.4 acres with a realistic capacity of 148 lower-income units and 148 moderate-income units. Sites 2-4, 2-5, and 2-6 were separated into a standalone row because they were evaluated using a different realisticcapacity methodology than the other MU-6-AH sites. Lower- and moderate-income unit calculations were rounded at the parcel level rather than the strategy level (resulting in 2 fewer lower income units and 3 fewer moderate income units) to ensure consistency with Tables B-1 and B-2 (included in this memorandum as Tables C-13a and C-13b).
c. Sites 2-4 and 2-5 were estimated to accommodate 117 moderate-income units in addition to capacity projected on Site 2-6 in the March 2026 Draft Housing Element Technical Report Amendments. However, following additional review, all housing-capacity assumptions associated with Sites 2-4 and 2-5 were removed. The sites remain in the inventory to reflect the City’s vision for the properties may redevelop together with residential mixed-use development.
3736
The Ontario Plan 2050 Policy Plan Amendments Projections Methodology April 2026 Appendix C Page 18 of 18
5d. Table C-12 in this document replicates Table 5-16 in the Housing Element Technical Report Amendments, which was incorrectly totaled in the March 2026 draft. In the March Draft, the number of lower income units identified in East Holt in Table 5-6 totaled 470, but they were incorrectly reported as 490 in Table 5-16; similarly, moderate income units identified in East Holt in Table 5-6 totaled 490, but were reported as 511 in Table 5-

16. The table above reflects the edits described in notes 5a-5c, and correctly reports the number of units associated with East Holt Blvd. With the edits above the total number of lower income units identified in East Holt changes from 470 to 468 (due to rounding differences described in note 5b), while the number of moderate income units changes from 490 to 371 (due to the changes in how sites 2-4 and 2-5 were counted as described in note 5c and rounding differences described in notes 5a and 5b), and the total units is summed correctly at 839. Tables 5-5, 5-6 and 5-16 in the Housing Element Technical Report will be updated to correctly reflect these edits noted above (Additional any changes may also be required as a result of HCD review).

6. Grand Park Corridor corrections to the March 2026 Draft Housing Element Technical Report include: The realistic capacity associated with APN 105320101 (site 2-80) was incorrectly excluded from the estimated realistic-capacity calculations in Table 5-13 in the March 2026 Draft Housing Element Technical Report Amendments. The adjustment factor applied to this site has been restored to the certified assumption previously used for the Grand Park Corridor opportunity area (37.7%), rather than the reduced adjustment factor (35.8%) noted in Table 5-13 in the March 2026 Draft.

As a result, the realistic-capacity totals for the Grand Park area was corrected from 1,906 units to 2,008 units. The RHNA income distribution for the Grand Park Corridor identified in Table 5-14 of the March 2026 Draft have also been revised to correctly reflect the housing development potential associated with APN 105320101, reflecting 1003 lower income units (instead of 953), 501 moderate income units (instead of 477), and 504 above moderate income units (instead of 477). Table 5-16 will also be revised to reflect the values in Table 5-14 and this methodology memo. The evaluation of the proposed land use change was appropriately calculated and evaluated for the Policy Plan Amendments, and this change in how the RHNA was tabulated will not impact the environmental analysis for the proposed project.

7. Sports Park corrections to the March 2026 Draft Housing Element Technical Report include: The proposed maximum density for the Sport Park designation was incorrectly reported in Table 5-13 the March 2026 Draft Housing Element Technical Report Amendments and has been corrected from 45 dwelling units per acre to 65 dwelling units per acre. As a result of this correction, the maximum theoretical capacity increased from 3,386 dwelling units to 4,888 dwelling units. Since the number of realistic units is not expected to change with this correction, the associated adjustment factor for sites with SP-AG-AH/MU-SP zoning/TOP designations was reduced from 20 percent to 13.8 percent. While this change did not directly impact the final realistic-capacity estimate (just the adjustment factor), the final realistic capacity identified in Tables 5-13 and 5-14 was adjusted from 677 dwelling units to 675 dwelling units to reflect data rounded at the parcel level rather than the strategy-area level to ensure consistency with Tables B-1 and B-2 (included in this memorandum as Tables C-13a and C-13b). Projected buildout of the proposed project was evaluated as described in earlier sections of this memo, and the table correction to the Housing Element will not impact the Land Use Element or the environmental analysis conducted for the proposed project.

8. Rounding adjustments: Unit calculations were updated to show units rounded at the parcel level rather than the strategy level to ensure consistency with Tables B-1 and B-2 (included in this memorandum as Tables C-13a and C-13b). This resulted in minor changes to associated housing element tables 5-x through 5-16 compared to the March 2026 Draft. The Housing Element Technical Report Amendments will be updated to reflect these minor rounding changes.

Attached Tables:

Table C-13a Housing Element Sites Inventory - Sites Already Suitably Zoned (Prior to the Start of the 6th Cycle) (Updated Version of Housing Element Table B-1, reflecting the proposed inventory. NOTE: proposed amendments to the Housing Element were undergoing HCD review at the time this memo was authored, and this table may subject to change in response to HCD comments)
Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program (Includes Sites That Were Rezoned During the 6th Cycle) (Updated Version of Housing Element Table B-2, reflecting the proposed inventory. NOTE: proposed amendments to the Housing Element were undergoing HCD review at the time this memo was authored, and this table may subject to change in response to HCD comments)
3737
Table C-13a Housing Element Sites Inventory - Sites Already Suitably Zoned This is an updated Version of Housing Element Table B-1, reflecting the proposed inventory as of March 2026 City of Ontario Policy Plan Amendments
Projections Methodology
Map
ID Opp Area Site Address/Intersection
5 Digit
ZIP
Code
APN Consolidate
d Sites Current GP Current Zone
Min
Density
(du/ac)
Max
Density
(du/ac)
Parcel
Size
(Acres)
Existing Use/ Vacancy Infrast
Available
PubliclyOwnedSite
Status
Identified in Last/
Last 2 Cycle(s) Lower Mod
Abov
Mod
Total Notes
1-3 West Holt 900 W HOLT BLVD 91762 101050176 - HDR HDR-45 25 45 1.5 Auto sales Yes NO Available Prior/Non-vacant 19 18 - 37 - 1-4 West Holt 1034 W HOLT BLVD 91762 101050206 J HDR HDR-45 25 45 0.3 Auto sales Yes NO Available No 4 4 - 8 - 1-5 West Holt 1020 W HOLT BLVD 91762 101050207 J HDR HDR-45 25 45 0.4 Auto sales Yes NO Available Prior/Non-vacant 5 5 - 10 - 1-6 West Holt 1134 W HOLT BLVD 91762 101052206 - HDR HDR-45 25 45 0.7 Strip retail Yes NO Available Prior/Non-vacant 8 8 - 16 - 1-7 West Holt 1156 W HOLT BLVD 91762 101052217 - HDR HDR-45 25 45 1.0 Auto sales Yes NO Available Prior/Non-vacant 12 12 - 24 - 1-8 West Holt 1228 W HOLT BLVD 91762 101054302 C HDR HDR-45 25 45 0.5 Parking lot Yes NO Available Prior/Non-vacant 6 6 - 12 - 1-9 West Holt 1234 W HOLT BLVD 91762 101054303 - HDR HDR-45 25 45 0.2 Parking Lot Yes NO Available Prior/Non-vacant 3 3 - 6 - 1-12 West Holt 1328 W HOLT BLVD 91762 101054310 E HDR HDR-45 25 45 0.3 Auto sales Yes NO Available Not Used 4 3 - 7 - 1-13 West Holt 1328 W HOLT BLVD 91762 101054311 E HDR HDR-45 25 45 0.3 Auto sales Yes NO Available Not Used 4 3 - 7 - 1-14 West Holt 1350 W HOLT BLVD 91762 101054313 F HDR HDR-45 25 45 0.4 Building supply Yes NO Available Prior/Non-vacant 4 4 - 8 - 1-15 West Holt 1360 W HOLT BLVD 91762 101054314 F HDR HDR-45 25 45 0.4 Building supply Yes NO Available Prior/Non-vacant 6 5 - 11 - 1-16 West Holt W OF SWC STONERIDGE CT &
MOUNTAIN AVE 91762 101054326 C HDR HDR-45 25 45 0.3 Parking lot Yes NO Available Not Used 3 4 - 7 - 1-17 West Holt 1203 W STONERIDGE CT 91762 101054327 C HDR HDR-45 25 45 0.4 Parking lot Yes NO Available Prior/Non-vacant 5 6 - 11 - 1-18 West Holt 1424 W HOLT BLVD 91762 101055204 G HDR HDR-45 25 45 0.3 Automotive Yes NO Available Not Used 4 4 - 8 - 1-19 West Holt 1426 W HOLT BLVD 91762 101055205 G HDR HDR-45 25 45 0.3 Automotive Yes NO Available Not Used 4 4 - 8 - 1-20 West Holt 1448 W HOLT BLVD 91762 101055207 H HDR HDR-45 25 45 0.3 Auto sales Yes NO Available Not Used 4 4 - 8 - 1-21 West Holt 1528 W HOLT BLVD 91762 101055212 I HDR HDR-45 25 45 0.3 Retail Yes NO Available Not Used 4 4 - 8 - 1-22 West Holt 1538 W HOLT BLVD 91762 101055213 I HDR HDR-45 25 45 0.3 Retail Yes NO Available Not Used 4 4 - 8 - 1-23 West Holt 120 N BENSON AVE 91762 101055216 D HDR HDR-45 25 45 0.7 Retail Yes NO Available Prior/Non-vacant 8 9 - 17 - 1-24 West Holt 1535 W STONERIDGE CT 91762 101055217 D HDR HDR-45 25 45 0.2 Retail Yes NO Available Not Used 3 2 - 5 - 1-25 West Holt 1420 W HOLT BLVD 91762 101055232 A HDR HDR-45 25 45 0.5 Parking lot Yes NO Available Prior/Non-vacant 6 6 - 12 - 1-26 West Holt 1414 W HOLT BLVD 91762 101055233 A HDR HDR-45 25 45 0.5 Building supply Yes NO Available Prior/Non-vacant 6 6 - 12 - 1-27 West Holt 1404 W HOLT BLVD 91762 101055234 A HDR HDR-45 25 45 0.4 Building supply Yes NO Available Prior/Non-vacant 5 6 - 11 - 1-28 West Holt 1502 W HOLT BLVD 91762 101055237 H HDR HDR-45 25 45 0.4 Auto sales Yes NO Available Prior/Non-vacant 5 5 - 10 - 1-29 West Holt 1512 W HOLT BLVD 91762 101055238 - HDR HDR-45 25 45 0.6 Auto sales Yes NO Available Prior/Non-vacant 7 7 - 14 - 1-30 East Holt 1323 E HOLT BLVD 91761 11006104 - MU-HE MU-6 14 40 1.0 Vacant Yes NO Available Not Used 11 11 - 22 - 1-31 East Holt 1207 E HOLT BLVD 91761 11006110 - MU-HE MU-6 14 40 1.1 Vacant Yes NO Available Not Used 13 13 - 26 - 1-32 East Holt 1217 E HOLT BLVD 91761 11006121 - MU-HE MU-6 14 40 1.2 Motel Yes NO Available Not Used 13 13 - 26 - 1-33 East Holt 1241 E HOLT BLVD 91761 11006125 - MU-HE MU-6 14 40 1.2 Motel Yes NO Available Not Used 14 14 - 28 - 1-34 East Holt 1111 E HOLT BLVD 91761 104847115 - MU-HE MU-6 14 40 0.5 Automotive / Open Storage Yes NO Available Not Used 6 6 - 12 - 1-35 East Holt 1101 E HOLT BLVD 91761 104847122 B MU-HE MU-6 14 40 0.8 Parking Lot Yes NO Available Not Used 9 9 - 18 - 1-36 East Holt 1101 E HOLT BLVD 91761 104847123 B MU-HE MU-6 14 40 0.5 Vacant Church Yes NO Available Not Used 5 5 - 10 - 1-37 East Holt 1031 E HOLT BLVD 91761 104848102 - MU-HE MU-6 14 40 1.8 Truck repair Yes NO Available Not Used 21 21 - 42 - 1-38 East Holt 1015 E HOLT BLVD 91761 104848103 - MU-HE MU-6 14 40 0.8 Auto parts store Yes NO Available Not Used 9 9 - 18 - 1-39 East Holt E OF NEC HOLT BLVD & ALLYN AVE 91761 104848106 Vista Verde II MU-HE PUD 14 40 0.6 Vacant Yes NO Available Not Used 17 - - 17 - 1-40 East Holt 935 E HOLT BLVD 91761 104848107 Vista Verde II MU-HE PUD 14 40 0.9 Independent Convenience Store Yes NO Available Not Used 24 - - 24 -
1-41 East Holt 1025 E HOLT BLVD 91761 104848122 - MU-HE MU-6 14 40 1.9 Truck repair Yes NO Available Not Used 21 21 - 42 - 1-42 East Holt 957 E HOLT BLVD 91761 104848128 Vista Verde II MU-HE PUD 14 40 1.2 Vacant Yes NO Available Not Used 31 - - 31 - 1-43 East Holt 957 E HOLT BLVD 91761 104848129 Vista Verde II MU-HE PUD 14 40 1.2 Vacant Yes NO Available Not Used 31 - - 31 - 1-44 East Holt 803 E HOLT BLVD 91761 104851212 - MU-HE MU-6 14 40 0.6 Car Sales Yes NO Available Not Used 7 7 - 14 - 1-45 East Holt 813 E HOLT BLVD 91761 104851213 - MU-HE MU-6 14 40 0.9 Laundromat / Automotive Yes NO Available Not Used 10 10 - 20 - 1-46 Ontario Center SP SWC CONCOURS ST & MERCEDES LN 91764 21020404 - MU-OC SP see note 1 see note 1 8.4 Vacant Yes NO Available Not Used 63 63 - 126 Note 1 Draft April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13a Page 13738 Table C-13a Housing Element Sites Inventory - Sites Already Suitably Zoned This is an updated Version of Housing Element Table B-1, reflecting the proposed inventory as of March 2026 City of Ontario Policy Plan Amendments
Projections Methodology
Map
ID Opp Area Site Address/Intersection
5 Digit
ZIP
Code
APN Consolidate
d Sites Current GP Current Zone
Min
Density
(du/ac)
Max
Density
(du/ac)
Parcel
Size
(Acres)
Existing Use/ Vacancy Infrast
Available
PubliclyOwnedSite
Status
Identified in Last/
Last 2 Cycle(s) Lower Mod
Abov
Mod
Total Notes
1-47 Ontario Center SP 888 N HAVEN AVE 91764 21020407 - MU-OC SP see note 1 see note 1 4.5 Vacant Yes NO Available Not Used 33 34 - 67 Note 1 1-50 East Holt 907 E HOLT BLVD 91761 104848127 - MU-HE MU-6 14 40 0.9 O'Reilly Auto Parts Yes NO Available Not Used 10 10 - 20 - 1-51 East Holt 905 E HOLT BLVD 91761 104848108 Vista Verde II MU-HE PUD 14 40 1.6 Vacant Yes NO Available Not Used 41 - - 41 - Notes:

Note 1: Specific Plan does not set density standards, however the types of projects allowed by zoning can accommodate products with 60+ du/ac. The Policy Plan Category allows for densities up to 125 du/ac.

Draft April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13a Page 23739 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

N OF NEC MAPLE ST &
EUCLID AVE 91762 105027201 5 3 8 0 0.6 HDR HDR-45 HDR HDR-45 25 45 16 Vacant Vacant - 2-16 Old Cardenas
Market 1652 S EUCLID AVE 91762 105027220 5 3 8 0 0.6 HDR HDR-45 HDR HDR-45 25 45 16 Vacant Vacant - 2-17 Old Cardenas
Market 1714 S EUCLID AVE 91762 105028401 11 6 16 0 2.2 MU-EF MU-8D MU-EF MU-8D 20 75 33 Nonvacant Vacant Grocery - 2-18 Ontario Mills 1050 ONTARIO MILLS DR 91764 23801402 6 4 5 5 1.3 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 20 Nonvacant Automotive
small outparcel: Car Wash ;
Note 4
2-19 Ontario Mills 990 ONTARIO MILLS DR
UNIT H 91764 23801403 9 5 7 7 1.8 MU-OM SP MU-OM
SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 28 Nonvacant Restaurant
small outparcel: Jamba Juice,
Mantra Indian ; Note 4
2-20 Ontario Mills 960 ONTARIO MILLS DR 91764 23801404 6 4 5 5 1.3 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 20 Nonvacant Restaurant
small outparcel: Starbucks,
Noodle World Jr, Donut Shop ;
Note 4
2-21 Ontario Mills 4900 E FOURTH ST 91764 23801405 94 55 74 74 18.6 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 297 Nonvacant Movie theater
large outparcel: Regal Edwards
; Note 4
2-22 Ontario Mills 980 ONTARIO MILLS DR
UNIT C 91764 23801406 6 4 5 5 1.2 MU-OM SP MU-OM
SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 20 Nonvacant Restaurant
small outparcel: Rubio's,
Fishbone, Corner Deli ; Note 4
2-23 Ontario Mills 880 N ROCHESTER AVE 91764 23801407 7 4 5 5 1.3 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 21 Nonvacant Retail
small outparcel: Car stereo
store ; Note 4
2-24 Ontario Mills 4421 E ONTARIO MILLS
PKWY 91764 23801410 17 10 13 13 3.3 MU-OM SP MU-OM
SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 53 Nonvacant Retail
small outparcel: The Love
Store (adult entertainment) ;
Note 4
2-25 Ontario Mills 950 ONTARIO MILLS DR 91764 23801418 7 4 5 6 1.4 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 22 Nonvacant Restaurant
small outparcel: Heroes
Restaurant and Brewhouse ;
Note 4
2-26 Ontario Mills 4549 MILLS CIR 91764 23801419 74 43 59 59 14.7 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 235 Nonvacant Movie theater &
mall parking lot
Mall Site: AMC Ontario Mills 30
; Note 4
DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 1 3740 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

Wienerschnitzel ; Note 4
2-35 Ontario Mills 4649 MILLS CIR 91764 23801434 5 3 4 3 0.9 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 15 Nonvacant Restaurant
small outparcel: Burger King ;
Note 4
2-36 Ontario Mills 1 MILLS CIR 91764 23801436 485 285 385 385 96.3 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 1540 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Mall and Parking Lot
; Note 4
2-37 Ontario Mills 4543 MILLS CIR 91764 23801437 6 4 5 5 1.2 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 20 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: The Children's Place
; Note 4
2-38 Ontario Mills 4449 MILLS CIR 91764 23801438 7 4 5 5 1.4 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 21 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Sketchers Factory
Outlet ; Note 4
2-39 Ontario Mills 4421 MILLS CIR 91764 23801439 6 3 5 5 1.1 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 19 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Mall ; Note 4
2-40 Ontario Mills 4380 MILLS CIR 91764 23801440 9 5 7 7 1.7 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 28 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Parking Lot ; Note 4
DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 2 3741 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

ONTARIO MILLS PKWY &
INLAND EMPIRE BLVD (TINTERSECTION)91764 23804132 23 13 18 18 4.5 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 72 Nonvacant Retail
large outparcel: Big Box
Parking Lot ; Note 4
2-51 Ontario Mills 4750 MILLS CIR 91764 23826101 24 14 19 19 4.8 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 76 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Nordstrom Rack &
Parking Lot ; Note 4
2-52 Ontario Mills 4758 MILLS CIR 91764 23826102 7 4 6 6 1.4 MU-OM SP MU-OM SP - City to ammend California
Commerce Center North
Specific Plan (The Mills)
25 85 23 Nonvacant
Ontario Mills
Mall & parking
lot
Mall Site: Sketchers Factory
Outlet & Parking Lot ; Note 4
2-76 Vineyard
Corridor

NEC EDISON AVE &
VINEYARD AVE 91762 21818101 100 58 79 79 9 MDR SP-AG-AH HDR SP-AG-AH 25 45 316 Nonvacant Crops / Nursery - DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 3 3742 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

N OF NEC EDISON AVE &
VINEYARD AVE 91762 21818102 78 45 61 63 7 MDR SP-AG-AH HDR SP-AG-AH 25 45 247 Nonvacant Crops / Nursery Note 2 2-78a Vineyard
Corridor 9064 EDISON AVE 91762 21818111 58 35 46 47 5.3 MDR SP-AG-AH HDR SP-AG-AH 25 45 186 Nonvacant nursery Note 2, Note 3 2-78b Vineyard
Corridor 9064 EDISON AVE 91762 21818111 3 3 2 3 0.5 LDR SP-AG MDR-AH SP-AG-AH 20 30 11 Nonvacant Truck parking Note 2, Note 3 2-79 Vineyard
Corridor 9060 EDISON AVE 91762 21818112 80 48 64 63 7.3 MDR SP-AG-AH HDR SP-AG-AH 25 45 255 Nonvacant nursery - 2-80 Grand Park 7330 EDISON AVE 91762 105320101 121 72 96 96 15.7 MDR SP-AG-AH MU-GP SP-AG-AH 20 65 385 Nonvacant Dairy/Vacant Note 2 2-81 Grand Park 7244 EDISON AVE 91762 105328101 65 38 51 51 8.4 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 205 Nonvacant Dairy / Truck Parking
Note 2
2-82 Grand Park 7218 EDISON AVE 91762 105328102 16 9 13 12 2.1 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 50 Nonvacant RV Storage Note 2 2-83 Grand Park NWC EDISON AVE &
EUCLID AVE 91762 105328103 21 12 17 17 2.7 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 67 Vacant vacant Note 2 2-102 Grand Park 7325 EDISON AVE 91762 105339101 194 113 154 154 25.1 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 615 Nonvacant Mulch Note 2 2-103 Grand Park 7225 EDISON AVE 91762 105341101 97 57 77 78 12.6 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 309 Nonvacant Nursery Note 2 2-105 Grand Park 14211 EUCLID AVE 91762 105342102 33 20 26 27 4.3 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 106 Nonvacant Truck Parking Note 2 2-106 Grand Park 14187 EUCLID AVE 91762 105342104 44 26 35 35 5.7 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 140 Nonvacant livestock Note 2 2-107 Grand Park S OF SEC EDISON AVE &
EUCLID AVE 91762 105342107 5 3 3 4 0.6 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 15 Nonvacant Truck Parking Note 2 2-108 Grand Park 14107 EUCLID AVE 91762 105342108 9 5 7 8 1.2 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 29 Nonvacant Convenience
store (Mark's
Market)
Note 2
2-109 Grand Park 7145 EDISON AVE 91762 105342109 27 16 22 22 3.5 MU-GP SP-AG-AH MU-GP SP-AG-AH 20 65 87 Nonvacant Truck Parking Note 2 2-131 Grove Corridor 8146 E CHINO AVE 91761 21617101 32 19 0 155 9.1 MU-GR SP-AG-AH MU-GR SP-AG-AH 20 65 206 Nonvacant Crops / Truck Parking
Note 2
2-132 Grove Corridor N OF NEC CHINO AVE &
GROVE AVE 91761 21617102 16 10 0 79 4.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 105 Nonvacant Crops Note 2 2-133 Grove Corridor S OF SEC RIVERSIDE DR
& GROVE AVE 91761 21617103 33 19 0 157 9.5 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 209 Nonvacant Crops Note 2 2-134 Grove Corridor SWC RIVERSIDE DR &
PARCO AVE 91761 21617105 32 19 0 151 9.2 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 202 Nonvacant Crops Note 2 2-135 Grove Corridor 0 COMET AVE 91761 21617106 33 20 0 159 9.6 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 212 Nonvacant Crops Note 2 2-136 Grove Corridor N OF NEC CHINO AVE &
GROVE AVE 91761 21617111 16 10 0 79 4.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 105 Nonvacant Crops Note 2 2-137 Grove Corridor 13605 S GROVE AVE 91761 21621101 79 46 0 187 8.9 MDR SP-AG-AH HDR SP-AG-AH 25 45 312 Nonvacant Former Dairy
(facilities
removed)
Note 2
2-138 Grove Corridor 13403 S GROVE AVE 91761 21621104 73 43 0 216 9.5 MDR SP-AG-AH HDR SP-AG-AH 25 45 332 Nonvacant Former Dairy
(facilities
removed)
Note 2
DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 4 3743 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

City of Ontario Policy Plan Amendments
Projections Methodology
Map
ID
Opportunity
Area Site Address/Intersection
5 Digit
ZIP
Code
APN
Very
LowIncome
LowIncome
ModerateIncome
Above
ModerateIncome
Parcel
Size
(Acres)
Current
General
Plan
Current
Zoning
Proposed
General
Plan
Proposed Zoning
Minimum
Density
Allowed
Maximum
Density
Allowed
Total
Capacity
Vacant/
Nonvacant
Description of
Existing Uses
Notes
2-139 Grove Corridor 8113 E CHINO AVE 91761 21621105 45 26 0 133 9 MU-GR SP-AG-AH MU-GR SP-AG-AH 20 65 204 Nonvacant Mulch /
Lanscaping
Supplies
Note 2
2-140 Grove Corridor 0 0 21621124 55 33 0 150 6.8 MDR SP-AG-AH HDR SP-AG-AH 25 45 238 Nonvacant Truck Parking Note 2 2-141 Grove Corridor N OF NEC EDISON AVE &
GROVE AVE 91761 21631103 95 56 76 76 8.6 MDR SP-AG-AH HDR SP-AG-AH 25 45 303 Nonvacant Dairy Note 2 2-142 Grove Corridor 13817 S GROVE AVE 91761 21631104 105 62 83 83 9.5 MDR SP-AG-AH HDR SP-AG-AH 25 45 333 Nonvacant Dairy Note 2 2-143 Grove Corridor 13715 S GROVE AVE 91761 21631105 100 59 79 79 9.1 MDR SP-AG-AH HDR SP-AG-AH 25 45 317 Nonvacant Truck Parking
and Open
Storage
Note 2
2-144 Grove Corridor N OF NWC CHINO AVE &
GROVE AVE 91761 105217102 33 19 0 157 9.5 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 209 Nonvacant Crops Note 2 2-145 Grove Corridor 8074 E CHINO AVE 91761 105218101 31 18 0 153 8.9 MU-GR SP-AG-AH MU-GR SP-AG-AH 20 65 202 Nonvacant Crops Note 2 2-146 Grove Corridor 8089 E CHINO AVE 91761 105246103 45 26 0 132 9 MU-GR SP-AG-AH MU-GR SP-AG-AH 20 65 203 Nonvacant Dairy / Hauling Agency
Note 2
2-147 Grove Corridor S OF SEC CHINO AVE &
GROVE AVE 91761 105247102 73 43 0 216 9.5 MDR SP-AG-AH HDR SP-AG-AH 25 45 332 Nonvacant Crops Note 2 2-148 Grove Corridor 13524 S GROVE AVE 91761 105248102 54 31 0 158 6.9 MDR SP-AG-AH HDR SP-AG-AH 25 45 243 Nonvacant Trucking Business
Note 2
2-149 Grove Corridor 13608 S GROVE AVE 91761 105249105 39 23 0 93 4.4 MDR SP-AG-AH HDR SP-AG-AH 25 45 155 Nonvacant Truck Parking Note 2 2-150 Grove Corridor 8010 SCHAEFER AVE 91761 105249106 37 22 0 89 4.2 MDR SP-AG-AH HDR SP-AG-AH 25 45 148 Nonvacant Truck Parking / Open Storage
Note 2
2-151 Grove Corridor 8025 SCHAEFER AVE 91761 105315104 51 30 40 40 4.6 MDR SP-AG-AH HDR SP-AG-AH 25 45 161 Nonvacant Truck Parking Note 2 2-152 Grove Corridor 8087 SCHAEFER AVE 91761 105315105 43 26 34 34 3.9 MDR SP-AG-AH HDR SP-AG-AH 25 45 137 Nonvacant Truck Parking Note 2 2-153 Grove Corridor 13814 S GROVE AVE 91761 105316102 105 61 83 83 9.5 MDR SP-AG-AH HDR SP-AG-AH 25 45 332 Nonvacant livestock Note 2 2-154 Grove Corridor 13908 S GROVE AVE 91762 105317103 47 28 37 37 4.3 MDR SP-AG-AH HDR SP-AG-AH 25 45 149 Nonvacant Truck Parking Note 2 2-155 Grove Corridor 13960 S GROVE AVE 91762 105317105 40 23 32 32 3.6 MDR SP-AG-AH HDR SP-AG-AH 25 45 127 Nonvacant poultry / eggs Note 2 2-156 Euclid Corridor 7247 E RIVERSIDE DR 91761 105207105 5 3 0 34 1.9 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 42 Nonvacant Ag Supply - 2-157 Euclid Corridor SEC RIVERSIDE DR &
EUCLIDE AVE 91761 105207107 62 36 0 295 15 MU-ER SP-AG-AH MU-ER SP-AG-AH 20 75 393 Nonvacant Crops - 2-158 Euclid Corridor E OF SEC RIVERSIDE DR
& EUCLIDE AVE 91761 105207108 25 15 0 121 7.3 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 161 Nonvacant Crops - 2-159 Euclid Corridor E OF SEC RIVERSIDE DR
& EUCLIDE AVE 91761 105207109 16 9 0 76 4.6 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 101 Nonvacant Crops - 2-160 Euclid Corridor 7297 E RIVERSIDE DR 91761 105207110 7 4 0 40 2.3 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 51 Vacant vacant - 2-161 Euclid Corridor S OF SWC RIVERSIDE DR
& SULTANA AVE 91761 105208104 33 19 0 157 9.5 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 209 Nonvacant Crops / Fallow Field -
DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 5 3744 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

City of Ontario Policy Plan Amendments
Projections Methodology
Map
ID
Opportunity
Area Site Address/Intersection
5 Digit
ZIP
Code
APN
Very
LowIncome
LowIncome
ModerateIncome
Above
ModerateIncome
Parcel
Size
(Acres)
Current
General
Plan
Current
Zoning
Proposed
General
Plan
Proposed Zoning
Minimum
Density
Allowed
Maximum
Density
Allowed
Total
Capacity
Vacant/
Nonvacant
Description of
Existing Uses
Notes
2-162 Euclid Corridor 7325 E RIVERSIDE DR 91761 105210101 27 16 0 151 8.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 194 Nonvacant Swap meet parking lot
-
2-163 Euclid Corridor 7192 E CHINO AVE 91761 105226101 18 10 0 84 5.1 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 112 Nonvacant Crops - 2-164 Euclid Corridor N OF NEC CHINO AVE &
EUCLID AVE 91761 105226102 20 12 0 96 5.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 128 Nonvacant Crops - 2-165 Euclid Corridor 13165 EUCLID AVE 91762 105226106 40 24 0 192 11.6 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 256 Nonvacant Crops / Dairy - 2-166 Euclid Corridor 13647 EUCLID AVE 91762 105238101 76 45 0 224 15.7 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 345 Nonvacant Fallow Field / Crops
-
2-167 Euclid Corridor N OF NEC SCHAEFER
AVE & EUCLID AVE 91762 105238102 5 2 0 14 1 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 21 Nonvacant Fallow Field / Crops -
2-168 Euclid Corridor 13583 EUCLID AVE 91762 105238104 19 12 0 57 4 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 88 Nonvacant Fallow Field / Crops
-
2-169 Euclid Corridor 13573 EUCLID AVE 91762 105238105 3 2 0 9 0.6 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 14 Nonvacant Fallow Field / Crops
-
2-170 Euclid Corridor 13555 EUCLID AVE 91762 105238106 3 2 0 10 0.7 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 15 Nonvacant Fallow Field / Crops
-
2-171 Euclid Corridor 13545 EUCLID AVE 91762 105238108 4 2 0 12 0.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 18 Nonvacant pool supply - 2-172 Euclid Corridor 7220 CHINO AVE 91761 105238110 72 42 0 211 14.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 325 Nonvacant Fallow Field / Dairy
Note 2
2-173 Euclid Corridor N OF NWC SCHAEFER
AVE & SULTANA AVE 91762 105238111 31 18 0 91 6.3 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 140 Nonvacant Fallow Field / Crops -
2-174 Euclid Corridor N OF NWC SCHAEFER
AVE & SULTANA AVE 91762 105238112 14 8 0 40 2.8 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 62 Nonvacant Fallow Field / Crops -
2-175 Euclid Corridor 13525 EUCLID AVE 91762 105238113 7 3 0 19 1.4 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 29 Nonvacant RV Rentals - 2-176 Euclid Corridor 13555 EUCLID AVE 91762 105238114 54 32 0 161 11.3 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 247 Nonvacant Fallow Field - 2-177 Euclid Corridor 13529 EUCLID AVE 91762 105238116 4 3 0 13 0.9 MDR SP-AG-AH MDR-AH SP-AG-AH 20 30 20 Nonvacant RV Rentals - 2-178 Sports Park S OF SWC RIVERSIDE DR
& VINEYARD AVE 91761 21617406 27 16 21 21 9.5 MDR SP-AG-AH MU-SP SP-AG-AH 25 65 85 Nonvacant RV storage - 2-179 Sports Park N OF NWC CHINO AVE &
VINEYARD AVE 91761 21617407 27 16 21 21 9.5 MDR SP-AG-AH MU-SP SP-AG-AH 25 65 85 Nonvacant Crops - 2-180 Sports Park NWC CHINO AVE &
VINEYARD AVE 91761 21617408 26 15 21 21 9.1 MDR SP-AG-AH MU-SP SP-AG-AH 25 65 83 Nonvacant Crops - 2-181 Vineyard
Corridor 8921 SCHAEFER AVE 91761 21818115 179 106 142 142 16.3 MDR SP-AG HDR SP-AG-AH 25 45 569 Nonvacant Dairy - 3-1 Vineyard
Corridor 8817 SCHAEFER AVE 91761 21631404 74 44 59 59 10.7 LDR SP-AG MDR-AH SP-AG-AH 20 30 236 Nonvacant Mulch /
Landscape
Supply
-
3-2 Vineyard
Corridor 8847 SCHAEFER AVE 91761 21631405 127 75 101 101 11.6 LDR SP-AG HDR SP-AG-AH 25 45 404 Nonvacant Mulch /
Landscape
Supply
-
3-3 Vineyard
Corridor 8691 SCHAEFER AVE 91761 21631403 22 12 17 17 3.1 LDR SP-AG MDR-AH SP-AG-AH 25 45 68 Nonvacant Mulch /
Landscape
Supply
-
DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 6 3745 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

S OF SWC SCHAEFER
AVE & HELLMAN AVE 91761 21631307 67 39 53 53 9.6 LDR SP-AG MDR-AH SP-AG-AH 25 45 212 Nonvacant Mulch /
Landscape
Supply
-
3-8 Vineyard
Corridor

S OF SEC SCHAEFER
AVE & WALKER AVE 91761 21631303 66 39 52 52 9.5 LDR SP-AG MDR-AH SP-AG-AH 25 45 209 Nonvacant Mulch /
Landscape
Supply
-
3-9 SCE Easement NWC OF SCE EASEMENT
& PARCO AVE 91761 21631108 61 36 48 49 8.8 LMDR SP-AG MDR-AH SP-AG-AH 25 45 194 Nonvacant Dairy Note 2 3-10 Vineyard
Corridor 13506 S ONTARIO AVE 91761 21812208 73 42 58 58 10.5 LDR SP-AG MDR-AH SP-AG-AH 25 45 231 Nonvacant Mulch /
Landscape
Supply
-
3-11 Vineyard
Corridor

NEC OF SCHAEFER AVE
& WHISPERING LAKES LN 91761 21812201 73 43 58 58 10.5 LDR SP-AG MDR-AH SP-AG-AH 25 45 232 Nonvacant Dairy - 3-12 Vineyard
Corridor 13656 S ONTARIO AVE 91761 21812209 73 42 58 58 10.5 LDR SP-AG MDR-AH SP-AG-AH 25 45 231 Nonvacant Mulch /
Landscape
Supply
-
3-17 Sports Park 8939 E CHINO AVE 91761 21812105 25 15 20 20 9 LDR SP-AG MU-SP SP-AG-AH 25 65 80 Nonvacant Dairy - 3-18 Sports Park 9145 E CHINO AVE 91761 21812204 29 18 23 23 10.4 LDR SP-AG MU-SP SP-AG-AH 25 65 93 Nonvacant Dairy - 3-19 Sports Park 9175 E CHINO AVE 91761 21812210 30 17 23 23 10.4 LDR SP-AG MU-SP SP-AG-AH 25 65 93 Nonvacant Dairy - 3-20 Sports Park S OF SWC OF CHINO AVE
& WHISPERING LAKES LN 91761 21812106 82 48 65 65 7.4 LDR SP-AG HDR SP-AG-AH 25 45 260 Nonvacant Dairy Note 2 3-21a Sports Park S OF SEC OF CHINO AVE
& WHISPERING LAKES LN 91761 21812203 64 38 51 51 5.8 LDR SP-AG HDR SP-AG-AH 25 45 204 Vacant vacant Note 2, Note 3 3-21b Vineyard
Corridor

S OF SEC OF CHINO AVE
& WHISPERING LAKES LN 91761 21812203 13 7 10 10 1.9 LDR SP-AG MDR-AH SP-AG-AH 20 30 40 Vacant vacant Note 2, Note 3 3-22a Sports Park 13434 S ONTARIO AVE 91761 21812206 64 37 51 51 5.8 LDR SP-AG HDR SP-AG-AH 25 45 203 Nonvacant Mulch /
Landscape
Supply
Note 2, Note 3
3-22b Vineyard
Corridor 13434 S ONTARIO AVE 91761 21812206 13 8 10 10 1.9 LDR SP-AG MDR-AH SP-AG-AH 20 30 41 Nonvacant Mulch /
Landscape
Supply
Note 2, Note 3
3-23 Vineyard
Corridor
13567 S WHISPERING
LAKES LN 91761 21812202 73 42 58 58 10.5 LDR SP-AG MDR-AH SP-AG-AH 20 30 231 Nonvacant Dairy - 3-24 East Holt 201 S BON VIEW AVE 91761 104913101 6 3 9 0 0.8 BP IP MU-HE MU-6-AH 20 40 18 Nonvacant Aging nonconforming
homes
-
3-28 SCE Easement NEC OF SCE EASEMENT
& CUCAMONGA AVE 91761 105317101 58 34 46 46 8.4 LMDR SP-AG MDR-AH SP-AG-AH 20 30 184 Nonvacant Truck Parking Note 2 3-30 SCE Easement NWC OF SCE EASEMENT
& CUCAMONGA AVE 91761 105318102 58 34 46 46 8.4 LDR SP-AG MDR-AH SP-AG-AH 20 30 184 Nonvacant Dairy Note 2 3-31 SCE Easement 13905 BON VIEW AVE 91762 105318101 57 33 45 45 8.2 LDR SP-AG MDR-AH SP-AG-AH 20 30 180 Nonvacant Dairy Note 2 3-32 SCE Easement NWC OF SCE EASEMENT
& BON VIEW AVE 91762 105319102 56 33 44 44 8.1 LDR SP-AG MDR-AH SP-AG-AH 20 30 177 Nonvacant Dairy Note 2 3-33 SCE Easement NEC OF SCE EASEMENT
& CAMPUS AVE 91762 105319101 56 33 45 45 8.1 LDR SP-AG MDR-AH SP-AG-AH 20 30 179 Nonvacant Dairy Note 2 DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 7 3746 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

N OF NEC OF SCHAEFER
AVE & VINEYARD AVE 91761 21812102 88 52 70 70 8 MDR SP-AG HDR SP-AG-AH 25 45 280 Nonvacant Crops Note 2 3-88 Vineyard
Corridor

N OF NWC OF SCHAEFER
AVE & VINEYARD AVE 91761 21621407 97 57 77 77 8.8 MDR SP-AG HDR SP-AG-AH 25 45 308 Nonvacant Crops Note 2 3-89 Vineyard
Corridor

NEC OF SCHAEFER AVE
& VINEYARD AVE 91761 21812101 96 57 76 76 8.7 MDR SP-AG HDR SP-AG-AH 25 45 305 Nonvacant Crops - 3-90 Vineyard
Corridor 8887 SCHAEFER AVE 91761 21631407 122 71 97 97 11.1 MDR SP-AG HDR SP-AG-AH 25 45 387 Nonvacant Dairy - 3-91 SCE Easement 7595 SCHAEFER AVE 91762 105311104 31 18 25 25 4.5 LDR SP-AG MDR-AH SP-AG-AH 20 30 99 Nonvacant Dairy Note 2 DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 8 3747 Table C-13b Housing Element Sites Inventory - Candidate Sites Subject to Rezoning Program Updated Version of Housing Element Table B-2, reflecting the proposed inventory.

Note 1: Lot Consolidation Proposed
Note 2: This parcel has multiple land use designations. Only the portion of the parcel that is suitable for residential use was included in the acreage in this table and used to calculate capacity.

Note 3: This parcel has been split in the inventory to reflect multiple proposed land uses. Only the portions of the parcel suitable for residential use was included in the acreage in this table and used to calculate capacity.

Note 4: The sites associated with strategy 5 are included in the inventory, but the City does not count the units herein towards its RHNA because the zoning is not in place. The City cannot count the units associated with these sites towards its RHNA obligation until zoning consistent with the Housing Element is implemented.

DRAFT April 2026 - (Proposed revisions to the Housing Element were undergoing HCD review at the time this memo was authored; this table may change in response to HCD comments.) Table C-13b Page 9 3748 APPENDIX D:

FRIANT RANCH WHITEPAPER AND

CO-BENEFITS RISK ASSESSMENT
.,
3749
3750

ASSESSING REGIONAL CRITERIA POLLUTANT EMISSIONS IMPACTS UNDER CEQA

IN LIGHT OF THE FRIANT RANCH RULING
Contributing Authors are the following members of the Association of Environmental Professionals Climate Change Committee: Dave Mitchell (First Carbon Solutions), Nicole Vermilion (PlaceWorks), Michael Hendrix (Atkins), and Rich Walter (ICF International, Inc.) 1 California Environmental Quality Act (CEQA) practitioners continually adapt and expand how environmental assessments address impacts. Over the years impact analyses and how lead agencies determine what a ‘significant’ impact is has been clarified through case law and updates to the CEQA Guidelines. However, sometimes court rulings create uncertainty for CEQA practitioners to address them based on the information and tools at hand. Such is the case for requests made on regional air quality impacts in the recent Sierra Club et al. v County of Fresno et al. and Friant Ranch, L.P, (2014 266 Cal. App. 5th Dist, Case No. F066798), referred to as Friant Ranch.

Background
The Friant Ranch project is a proposed master planned retirement community for active adults (55 and older) on approximately 942 acres in central Fresno County. The regional air quality analysis was prepared in accordance with the adopted San Joaquin Valley Air Pollution Control District’s (SJVAPCD) Guide for Assessing and Mitigating Air Quality Impacts (GAMAQI). While the SJVAPCD is in the process of updating the GAMAQI, in general, the regional air quality significance thresholds and methodology between the adopted 2002 GAMAQI and the draft GAMAQI are similar. The EIR used a threshold for ozone precursors from the SJVAPCD GAMAQI which was based on the New Source Review offset thresholds for stationary sources 10 tons per year for (ROG and NOx). This threshold approach was adopted by most air pollution control districts in California who have adopted CEQA air quality guidelines.

Based on the SJVAPCD significance thresholds for criteria air pollutants, air quality impacts of the Friant Ranch project were identified as a significant unavoidable impact of the project in the Environmental Impact Report (EIR). Due to the size of the project, emissions of reactive organic gases (ROGs) and oxides of nitrogen (NOx) exceed the SJVAPCD’s significance thresholds by 10 times in a region that is designated as non-attainment under the California and National ambient air quality standards (AAQS) for these pollutants. At build-out, the proposed Friant Community Plan would emit approximately 117.38 tons per year of PM10, 109.52 tons per year of ROGs, and 102.19 tons per year of NOx. 1 This paper represents the personal opinions of the contributing authors and not an opinion of the consulting firms for which they work. This paper does not represent legal advice. CEQA lead agencies are advised to consult with their legal counsel in matters of CEQA legal adequacy. 3751
The central issue in the court case was not the methodology applied to quantifying regional air quality impacts or the determination that the magnitude of emissions was substantial and therefore ‘significant’ but, that the EIR did not adequately relate the magnitude of emissions over the threshold to the healthbased effects of the criteria air pollutants emissions. The court found that simply reporting that the emissions exceed the threshold was not sufficient. Per the Friant Ranch ruling:
“The discussion of the adverse health effects, however, was not connected to the levels of the pollutant that would be emitted by the completed project. Instead, the discussion of adverse health effects was general in nature.”
The court agreed with the plaintiffs that the EIR was inadequate because it did not explain what it meant to exceed the significance threshold by 10 times and because it didn’t provide a meaningful analysis of the adverse health effects that would be associated with the project’s estimated emissions. Per the ruling, the
“EIR, however, provided no information about the composition of the particulate matter that was expected to be produced by the project.”
Drawing from Bakersfield Citizens (supra, 124 Cal. App. 4th 1184), health impacts resulting from adverse air quality impacts must be identified and analyzed. Despite the fact that the Friant Ranch EIR included a general discussion of adverse health effects, the court found it was ‘short’ on analysis as it did not correlate the additional tons per year of regional emission that would be generated by the project (i.e., the adverse air quality impacts) to adverse human health impacts that could be expected to result from those regional emissions. The Friant Ranch ruling indicated that the EIR should have provided an analysis of the correlation between the project’s regional emissions and human health impacts.

Specific examples cited include:
 “The information provided doesn’t enable a reader to determine whether the 100-plus tons per year of PM10, ROG, and NOx will require people with respiratory difficulties to wear filtering devices when they go outdoors in the project area or nonattainment basis or, in contrast, will be no more than a drop in the bucket to those people breathing the air containing the additional pollutants.”
 “If an estimate of the project’s impact on the “days exceeding standards” had been provided, the public and decision makers might have some idea of the magnitude of the air pollutant impact on human health. As presently written, the final EIR does not inform the reader what impact, if any, the project is likely to have on the days of nonattainment per year—it might double those days or it might not even add a single a day per year. Similarly, no connection or correlation is made between (1) the EIR’s statement that exposure to ambient levels of ozone ranging from

0.10 to 0.40 parts per million for one to two hours has been found to significantly alter lung functions and (2) the emissions that the project is expected to produce.” 3752
These examples, highlight the difficulty that air quality practitioner have with the Friant Ranch ruling, as discussed further below. The current practice in addressing air quality impacts in CEQA documents is to relate health impacts to the regional significance thresholds, which are related to the AAQS. However, as cited in the Friant Ranch ruling, the court found this disclosure too general and not specific enough.

However, on the other end of the spectrum, there are not adequate tools available to characterize health impacts of a single project to the degree requested. The court did not provide guidance in this.

Rather, the court cited that the “County has discretion in choosing what type of analysis to provide and we will not direct County on how to exercise that discretion. (§ 21168.5.) Nonetheless, there must be some analysis of the correlation between the project’s [regional] emissions and human health impacts.” So CEQA air quality practitioners are now left scratching their heads on how to comply with the ruling and are seeking guidance from the regional air districts.

The court in the Friant Ranch ruling is clearly viewing the EIR analysis of regional criteria pollutants through a project-level paradigm, when this is clearly a cumulative impact issue. As such, the court’s suggestions as to a remedy are based on the wrong paradigm, which creates further substantial challenge for the CEQA practitioner.

Origin of Criteria Air Pollutant Significance Thresholds Before we get into addressing the specific components of the ruling, it is important to discuss how the regional significance thresholds (lbs per day and/or tons per year) were developed for CEQA significance findings and how they related to human health and welfare.

Regional significance thresholds are derived from the United Stated Environmental Protection Agency (USEPA) health-based standards. Regional significance thresholds have been used by air districts in California for the last 25 years. Air quality practitioners prior to the development of bright-line significance threshold struggled with the need to define the level at which a project’s emissions are deemed significant. It is important to note that regional criteria air pollutant emissions are by definition a cumulative impact.

Regional air quality impacts, similar to greenhouse gas (GHG) emissions impacts are inherently cumulative in nature. Land use projects on their own would not single-handedly cause emissions that exceed the ambient air quality standards. In fact, localized emissions modeling requires air quality modelers to consider the background concentrations when calculating localized impacts. An analysis of regional emissions impacts addresses whether the additional amount of emissions generated by a project should be considered significant in the context of the existing cumulative effect, which is based on criteria air pollutant emissions for which the air basin is designated as nonattainment for. Therefore, the regional criteria air pollutant analysis in a CEQA document is not a project-level analysis, but a cumulative impact analysis.

Therefore, the “one molecule rule”, as defined in the Communities for a Better Environment v. California Resources Agency and California Building Industry Association (2002 126 Cal. Rptr. 2d. 441, Cal.App.3 Dist., 2002) (CBE Case), applies. As defined in the CBE case, just because criteria air pollutant emissions 3753
adds to the effect in the nonattainment area does not necessarily create a significant cumulative effect, and the “one [additional] molecule rule” is not the law. Consequently, air districts have developed these bright-line thresholds to define what constitutes a significant impact.

For example, in the South Coast Air Basin, these bright-line significance thresholds were originally developed based on the annual emissions permitting thresholds in the USEPA Prevention of Significant Deterioration (PSD) of Air Quality regulation. The USEPA thresholds are the increment of air pollution an area is allowed to increase. PSD increments prevent the air quality in clean areas from deteriorating to the level set by the National AAQS. Similar to CEQA thresholds, the USEPA thresholds require projects that generate regulated sources of emissions to demonstrate that new emissions emitted from a proposed major stationary source or major modification, in conjunction with other applicable emissions increases and decreases from existing sources, will not cause or contribute to a violation of any applicable National AAQS or PSD increment. Consequently, the thresholds used by air districts in California to determine significant impacts are derived from the health based AAQS. Table 1 shows the primary health and welfare effects from the criteria air pollutant emissions of concern for land use projects.

Table 1 Primary Sources and Effects of Criteria Air Pollutants Pollutants Primary Health and Welfare Effects
Carbon Monoxide Aggravation of some heart diseases (angina); Reduced tolerance for exercise;
Impairment of mental function;
Impairment of fetal development;
Death at high levels of exposure
Nitrogen Dioxide Aggravation of respiratory illness
Ozone (O3)1 Aggravation of respiratory and cardiovascular diseases; Reduced lung function, Increased cough and chest
discomfort
Particulate Matter
(PM10 and PM2.5)
Reduced lung function;
Aggravation of respiratory & cardio-respiratory diseases; Increases in mortality rate;
Reduced lung function growth in children
Source: South Coast Air Quality Management District (SCAQMD). 2005, May 6. Guidance Document for Addressing Air Quality, Issues in General Plans and Local Planning, http://www.aqmd.gov/docs/defaultsource/planning/air-quality-guidance/complete-guidance-document.pdf?sfvrsn=4 1 Ozone is a secondary criteria air pollutant and not emitted directly by a project.

One way to think about the existing thresholds is to think about the regional inventory of criteria pollutants. While a project that exceeds the thresholds by itself cannot “bounce the needle” on the ambient concentrations of criteria pollutants, when you amalgamate all the land use and other sources that exceed the thresholds, then you are dealing with a meaningful majority of the regional criteria 3754
pollutant emissions. The existing thresholds are a tool by which to ensure that CEQA evaluations are conducted for projects that meaningfully contribute to the regional inventory. But this does not mean that a single project would substantially change ambient conditions in a specifically measurable way in terms of health effect. Rather it means that without control, the cumulative projects above this threshold would contribute to meaningfully changes in ambient concentrations which would have measurable changes in health effects. Using CEQA terminology, the thresholds do not identify the level at which a project results in a significant impact, instead the thresholds identify when the project’s emissions are a considerable contribution to a cumulatively significant impact.

Limits of Air Quality Dispersion Modeling for Regional Criteria Pollutants As a measure of cumulative contribution, the regional significance thresholds for criteria pollutants only indirectly tie emissions generated by a project to the health-based standards of the AAQS. The healthbased standards of the AAQS are based on the concentration of air pollutant emissions in the air and not the quantity of emissions (mass emissions) generated within an air basin. If the ruling requires something more than a general discussion of the health implications of exceeding the regional significance thresholds of the air district, how can lead agencies comply with the ruling? Ozone and Secondary Particulate Matter
Ozone and secondary PM cannot be modeled with one of the dispersion models used for localized pollutants (such as diesel particulate matter) because they are formed with complex chemical reactions in the atmosphere sometimes many miles from the source of emissions. The models need to simulate dispersion, deposition, atmospheric chemistry, and meteorology, in a three dimensional scale. The models need to include all precursor emission sources in a gridded inventory that accounts for the time of day and location of the emission sources throughout a modeling domain. Some simplified models referred to as mesoscale models have been developed to model the impact on a smaller scale for large point sources such as power plants, but according to USEPA they are not considered a reliable predictor of actual concentrations of ozone. In addition, emissions from development projects are primarily generated by mobile sources. Cars and trucks travel an average of 7-10 miles for each trip resulting in emissions being spread throughout the road network, not from a single project site. Therefore, ozone, and to lesser extent PM10 must be modeled using a regional atmospheric model.

Ozone air quality attainment plans use regional atmospheric models to determine the emission carrying capacity of the air basin. If the carrying capacity is exceeded, locations within the modeling domain will exceed the ambient air quality standard. The more that the carrying capacity is exceeded, the higher the concentration experienced in the areas exceeding the standard. When air basins are close to attainment, the areas that exceed the standard become more isolated. Attainment modeling is used to determine the amount of reductions needed to reach attainment at the last location within the basin.

This means that most locations in a basin, including those with very large projects, may have no exceedances of the standard and areas with less favorable meteorology with no projects and limited local sources can exceed the standard.
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Nitrogen Dioxide/Oxides of Nitrogen
Theoretically, it would be possible to add the emissions from a large project such as Friant Ranch into the regional attainment model and look for increases in concentration throughout the air basin. This would be considered a sensitivity analysis. The analysis could hold the emissions in the rest of the grid constant and see what happens when emissions are added to the appropriate grid squares. The SJVAB inventory for the ozone precursor NOx is about 545 tons per day or 198,925 tons per year. Friant Ranch would produce approximately 102 tons per year of NOx or a 0.051 percent increase. A small increase in emissions of less than a tenth of a percent spread over several grid cells is not likely to move the concentration by an amount beyond the uncertainty in the model.

The regional models account for phenomenon like low level jet streams that can quickly transport emissions from where they are generated to distant locations and wind eddies that recirculate polluted air on a sub-regional basis. In addition, photochemical modeling, in the case of ozone, is dependent of the amount of the individual precursors at all locations in the domain. This is because in the absence of sunlight, NOx destroys ozone, and areas deficient in NOx such as rural and mountain areas will experience high ozone concentrations well into the evening while urban areas with many NOx sources will see rapid decreases in ozone in the evening. Therefore, modeling that adds emissions from a development project in one part of the modeling domain may have a beneficial effect in one area and a negative effect in another. For Friant Ranch, the regional model would be the only way to accurately measure the increase in concentration, if any that would occur by adding the emissions at the project site on the road network receiving traffic from the project. However, the scale of the additional emissions is so small compared to the basin scale variables, that the effect on ambient concentrations would be lost in the “noise” of the model and would be highly unlikely to be directly attributable (or “correlated” in court’s language) to the project itself The San Joaquin Valley Air Basin (SJVAB) is considered NOx limited, meaning reductions in NOx have the most effect on ozone formation while the other ozone precursor, ROG, would have little effect because of its abundance in the atmosphere. Most ROG compounds are not considered to have health impacts except for those classified as toxic air contaminants that are regulated separately. ROG is generated by plants. Eliminating all ROG from manmade sources will still leave sufficient biogenic ROG to participate in the photochemical reaction to form ozone.

Inhalable Particulate Matter
PM10 is formed by chemical reactions in the atmosphere with precursor emissions and directly from combustion and from fugitive dust. For example, the particulate ammonium nitrate is formed when NOx and ammonia react in a series of complex chemical reactions. PM10 is an amalgamation of numerous particles, and aerosols. The mix of chemicals varies day to day and season to season. In winter, wood smoke and ammonium nitrate are larger portions the mix with occasional days heavily influenced by fugitive dust. In the summer, fugitive dust provides a larger fraction and nitrate is reduced. This variation somewhat complicates the modeling process. Regional PM10 emissions in the SJVAB were modeled using a procedure called chemical mass balance. The individual PM10 species are allocated into 3756
a grid and reductions from the control measures designed to reduce each constituent are applied to the inventory in each grid square to demonstrate attainment. This process could be done in reverse by adding pollutants generated by the project into the appropriate grid square to see if it would substantially increase concentrations to unhealthful levels. However, again, the regional scale effects and dispersion dwarf nearly all project level emission contributions such that meaningful attribution of ambient concentrations to the project itself will be difficult. More sophisticated atmospheric models for PM10 and PM2.5 exist, but to our knowledge have not been used in the SJVAB.

Number of Days Air Basins Exceed AAQS
In addition to effects on peak concentration, it is also important to know how many days people are exposed to the unhealthful levels and whether a project would increase the number of days each year in which the air quality standard is exceeded. The modeling only tells what conditions will be during days with the worst conditions (most favorable for forming ozone) called an episode. Poor air quality builds up over a number of days when stagnant conditions occur. Eventually, more favorable conditions return and the air quality is improved. In Fresno the number of days exceeding the federal 8-hour ozone standard of 0.075 ppm was between 50 and 70 days per year between 2009 and 2012. This is down from over 100 days per year in the past. The peak 8-hour readings were as high as 0.116 ppm in 2013.

This indicates that the existing conditions are well over the standard on many days. A sufficient regional cumulative increase in emissions could cause the concentration to go up on the worst days and to increase the number of days exceeding the standard. However, as described above, the Friant Ranch emissions are so small relevant to the regional inventory (NOx emission are only 0.051 percent of regional emissions) that any project-attributable change in conditions is likely to be within the model uncertainty and thus would not be a valid result that could be used as the basis for a significant determination under CEQA.

Criteria Air Pollutant Burden: Number of People Exposed to Unhealthful Concentrations Another factor that is considered in assessing air quality health impacts is the number of people exposed to unhealthful air quality from regional criteria pollutants. Areas with large populations with high pollutant concentrations would expose more people to bad air than areas with small populations and equally poor air quality. It is not acceptable to expose anyone to poor air quality, but it may help prioritize actions to reduce the impacts by where the most people would be helped.

The health impacts of ozone can be presented in a number of ways. The clearest in comparison is to the state and federal ambient ozone standards. If ambient concentrations are below the standard, it is safe to say that no health impact would occur to anyone. When concentrations exceed the standard, impacts will vary based on how much the standard is exceeded. The USEPA developed the Air Quality Index (AQI) as an easy to understand measure of health impact. 75 ppb: AQI 100 – Moderate:

Sensitive Groups: Children and people with asthma are the groups most at risk.

Health Effects Statements: Unusually sensitive individuals may experience respiratory symptoms.
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Cautionary Statements: Unusually sensitive people should consider limiting prolonged outdoor exertion.
95 ppb: AQI 150 – Unhealthful for Sensitive Groups:

Sensitive Groups: Children and people with asthma are the groups most at risk.

Health Effects Statements: Increasing likelihood of respiratory symptoms and breathing discomfort in active children and adults and people with respiratory disease, such as asthma.

Cautionary Statements: Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.
115 ppb: AQI 200 – Unhealthy:

Sensitive Groups: Children and people with asthma are the groups most at risk.

Health Effects Statements: Greater likelihood of respiratory symptoms and breathing difficulty in active children and adults and people with respiratory disease, such as asthma; possible respiratory effects in general population.

Cautionary Statements: Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion.
139 ppb: AQI 210 – Very Unhealthful:

Sensitive Groups: Children and people with asthma are the groups most at risk.

Health Effects Statements: Increasingly severe symptoms and impaired breathing likely in active children and adults and people with respiratory disease, such as asthma; increasing likelihood of respiratory effects in general population.

Cautionary Statements: Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion.

Based on the AQI scale, Fresno experienced one day in the last three years that would be categorized as unhealthful, and as many as 56 days that was unhealthful for sensitive groups or moderate at the worst monitoring station. This raises the question of what would be considered a significant project impact.

Would a project need to be solely responsible for increasing the days over the standard by one day, five days, or ten days? Would a project need to increase the AQI to the next higher level – moderate to unhealthful for sensitive groups? This line of reasoning leads back to the basis of the 10 ton per year ozone precursor threshold that is based on a policy determination that this amount is a cumulative contribution deserving mitigation in consideration to the existing impact. Although this approach might in concept be thought useful, since nearly all projects would have such a small contribution in isolation2 ,
it is unlikely that any one project would change the modeled population exposed to concentrations above ambient thresholds.
2 In the Friant Ranch case, using ozone precursors as an example, if regional cumulative emissions result in an AQI of 210 and the AQI is linearly related to the amount of emissions, then the project-only contribution would be an increase in the AQI of 0.1. Given the photochemistry issues described above for ozone formation, such a direct linear relationship is not a valid presumption and this points out that ultimately this is a cumulative impact, not a project-only impact.
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The Air District’s Shoes
The discussion above highlights the difficulties with doing macro-level dispersion modeling and relating mass emissions to the number of people requiring “filtering devices when they go outdoors” or the number of additional days a region may be in nonattainment for. However, do CEQA practitioners really need to go that far? As identified above, regional significance thresholds (lbs per day, and/or tons per year thresholds) are derived from the AAQS. Air basins are identified as either attainment or nonattainment of the California and National AAQS for criteria pollutants. If an air basin is designated as nonattainment, the regional air districts are required to prepare air quality management plans detailing strategies to meet the AAQS in the timeline specified. If air districts already need to forecast future emission levels based on growth, does the decision made by the court overstep into the shoes of the air district?
Projects that exceed the regional significance threshold contribute cumulatively to the nonattainment designation, but do not cause nonattainment in isolation. As the attainment designation is based on the AAQS, which are set at levels of exposure that are determined to not result in adverse health, a project in a nonattainment area with criteria pollutant emissions would cumulatively contribute to health impacts within the air basin. Therefore, in the absence of tools for equating regional emissions to more specific health-based affects, the appropriate place for evaluating how growth within the air basins affects the ability to meet the AAQS and attain the health based standards established by the State and EPA is the regional air quality management plans. Regional air quality management planning specifically accounts for new development in the region based on development patterns set forth in General Plan.

The air district runs regional model simulations to determine whether or not an air basin can meet the AAQS. As addressed above, it would be exceedingly difficult to impossible for an individual project to accurately identify how it affects basin-wide concentrations within the uncertainty levels of available regional modeling tools. The air districts are the primary agencies responsible for ensuring the health and welfare of sensitive individuals to elevated concentrations of air quality. Therefore, the most appropriate discussion may be to relate when the air district anticipates the region attaining the healthbased standards of the AAQS.

So….what is the CEQA practitioner to do?
As discussed above, regional scale modeling of project-level criteria pollutant emissions will be unrevealing and in nearly all cases will not result in any meaningful identification of changes in ambient levels and human health effects with any certainty. The court ruling is logically flawed in applying a project-level paradigm to a cumulative-level contribution and is asking for an unrealistic and unscientific level of disclosure. As the California Supreme Court has taken up the case, it may resolve the issues in its determination.

In the meantime, lead agencies would be wise to provide the following disclosure when analyzing regional criteria pollutant emissions to better “correlate” project-level criteria pollutant emissions to human health impacts:

1. Describe the cumulative context of regional criteria pollutant emissions and that regional health 3759
effects occur due to the cumulative emissions of existing and future criteria pollutant sources.

2. Characterize the level of project criteria pollutant emissions in comparison to the regional inventory both in terms of tons and percentages.

3. Describe that regional criteria pollutant modeling cannot accurately capture the project-level effect on ambient pollutant concentrations beyond the uncertainty level of the modeling.

4. Disclose that cumulative contributions of regional criteria pollutant emissions collectively can and do have a real-world effect on human health and describe those in the impact analysis (not just in the setting section).

5. Disclose that in general, more criteria pollutant emissions will contribute to more health effects regionally, but that specifically attributing the project’s emissions to a specifically defined quantitative or geographic health effect is beyond the resolution of current tools.

6. Differentiate between regional criteria pollutants that are a concern for regional air pollution and localized pollutants (like toxic air contaminants) that are a project-scale concern for the immediate surrounding area of a project. If localized pollutants are studied for their impacts on ambient air quality near the project, explain why this is not appropriate for regional-scale pollutants like ozone precursors.

The situation with the Friant Ranch ruling is not unlike the recent court rulings concerning CEQA baselines. In the first Sunnyvale baseline case (Sunnyvale West Neighborhood Assn. v. City of Sunnyvale City Council, 2010), the appellate court described in rather bold terms that sole reliance on a future year baseline is never appropriate and that only an existing year baseline is appropriate under CEQA. In a second Sunnyvale baseline case (Pfeiffer v. City of Sunnyvale City Council 2011), the same appellate court found that a future year baseline could be appropriate in a CEQA document provided that the analysis also compares project effects to existing conditions. A California Supreme Court ruling (Neighbors for Smart Rail v. Exposition Metro Line Construction Authority, 4th Appellate District, 2013) then resolved the issue by finding that use of a future-year baseline only without comparison to existing conditions could actually be appropriate when comparing project conditions to existing conditions would be misleading to the public and decision-makers. The Supreme Court ultimately overturned the original court’s key finding, while adding requirements of substantiation that did not exist previously.

Depending on the actions of the Supreme Court, the Friant Ranch ruling may or may not be legal precedent on this issue. Given the pragmatic problems with trying to quantitatively correlate projectlevel criteria pollutant emissions to regional human health effects, it is hoped that future rulings are better informed by the science underlying regional criteria air pollution and associated health effects. 3760

TECHNICAL MEMORANDUM
20341 SW Birch Street | Suite 230 | Newport Beach, CA 92660 | (949) 660-1994 | urbanxroads.com DATE: May 7, 2026
TO: Scott Mulkay, Ontario Ranch Venture II, LLC
FROM: Haseeb Qureshi, Urban Crossroads, Inc.

JOB NO: 16285-02 Technical Memo
SUBJECT: EUCALYPTUS BUSINESS PARKSPECIFIC PLANCOMPARATIVE

HEALTH IMPACT ANALYSIS
The purpose of this memorandum is to provide additional analysis of the Eucalyptus Business Park Specific Plan (proposed Project), specifically a comparative analysis of air quality impacts and health risks for the proposed Project by leveraging the conclusions from similar projects such as the Friant Ranch Community Development Project (Friant Ranch project) and the California State University Dominguez Hills (CSUDH) Campus Master Plan (CSUDH project).

COMPARATIVE HEALTH IMPACT ANALYSIS: FRIANT RANCH AND CSUDH This section presents a comparative analysis of air quality impacts and health risk assessments for the Eucalyptus Business Park Specific Plan (proposed Project), leveraging the conclusions from similar projects such as the Friant Ranch project and the CSUDH project. These projects underwent comprehensive health impact modeling to evaluate the potential health effects of increased ozone and PM2.5 concentrations due to emissions from these projects. These analyses concluded that emissions from these projects would not result in significantly higher concentrations of ozone and PM2.5 in the region, and that these marginally increased pollutant concentrations would not result in meaningful health effects in the region. This memorandum demonstrates that the proposed Project’s emissions would similarly not result in a meaningful increase in health effects due to increased concentrations of ozone and PM2.5 in the region, and that performing comprehensive health impact modeling would not yield additional meaningful information.

The comparison focuses primarily on emissions of Nitrogen Oxides (NOx), Particulate Matter (PM2.5 and PM10), and Reactive Organic Gases (ROG)/Volatile Organic Compounds (VOC)—key pollutants for evaluating potential adverse health effects and ozone precursor formation. The findings of the Friant Ranch and CSUDH health impacts analyses serve as a foundation to support the conclusions for the proposed Project.

HEALTH IMPACT ANALYSIS BACKGROUND
National and California Ambient Air Quality Standards
The National Ambient Air Quality Standards (NAAQS) and California Ambient Air Quality Standards (CAAQS) are air quality regulations designed to define clean air in such a way that even the most sensitive individuals are protected. The NAAQS were established by the United States Environmental Protection Agency (US EPA) under the Clean Air Act. The NAAQS include standards 3761
16285-02 Technical Memo 2 Eucalyptus Business Park Specific Plan for six criteria air pollutants, including ozone, particulate matter (PM10 and PM2.5), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and lead (Pb).

The CAAQS were established by the California Air Resources Board (CARB) and set stricter standards for criteria pollutants compared to the NAAQS. Additionally, the CAAQS include additional standards for pollutants not covered under the NAAQS, including hydrogen sulfide, vinyl chloride, sulfites, and visibility-reducing particles.

Criteria Pollutant Health Effects
Carbon Monoxide (CO): CO is a colorless, odorless gas produced by the incomplete combustion of carbon-containing fuels, such as gasoline or wood. CO concentrations tend to be the highest during the winter morning, when little to no wind and surface-based inversions trap the pollutant at ground levels. Because CO is emitted directly from internal combustion engines, unlike ozone (O3), motor vehicles operating at slow speeds are the primary source of CO in the SCAB. The highest ambient CO concentrations are generally found near congested transportation corridors and intersections.

Individuals with a deficient blood supply to the heart are the most susceptible to the adverse effects of CO exposure. The effects observed include earlier onset of chest pain with exercise, and electrocardiograph changes indicative of decreased oxygen supply to the heart. Inhaled CO has no direct toxic effect on the lungs but exerts its effect on tissues by interfering with O2 transport and competing with O2 to combine with hemoglobin present in the blood to form carboxyhemoglobin.

Hence, conditions with an increased demand for O2 supply can be adversely affected by exposure to CO. Individuals most at risk include fetuses, patients with diseases involving heart and blood vessels, and patients with chronic hypoxemia (oxygen deficiency) as seen at high altitudes.

Sulfur Dioxide (SO2): SO2 is a colorless, extremely irritating gas or liquid. It enters the atmosphere as a pollutant mainly as a result of burning high sulfur-content fuel oils and coal and from chemical processes occurring at chemical plants and refineries. When SO2 oxidizes in the atmosphere, it forms SO4. Collectively, these pollutants are referred to as sulfur oxides (SOX).

A few minutes of exposure to low levels of SO2 can result in airway constriction in some asthmatics, all of whom are sensitive to its effects. In asthmatics, increase in resistance to air flow, as well as reduction in breathing capacity leading to severe breathing difficulties, are observed after acute exposure to SO2. In contrast, healthy individuals do not exhibit similar acute responses even after exposure to higher concentrations of SO2.

Animal studies suggest that despite SO2 being a respiratory irritant, it does not cause substantial lung injury at ambient concentrations. However, very high levels of exposure can cause lung edema (fluid accumulation), lung tissue damage, and sloughing off of cells lining the respiratory tract.

Some population-based studies indicate that the mortality and morbidity effects associated with fine particles show a similar association with ambient SO2 levels. In these studies, efforts to separate the effects of SO2 from those of fine particles have not been successful. It is not clear whether the two pollutants act synergistically, or one pollutant alone is the predominant factor.

Oxides of Nitrogen (NOX): NOX consist of nitric oxide (NO), nitrogen dioxide (NO2) and nitrous oxide (N2O) and are formed when nitrogen (N2) combines with O2. Their lifespan in the atmosphere ranges from one to seven days for nitric oxide and nitrogen dioxide, to 170 years for nitrous oxide. NOX is typically created during combustion processes and are major contributors to smog formation and acid deposition. NO2 is a criteria air pollutant and may result in numerous adverse health effects; it 3762
16285-02 Technical Memo 3 Eucalyptus Business Park Specific Plan absorbs blue light, resulting in a brownish-red cast to the atmosphere and reduced visibility. Of the seven types of nitrogen oxide compounds, NO2 is the most abundant in the atmosphere. As ambient concentrations of NO2 are related to traffic density, commuters in heavy traffic may be exposed to higher concentrations of NO2 than those indicated by a regional monitoring station.

Population-based studies suggest that an increase in acute respiratory illness, including infections and respiratory symptoms in children (not infants), is associated with long-term exposure to NO2 at levels found in homes with gas stoves, which are higher than ambient levels found in Southern California. Increase in resistance to air flow and airway contraction is observed after short-term exposure to NO2 in healthy subjects. Larger decreases in lung functions are observed in individuals with asthma or chronic obstructive pulmonary disease (e.g., chronic bronchitis, emphysema) than in healthy individuals, indicating a greater susceptibility of these sub-groups.

In animals, exposure to levels of NO2 considerably higher than ambient concentrations result in increased susceptibility to infections, possibly due to the observed changes in cells involved in maintaining immune functions. The severity of lung tissue damage associated with high levels of O3 exposure increases when animals are exposed to a combination of O3 and NO2.

Ozone (O3): O3 is a highly reactive and unstable gas that is formed when VOCs and NOX, both byproducts of internal combustion engine exhaust, undergo slow photochemical reactions in the presence of sunlight. O3 concentrations are generally highest during the summer months when direct sunlight, light wind, and warm temperature conditions are favorable to the formation of this pollutant.

Individuals exercising outdoors, children, and people with pre-existing lung disease, such as asthma and chronic pulmonary lung disease, are considered to be the most susceptible sub-groups for O3 effects. Short-term exposure (lasting for a few hours) to O3 at levels typically observed in Southern California can result in breathing pattern changes, reduction of breathing capacity, increased susceptibility to infections, inflammation of the lung tissue, and some immunological changes.

Elevated O3 levels are associated with increased school absences. In recent years, a correlation between elevated ambient O3 levels and increases in daily hospital admission rates, as well as mortality, has also been reported. An increased risk for asthma has been found in children who participate in multiple outdoor sports and live in communities with high O3 levels.

O3 exposure under exercising conditions is known to increase the severity of the responses described above. Animal studies suggest that exposure to a combination of pollutants that includes O3 may be more toxic than exposure to O3 alone. Although lung volume and resistance changes observed after a single exposure diminish with repeated exposures, biochemical and cellular changes appear to persist, which can lead to subsequent lung structural changes.

Particulate Matter (PM10 and PM2.5): PM10 is a major air pollutant consisting of tiny solid or liquid particles of soot, dust, smoke, fumes, and aerosols. Particulate matter pollution is a major cause of reduced visibility (haze) which is caused by the scattering of light and consequently the significant reduction in air clarity. The size of the particles (10 microns or smaller, about 0.0004 inches or less) allows them to easily enter the lungs where they may be deposited, resulting in adverse health effects. Additionally, it should be noted that PM10 is considered a criteria air pollutant.

PM2.5 is a similar air pollutant to PM10 consisting of tiny solid or liquid particles which are 2.5 microns or smaller (which is often referred to as fine particles). These particles are formed in the atmosphere from primary gaseous emissions that include SO4 formed from SO2 release from power plants and 3763
16285-02 Technical Memo 4 Eucalyptus Business Park Specific Plan industrial facilities and nitrates that are formed from NOX release from power plants, automobiles, and other types of combustion sources. The chemical composition of fine particles highly depends on location, time of year, and weather conditions. PM2.5 is a criteria air pollutant.

A consistent correlation between elevated ambient fine particulate matter (PM10 and PM2.5) levels and an increase in mortality rates, respiratory infections, number and severity of asthma attacks and the number of hospital admissions has been observed in different parts of the United States and various areas around the world. In recent years, some studies have reported an association between long-term exposure to air pollution dominated by fine particles and increased mortality, reduction in lifespan, and an increased mortality from lung cancer.

Daily fluctuations in PM2.5 concentration levels have also been related to hospital admissions for acute respiratory conditions in children, to school and kindergarten absences, to a decrease in respiratory lung volumes in children without asthma, and to increased medication use in children and adults with asthma. Recent studies show lung function growth in children is reduced with long term exposure to particulate matter.

The elderly, people with pre-existing respiratory or cardiovascular disease, and children appear to be more susceptible to the effects of high levels of PM10 and PM2.5.

Volatile Organic Compounds (VOCs): VOCs are hydrocarbon compounds (any compound containing various combinations of hydrogen and carbon atoms) that exist in the ambient air. VOCs contribute to the formation of smog through atmospheric photochemical reactions and/or may be toxic. Compounds of carbon (also known as organic compounds) have different levels of reactivity; that is, they do not react at the same speed or do not form O3 to the same extent when exposed to photochemical processes. VOCs often have an odor, and some examples include gasoline, alcohol, and the solvents used in paints. Exceptions to the VOC designation include CO, carbon dioxide, carbonic acid, metallic carbides or carbonates, and ammonium carbonate. VOCs are a criteria pollutant since they are a precursor to O3, which is a criteria pollutant. The terms VOC and ROG (see below) are used interchangeably.

Breathing VOCs can irritate the eyes, nose, and throat, can cause difficulty breathing and nausea, and can damage the central nervous system as well as other organs. Some VOCs can cause cancer.

Not all VOCs have all these health effects, though many have several.

Reactive Organic Gases (ROGs): Similar to VOCs, ROGs are also precursors in forming O3 and consist of compounds containing methane, ethane, propane, butane, and longer chain hydrocarbons, which are typically the result of some type of combustion/decomposition process.

Smog is formed when ROG and NOX react in the presence of sunlight. ROGs are a criteria pollutant since they are a precursor to O3, which is a criteria pollutant. The terms ROG and VOC (see previous) are used interchangeably. The health effects of ROGs are similar to those of VOCs.

AIRDISTRICTRESPONSES TOFRIANTRANCHDECISION
San Joaquin Valley APCD and SCAQMD Briefs
Both the San Joaquin Valley Air Pollution Control District (SJVAPCD) and the SCAQMD submitted amicus briefs during the Friant Ranch judicial proceedings, emphasizing the difficulty of linking individual project emissions to specific health impacts. Their briefs argued that the available air quality modeling tools are primarily designed for regional assessments rather than project-specific 3764
16285-02 Technical Memo 5 Eucalyptus Business Park Specific Plan evaluations, given the complexities involved in the formation and dispersion of secondary pollutants. These points bolster the argument that the proposed Project, with emissions well below those that occur at the regional level, will not result in significant regional health impacts. However, the Court found that reliance on statements in the amicus briefs, alone, did not constitute substantial evidence for purposes of the EIR prepared for the proposed Project in this case. (Ruling at p. 41).

Although the Antelope Valley AQMD has not prepared Friant Ranch recommendations, other agencies have prepared guidance based on extensive photochemical modeling performed to evaluate the potential for emissions from individual projects to be tied to specific health effects at the regional level. These analyses have concluded that there is no reliable methodology for performing this analysis and that the results are not meaningful or statistically significant when evaluated at the regional level. Therefore, SMAQMD guidance is utilized for the proposed Project, as discussed below.

Sacramento Metropolitan Air Quality Management District Interim Recommendations The SMAQMD issued interim recommendations in response to the Friant Ranch decision, stating that there is no reliable quantitative methodology to correlate emissions from individual projects with specific health consequences. The proposed Project follows these recommendations by relying on established regional analyses and using health impact conclusions drawn from other large projects such as the Friant Ranch project, where no significant health impacts were identified at similar emission levels.

Bay Area Air Quality Management District Recommendations In its updated 2022 CEQA Guidelines, the Bay Area Air Quality Management District (BAAQMD) released additional guidance for demonstrating compliance with the 2018 Friant Ranch decision. Per the BAAQMD Guidelines, lead agencies should explain the nature and magnitude of any health impacts that may result from criteria air pollutants and “make a reasonable effort to connect a project’s emissions, where significant, to foreseeable health impacts or provide evidence as to why such an analysis is not scientifically possible.”

CITY OF LOS ANGELES
City of Los Angeles Technical Advisory
The City of Los Angeles Department of City Planning, together with its Technical Advisory Panel of air quality experts, created a guidance document on Air Quality and Health Effects in response to the Friant Ranch case. This document includes an introduction, background, and methodology that covers CEQA thresholds of significance, available models, various air quality and health effects considerations, health effects addressed in plans and regulatory standards, health effects of criteria pollutants and TACs, and the relationship between adverse air quality impacts and health effects conclusions.

The introduction states, "This paper provides information to the public regarding health consequences associated with exposure to air pollutants and explains why direct correlation of a project's pollutant emissions and anticipated health effects is currently infeasible, as no expert agency has approved a quantitative method to reliably and meaningfully translate mass emission 3765
16285-02 Technical Memo 6 Eucalyptus Business Park Specific Plan estimates of criteria air pollutants to specific health effects for the scale of projects typically analyzed in City EIRs" (City of Los Angeles 20191
).

The City's guidance further explains that "directly correlating a single project's emissions in a typical City EIR to quantifiable human health consequences is currently not scientifically feasible, as it is not possible to conduct such an analysis that would provide reliable or meaningful results. It is also infeasible to correlate regional emissions from local area-wide projects or plans identified in City EIRs to quantified human health consequences in any reliable or meaningful way, for many of the same reasons, and with additional challenges associated with separating and anticipating reasonably foreseeable emissions from other sources" (City of Los Angeles 2019).

ASSOCIATION OF ENVIRONMENTAL PROFESSIONALS –CLIMATE CHANGE COMMITTEE We Can Model Regional Emissions, But Are the Results Meaningful for CEQA? The California Association of Environmental Professionals Winter 2020 Environmental Monitor newsletter featured an article titled "We Can Model Regional Emissions, But Are the Results Meaningful for CEQA?" authored by members of the AEP Climate Change Committee, which comprises air quality and climate change experts from environmental consulting firms2 . The article
examines the Friant Ranch case and Health Impact Assessments (HIAs) that utilize Photochemical Grid Models (PGMs) and BenMAP to estimate health impacts resulting from increases in O3 and PM2.5 concentrations.

The article acknowledges the technical feasibility of conducting regional-scale criteria air pollutant modeling for individual projects but underscores the limitations of such models. These limitations include low resolution and spatial averaging, which produce "noise" and model uncertainty that can surpass a project's specific emissions. Additionally, regional-scale models are highly dependent on background concentrations where factors such as meteorology and topography significantly influence the certainty levels of predicted concentrations at receptor points. Consequently, there are statistical ranges of uncertainty throughout all modeling steps, making it challenging to predict ground-level secondary PM and O3 concentrations associated with relatively small emission sources with high accuracy (AEP 2020).

Moreover, the article highlights the limitations and uncertainties associated with BenMAP. For instance, regional models assign the same toxicity to PM regardless of its source (e.g., road dust versus exhaust), potentially leading to overpredicted adverse health effects. BenMAP assumes that health effects can occur at any concentration, including small incremental concentrations, and posits that impacts observed at large concentration differences can be linearly scaled down to small increases, without considering potential thresholds below which health impacts may not occur.

Additionally, BenMAP is designed for assessing impacts over large areas and populations and was not intended for use in individual projects. The number of hospitalizations or morbidity increases predicted by BenMAP is significantly influenced by population characteristics. Thus, small emission increases in densely populated areas have a far greater impact than substantial emission increases in sparsely populated regions. As a result, identical emissions in urban areas could lead to more 1 https://planning.lacity.gov/odocument/e1a00fbf-6134-4fa9-b6fd-54eee631effb/City_of_LA_- _Air_Quality_and_Health_Effects_and_Attachments.pdf
2 https://www.califaep.org/climate_change.php
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16285-02 Technical Memo 7 Eucalyptus Business Park Specific Plan significant health consequences compared to emissions on the urban periphery, where fewer individuals might be affected (AEP 2020).

The article concludes that "given the margin of uncertainty at each step in the process (regional scale modeling, existing ambient air quality effects on health, population health conditions vulnerability, and marginal health effects of air pollution), the identification of marginal health effects due to individual projects using regional air quality modeling and tools such as BenMap are likely to fall within the level of uncertainty and thus be deemed 'speculative' per CEQA" (AEP 2020).

SCIENTIFIC AND MODELING APPROACHES
Criteria Air Pollutants Versus Toxic Air Contaminants
Toxic Air Contaminants (TACs) are pollutants which may cause or contribute to an increase in illness or death, and are identified by CARB. It should be noted that TACs are subject to a different regulatory process than criteria pollutants, which are subject to the NAAQS and CAAQS. It should be noted that health impacts may result from exposure to small quantities of TACs, and it is often not possible to definitively quantify TAC exposures that would not result in any adverse health effects. However, for criteria air pollutants, acceptable levels of exposure have been established, and are the general basis for the NAAQS and CAAQS levels. As SJVAPCD noted in their amicus brief3 :
“Although criteria air pollutants can also be harmful to human health, they are distinguishable from toxic air contaminants and are regulated separately. The most relevant difference between criteria pollutants and TACs for purposes of this case is the manner in which human health impacts are accounted for. While it is common practice to analyze the correlation between an individual facility’s TAC emissions and the expected localized human health impacts, such is not the case for criteria pollutants. Instead, the human health impacts associated with criteria air pollutants are analyzed and taken into consideration when EPA sets the NAAQS for each criteria air pollutant.”
The SJVAPCD concluded that although it is possible to evaluate the health impacts for TACs, it is not feasible to conduct a similar analysis for criteria air pollutants due to the lack of currently available computer modeling tools equipped for the task.

Air Quality and Health Impact Modeling
Community Multiscale Air Quality Modeling System and Comprehensive Air Quality Model with Extensions: The Community Multiscale Air Quality modeling system (CMAQ) and the Comprehensive Air Quality Model with Extensions (CAMx) are both photochemical air quality models used to simulate air quality at scales ranging from the local to regional levels. These models are often used in health impact analyses to estimate the potential increases in pollutant concentrations over a large geographic area as a result of criteria and precursor pollutant emissions generated by a project. These models are able to fully characterize the physical processes that occur in the atmosphere and can predict concentrations of ozone, PM2.5, and other pollutants based on mass emissions of precursor pollutants.
3 https://www.courts.ca.gov/documents/7-s219783-ac-san-joaquin-valley-unified-air-pollution-control-dist-041315.pdf 3767
16285-02 Technical Memo 8 Eucalyptus Business Park Specific Plan Environmental Benefits Mapping and Analysis Program-Community Edition: The Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) is a modeling program that estimates the number of air pollution-related deaths and illnesses, as well as the associated economic impacts. The program utilizes population data as well as health and economic data to evaluate the potential health impacts that may result from increased pollutant concentrations in a given area. As such, emissions that occur in a heavily populated area would result in greater health impacts compared to those that would occur in a sparsely populated area. BenMAP-CE focuses on the impacts associated with increased ozone and PM2.5 concentrations.

Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool: The Co-Benefits Risk Assessment Health Impact Screening and Mapping Tool (COBRA) is a tool published by the United States Environmental Protection Agency (U.S. EPA) that is designed to estimate the impacts to human health that may occur as a result of changes in ozone and PM2.5 concentrations. COBRA includes a built-in air quality model for estimating ozone and PM2.5 concentrations based on precursor pollutant mass emissions. As such, while BenMAP-CE requires the use of a model such as CMAQ or CAMx to calculate pollutant concentrations, this function is built into the COBRA model.

Similar to BenMAP-CE, COBRA evaluates how concentrations of precursor pollutants result in ozone and PM2.5 health impacts, including premature mortality, asthma, heart attacks, and lost work and school days. The model also estimates the monetary impacts that arise from these health outcomes.

It should be noted that COBRA takes into account the population density of a given area when calculating potential health impacts, and as a result emissions that occur in a more population-dense area would result in greater health impacts.

Based on EPA’s description of the model, COBRA is intended for use by state and local governments to explore how changes in air pollution from clean energy policies and programs can affect human health at the county, state, regional, and national levels, to estimate the economic value of the health benefits associated with clean energy policies and programs to compare against program costs, and to map the air quality and health related benefits from reductions in emissions of PM2.5, SO2, NOX, and VOCs that result from clean energy policies and programs4 .

SUMMARY OF FRIANT RANCH AND CSUDHHEALTH IMPACT ANALYSES Friant Ranch Analysis
The Friant Ranch project consisted of a 942-acre Specific Plan including 2,500 residential units and other uses, including a commercial center and a neighborhood electric vehicle network. The Friant Ranch project underwent a comprehensive health impact assessment using CAMx and BenMAP-CE to quantify the potential health impacts of NOX, PM2.5, and PM10 emissions. Key findings from the Friant Ranch analysis include:
• Peak Daily Emissions: The estimated peak daily emissions for the Friant Ranch Project were estimated at 342 pounds per day of ROG/VOC, 438 pounds per day of NOX, 174.7 pounds per day of PM10, and 70.7 pounds per day of PM2.5. Despite these emissions, the health impact modeling concluded that health risks from these pollutants were not significant or were within a margin of error, even under worst-case scenario assumptions. • Modeling Approach: The CAMx model was used to model the potential incremental increase in ozone and PM2.5 concentrations in the region as a result of project emissions. This modeling indicated that project emissions would result in a maximum increase in pollutant concentration of 0.019% for ozone and 0.41% for PM2.5 at the most 4 https://www.epa.gov/cobra/what-cobra
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16285-02 Technical Memo 9 Eucalyptus Business Park Specific Plan impacted grid cell compared to baseline 2020 background concentrations. The BenMAP model was used to estimate health effects in the region that would be expected as a result of these increased pollutant concentrations.

Even with these stringent parameters, the health impacts—quantified in terms of mortality—were minimal. The findings emphasized that the potential increase in health incidences was negligible, particularly compared to the background health conditions of the region.

CSUDH Analysis
The CSUDH project consisted of a 344-acre campus plan including student housing, university buildings and a 30,000 seat stadium. The CSUDH project also underwent a similar air quality impact analysis using CAMx and BenMAP-CE to estimate changes in ambient PM2.5, PM10, and ozone concentrations due to project-related emissions:
• Peak Daily Emissions: The estimated peak daily emissions for the CSUDH Project were estimated at 482.6 pounds per day of ROG/VOC, 240.1 pounds per day of NOX, and 79.5 pounds per day of PM2.5. Despite these emissions, the health impact modeling concluded that health risks from these pollutants were not significant or were within a margin of error.
• Health Impact Findings: The results of the health impact modeling for the CSUDH project indicated a very small increase in health incidences, including mortality, asthma-related emergency room visits and respiratory-related hospital admissions, all of which were considered negligible. For all endpoints, the number of estimated incidences was less than 0.0058% of the background health incidences, indicating that health impacts were well within the margin of error.
• Conservative Emissions Estimates: The analysis utilized peak operational emissions, and these were modeled across the entire operational year, further emphasizing the conservative nature of the health impact conclusions. Even with this conservative approach, the resulting health impacts were deemed negligible.

GEOGRAPHIC AND DISPERSION CONSIDERATIONS
The Friant Ranch project is located within the San Joaquin Valley Air Basin; and the CSUDH project is located within the South Coast Air Basin (SCAB), while the proposed Project is located in the South Coast Air Basin (SCAB). The background conditions and existing attainment status for each respective air basin are taken into account in this analysis and are reflected in the regional thresholds adopted by each respective air district. The Project site benefits from strong coastal wind patterns that enhance the dispersal of pollutants, reducing the likelihood of pollutant accumulation near the Project site and mitigating any localized health impacts.

Health Effects of Criteria Air Pollutants and Their Precursors Health effects associated with criteria air pollutants are diverse, including premature mortality, cardiovascular effects, increased healthcare utilization, respiratory illnesses, and reduced lung function. These impacts are particularly acute in sensitive receptors, such as children, the elderly, and those with preexisting respiratory conditions. Although the proposed Project’s emissions of ROG/VOC, NOX, SO2, PM10, and PM2.5 are lower than those observed for Friant Ranch and the CSUDH project. As a result, the potential health impacts are expected to be similar to or less than those expected to result from these projects, resulting in minimal increases in pollutant concentrations and health risk that is considered negligible. Because the proposed Project’s emissions are lower than those of the Friant Ranch and CSUDH projects, the proposed Project’s potential health impacts would also be lower, and thus considered negligible.

Table 1 below presents a comparison of the proposed Project’s daily criteria pollutant emissions to those of the Friant Ranch and CSUDH projects.
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16285-02 Technical Memo 10 Eucalyptus Business Park Specific Plan TABLE 1:CRITERIA POLLUTANT EMISSIONS COMPARISON
Project Emissions (lbs/day)
ROG NOX SOX PM10 PM2.5
Proposed Project – Phase 1,
2, and 3 (Buildout) 129.36 204.68 2.52 169.81 46.66
Friant Ranch Project 342.00 438.00 N/A 174.70 70.70
CSUDH Project 482.60 240.10 2.70 N/A 79.50
Scientific and Technological Complexities
The Friant Ranch decision emphasized the need to correlate project-specific emissions to health outcomes, a task complicated by the scientific and technological challenges inherent in modeling secondary pollutants such as ozone and PM2.5. Secondary pollutants are formed via complex chemical reactions involving multiple precursor emissions, influenced by atmospheric conditions, which makes the direct correlation of emissions to health outcomes challenging. The SJVAPCD and SCAQMD briefs highlight these complexities, asserting that currently available modeling tools are not equipped to provide reliable project-level health impact analyses. This underscores the lack of reliability in calculating regional health impacts, as emissions at an individual project level are often insufficient to affect regional pollutant concentrations and thus health impacts in a meaningful way.

PROPOSED PROJECT HEALTH EFFECTS
In order to estimate regional health impacts that may result from criteria and precursor pollutants emitted by the proposed Project, the peak daily emissions of ROG/VOC, NOX, SO2, and PM2.5 were utilized in the COBRA model. Table 2 below presents the estimated health effects and the incremental increase in mortality from all causes that is estimated to occur as a result of emissions generated by the proposed Project at the regional level (Riverside, San Bernardino, Los Angeles, and Orange Counties). COBRA model outputs are presented in Appendix 2.

TABLE 2: PROPOSED PROJECT HEALTH AND MORTALITY INDICATORS Health Indicator Proposed Project –
Specific Plan Buildout Baseline % Increase
Total Mortality (low – high estimate)1 0.632 - 1.131 104,795 0.00060%–

0.00108%
Total Asthma Symptoms 532.128 N/A N/A
Total Incidence, Asthma2 3.132 1,327,861 0.00024%
Total Incidence, Hay Fever/Rhinitis 20.175 N/A N/A
Total ER Visits, Respiratory3 0.864 52,572 0.00164%
Total Hospital Admits, All Respiratory4 0.083 107,348 0.00008% 1 Source: California Department of Public Health 2020-2022 All Causes Mortality, with deaths due to drug overdoses, firearms, homicide, suicide, traffic crashes, and accidents removed (https://www.cdph.ca.gov/Programs/CHSI/Pages/County-HealthStatus-Profiles.aspx) 2 Source: California Department of Public Health 2021-2022 Asthma Prevalence (https://data.chhs.ca.gov/dataset/asthmaprevalence) 3 Source: California Department of Public Health Asthma Emergency Department Visit Rates (https://data.chhs.ca.gov/dataset/asthma-emergency-department-visit-rates) 3770
16285-02 Technical Memo 11 Eucalyptus Business Park Specific Plan 4 Source: California Department of Health Care Access and Information 2023 Hospital Inpatient Characteristics (https://data.chhs.ca.gov/dataset/hospital-inpatient-characteristics-by-patient-county-of-residence) As shown in Table 2, based on the COBRA model the proposed Project is estimated to result in an incremental increase in mortality of 1.131 (high estimate) as a result of increased concentrations of ozone and PM2.5 in the region due to Project emissions (0.188 attributable to ozone and 0.944 attributable to PM2.5). Compared to baseline mortality estimates for the region, this represents an increase of approximately 0.00108%.

It should be noted that this incremental increase in mortality is consistent with other large projects.

The incremental incidence increase in mortality from all causes for the Friant Ranch project was estimated at 0.000014% for ozone and 0.00039% for PM2.5, for a total incremental increase of

0.00041%, which is lowerthan what would be expected from the proposed Project. However, for the CSUDH project, the project’s emissions were estimated to result in an increase in mortality from all causes of 0.0032% attributable to ozone and PM2.5 combined, which is higher than would be expected for the proposed Project.Although the increase in total mortality is higher for the proposed Project compared to the Friant Ranch project and lower than that of the CSUDH projects, it should be noted that the overall increase in mortality as a result of the proposed Project’s emissions remains minimal, and at approximately 66% lower than the increase that would result from the CSUDH project, consistent with other large projects. As such, emissions associated with the proposed Project remain minimal and consistent with other large projects, and would not result in significant health impacts due to increases in ozone and PM2.5 concentrations in the region.

CONCLUSION
The comparative analysis of emissions and health impacts for the proposed Project relative to the Friant Ranch and CSUDH projects supports the conclusion that although the proposed Project’s emissions are greater than those reported forthe Friant Ranch project and lower than those reported for the CSUDH project, the increase in health risks in the region posed by the proposed Project’s emissions remains negligible, and consistent with those identified in the CSUDH project.

As discussed in this memorandum, no available modeling tools have been proven to provide a reliable and meaningful analysis to correlate an increase in mass totals or concentrations of criteria air pollutants from an individual project to specific health effects or estimate additional pollutant nonattainment days relative to the NAAQS and CAAQS due to a single project at this time.

Further, as explained in the SJVAPCD brief and noted previously, running a photochemical model used for predicting O3 attainment with the emissions solely from an individual project like the Friant Ranch project or the proposed Project is not likely to yield valid information given the relative scale involved. The examples discussed in this memorandum support the SJVAPCD’s brief contention that consistent, reliable, and meaningful results may not be provided by methods applied at this time.

None of the health impact analyses that were reviewed has concluded that health effects estimated using a photochemical model and BenMAP approach are substantial provided that the estimated project-generated incidences represent a very small percent of the number of background incidences, potentially within the models’ margin of error and therefore the findings may not be meaningful.
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16285-02 Technical Memo 12 Eucalyptus Business Park Specific Plan APPENDIX 1:

FRIANT ANALYSES
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AIR EMISSIONS REPORT
for the

FRIANT RANCH COMMUNITY DEVELOPMENT

PROJECT
Prepared for:

Friant Ranch LP
Fresno, California
Prepared by:

BlueScape Environmental
16870 West Bernardo Drive, Suite 400
San Diego, California 92127
March 4, 2021
j) B L U E S C A P E

ENVIRONMENTAL
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Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental i March 4, 2021

TABLE OF CONTENTS

1.0 INTRODUCTION ..................................................................... 3

2.0 PROJECT DESCRIPTION.......................................................... 4

3.0 EMISSION CALCULATION METHODS........................................ 6

3.1 Calculation Methodology for Friant Ranch Emissions................ 7

3.1.1 Construction Emissions Methodology.......................... 8

3.1.2 Operational Emissions Methodology ........................... 9

4.0 AIR EMISSIONS ESTIMATES ..................................................10

5.0 EVALUATION OF MITIGATION STRATEGIES ...........................14

6.0 PEAK DAILY EMISSIONS FOR HEALTH IMPACT MODELING .....19

7.0 REFERENCES .........................................................................23 TABLES
Table 2-1 Friant Ranch Land Use Types: Specific Plan and Community Plan 5 Table 4-1 Annual UnMitigated Construction Emissions ...........................11 Table 4-2 Annual Unmitigated Operational Criteria Pollutant Emissions ....12 Table 4-3 Unmitigated Annual Operation and Construction.....................13 Table 5-1 Annual Mitigated Construction Emissions...............................16 Table 5-2 Annual Mitigated Operational Criteria Pollutant Emissions ........17 Table 5-3 Mitigated Annual Operation and Construction Emissions ..........18 Table 6-1 Peak Daily Operation and Construction Emissions...................20 Table 6-2 Unmitigated Peak Daily Operation and Construction................21 Table 6-3 Mitigated Peak Daily Operation and Construction....................22

APPENDICES
Appendix A: Supporting Tables
Appendix B: CalEEMod Files (Provided Electronically)
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Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental ii March 4, 2021

GLOSSARY OF TERMS AND ACRONYMS
Acronym Description
AE Alternative Energy
BE Building Energy
CalEEMod California Emissions Estimator Model
Cal/EPA California Environmental Protection Agency
CAMx Comprehensive Air Quality Model with Extensions
CAPCOA California Air Pollution Control Officers Association CARB California Air Resources Board
CEC California Energy Commission
CEQA California Environmental Quality Act
CEUS California Commercial End Use Survey
CO Carbon Monoxide
csv Comma Separated Value
DEIR Draft Environmental Impact Report
DPM Diesel particulate matter
DU Dwelling Unit
EMFAC EMission FACtor Model
FEIR Final Environmental Impact Report
HIA Health Impact Analysis
ISR Indirect Source Review
ITE Institute of Transportation Engineers
kg Kilograms
km Kilometers
kWh Kilowatt hour
lb/day Pounds per Day
lb/yr Pounds per Year
LE Lighting Energy
LUT Land Use Transportation
NO2 Nitrogen Dioxide
NOx Oxides of Nitrogen
O3 Ozone
PM Particulate Matter
PM10 Particulate Matter 10 Micrometers or less in diameter PM2.5 Particulate Matter 2.5 Micrometers or less in diameter ppm Parts per million
RASS Residential Appliance Saturation Survey
ROG Reactive Organic Gases
SAFE Safer Affordable Fuel-Efficient Vehicles Rule
SDT Site Design Transportation
SJVAPCD San Joaquin Valley Air Pollution Control District SO2 Sulfur Dioxide
SOx Oxides of Sulfur
sqft Square Feet
SW Solid Waste
TPY Tons Per Year
TRT Trip Reduction Transportation
USEPA US Environmental Protection Agency
VOC Volatile Organic Compounds
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Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 3 March 4, 2021

1.0 INTRODUCTION
At the request of Friant Ranch LP, BlueScape Environmental (BlueScape) prepared this Air Emissions Report for pollutant emissions from the Friant Ranch Development Project (Project), including criteria pollutants. The Project consists of the Friant Ranch Specific Plan, an update to the Friant Ranch Community Plan, a new wastewater treatment facility, and improvements at such locations as the Depot Parcel. The purpose of the report is to provide the Project air quality modeling emissions inputs for the regional Health Impact Analysis (HIA). A discussion on the baseline regional emissions inputs used in the HIA is provided in a separate report (BlueScape 2021).

The Air Emissions Report is organized into the following sections:
• Section 2.0 – Project Description: The Friant Ranch Development Project is described in this section, including details that were used to complete the emission calculations.
• Section 3.0 – Air Emissions Calculation Methods: The methods used to calculate criteria pollutant emissions are described in this section, for the Specific Plan and Community Plan, for each phase of Project construction and for operations. Emissions from a proposed new wastewater treatment plant were evaluated and determined to be negligible for regional impact analysis modeling, but were included in the HRA. This section also provides a discussion on nonmitigated and mitigated emission scenarios. • Section 4.0 – Air Emissions Estimates: The emission calculations are summarized in this section.
• Section 5.0 – Evaluation of Mitigation Measures: A brief discussion of construction and operational mitigation measures used in emissions modeling are described in this section.
• Section 6.0 – Peak Daily Emissions for Health Impact
Modeling: This section presents the peak daily criteria pollutant estimates that were used in HIA modeling.
• Section 7.0 – References: includes a list of references cited in this report.
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2.0 PROJECT DESCRIPTION
The Project is located in Friant, California, in Fresno County, approximately five miles north of the Fresno city limits and 21 miles east of the city of Madera, within the Friant Community Plan Area and the Friant Specific Plan Area. In February 2011, the County of Fresno approved the Friant Ranch Project, a master planned community incorporating age-restricted (ages 55+) and nonage restricted family and multi-family residential units, a commercial village center, a recreation center, trails, open spaces, parks and parkways. The county’s approvals included a new Specific Plan called the Friant Ranch Specific Plan and an update to the pre-existing Friant Community Plan.

The proposed Project is a mixed-use development proposed by Friant Ranch LP (Project sponsor). The Project would develop retail, residential, and open space uses on a 942-acre site in Friant, California. The Project will be primarily residential, with up to 2,270 dwelling units age restricted to over 55, and up to 230 dwelling units without age restriction.

The site is in a mostly rural area, with scattered neighborhoods and houses surrounding it to the northwest, northeast, and southwest. Off-site roads near the Project site include North Friant Road, which runs in a northwest/southwest direction directly along the eastern property line of the Project. North Friant Road becomes Millerton Road at the northern tip of the Project site where the direction changes to mostly an east/west direction. North Fork Road / Road 206 branches off at the N. Friant Rd. / Millerton Rd. transition, and runs in an east/west direction.

The Project also includes a land use designation and zone change for the Depot Parcel, which is outside of the Friant Ranch Specific Plan area, but within the boundaries of the 1983 Friant Community Plan area.

Table 2-1 defines the land use types and total square footage for the proposed development at full-build-out, including both the Specific Plan and the Community Plan land use types.
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TABLE 2-1
FRIANT RANCH LAND USE TYPES: SPECIFIC PLAN AND COMMUNITY PLAN Land Use Specific Plan Community Plan
Type Subtype Amount Size
Metric Population Amount Size Metric Population
Commercial Medical Office
Building 10 1000sqft -- -- -- --
Commercial Office Park 100 1000sqft -- -- -- --
Recreational
Fast Food
Restaurant w/Drive
Thru

5.0 1000sqft -- 10 1000sqft --
Recreational Health Club 42 1000sqft -- -- -- --
Recreational High Turnover (Sit
Down Restaurant) 10 1000sqft -- 80 1000sqft --
Residential Apartments Low
Rise 230 Dwelling Unit 658 146 Dwelling Unit 418
Residential Condo/Townhouse 166 Dwelling
Unit 475 -- -- --
Residential
Retirement
Community/Single
Family Housing
2,104 Dwelling
Unit 4,158 419 Dwelling Unit 1,198
Retail Regional Shopping
Center 125 1000sqft -- 74 1000sqft --
Commercial Pharmacy/Drugstore
with Drive Thru -- -- -- 50 1000sqft --
Recreational City Park -- -- -- 6.1 Acre --
Recreational Hotel -- -- -- 200 Room --
Recreational Motel -- -- -- 25 Room --
Recreational Quality Restaurant -- -- -- 40 1000sqft -- Retail Convenience Market
(24-hr) -- -- -- 10 1000sqft --
Retail Gasoline/Service
Station -- -- -- 10 Pump --
Retail Strip Mall -- -- -- 100 1000sqft --
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3.0 EMISSION CALCULATION METHODS
Emissions of criteria pollutants were estimated for the Friant Ranch Specific Plan, as well as the Community Plan and new wastewater treatment plant discussed in the DEIR. Emissions are revised relative to the Draft Environmental Impact Report (DEIR, County of Fresno, 2009) for two reasons:
1) The DEIR used URBEMIS, an emissions model that is currently outdated, to estimate operational emissions for the Project. Currently, most air districts in California recommend the use of CalEEMod (CAPCOA, 2017), a more recently developed land use emissions model.
2) The DEIR estimated emissions for the Community Plan and the full Specific Plan at build-out, which was projected to be in year 2020. Based on an updated construction start date of 2022, operational Project emissions are estimated for full-build out in 2031.

This section describes the calculation of the Friant Ranch Project estimated emissions using CalEEMod and preparation of the model-ready Project emissions for regional Health Impact Analysis (HIA) modeling. Emissions of reactive organic gases (ROG), oxides of nitrogen (NOx), carbon monoxide (CO), sulfur dioxide (SO2), particulate matter 10 micrometers or less in diameter (PM10), and particulate matter 2.5 micrometers or less in diameter (PM2.5) were estimated. There are minor differences between volatile organic compounds (VOC) and ROG that define each term, however for purposes of this study we will assume they are equivalent due to the fact that air quality models directly calculate ROG in place of VOC. The development of Friant Ranch Project emissions is provided in Section 3.1. The annual unmitigated Friant Ranch Project emissions are summarized in Section 4.0.

The highest peak daily Project emission rate for each pollutant was applied to the 2020 and 2031 background regional emissions data that is discussed further in the HIA report (BlueScape 2021). The 2020 scenario year represents the CEQA baseline for existing conditions prior to Project development. The 2031 scenario year represents the “no project” conditions that account for reasonably foreseeable development that will have occurred by that year, and is adequate as a cumulative CEQA baseline. The 2031 year full-buildout Project impacts were then added to these two CEQA baseline years, to understand the potential specific health impacts above baseline.

There are conservative assumptions and methodologies that were used during the development of Friant Ranch Project emissions, which result in conservatively high estimates of Project emissions and associated health effects. This includes a conservatively high estimate of land use areas and an overlap of all emissions that may happen in any given year. The operational emissions associated with the Specific Plan and the Community Plan were combined with the construction emissions to determine the highest emission rates for modeling.
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First, the emissions were determined using the land use described in Section 2 of this report, for operational year 2031. Some of the land use types proposed for the Community Plan in the DEIR have been adjusted based on more recent and accurate information. The land use was revised to remove the light industrial building type and more realistic estimates of the sizes of hotels, motels, and park areas were applied.

Second, Friant Ranch Project emissions are estimated for each year of the development, from 2022 through 2031. Although 2031 is shown to be the full build-out year, with the highest Specific Plan and Community Plan operational emissions, the emissions from construction of the development were applied to each individual year to predict the worst-case year for health effects.

Third, both annual and peak daily emissions are estimated for all Friant Ranch Project sources; however, in order to be conservative, peak daily emissions for each emission source are selected for modeling and are modeled throughout the full annual air quality model simulation. Thus, the modeled annual impacts and the health effects that are dependent on annual concentrations have incorporated layers of conservatism:

1. Peak daily emissions for all activities occur concurrently;

2. Peak daily emissions would occur over a full year;

3. The highest unmitigated peak daily emissions for NOx occur in 2023, but the highest peak daily emissions for ROG, PM10, and PM2.5 occur in 2029.

Highest emissions are combined and overlayed onto the 2020 existing conditions baseline and the 2031 Project full build-out baseline to predict conservative health effects; and

4. Similarly, the highest mitigated peak daily emissions for NOx occur in 2022, but the highest peak daily emissions for ROG, PM10, and PM2.5 occur in 2029. Highest emissions are combined and overlayed onto the 2020 existing conditions baseline and the 2031 Project full build-out baseline to demonstrate more likely, but still conservative, health effects after mitigation strategies are implemented.

Therefore, the estimated emissions utilized for health impacts modeling should be considered as worst-case scenarios.

3.1 Calculation Methodology for Friant Ranch Emissions
Emissions associated with construction and operation of the proposed Project and existing development were estimated using the California Emissions Estimator Model (CalEEMod) version 2016.3.2. Emission estimates are based on CalEEMod land use types and total square feet (as shown in Table 2-1), as well as population.
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Default values used in CalEEMod version 2016.3.2 are based on the California Energy Commission (CEC), sponsored California Commercial End Use Survey (CEUS), and Residential Appliance Saturation Survey (RASS) studies.

CalEEMod provides both construction-related and operational emissions of air pollutants. This methodology has been subjected to peer review by numerous public and private stakeholders, and in particular by the CEC; and therefore, is considered reasonable and reliable for use in estimating air emissions for development projects in California. CalEEMod is considered a conservative tool because many of the default parameters used to calculate emissions are based on conservative assumptions for land use developments throughout the State of California to be protective of the environment and air quality standards.

3.1.1 Construction Emissions Methodology
Construction of the proposed Project would generate temporary air emissions, primarily associated with the operation of construction equipment and truck trips. These impacts are associated with fugitive dust (PM10 and PM2.5) from soil disturbance, and exhaust emissions, such as ROG, NOx, CO, SO2, and DPM from heavy diesel-fueled construction vehicles.

Site preparation and grading typically generate the highest emission quantities because the use of heavy equipment is greatest during this phase of construction. Emissions associated with the construction phases were estimated based on the projected maximum amount of equipment that would be used onsite at one time, in each given year.

The development schedule for the Specific Plan and Depot Parcel is based on the annual phased schedule in Table 2-2 of the DEIR and the duration and sequence of construction activities presented in the DEIR. Construction emissions are developed for the Specific Plan, phases 1 through 5 and the Depot Parcel. Each year of active construction in a development phase is run separately in CalEEMod and results for overlapping phases in the same year are added together to yield worst-case annual and daily emissions.

The duration and timing of construction activities are phased in 3-year increments, as presented in the FEIR. The following example shows the construction duration for phase 1 through phase 5.
• The first year of each phase includes grading that will occur from January 1 through May 2 and building construction from April 16 through December 31.
• The second year of each phase includes building construction from January 1 through December 31.
• The third year of each phase includes building construction from January 1 through December 31, architectural coating from June 28 through October 7, and paving from October 21 through December 3. • In addition, the next phase of construction will begin in the second year of the previous phase, so construction activities occur in different locations, but emissions will overlap.
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The construction equipment and activities are in accordance with CalEEMod defaults, with slight adjustments in equipment load factors, based on those discussed in the FEIR. Worker, vendor, and haul trips are also based on CalEEMod defaults. The silt loading factor is from the California Air Resources Board (CARB) 2018 Miscellaneous Process Methodology 7.9 for Entrained Road Travel, Paved Road Dust using Fresno County data. Construction mitigation strategies will be discussed in a subsequent section of this report.

After construction is complete, three types of sources will generate emissions at the proposed Project site. These are area sources, energy use, and Projectgenerated traffic, which are defined as operational emissions. The emission calculation methodologies of these sources are discussed below.

3.1.2 Operational Emissions Methodology
Operational emissions include emissions from electricity consumption (energy sources), vehicle trips (mobile sources), and area sources (landscape equipment and consumer products use or reapplication of paint). The majority of operational emissions are associated with vehicle trips to and from the Project site.

Area Sources
All direct area source emissions were based on CalEEMod defaults for the designated land use category with the exception of consumer product emissions and fireplaces. For consumer products, a (VOC) emission factor was derived using methodology consistent with CalEEMod, but with updated statewide parameters. The CalEEMod default emission factor assumes 2008 statewide VOC inventory and building square footage. An updated VOC inventory for 2017 was taken from CARB (CARB, 2017), and 2017 population estimates based on the State of California's Department of Finance demographic projections (DOF, 2017) were used to estimate a statewide VOC EF of 1.62 x 10-5 lb/sf/day for consumer products use, for 2017 going forward.

Residential dwellings within the Specific Plan do not include wood burning fireplaces. However, residential dwellings within the Community Plan may have already been built with existing wood burning fireplaces. So, it was estimated that 12.4% of multi-family dwellings and 10% of single-family dwellings would include wood burning fireplaces. In addition, 55% of all dwelling types in the Specific Plan and Community Plan are assumed to have a natural gas hearth, consistent with CalEEMod defaults, and the remaining units have no fireplace.

Reapplication of architectural coatings assumes 10% of each building interior and exterior surface area, which incorporates low VOC paints.

Energy Use
Residences and other land uses will use natural gas and the associated emissions were estimated using CalEEMod. Electricity use would contribute indirectly to criteria air pollutant emissions; however, the emissions from electricity use are only quantified for GHGs in CalEEMod. Natural gas use was based on CalEEMod defaults.
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Project Traffic
The Project would generate criteria pollutant and DPM emissions from mobile sources (vehicular traffic), as a result of the residents and workers associated with the Project. The daily vehicle miles traveled were estimated using the CalEEMod calculation methodology for each land use type. The daily trip rates are based on the Institute of Transportation Engineers (ITE) Trip Generation Manual, 8th edition, which are incorporated into CalEEMod as average trip rates for each respective land use category. However, trip lengths were adjusted to account for mixed-use development criteria. Under the mixed-use design criteria, the community is planned to create working and shopping opportunity within the residential development design. Therefore, trip lengths for residential land uses traveling for work, shopping, or other activities are assumed to be mainly originating from the community, with destinations within the community. Using a weighted average of default trip lengths and a general layout of the development roads, the trip lengths were adjusted. It was assumed that 80% of the vehicle trips would travel an average of 2 miles for on-community destinations and 20% of the vehicle trips would use default trip lengths in CalEEMod, for off-community destinations (likely to the Fresno area).

Project-related traffic was assumed to include a mixture of vehicles in accordance with the fleet mix distribution values defined by CARB’s EMission FACtor Model (EMFAC) (CARB 2017). Emission factors representing the vehicle mix and emissions for each year were adjusted using EMFAC2017 emission factors for the Fresno/San Joaquin Valley region, with the Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule emission factor adjustment. A commaseparated value (csv) file was generated for each year of the development Project duration, from 2022 through 2031 and imported into the CalEEMod model for the calculation of vehicle emissions.

4.0 AIR EMISSIONS ESTIMATES
Full Project build-out emissions are presented, consistent with the Friant Community Plan Update & Friant Ranch Specific Plan DEIR (County of Fresno 2009). Emissions were calculated assuming full Project build-out of residential and commercial areas in 2031 noting that, according to the Project sponsor, the Project will be developed in phases over multiple years. The Depot Parcel will be developed after the full build-out year in 2032 or 2033 in a much smaller area than previous Project phases, and construction-related emissions will be much less than in the 2031 full build-out year. Operational emissions will be slightly less in 2022 and 2023 and the Depot Parcel will add little to operational emissions. Therefore, the Depot Parcel was not included in the operational air emissions estimates. The unmitigated annual construction emissions for the Friant Ranch Specific Plan are presented in Table 4-1. Emissions are shown for each of the phases for Project build-out, from 2022 through 2031. 3783
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TABLE 4-1

ANNUAL UNMITIGATED CONSTRUCTION EMISSIONS
Year
Friant Ranch - Annual Construction Emissions - Unmitigated (tons/year) Phase ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
Total
2022 P1 0.551 4.92 5.11 0.010 0.778 0.220 0.997 0.206 0.203 0.410 Total
2023 P1, P2 0.987 8.73 9.63 0.019 1.20 0.375 1.57 0.320 0.347 0.667 2023 P1 0.390 3.11 3.91 0.008 0.182 0.129 0.311 0.049 0.120 0.170 2023 P2 0.597 5.62 5.72 0.011 1.01 0.245 1.26 0.271 0.227 0.498 Total
2024 P1, P2 3.13 6.07 7.83 0.016 0.285 0.245 0.529 0.077 0.228 0.305 2024 P1 2.779 3.14 4.11 0.008 0.161 0.129 0.290 0.044 0.120 0.163 2024 P2 0.347 2.93 3.73 0.007 0.123 0.116 0.239 0.033 0.108 0.141 Total
2025 P2, P3 0.767 6.51 8.33 0.017 0.916 0.251 1.17 0.254 0.233 0.487 2025 P2 0.345 2.83 3.78 0.008 0.185 0.101 0.285 0.050 0.094 0.144 2025 P3 0.422 3.69 4.54 0.009 0.731 0.151 0.882 0.204 0.139 0.343 Total
2026 P2, P3 5.07 5.67 7.63 0.016 0.267 0.212 0.479 0.072 0.198 0.270 2026 P2 4.74 2.91 3.95 0.008 0.119 0.112 0.231 0.032 0.104 0.136 2026 P3 0.329 2.76 3.68 0.008 0.148 0.100 0.249 0.040 0.093 0.134 Total
2027 P3, P4 3.88 6.96 8.87 0.018 1.14 0.271 1.41 0.281 0.251 0.532 2027 P3 3.42 2.96 3.97 0.008 0.143 0.112 0.255 0.039 0.104 0.143 2027 P4 0.460 4.00 4.90 0.010 0.996 0.159 1.15 0.242 0.147 0.389 Total
2028 P4 0.354 2.95 3.82 0.009 0.251 0.101 0.351 0.068 0.094 0.161 Total
2029 P4, P5 4.63 7.64 9.70 0.020 0.960 0.295 1.26 0.265 0.274 0.539 2029 P4 4.08 2.85 3.83 0.007 0.073 0.112 0.185 0.020 0.104 0.124 2029 P5 0.553 4.78 5.88 0.013 0.887 0.183 1.07 0.245 0.170 0.415 Total
2030 P5 0.312 1.59 3.41 0.008 0.113 0.029 0.142 0.031 0.029 0.060 Total
2031 P5 3.49 1.66 3.64 0.008 0.057 0.037 0.093 0.015 0.037 0.052 Depot
2032 P6 0.191 0.955 1.804 0.004 0.230 0.024 0.254 0.116 0.024 0.140 Depot
2033 P6 0.247 0.430 0.822 0.002 0.022 0.010 0.032 0.006 0.010 0.016 Note: Fug PM10/PM2.5 represents fugitive (dust) particulate matter and Exh PM10/PM2.5 represents exhaust particulate matter.
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The unmitigated annual operational emissions for the Friant Ranch Specific Plan and the Friant Community Plan are presented in Table 4-2.

TABLE 4-2

ANNUAL UNMITIGATED OPERATIONAL CRITERIA POLLUTANT EMISSIONS Friant Ranch Specific Plan
Category
2031
ROG NOx CO SO2 PM10 PM2.5
Tons/Year
Area (Total) 10.7 1.15 18.9 0.007 0.179 0.179
Architectural Coatings 1.36 0 0 0 0 0
Consumer Products 8.65 0 0 0 0 0
Hearth 0.109 0.935 0.398 0.006 0.076 0.076
Landscaping 0.554 0.214 18.5 0.001 0.103 0.103
Energy (Natural Gas) 0.247 2.13 1.07 0.014 0.171 0.171
Mobile (Total) 4.90 4.00 27.0 0.062 7.86 2.13
Specific Plan Total 15.8 7.28 46.9 0.083 8.21 2.48
Friant Community Plan
Category
2031
ROG NOx CO SO2 PM10 PM2.5
Tons/Year
Area (Total) 6.36 0.350 9.83 0.020 0.946 0.946
Architectural Coatings 0.846 0 0 0 0 0
Consumer Products 4.75 0 0 0 0 0
Hearth 0.636 0.302 5.64 0.020 0.923 0.923
Landscaping 0.126 0.048 4.19 0.0002 0.023 0.023
Energy (Natural Gas) 0.277 2.48 1.84 0.015 0.192 0.192
Mobile (Total) 11.7 39.6 69.4 0.182 13.4 3.78
Community Plan Total 18.3 42.4 81.0 0.217 14.5 4.91
Friant Ranch Specific Plan and Community Plan
Unmitigated
Project Total 34.2 49.7 128 0.300 22.8 7.40
A summary of the total annual emissions for each year of the development Project are shown below in Table 4-3 for the Specific Plan, Community Plan, and Construction.
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TABLE 4-3

UNMITIGATED ANNUAL OPERATION AND CONSTRUCTION
ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 1.91 0.99 5.97 0.010 0.784 0.043 0.827 0.210 0.043 0.253 2023 3.70 1.81 11.4 0.020 1.57 0.082 1.65 0.420 0.081 0.501 2024 5.41 2.60 16.5 0.029 2.35 0.121 2.47 0.630 0.120 0.750 2025 7.04 3.34 21.4 0.038 3.13 0.161 3.29 0.839 0.160 1.00 2026 8.62 4.05 26.0 0.046 3.92 0.201 4.12 1.05 0.199 1.25 2027 10.2 4.91 30.5 0.055 4.70 0.254 4.95 1.26 0.252 1.51 2028 11.8 5.37 34.7 0.061 5.48 0.277 5.76 1.47 0.275 1.74 2029 13.1 6.02 38.8 0.069 6.26 0.315 6.58 1.68 0.312 1.99 2030 14.5 6.65 42.9 0.076 7.04 0.352 7.40 1.89 0.350 2.24 2031 15.8 7.28 46.9 0.083 7.82 0.389 8.21 2.10 0.387 2.48 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 25.5 51.7 104 0.266 13.2 1.47 14.7 3.58 1.45 5.03
2023 24.0 46.9 100 0.258 13.2 1.38 14.6 3.58 1.37 4.95
2024 23.0 46.1 96.6 0.253 13.2 1.38 14.6 3.58 1.37 4.95 2025 22.1 45.3 93.2 0.246 13.2 1.38 14.6 3.58 1.36 4.94 2026 21.4 44.6 90.6 0.241 13.2 1.37 14.6 3.58 1.36 4.94 2027 20.7 44.0 88.1 0.235 13.2 1.37 14.6 3.58 1.36 4.93 2028 20.0 43.5 85.9 0.230 13.2 1.36 14.6 3.58 1.35 4.93 2029 19.4 43.0 84.0 0.225 13.2 1.36 14.6 3.58 1.35 4.92 2030 18.9 42.7 82.4 0.221 13.2 1.35 14.5 3.58 1.34 4.92 2031 18.3 42.4 81.0 0.217 13.2 1.35 14.5 3.58 1.34 4.91 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 0.551 4.92 5.11 0.010 0.778 0.220 1.00 0.206 0.203 0.410 2023 0.987 8.73 9.63 0.019 1.20 0.375 1.57 0.320 0.347 0.667 2024 3.13 6.07 7.83 0.016 0.285 0.245 0.53 0.077 0.228 0.305 2025 0.767 6.51 8.33 0.017 0.92 0.251 1.17 0.254 0.233 0.487 2026 5.07 5.67 7.63 0.016 0.267 0.212 0.48 0.072 0.198 0.270 2027 3.88 6.96 8.87 0.018 1.14 0.271 1.41 0.281 0.251 0.532 2028 0.354 2.95 3.82 0.009 0.251 0.101 0.35 0.068 0.094 0.161 2029 4.63 7.64 9.70 0.020 0.960 0.295 1.26 0.265 0.274 0.539 2030 0.312 1.59 3.41 0.008 0.113 0.029 0.14 0.031 0.029 0.060 2031 3.49 1.66 3.64 0.008 0.057 0.037 0.09 0.015 0.037 0.052 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 28.0 57.6 115 0.286 14.8 1.73 16.5 4.00 1.70 5.70
2023 28.7 57.4 121 0.297 16.0 1.84 17.8 4.32 1.80 6.12
2024 31.6 54.8 121 0.297 15.8 1.75 17.6 4.29 1.72 6.00
2025 30.0 55.1 123 0.301 17.2 1.79 19.0 4.67 1.76 6.43
2026 35.1 54.4 124 0.302 17.4 1.79 19.2 4.70 1.76 6.46
2027 34.7 55.9 127 0.308 19.0 1.89 20.9 5.12 1.86 6.98
2028 32.1 51.8 124 0.300 18.9 1.74 20.7 5.11 1.72 6.83
2029 37.1 56.7 133 0.314 20.4 1.97 22.4 5.52 1.93 7.45
2030 33.6 50.9 129 0.304 20.3 1.73 22.1 5.49 1.72 7.21
2031 37.6 51.3 132 0.308 21.1 1.77 22.8 5.69 1.76 7.45
Year
Friant Ranch - Total Annual Construction Emissions - Unmitigated (tons/year) Year
Specific Plan - Total Operational Emissions - Unmitigated (tons/year) Year
Community Plan - Total Operational Emissions - Unmitigated (tons/year) Year
Friant Ranch - Total Annual Operational and Construction Emissions - Unmitigated (tons/year) 3786
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5.0 EVALUATION OF MITIGATION STRATEGIES
Construction mitigation strategies would mainly rely on the use of construction equipment considered construction clean fleet, to comply with the emission reductions required by SJVAPCD District Rule 9510: Indirect Source Review (ISR). The rule requires construction equipment greater than fifty (50) horsepower to reduce construction equipment emissions by 20% of the total NOx emissions and by 45% of the total PM10 exhaust emissions. Although the construction fleet will vary based on the phase and subphase of construction activity, a combination of Tier 3 and Tier 4 engines were modeled to demonstrate the overall emission reductions. Lower emitting equipment or add-on control technology may become more readily available in future construction years.

Construction mitigation measures to comply with SJVAPCD District Rule 8021 were also included to demonstrate the reduction of fugitive dust emissions associated with the Project. At a minimum, watering of disturbed areas at least 3 times per day or as necessary; and limiting vehicle travel speeds on unpaved roads within the construction areas to a maximum of 15 miles per hour were incorporated.

Operational mitigation strategies were incorporated to quantify the reduction in emissions due to the SJVAPCD District Rule 9510: Indirect Source Review (ISR) strategies. The CAPCOA traffic mitigation measures have been incorporated into CalEEMod and correlate well with the ISR mitigation measures.

LUT-1 / Mitigation Measure 2a: Increase Density was applied as 2,000 dwelling units (du) within ½-mile radius (3.98 du/acre) and 1,500 jobs within ½-mile radius (2.9 jobs/acre).

LUT-3 / Mitigation Measure 2b: Increase Diversity can be included if the Project contains various land uses, such as office, commercial, institutional, and residential land uses present within ¼ mile.

LUT-9 / Mitigation Measure 2c: Improve Walkability Design included a calculation of 3-way and 4-way intersections compared to the distance from the center of the Project to its farthest boundary. This value was calculated to be 5.62 intersections per square mile.

LUT-4 / Mitigation Measure 2d: Improve Destination Accessibility was applied as the distance to downtown per job center is within ¼ mile.

LUT-5 / Mitigation Measure 2e: Increase Transit Accessibility was applied as the distance to a transit station is within ¼ mile.

SDT-1 / Mitigation Measure 3a: Improve Pedestrian Network was applied as providing a pedestrian access network within the Project site and connecting off-site.
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SDT-2 / Mitigation Measure 3b: Provide Traffic Calming Measures was applied as 100% of streets containing improvements within ½ mile of the Project site and 25% of intersections improved within ½ mile of the Project site.

SDT-3 / Mitigation Measure 3c: Implement Neighborhood Electric Vehicle (NEV) Network was included for the Project.

TRT-6 / Mitigation Measure 5c: Encourage Telecommuting and Alternative Work Schedules included 25% of employees to participate in telecommuting

1.5 days per week.

A-1 / Mitigation Measure 7b: Landscape Equipment included 3% electric lawnmower, leaf blower, and chainsaws.

BE-1 / Mitigation Measure 7a: Exceed Title 24 included as a minimum of 7% improvement over the 2016 building standards as applied by CalEEMod.

LE-1 / Mitigation Measure 7a: Install High Efficiency Lighting applied as a 20% reduction in energy use.

AE-1, AE-2, AE-3 / Mitigation Measure 8: Solar Panels are incorporated as 3 kW times the number of dwelling units, or a maximum of 7,500 kWh for 2031.

SW-1 would institute recycling and composting services to provide a reduction of 50% to 75% of the total waste disposed.

Use of low VOC paints will be included in the baseline scenario, but additional mitigation is considered for SJV Rule 4601, which requires a VOC limit of 50 g/L for flat and non-flat coatings, effective after 1/1/2022.

These mitigation strategies were quantified to demonstrate emission reductions, using the CalEEMod model. Mitigation strategies that were defined in the FEIR are now considered outdated, but are shown with the newer ISR reduction strategies, submitted in 2019. The comparison of FEIR mitigation strategies and those applied based on the ISR application is shown in Table A-1 in Appendix A.

The mitigated annual construction emissions for the Friant Ranch Specific Plan are presented in Table 5-1. Emissions are shown for each of the phases of Project build-out, from 2022 through 2031.
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TABLE 5-1

ANNUAL MITIGATED CONSTRUCTION EMISSIONS
Year
Friant Ranch - Annual Construction Emissions - Mitigated (tons/year) Phase ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
Total
2022 P1 0.293 3.86 5.42 0.010 0.086 0.190 0.475 0.104 0.085 0.189 Total
2023 P1, P2 0.483 5.07 10.6 0.019 0.610 0.209 0.819 0.163 0.209 0.373 2023 P1 0.210 1.01 4.19 0.008 0.182 0.027 0.209 0.049 0.027 0.076 2023 P2 0.273 4.06 6.37 0.011 0.428 0.183 0.611 0.114 0.183 0.297 Total
2024 P1, P2 2.77 2.92 8.50 0.016 0.285 0.121 0.405 0.077 0.120 0.197 2024 P1 2.60 1.18 4.43 0.008 0.161 0.039 0.200 0.044 0.038 0.082 2024 P2 0.170 1.74 4.06 0.007 0.123 0.082 0.205 0.033 0.082 0.115 Total
2025 P2, P3 0.368 4.16 9.26 0.017 0.506 0.195 0.700 0.139 0.194 0.334 2025 P2 0.187 1.85 4.16 0.008 0.185 0.081 0.266 0.050 0.081 0.131 2025 P3 0.181 2.31 5.09 0.009 0.321 0.113 0.434 0.089 0.113 0.203 Total
2026 P2, P3 4.73 2.84 8.41 0.016 0.267 0.111 0.378 0.072 0.111 0.183 2026 P2 4.55 1.05 4.34 0.008 0.119 0.030 0.149 0.032 0.030 0.062 2026 P3 0.171 1.79 4.06 0.008 0.148 0.081 0.229 0.040 0.081 0.121 Total
2027 P3, P4 3.44 3.68 9.84 0.018 0.603 0.149 0.752 0.153 0.149 0.302 2027 P3 3.25 1.99 4.36 0.008 0.143 0.096 0.239 0.039 0.096 0.135 2027 P4 0.188 1.70 5.48 0.010 0.460 0.053 0.513 0.114 0.053 0.167 Total
2028 P4 0.197 1.98 4.20 0.009 0.251 0.081 0.332 0.068 0.081 0.149 Total
2029 P4, P5 4.17 4.98 10.8 0.020 0.551 0.236 0.788 0.151 0.236 0.388 2029 P4 3.91 1.88 4.22 0.007 0.073 0.097 0.170 0.020 0.097 0.116 2029 P5 0.261 3.10 6.55 0.013 0.478 0.140 0.618 0.132 0.140 0.271 Total
2030 P5 0.122 0.878 3.93 0.008 0.113 0.011 0.125 0.031 0.011 0.042 Total
2031 P5 3.28 0.985 4.16 0.008 0.057 0.026 0.082 0.015 0.026 0.041 Depot
2032 P6 0.063 0.671 2.22 0.004 0.111 0.018 0.129 0.051 0.018 0.069 Depot
2033 P6 0.202 0.318 0.943 0.002 0.022 0.009 0.031 0.006 0.009 0.015 Note: Fug PM10/PM2.5 represents fugitive (dust) particulate matter and Exh PM10/PM2.5 represents exhaust particulate matter.
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Mitigated Specific Plan and Community Plan operational emissions for 2031 are shown in Table 5-2, which demonstrates the annual reduction in each pollutant by using the mitigation strategies defined above.

TABLE 5-2

ANNUAL MITIGATED OPERATIONAL CRITERIA POLLUTANT EMISSIONS Friant Ranch Community: Operational Criteria Pollutant Emissions (tons/year) Friant Ranch Specific Plan
Category
2031
ROG NOx CO SO2 PM10 PM2.5
tons
Area (Total) 10.2 1.15 18.8 0.007 0.178 0.178
Architectural Coatings 0.892 0 0 0 0 0
Consumer Products 8.65 0 0 0 0 0
Hearth 0.109 0.935 0.398 0.006 0.076 0.076
Landscaping 0.546 0.212 18.4 0.001 0.102 0.102
Energy (Natural Gas) 0.234 2.02 1.01 0.013 0.162 0.162
Mobile (Total) 4.84 3.60 24.7 0.052 6.45 1.75
Specific Plan Total 15.3 6.77 44.5 0.071 6.79 2.09
Mitigation Reduction % 3.51 6.96 5.17 13.6 17.4 15.8
Friant Community Plan
Category
2031
ROG NOx CO SO2 PM10 PM2.5
tons
Area (Total) 5.34 0.259 4.25 0.002 0.040 0.040
Architectural Coatings 0.445 0 0 0 0 0
Consumer Products 4.75 0 0 0 0 0
Hearth 0.025 0.211 0.090 0.001 0.017 0.017
Landscaping 0.124 0.048 4.16 0.0002 0.023 0.023
Energy (Natural Gas) 0.277 2.48 1.84 0.015 0.192 0.192
Mobile (Total) 11.6 37.7 66.8 0.165 11.8 3.33
Community Plan Total 17.2 40.5 72.9 0.181 12.1 3.56
Mitigation Reduction % 5.99 4.58 9.99 16.5 17.1 27.5
Friant Ranch Specific Plan and Community Plan
Project Total 32.5 47.2 117 0.253 18.8 5.65
A summary of the total annual mitigated emissions for each year of the development Project are shown below in Table 5-3 for the Specific Plan, Community Plan, and Construction.
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TABLE 5-3

MITIGATED ANNUAL OPERATION AND CONSTRUCTION EMISSIONS
ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 1.85 0.90 5.59 0.009 0.642 0.041 0.683 0.172 0.041 0.213 2023 3.58 1.67 10.7 0.017 1.28 0.078 1.36 0.344 0.077 0.421 2024 5.23 2.40 15.6 0.025 1.93 0.116 2.04 0.516 0.115 0.631 2025 6.81 3.09 20.1 0.032 2.57 0.154 2.72 0.688 0.153 0.841 2026 8.34 3.75 24.6 0.039 3.21 0.192 3.40 0.860 0.191 1.05 2027 9.83 4.57 28.8 0.047 3.85 0.245 4.09 1.03 0.243 1.27 2028 11.3 4.98 32.8 0.053 4.49 0.266 4.76 1.20 0.264 1.47 2029 12.6 5.59 36.8 0.059 5.13 0.303 5.43 1.37 0.300 1.67 2030 14.0 6.18 40.7 0.065 5.77 0.339 6.11 1.55 0.336 1.88 2031 15.3 6.77 44.5 0.071 6.41 0.374 6.79 1.72 0.372 2.09 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 24.3 49.0 94.5 0.225 11.6 0.531 12.2 3.16 0.515 3.67 2023 22.9 44.6 91.1 0.218 11.6 0.454 12.1 3.16 0.442 3.60 2024 21.9 43.9 87.6 0.213 11.6 0.451 12.1 3.16 0.439 3.60 2025 21.0 43.1 84.5 0.207 11.6 0.447 12.1 3.16 0.436 3.59 2026 20.2 42.5 82.0 0.203 11.6 0.444 12.1 3.16 0.432 3.59 2027 19.5 41.9 79.6 0.198 11.6 0.439 12.1 3.16 0.428 3.58 2028 18.9 41.5 77.6 0.193 11.6 0.434 12.1 3.16 0.423 3.58 2029 18.3 41.0 75.7 0.188 11.6 0.429 12.1 3.15 0.419 3.57 2030 17.8 41.0 74.6 0.187 11.9 0.428 12.3 3.22 0.418 3.64 2031 17.2 40.5 72.9 0.181 11.6 0.421 12.1 3.15 0.411 3.56 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 0.293 3.86 5.42 0.010 0.086 0.190 0.475 0.104 0.085 0.189 2023 0.483 5.07 10.6 0.019 0.610 0.209 0.819 0.163 0.209 0.373 2024 2.77 2.92 8.50 0.016 0.285 0.121 0.405 0.077 0.120 0.197 2025 0.368 4.16 9.26 0.017 0.506 0.195 0.700 0.139 0.194 0.334 2026 4.73 2.84 8.41 0.016 0.267 0.111 0.378 0.072 0.111 0.183 2027 3.44 3.68 9.84 0.018 0.603 0.149 0.752 0.153 0.149 0.302 2028 0.197 1.98 4.20 0.009 0.251 0.081 0.332 0.068 0.081 0.149 2029 4.17 4.98 10.8 0.020 0.551 0.236 0.788 0.151 0.236 0.388 2030 0.122 0.878 3.93 0.008 0.113 0.011 0.125 0.031 0.011 0.042 2031 3.28 0.985 4.16 0.008 0.057 0.026 0.082 0.015 0.026 0.041 ROG NOx CO SO2 Fug PM10 Exh PM10 Total PM10 Fug PM2.5 Exh PM2.5 Total PM2.5 2022 26.5 53.7 106 0.244 12.4 0.761 13.3 3.43 0.64 4.07 2023 27.0 51.3 112 0.254 13.5 0.741 14.3 3.67 0.73 4.39 2024 29.9 49.2 112 0.254 13.9 0.688 14.5 3.75 0.67 4.42 2025 28.2 50.4 114 0.257 14.7 0.796 15.5 3.98 0.78 4.77 2026 33.3 49.1 115 0.257 15.1 0.747 15.9 4.09 0.73 4.82 2027 32.8 50.2 118 0.263 16.1 0.833 16.9 4.34 0.82 5.16 2028 30.4 48.4 115 0.254 16.4 0.781 17.2 4.43 0.77 5.19 2029 35.1 51.6 123 0.267 17.3 0.968 18.3 4.68 0.96 5.64 2030 31.9 48.0 119 0.261 17.8 0.778 18.5 4.80 0.77 5.56 2031 35.8 48.2 122 0.260 18.1 0.821 18.9 4.89 0.81 5.69 Year
Friant Ranch - Total Annual Construction Emissions - Mitigated (tons/year) Year
Friant Ranch - Total Annual Operational and Construction Emissions - Mitigated (tons/year) Year
Specific Plan - Total Operational Emissions - Mitigated (tons/year) Year
Community Plan - Total Operational Emissions - Mitigated (tons/year) 3791
Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 19 March 4, 2021

6.0 PEAK DAILY EMISSIONS FOR HEALTH IMPACT MODELING
Peak daily emissions are required for regional Health Impact Analysis (HIA) modeling. To determine the highest peak daily emissions, the combined operational plus construction emissions for each year were evaluated. The highest peak daily emission rate for each pollutant was applied to the 2020 and 2031 background regional emissions data to demonstrate the most conservative health impacts. The 2020 and 2031 regional emissions data inputs used for the HIA were developed from modeling information obtained from CARB, as developed for the San Joaquin Valley 2018 PM2.5 State Implementation Plan (SJVAPCD 2018). A discussion on the regional emissions inputs to the HIA is provided in the separate HIA report (BlueScape 2021).

Without the Project emissions included, the 2020 scenario year represents the CEQA baseline for existing conditions prior to Project development. The 2031 scenario year represents the “no project” conditions that account for reasonably foreseeable development that will have occurred by that year, and is adequate as a cumulative CEQA baseline. The 2031 year full-buildout Project impacts were then added to these two CEQA baseline years, to understand the potential specific health impacts above baseline.

The following tables show unmitigated and mitigated peak daily emissions for operational and construction emissions combined. Table 6-1 shows the unmitigated peak daily emissions and then the mitigated peak daily emissions that were applied to the regional health impact model, for the Project.

Construction emissions and operational emissions are defined separately, by source category. Peak daily NOx emissions occur in year 2023 for the unmitigated scenario and year 2022 for the mitigated scenario. Peak daily ROG and PM emissions occur in year 2029 for both the unmitigated and mitigated scenarios. Although operational emissions associated with the Specific Plan and Community Plan are highest at Project build-out, year 2031, the combination of construction emissions with operational emissions in these years (2022, 2023 and 2029) represents a worst-case peak day scenario for those pollutants.
3792
Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 20 March 4, 2021
TABLE 6-1

PEAK DAILY OPERATION AND CONSTRUCTION EMISSIONS
Category
Peak Day Unmitigated Emissions (lb/day)
ROG NOx
Fugitive
PM10
Exhaust
PM10
Fugitive
PM2.5
Exhaust
PM2.5
Construction
On-Road Mobile 0.773 3.13 2.59 0.014 0.699 0.013
Off-Road Mobile 9.36 111 15.6 4.10 4.34 3.79
Area/Energy 104 0 0 0 0 0
Operational (Specific Plan plus Community Plan)
On-Road Mobile 127 294 124 1.59 33.4 1.51
Off-Road Mobile 0 0 0 0 0 0
Area/Energy 101 28.9 0 27.0 0 27.0
Total 342 438 142 32.7 38.4 32.3
Category
Peak Day Mitigated Emissions (lb/day)
ROG NOx
Fugitive
PM10
Exhaust
PM10
Fugitive
PM2.5
Exhaust
PM2.5
Construction
On-Road Mobile 0.773 2.63 2.59 0.014 0.699 0.013
Off-Road Mobile 3.83 60.4 6.08 3.49 1.69 3.49
Area/Energy 104 0 0 0 0 0
Operational (Specific Plan plus Community Plan)
On-Road Mobile 126 304 107 1.42 28.8 1.35
Off-Road Mobile 0 0 0 0 0 0
Area/Energy 81.8 22.9 0 4.82 0 4.82
Total 316 389 115 9.74 31.2 9.66
Note: Area source exhaust PM originates from natural gas combustion and landscaping.

Table 6-2 shows the unmitigated peak daily emissions, on an annual basis from 2022 through 2031. The maximum daily emission rate for each pollutant was selected for the peak daily rate. Table 6-3 shows the mitigated peak daily emissions, on an annual basis from 2022 through 2031. These tables include all of the criteria pollutant emissions, but since contributions to regional ozone and fine particulate are minimal, CO and SO2 were not included in the regional HIA modeling.
3793
Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 21 March 4, 2021
TABLE 6-2

UNMITIGATED PEAK DAILY OPERATION AND CONSTRUCTION
Year
Friant Ranch - Total Peak Daily Operational Emissions - Unmitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 199 346 869 2.13 89.0 26.3 115 24.1 26.2 50.3
2023 201 323 888 2.15 94.0 26.2 120 25.4 26.1 51.5
2024 205 325 905 2.18 99.0 26.6 126 26.7 26.5 53.3
2025 209 326 922 2.21 104 27.0 131 28.1 26.9 55.0
2026 213 328 943 2.24 109 27.4 136 29.4 27.3 56.7
2027 219 334 965 2.29 114 28.2 142 30.7 28.1 58.8
2028 224 332 985 2.29 119 28.2 147 32.1 28.1 60.2
2029 228 335 1,008 2.32 124 28.5 153 33.4 28.5 61.9
2030 233 338 1,032 2.34 129 28.9 158 34.7 28.8 63.6
2031 237 342 1,057 2.38 134 29.3 163 36.1 29.2 65.3
Year
Friant Ranch - Total Peak Daily Construction Emissions - Unmitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 9.09 83.2 83.0 0.155 16.5 3.76 20.3 4.38 3.47 7.84 2023 12.6 114 122 0.235 19.0 4.94 23.9 5.06 4.57 9.63
2024 72.2 56.2 72.5 0.138 2.36 2.41 4.74 0.636 2.24 2.87 2025 10.1 86.2 107 0.217 17.7 3.46 21.2 4.94 3.20 8.14
2026 127 53.7 71.6 0.140 2.19 2.16 4.36 0.591 2.01 2.60 2027 95.5 98.6 121 0.241 22.8 4.09 26.8 5.54 3.78 9.31
2028 2.78 22.7 30.0 0.070 1.98 0.773 2.75 0.535 0.719 1.25 2029 114 99.2 122 0.242 18.2 4.12 22.3 5.04 3.81 8.85
2030 2.41 12.2 26.3 0.062 0.89 0.223 1.11 0.241 0.223 0.463 2031 90.2 21.4 42.3 0.084 0.58 0.67 1.25 0.156 0.668 0.824 Year
Friant Ranch - Total Peak Daily Operational and Construction Emissions - Unmitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 209 429 952 2.28 105 30.1 136 28.4 29.7 58.1
2023 214 438 1,009 2.39 113 31.2 144 30.5 30.7 61.2
2024 277 381 977 2.32 101 29.0 130 27.4 28.8 56.1
2025 219 412 1,029 2.42 122 30.5 152 33.0 30.1 63.1
2026 341 382 1,015 2.38 111 29.6 141 30.0 29.3 59.3
2027 314 433 1,087 2.53 137 32.3 169 36.3 31.9 68.1
2028 226 355 1,015 2.36 121 28.9 150 32.6 28.8 61.4
2029 342 434 1,130 2.56 142 32.7 175 38.4 32.3 70.7
2030 235 350 1,058 2.41 130 29.2 159 35.0 29.1 64.0
2031 328 364 1,100 2.46 135 30.0 165 36.2 29.9 66.1
3794
Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 22 March 4, 2021
TABLE 6-3

MITIGATED PEAK DAILY OPERATION AND CONSTRUCTION
Year
Friant Ranch - Total Peak Daily Operational Emissions -Mitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 181 326 707 1.53 78.2 4.02 82.2 21.1 3.92 25.1
2023 182 306 725 1.55 82.3 3.95 86.2 22.2 3.87 26.1
2024 186 308 741 1.57 86.4 4.34 90.7 23.3 4.26 27.6
2025 190 309 759 1.60 90.5 4.73 95.2 24.4 4.65 29.1
2026 194 311 778 1.62 94.5 5.12 99.7 25.5 5.04 30.5
2027 199 317 800 1.67 98.6 5.86 105 26.6 5.78 32.4
2028 203 315 819 1.67 103 5.87 109 27.7 5.79 33.5
2029 208 318 841 1.69 107 6.24 113 28.8 6.16 34.9
2030 212 323 866 1.73 112 6.63 119 30.3 6.56 36.8
2031 217 325 888 1.75 115 6.99 122 31.0 6.91 37.9
Year
Friant Ranch - Total Peak Daily Construction Emissions - Mitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 4.55 63.0 87.0 0.155 7.48 1.50 8.98 1.99 1.47 3.45 2023 5.94 72.4 134.0 0.235 8.75 3.10 11.8 2.34 3.10 5.44 2024 69.5 31.5 78.4 0.138 2.36 1.57 3.90 0.64 1.57 2.20 2025 4.53 54.7 119 0.217 8.29 2.60 10.9 2.30 2.60 4.90
2026 125 32.1 78.4 0.140 2.19 1.59 3.78 0.59 1.59 2.18
2027 89.7 56.1 135 0.241 10.5 2.55 13.0 2.59 2.55 5.13
2028 1.57 15.2 32.9 0.070 1.98 0.624 2.60 0.53 0.62 1.16 2029 109 67.9 136 0.242 8.67 3.50 12.2 2.39 3.50 5.89
2030 0.955 6.7 30.3 0.062 0.890 0.088 0.978 0.241 0.087 0.328 2031 88.7 18.0 46.2 0.084 0.580 0.939 1.52 0.156 0.939 1.10 Year
Friant Ranch - Total Peak Daily Operational and Construction Emissions - Mitigated (lb/day) ROG NOx CO SO2
Fug
PM10
Exh
PM10
Total
PM10
Fug
PM2.5
Exh
PM2.5
Total
PM2.5
2022 185 389 794 1.68 85.6 5.52 91.2 23.1 5.39 28.5
2023 188 379 859 1.78 91.0 7.05 98.1 24.6 6.97 31.5
2024 255 340 820 1.71 88.7 5.91 94.6 24.0 5.83 29.8
2025 194 364 878 1.81 98.7 7.34 106 26.7 7.25 34.0
2026 319 343 857 1.76 96.7 6.71 103 26.1 6.62 32.7
2027 289 374 935 1.91 109 8.41 117 29.2 8.33 37.5
2028 205 330 852 1.74 105 6.49 111 28.2 6.41 34.6
2029 316 386 977 1.93 115 9.74 125 31.2 9.66 40.8
2030 213 330 896 1.80 113 6.72 120 30.5 6.64 37.2
2031 305 343 934 1.83 116 7.93 124 31.1 7.85 39.0
3795
Friant Ranch Community Development Project Air Emissions Report BlueScape Environmental 23 March 4, 2021

7.0 REFERENCES
BAAQMD 2017. California Environmental Quality Act Air Quality Guidelines, Bay Area Air Quality Management District, May 2017.

Available online at:www.baaqmd.gov/~/media/files/planning-andresearch/ceqa/ceqa_guidelines_may2017-pdf.pdf?la=en BlueScape 2021. Regional Air Quality Modeling and Health Impact Analysis:

Friant Ranch Community Development. 2021.

CAPCOA 2017. California Air Pollution Control Officer’s Association, Recommendation of CalEEMod.

Available online at: www.capcoa.org/caleemod/
CARB 2017. Consumer Products Program, low vapor pressure VOC. Available online at:
ww2.arb.ca.gov/our-work/programs/consumer-products-program/lvp-vocresearch CARB 2017. California Air Resources Board, Mobile Source Emission Factors for On-road vehicles, EMFAC2017. Available online at:
https://arb.ca.gov/emfac/
Caltrans 2017. Peak Hour Volume Data, Fresno County. Available online at:
https://dot.ca.gov/programs/traffic-operations/census
County of Fresno 2009. Friant Community Plan Update and Friant Ranch Specific Plan, Draft Environmental Impact Report. October 2009.

Available online at:
www.co.fresno.ca.us/home/showpublisheddocument?id=14246 Department of Finance (DOF) 2017, State of California. Demographics forecasting website,
Available online at: www.dof.ca.gov/Forecasting/Demographics/ SJVAPCD 2012. Draft Guidance for Assessing and Mitigating Air Quality Impacts – 2012, SJVAPCD, May 2012. Available online at:
www.valleyair.org/transportation/gamaqidraft-2012/gamaqi-2012-draftmay312012.pdf SJVAPCD, 2018. Photochemical Modeling for the 2018 San Joaquin Valley Annual/24-Hour PM2.5 State Implementation Plan, prepared by the California Air Resources Board, October 2018. www.valleyair.org/pmplans/ 3796
Friant Ranch Community Development Project Air Emissions Report

APPENDIX A
TABLES
3797
Friant Ranch Community Development Project Air Emissions Report TABLE A-1

MITIGATION STRATEGIES FOR EMISSION REDUCTION
Mitigation
Strategy FEIR 3.3.1/3.3.2 ISR Application CalEEMod Applied Construction Clean
Fleet

3.3.1: Diesel oxidation
catalyst with 40% NOx
reduction (cranes/forklift
15%)
Mitigation Measure 1:

District Rule 9510 (ISR
requires the clean fleet
data template to verify the
NOx and PM10 emission
reductions)
CalEEMod can apply
oxidation catalysts, diesel
particulate filters, and
upgraded engine Tier 2, 3,
or 4, or alternate fuels (Bio,
CNG, Electric)1
Increase Density
Mitigation Measure 2a:
2,000 DU within ½ mi
radius of Project
Applied as 2,000 in
CalEEMod
Increase Diversity Mitigation Measure 2b:

Mixed-Use development
Applied as increased
diversity in CalEEMod
Increase Walkability
Design
Mitigation Measure 2c: 7
intersections
Applied as intersections per
square miles in CalEEMod
Improve Destination
Accessibility
Mitigation Measure 2d: ¼
mile
Applied as ¼ mi to job
center or downtown in
CalEEMod
Increase Transit
Accessibility

3.3.2 Transportation
related mitigation
measures
Mitigation Measure 2e: ¼
mi to transit station
Applied as ¼ mi to transit
station in CalEEMod
Improve pedestrian
network
Bicycle usage is considered
a design strategy, not
mitigation
Mitigation Measure 3a:

Provide pedestrian access
network within Project site
and connecting offsite
Applied as pedestrian access
network within Project site
and connecting offsite in
CalEEMod
Provide traffic
calming measures
Mitigation Measure 3b:
100% of streets with
improvement within ½ mile
of Project site
Applied as 100% of streets
with improvement within ½
mile of Project site in
CalEEMod
Implement
Neighborhood Electric
Vehicle Network
Mitigation Measure 3c:

Provide NEV Network
Applied as NEV network
provided in CalEEMod
Encourage
Telecommuting and
Alt Work Schedules
Mitigation Measure 5c:
25% of employees to
participate in
telecommuting 1.5 days
Applied as 25% of
employees to participate in
telecommuting 1.5 days in
CalEEMod
Hearth Mitigation Measure 6: NG
only hearth Applied as NG only hearth
Exceed Title 24

3.3.2: Reduce residential
energy consumption 10-
20% from 2008 Title 24.
(These energy efficiency
standards are now
considered design
requirements and are
outdated as mitigation.)
Mitigation Measure 7a: ISR
states: TBD
Apply Title 24 reduction (see
below)
Single family homes built
under 2019 Energy
Standards use 7% less
energy than 2016
Standards. Assume 7%
reduction to 2031.

Lighting energy reduction
based on an average 29%
for the lighting power
density from 2016 to 2019
(non-residential buildings).

Assume 20% reduction to
2031.

Low-flow plumbing, toilets,
sinks, showers in accordance
with Title 24 as of 2019 and
water efficient irrigation, in
CalEEMod.

Landscape
Equipment
Mitigation Measure 7b: ISR
states “yes”
3% electric will be applied in
CalEEMod, as noted to be
the assumed statewide
average
3798
Friant Ranch Community Development Project Air Emissions Report TABLE A-1

MITIGATION STRATEGIES FOR EMISSION REDUCTION
Mitigation
Strategy FEIR 3.3.1/3.3.2 ISR Application CalEEMod Applied Solar Panels Mitigation Measure 8: ISR
states “yes”
kW of total power output will
be entered into CalEEMod
Electric Vehicle
Charger
Mitigation Measure 9: ISR
states “TBD”
Number of charging outlets
to be entered into CalEEMod
Solid Waste
Reduction
Fresno County is required to
meet the state mandated
75% reduction in solid waste
by 2020. CalEEMod will
apply 75% recycling and
composting.

Low VOC Paints

3.3.1: 45% ROG emission
reduction compared to
existing architectural
coating rules (defined
defaults were Res: 130 g/L
and Non-res: 250 g/L)
The FEIR measure is
outdated. SJV Rule 4601
requires VOC limit of 50 g/L
for flat and non-flat
coatings, effective after
1/1/2022. 50 g/L will be
used in CalEEMod.

Fugitive Dust SJV Reg VIII Control
Measures
Apply 3x per day watering
frequency (61% reduction in
PM10/2.5), vehicle speed 15
mph
EMFAC 2017 and
SAFE Factor
Adjustment
Emission factors for mobiles
sources will be adjusted in
CalEEMod to update vehicle
emissions

1. The ISR does not state specific measures, only the general clean fleet data, which is submitted to verify emission reductions meet the rule after each construction phase is completed. A combination of Tier 3 and Tier 4 engines was applied in CalEEMod to demonstrate the reductions, but they are not specified in the ISR or the EIR. 3799
Friant Ranch Community Development Project Air Emissions Report

APPENDIX B

CALEEMOD FILES
(PROVIDED ELECTRONICALLY)
3800
Appendix E
Regional Air Quality Modeling and
Health Impact Analysis
3801

REGIONAL AIR QUALITY MODELING
& HEALTH IMPACT ANALYSIS
for the

FRIANT RANCH COMMUNITY DEVELOPMENT

PROJECT
Prepared for:

Friant Ranch, LP
Fresno, California
Prepared by:

James A. Westbrook, CCM
Principal Air Quality Scientist
Francisco Matamala
Principal Photochemical Modeler
James G. Wilkinson, PhD
Principal Photochemical Modeler
BlueScape Environmental
16870 West Bernardo Drive, Suite 400
San Diego, CA 92127
August 4, 2021

BLUE SCAPE

ENVIRONMENTAL
3802
Friant Ranch Project
Friant, California
Regional Air Quality Modeling
& Health Impact Analysis
BlueScape Environmental i August 4, 2021

TABLE OF CONTENTS
EXECUTIVE SUMMARY .............................................................................. 1

1.0 INTRODUCTION............................................................................... 4

2.0 SIGNIFICANT IMPACT LEVELS FOR REGIONAL AIR QUALITY
MODELING AND HEALTH IMPACT ANALYSIS..................................... 6

3.0 CAMX PHOTOCHEMICAL MODELING FOR THE FRIANT RANCH
PROJECT.......................................................................................... 8

3.1 Summary of the Modeling System ................................................ 8

3.2 Modeling Domain..................................................................... 10

3.3 WRF – Weather Research and Forecasting Model Data ................... 11

3.4 CAMx Model Setup................................................................... 12

3.5 Emissions Inventories............................................................... 13

3.6 Model Performance Evaluation ................................................... 16

3.7 CAMx Modeling Results............................................................. 16

4.0 FRIANT RANCH REGIONAL HEALTH IMPACTS ANALYSIS................. 24

4.1 BenMAP-CE Overview ............................................................... 24

4.2 Application of BenMAP-CE to the Friant Ranch Project.................... 25

4.3 BenMAP-CE Modeling Results and Results Interpretation ................ 28

5.0 UNCERTAINTY IN PHOTOCHEMICAL MODELING AND HEALTH IMPACT ANALYSIS...................................................................................... 39

5.1 Uncertainty in Photochemical Modeling........................................ 39

5.2 Uncertainty in Health Impact Analysis ......................................... 41

6.0 CONCLUSIONS............................................................................... 44

7.0 REFERENCES.................................................................................. 45 3803
Friant Ranch Project
Friant, California
Regional Air Quality Modeling
& Health Impact Analysis
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TABLES
Table 1 Ambient Health Standards .............................................................. 6 Table 2 EPA and SJVAPCD Significant Impact Levels....................................... 7 Table 3 WRF Modeling Configuration.......................................................... 12 Table 4 General CAMx Configuration.......................................................... 12 Table 5 4-Km Grid Configuration............................................................... 12 Table 6 Project Peak Daily Unmitigated Operation and Construction Emissions (lb/day) ..................................................................................... 14 Table 7 Project Peak Daily Mitigated Operation and Construction Emissions (lb/day) ..................................................................................... 14 Table 8 2020 CAMx Modeling Results for the Baseline and Unmitigated Project Scenarios, Maximum Daily 8-Hour Average (MDA8) Ozone, at the Grid Cell with the Highest Change......................................................... 17 Table 9 2020 CAMx Modeling Results for the Baseline and Mitigated Project Scenarios, Maximum Daily 8-Hour Average (MDA8) Ozone, at the Grid Cell with the Highest Change......................................................... 17 Table 10 2031 CAMx Modeling Results for the Future and Unmitigated Project Scenario, Maximum Daily 8-Hour Average (MDA8) Ozone, at the Grid Cell with the Highest Change............................................................... 18 Table 11 2031 CAMx Modeling Results for the Future and Mitigated Project Scenarious, Maximum Daily 8-Hour Average (MDA8) Ozone, at the Grid Cell with the Highest Change......................................................... 18 Table 12 2020 CAMx Modeling Results for the Baseline and Unmitigated Project Scenarios, Annual Average 24-Hour PM2.5, at the Grid Cell with the Highest Change........................................................................... 21 Table 13 2020 CAMx Modeling Results for the Baseline and Mitigated Project Scenarios, Annual Average 24-Hour PM2.5, at the Grid Cell with the Highest Change........................................................................... 21 Table 14 2031 CAMx Modeling Results for the Baseline and Unmitigated Project Scenarios, Annual 24-Hour Average PM2.5, at the Grid Cell with the Highest Change........................................................................... 22 Table 15 2031 CAMx Modeling Results for the Baseline and Mitigated Project Scenarios, Annual 24-Hour Average PM2.5, at the Grid Cell with the Highest Change........................................................................... 22 Table 16 BenMAP-CE Modeling Results for Maximum Daily 8-Hour (MDA8) Average Ozone, Existng Baseline Year 2020, Unmitigated .............................. 30 Table 17 BenMAP-CE Modeling Resuts for Maximum Daily 8-Hour Average (MDA8) Ozone, Existing Baseline Year 2020, Mitigated ................................. 31 Table 18 BenMAP-CE Modeling Results for Maximum Daily 8-Hour (MDA8) Average Ozone, Future Baseline Year 2031, Unmitigated ............................... 32 Table 19 BenMAP-CE Modeling Results for Maximum Daily 8-Hour Average (MDA8) Ozone, Future Baseline Year 2031, Mitigated ................................... 33 Table 20 BenMAP-CE Modeling Results for Maximum Annual 24-Hour Average PM2.5, existing Baseline Year 2020, Unmitigated ............................... 34 Table 21 BenMAP-CE Modeling Results for Maximum Annual 24-Hour Average PM2.5, Existing Baseline Year 2020, Mitigated ................................... 35 3804
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Table 22 BenMAP-CE Modeling Results for Maximum Annual 24-Hour Average PM2.5, Future Baseline Year 2031, Unmitigated ................................. 36 Table 23 BenMAP-CE Modeling Results for Maximum Annual 24-Hour Average PM2.5, Future Baseline Year 2031, Mitigated ..................................... 38

FIGURES
Figure 1. Components of the CAMx Modeling System ....................................... 9 Figure 2. CARB CMAQ and CAMX Modeling Grid ............................................. 11 Figure 3. Project Emissions Apportionment to Grid Cells.................................. 15 Figure 4. Ozone 2020 Scenario Maximum Daily 8-hour (MDA8) Average Concentrations Comparison of Baseline to Unmitigated Project Emissions and Project Mitigated Emissions..................................................... 19 Figure 5. Ozone 2031 Scenario Maximum Daily 8-Hour (MDA8) Average Concentrations Comparison of Baseline to Unmitigated Project Emissions and Project Mitigated Emissions..................................................... 20 Figure 6. PM2.5 2020 Scenario Annual 24-Hour Average Concentrations Comparison of Baseline to Unmitigated Project Emissions and Project Mitigated Emissions................................................................................... 23 Figure 7 . PM2.5 2031 Scenario Annual 24-Hour Average Concentrations Comparison of Baseline to Unmitigated Project Emissions and Project Mitigated Emissions................................................................................... 24 Figure 8. Overview of the Process of Health Impact Calculations in BenMAP-CE ... 24

APPENDICES
Appendix A: WRF/CAMx/BenMAP-CE Modeling Input and Output (list of electronic files)
Appendix B: BenMAP-CE Audit Trial Reports
Appendix C: Resumes
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GLOSSARY OF TERMS AND ACRONYMS
Acronym Description
3D Three-dimensional
AAQA Ambient Air Quality Analysis
agl Above ground level
AQMP Air Quality Management Plan
Avg Average
BAAQMD Bay Area Air Quality Management District
BenMAP-CE Benefits Mapping and Analysis Program – Community Edition C-R Concentration-Response function
CAAQS California Ambient Air Quality Standards
CalEEMod California Emissions Estimator Model
CAMx Comprehensive Air Quality Model with Extensions
CARB California Air Resources Board
CB06 Carbon Bond 06 chemical mechanism
CEPAM California Standard Emission Tool
CEQA California Environmental Quality Act
CI Confidence Interval
CMAS Community Modeling and Analysis System
CMAQ Community Multiscale Air Quality Model
CO Carbon Monoxide
CSV Comma Separated Value
DPM Diesel Particulate Matter
EBI Euler Backward Iterative solver
EIR Environmental Impact Report
EMFAC California Air Resources Board’s Mobile Source Emission Factor inventory
EPA U.S. Environmental Protection Agency
ESMF Earth System Modeling Framework software
GIS Geographic Information System
GREASD Greatly Reduced Execution and Simplified Dynamics Submodel GRIB Gridded Binary form
HARP2 Hotspots Analysis and Reporting Program Version 2 HDF Hierarchical Data Format
HIA Health Impact Analysis
HRA Health Risk Assessment
ISA Integrated Science Assessments
km Kilometers
lb/day Pounds per day
LP Limited Partnership
MDA8 Maximum Daily 8-hour Average Ozone
MPE Model Performance Evaluation
NAAQS National Ambient Air Quality Standards
NAM North American Mesoscale
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GLOSSARY OF TERMS AND ACRONYMS
Acronym Description
NCEP National Centers for Environmental Protection
NCL NCAR Command Language
netCDF Network Common Data form
NOx Oxides of Nitrogen
OFFROAD California Air Resources Board’s Offroad Source Emission Factor Inventory
PBL Planetary Boundary Layer
PGM Photochemical Grid model
PM2.5
Particulate Matter 2.5 Micrometers or less in aerodynamic diameter
PM10
Particulate Matter 10 Micrometers or less in aerodynamic diameter
ppb Parts per billion
ppbv Parts per billion by volume
PopGrid EPA gridded population data for BenMAP
PPM Piecewise Parabolic Model
ROG Reactive Organic Gases
SAPRC07 Chemical Mechanism Model 2007
SIL Significant Impact Level
SIP State Implementation Plan
SMAQMD Sacramento Metropolitan Air Quality Management District SMOKE Sparse Matrix Operator Kerner Emissions modeling system SCAQMD South Coast Air Quality Management District
SJV San Joaquin Valley
SJVAPCD San Joaquin Valley Air Pollution Control District SOx Oxides of Sulfur
TOG Total Organic Gases
µg/m3 Micrograms per Cubic Meter
UTM Universal Transverse Mercator
VERDI Visual Environment for Rich Data Interpretation program VOC Volatile Organic Compounds
WRF Weather Research and Forecasting model
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EXECUTIVE SUMMARY
In the California Supreme Court decision in Sierra Club versus County of Fresno (S219783, December 24, 2018), or the “Friant Ranch” Case, the Court found the Environmental Impact Report (EIR) to be inadequate because it failed to make a reasonable effort to include an analysis that correlates Project emissions to impacts on human health. In particular, the Court focused on air quality impact analysis for the Project’s significant emissions of oxides of nitrogen (NOx) and Reactive Organic Gases (ROG), which are precursors to ozone formation, and of particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) emissions. The Court concluded that an EIR should meet evidentiary requirements, such as: 1) including sufficient detail to enable reviewers to understand and to consider meaningfully the issues the project raises, and 2) making a reasonable effort to connect a project’s air quality impacts to likely health consequences, or explain why such air quality analysis is not scientifically feasible at the time of drafting the analysis.

This technical study describes the work completed to satisfy the Supreme Court’s EIR evidentiary requirements as applied to the Friant Ranch Community Development Project (or “the Project”). BlueScape completed photochemical modeling work to support the Project Health Impact Analysis (HIA) as described in this technical study.

BlueScape generally followed the California Air Resources Board’s (CARB’s) 2018 PM2.5 State Implementation (SIP) study methods (CARB 2018), to develop regional ozone and PM2.5 air quality concentration predictions that were then entered into the U.S. Environmental Protection Agency (EPA) Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE, EPA 2019a) to estimate Project health impacts.

CARB used 2013 emissions and modeling files as the base case year for its photochemical modeling effort. In order to determine that the entire modeling system and supporting data bases adequately replicated observed conditions for 2013, CARB compared the 2013 air quality modeling concentration predictions to available 2013 observed air quality concentrations and determined that the modeling system was performant. CARB then projected emissions from 2013 to the 2020 future year, and the modeling system was applied in order to predict 2020 air quality concentrations.

In the Project HIA study, it was deemed prudent to set 2020 as the Existing Baseline year for the regional emissions, and use the 2013 meteorological data year to complete the modeling, to ensure consistent and valid model performance with the work CARB performed.

The electronic meteorological and emissions data files used by CARB for the San Joaquin Valley (SJV) for photochemical modeling completed with the Community Multiscale Air Quality (CMAQ) model were initially obtained by Ramboll Corporation (Ramboll) and processed prior to BlueScape obtaining the files and using them to complete the HIA. The 2020 CMAQ files obtained by Ramboll from CARB, which included speciated NOx, Total Organic Gases (TOG), particulate matter with an aerodynamic diameter less than or equal to 10 micrometers (PM10), and PM2.5 3808
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emissions, were converted for use in the Comprehensive Air Quality Model with Extensions (CAMx) model (Ramboll ENVIRON 2017) with appropriate CMAQ-to-CAMx conversion tools. CARB also provided its 2013 Weather Research and Forecasting Model (WRF v3.8 or later) meteorological data files to Ramboll, which were also converted for use in CAMx using appropriate tools. BlueScape obtained these files prepared by Ramboll from the Project proponent and further developed them for use in the HIA discussed in this report.

Using the 2013 WRF meteorological data coupled with the converted CMAQ input files, CAMx (v.6.5) photochemical modeling was performed for six (6) scenarios:
1) 2020 Existing Baseline year without Project emissions; 2) 2020 Existing Baseline year with unmitigated Project emissions; 3) 2020 Existing Baseline year with mitigated Project emissions; 4) 2031 Future Baseline year without Project emissions; 5) 2031 Future Baseline year with unmitigated Project emissions; and 6) 2031 Future Baseline year with mitigated Project emissions.

The intent of including a 2020 Existing Baseline year is to describe environmental conditions in 2020 and then to add Project emissions to this baseline. The Project emissions include construction emissions, as well as operational emissions from prior completion of Project work, that will occur in the same year. The Future Baseline year describes 2031 environmental conditions without the Project to which Project emissions at full Project buildout are added for a cumulative result. For the 2031 Future Baseline year, the 2020 CAMx regional emissions inventory was “grown” to a 2031 regional emissions inventory based upon growth factors developed using the CARB 2016 SIP - Standard Emission Tool (CEPAM, CARB 2018). The 2031 regional emissions inventory thus accounts for reasonably foreseeable population growth in the affected region by 2031.

A Model Performance Evaluation (MPE) of the 2020 CAMx air quality modeled results was completed. The CAMx model was run for the entire 2020 Existing Baseline year.

The MPE was conducted as a comparison of the hourly CAMx model predictions for the Project study, to CARB’s original hourly CMAQ model predictions for 2020, using CMAQ modeling output files obtained from CARB. The Environmental Protection Agency (EPA) has issued guidance on how air quality simulation models are to be judged to be adequately performing in order to be used for studies such as conducted here (EPA 2018a). Two statistics that are used to judge such model performance are mean gross error and mean bias. If the gross error is less than 15% and bias is within +/-5%, it is an indicator that the model is adequately performing (Emery et. al. 2017). Average gross error and bias statistics were estimated at the county-level for ozone and PM2.5 for each month of 2020 between the base case modeling results supplied to our team and the base case modeling results that we ran. Because the 3809
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gross error and bias values that resulted were less than 5% for gross error and were near zero for bias (i.e., a few tenths of a part per billion (ppb) for ozone and a few hundredths of a µg/m3 for PM2.5), the CAMx modeling system results for 2020 and 2031 were deemed suitable for use in the Friant Ranch Project HIA.

For the six modeling scenarios, maximum daily 8-hour average (MDA8) ozone concentrations and maximum annual 24-hour average PM2.5 concentrations were developed using CAMx for import into EPA’s BenMAP-CE model. BenMAP-CE is opensource software that can be configured to relate the human health impacts and/or benefits to air quality changes. The latest version of BenMAP-CE is Version 1.5, which the EPA released in March 2019. The program includes a subset of air quality monitoring data in preloaded databases for areas within the Continental United States, with recent and projected demographic and baseline health data, concentration-response (C-R) relationships drawn from the published epidemiological literature, and economic value estimates based on the published economics literature. The principal function of the software is to apply the findings of epidemiological and economics studies to estimate the health impacts and economic value of air pollution changes.

To estimate human health effects, BenMAP-CE first determines the change in ambient air pollution using user-specified air quality data for the baseline and controlled scenarios. The software then applies the relationship between the air pollution with certain health effects (also known as health endpoints) using health impact function or the C-R functions that were derived from epidemiology studies. BenMAP-CE applies that relationship to the exposed population to calculate health impacts based on baseline health incidence data.

The Project maximum estimated change in ozone concentration above both the 2020 Existing Baseline and the 2031 Future Baseline does not exceed the EPA’s Significant Impact Level (SIL) for ozone of 1 ppb, 8-hour average. The Project maximum estimated change in PM2.5 concentration impacts above baseline levels does not exceed the EPA’s and San Joaquin Valley Air Pollution Control District’s (SJVAPCD) SIL of 0.2 µg/m3, annual average. As discussed in Section 2.0, concentration results that do not exceed a SIL can be used by Fresno County, the lead agency for this Project, as one indicator that the Project concentration impacts, and therefore the related specific health impacts, are likely not meaningful.

The Project-related specific health incidences estimated using BenMAP for ozone and PM2.5 concentration changes above baseline, as compared with regional background health incidences, are very small relative to background incidences. For example, for ozone asthma-related emergency room visits for children aged 0 to 17, the Projectrelated health incidences per year were estimated to be 0.0427, or about 0.00026% of regional background occurrences. For PM2.5 and mortality, the Project-related health incidences per year were estimated to be 0.35, or about 0.00054% of regional background occurrences. As the health incidence values estimated for Friant Ranch Project emissions are less than one, and a negligible fraction of the background incidence values, it is not expected that the Project will result in any meaningful health impact greater than zero, that can be established with any certainty. 3810
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1.0 INTRODUCTION
This HIA study provides a description of the methodologies used to develop concentration data for ozone and PM2.5 for potential health impacts from Friant Ranch Project emissions. The intent of the HIA is to provide quantitative photochemical modeling and specific health impact information that meets the EIR evidentiary requirements for HIAs, as stated by the California Supreme Court in the Friant Ranch Case decision.

The proposed Project is located within the jurisdiction of the San Joaquin Valley Air Pollution Control District (SJVAPCD). CARB conducts photochemical modeling studies for the San Joaquin Valley to address attainment of the ozone and PM2.5 state and federal ambient air quality standards. To review regional impacts from these pollutants, CARB recently completed photochemical modeling for the 2018 PM2.5 SIP study using the CMAQ model for 2020 emissions (SJVAPCD 2018). As presented in this report, BlueScape obtained electronic files from Ramboll that were developed using CARB’s CMAQ files, to run another photochemical model, the CAMx model.

Using the CAMx files, BlueScape developed the HIA study to review potential ozone and PM2.5 impacts from the Project. As addressed in the HIA, the primary emissions precursors to ozone formation are NOx and Volatile Organic Compounds (VOC). VOC includes ROG, which is included within the larger category of TOG that is included in CAMx for complete photochemistry calculations. Air quality modeling concentration data developed with the CAMx model were used in health impact calculations completed with BenMAP-CE software.

The following discussion provides the background on California air district guidance, as response to the Friant Ranch Case decision. At the time the decision was released, the SJVAPCD did not develop general guidance to be used for California Environmental Quality Act (CEQA) review, and more specifically, the SJVAPCD did not develop procedures on how to evaluate specific health impacts from a project’s ozone precursor and PM2.5 emissions. However, the SJVAPCD and the South Coast Air Quality Management District (SCAQMD) did release legal positions on the Friant Ranch case. During the litigation before the California Supreme Court, SJVAPCD submitted an Amicus Curiae brief stating that models are not available to conduct an appropriate analysis (SJVAPCD 2015a). In its brief, SJVAPCD acknowledged that while health risk assessments (HRAs) for localized air toxics impacts, such as diesel particulate matter (DPM), are commonly prepared, “it is not feasible to conduct a similar analysis for criteria air pollutants because currently available computer modeling tools are not equipped for this task.” The SJVAPCD further noted that an analysis of health impacts solely from the Friant Ranch Project emissions (which equate to less than one-tenth of one percent of the total NOx and VOC emissions in the San Joaquin Valley) is not likely to yield valid information, and that any such information should not be “accurate when applied at the local level.” SJVAPCD has followed up the brief with general guidance to project developers to make “a reasonable effort to discuss relevant specifics regarding the connection between potential adverse air quality impacts from the Project with the likely nature and magnitude of the potential health impacts. If the potential health impacts from 3811
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the project cannot be specifically correlated, explain what is known and why, given scientific constraints, potential health impacts cannot be translated (SJVAPCD 2020).”
The SCAQMD also submitted an Amicus Curiae brief to the California Supreme Court (SCAQMD 2015). Its brief stated the difficulty of quantifying a project’s health impacts for such pollutants as ozone, due to the need to consider the impact of a relatively small amount of emissions on overall regional pollution impacts. However, the SCAQMD did conclude that such regional impact modeling could be feasible for larger projects, considered to be much larger in terms of total emissions, than the Friant Ranch Project. The SCAQMD has demonstrated the typical use of a photochemical modeling concentration analysis for regional (not source-specific) ozone and PM2.5 emissions impacts, connecting the results to specific predicted health impacts, in the 2016 Air Quality Management Plan (AQMP, SCAQMD 2017) and related reports.

In late 2019, the Sacramento Metropolitan Air Quality Management District (SMAQMD 2019) released Interim Guidance suggesting that, where air quality emission impacts are deemed to be significant under CEQA, project applicants should provide a technical (or quantitative) analysis and make a finding on whether specific health impact review of ozone and PM2.5 is feasible. If such a health impact analysis (for example using a photochemical model with BenMAP) is not feasible, the guidance suggests that the CEQA documents explain why the analysis is not feasible. Such an explanation could include review and discussion of models typically used currently for health impact analysis, including CalEEMod, EMFAC, OFFROAD, BenMAP-CE and HARP2, combined with an explanation of the extent to which these and any other tools identified could assist in describing the project’s health impacts from ozone and PM2.5 emissions.

Since releasing the Interim Guidance, the SMAQMD has released the Guidance to Address the Friant Ranch Ruling for CEQA Projects in the Sac Metro Air District, which replaces the Interim Guidance (SMAQMD 2020). The guidance document provides insight on potential ozone and PM2.5 impacts that may occur due to project development in the Sacramento region, from a project’s NOx, VOC, carbon monoxide (CO) and oxides of sulfur (SOx) emissions. The guidance provides screening look-up tables that can be used to estimate specific health effects in certain strategic growth areas. The screening look-up tables were developed using CAMx photochemical modeling. For projects with significant emissions outside of the strategic growth areas, the document provides guidance on the use of a photochemical model to develop quantitative estimates of ozone and PM2.5 concentrations and health impacts from these pollutants.

This report describes the methodology used to complete the ozone and PM2.5 concentration modeling for the Friant Ranch Project, including the CAMx modeling system, WRF model meteorological data inputs, CAMx model emission inputs and other input assumptions, and the output files prepared for BenMap-CE. The concentration results are described in tables and figures. A discussion regarding use of SILs for the HIA is provided in Section 2.0. The concentration impacts are discussed 3812
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in detail in Section 3.7. The health impacts calculated using BenMAP-CE are presented in Section 4.3.

2.0 SIGNIFICANT IMPACT LEVELS FOR REGIONAL AIR QUALITY

MODELING AND HEALTH IMPACT ANALYSIS
The Friant Ranch Case decision did not establish, nor provide the means to develop, CEQA significance thresholds for regional air quality and health impact modeling. The Case only states that specific health impact information should be presented to the public in a reliable and meaningful way. This HIA analysis for the Friant Ranch Project has been prepared due to the finding that one or more of the pollutants, NOx, ROG (or VOC), PM10 or PM2.5 emitted by the Project may exceed the CEQA mass emissions significance thresholds listed by the SJVAPCD (SJVAPCD 2015b). This section provides a discussion on use of concentration significant impact levels (SILs) for consideration by Fresno County, should the county decide to complete further review on whether the specific health impacts described in the HIA are meaningful.

The EPA and CARB have set ambient health standards – the National Ambient Air Quality Standards (NAAQS) and the California Ambient Air Quality Standards (CAAQS) – for the purpose of meeting regional air pollution management goals. The ambient standards are based upon health impact studies, including health impact studies included in the BenMAP-CE model database and used in this HIA. For ozone and PM2.5 health studies used to establish the NAAQS, see the Integrated Science Assessments (ISAs) (EPA 2019b). According to CARB, an ambient air quality standard is established to protect the health of the most sensitive groups in communities (CARB 2021a). The ambient standards represent an acceptable regional level of pollution for a specific compound. The federal and California state ambient health standards for ozone and PM2.5 are listed in Table 1 below (CARB 2021b).

TABLE 1

AMBIENT HEALTH STANDARDS
Pollutant Federal California
Ozone 70 ppb 8-Hour Avg 90 ppb 1-Hour Avg
70 ppb 8-hour Avg
PM2.5
35 µg/m3 24-Hour Avg

12.0 µg/m3 Annual Avg
12 µg/m3 Annual Avg
The SJVAPCD has established SILs as thresholds that are a small fraction of the ambient standards, to be used for ambient air quality analysis (AAQA) for air permitting, in APR 1925 Policy for District Rule 2201 AAQA Modeling (SJVAPCD 2019).

The SILs are used to “determine whether a proposed source’s emissions will have a significant impact on air quality in an area.” In areas that are in nonattainment of the ambient standards, such as is the case for PM2.5 within the San Joaquin Valley, the 3813
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SILs are thresholds that cannot be exceeded by an air permit applicant and still obtain an air permit authorization.

The SJVAPCD recommends use of ambient air quality analysis to determine whether impacts from development projects are significant under CEQA. The approach for development projects is provided in Section 8.4 of the Guidance for Assessing and Mitigating Air Quality Impacts (SJVAPCD 2015c) and refers to the APR 1925 AAQA modeling guidance. The PM2.5 SILs for non-fugitive sources (or projects) were obtained by SJVAPCD from EPA, as described in the document Guidance on Significant Impact Levels for Ozone and Fine Particles in the Prevention of Significant Deterioration Permitting Program (EPA 2018b). In that EPA guidance document (pages 7, 13), the EPA addresses why, in its judgment, source concentration impacts below a SIL are insignificant: “the EPA believes an insignificant impact is an impact on air quality concentrations that is small and not meaningful.” Thus, as related to the ambient health standards, a regulating authority can use the SILs as an indication that impacts below the SILs can be considered to be insignificant, or not causing a meaningful difference in a region’s air quality as well as in any associated health impacts.

For PM2.5, which is a nonattainment pollutant, the SJVAPCD considers project impacts on air quality to be insignificant if the PM2.5 SILs are not exceeded. In that case, no further analysis of impacts is required. The SJVAPCD does not list a SIL for regional ozone impacts, because such analysis is not required by APR 1925. The SIL for regional ozone impacts from single projects, 1 ppb 8-hour average, can be obtained from EPA’s Guidance on the SILs referenced above.

The air quality analysis SILs listed in the EPA SIL Guidance and SJVAPCD APR 1925 for ozone and PM2.5 are shown in Table 2.

TABLE 2

EPA AND SJVAPCD SIGNIFICANT IMPACT LEVELS
Pollutant EPA SJVAPCD
Ozone 1 ppb, 8-Hour Avg ---
PM2.5
All sources:

1.2 µg/m3, 24-Hour Avg

0.2 µg/m3, Annual Avg
Non-Fugitive Sources:

1.2 µg/m3, 24-Hour Avg

0.2 µg/m3
, Annual Avg
Fugitive dust:

2.5 µg/m3, 24-Hour Avg

0.63 µg/m3, Annual Avg
The ozone 8-hour average SIL of 1 ppb is about 1.4% of the 70 ppb federal and state 8-hour ozone standards. The most conservative PM2.5 SIL listed in Table 2, which 3814
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would be associated with long-term health effects of 0.2 µg/m3, is about 1.7% of the 12 µg/m3 federal and state annual average PM2.5 standard. While the purpose of an HIA is not to show compliance with ambient health standards as discussed above (rather to address specific health impacts of ozone and PM2.5), if HIA concentration impacts due to a project above the existing regional baseline do not exceed these SIL thresholds, then Fresno County could use this finding as an indication that the project’s concentration impacts on regional public health are likely not meaningful, because the SILs are related to regional health studies used to develop ambient health standards.

While this HIA report concludes that Friant Ranch Project concentration impacts (and therefore likely the related health impacts) are not meaningful, to provide Fresno County and the public with additional information, as well as meet the evidentiary requirements of the Friant Ranch Case, the report also provides specific health impact modeling results, as compared to baseline health impacts on the regional population from all of the emissions occurring in the region.

3.0 CAMX PHOTOCHEMICAL MODELING FOR THE FRIANT RANCH

PROJECT
This section provides a discussion of regional photochemical modeling completed for ozone and PM2.5 impacts using the CAMx model for the Friant Ranch Project. CAMx was used to develop regional air concentration data for the health impact analysis described in Section 4.0.

3.1 Summary of the Modeling System
Photochemical grid models (PGMs) are recognized and routinely utilized tools for regulatory analysis such as assessing emission control strategies in pursuit of attaining a particular ambient health standard. PGMs are large-scale mathematical air quality models that represent the physicochemical processes that occur in the atmosphere. PGMs simulate the changes of atmospheric pollutant concentrations due to changes in the constituent makeup of anthropogenic, biogenic, and geogenic emissions emitted into the atmosphere. PGMs are applied at multiple spatial scales from local, regional, national, and global over periods of time that span weekly, monthly, and annual scales.

Over the last five decades, EPA has devoted significant resources to develop PGMs for the assessment of air pollution issues, including health impact assessments and evaluation of emissions control strategies. The EPA's Air Quality Modeling Group has used photochemical models as part of its modeling analyses to support policy and regulatory decisions. Finally, EPA has developed guidance on the use of these models (EPA 2017).

CAMx is a state-of-the-art regional air quality modeling system developed by Ramboll that can be used to simulate the physical and chemical processes that govern the formation, transport, and deposition of gases and particulates in the atmosphere.

The structure of the CAMx modeling system is shown in Figure 1 below. 3815
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The components shown in Figure 1 are:
• WRF: Weather Research and Forecasting Model, v3.8 or later. WRF was used by CARB on behalf of the SJVAPCD, to generate the meteorology data for CMAQ modeling completed for the San Joaquin Valley. WRF (year 2013) output files from CARB used for the SJVAPCD 2018 PM2.5 SIP study 2020 model run were post-processed by Ramboll for use in CAMx. The 2013 meteorological data was used in this HIA for consistency with the CARB modeling study, thus ensuring valid CAMx model performance.

FIGURE 1. COMPONENTS OF THE CAMX MODELING SYSTEM
• CAMx: Comprehensive Air Quality Model with Extensions, v6.5. The CAMx model was run by Ramboll using converted CARB CMAQ input files and meteorology post-processed from CARB WRF output files.
• Initial and Boundary Conditions: Initial and boundary photochemical conditions for the spatial domain and the modeling time period. Initial conditions represent the state of the air quality of every grid cell of the domain and for every chemical species at the start of the simulation. The boundary conditions represent the estimated, incoming air quality of every chemical species at the north, south, east, and west lateral boundaries of the 4 km domain. The boundary conditions and initial conditions were derived based on modeling from the 12 km domain.
• Emissions Inventory: An emissions database for the scenario, spanning the time period, spatial domain, and species list required by CAMx. Generally, CAMx obtains its emissions inventories from the EPA’s SMOKE (Sparse Matrix Operator Kernel Emissions) Modeling System (CMAS 2018). In this Project, a complete emissions inventory for Year 2020 in CMAQ-format files was provided 3816
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by CARB to Ramboll, then converted by Ramboll to CAMx format to be used in this analysis. BlueScape also obtained the CMAQ files from CARB and used them for model performance evaluation.
• Post-Processing and Analysis: The following software tools were available to post-process CAMx results for further analysis and data visualization:
o VERDI: Java program for visualizing meteorology, emissions, and air quality modeling data. With options for overlaying GIS Shapefiles and observational data onto model output, VERDI offers a range of options for viewing atmospheric modeling data. VERDI was used in this Project to validate emissions re-gridding and output concentrations from CAMx. o Panoply: netCDF, HDF and GRIB Data Viewer. Used alongside VERDI to visualize CAMx output data and to extract CSV files from CAMx postprocessed output files, stored in netCDF format. o NCL Scripts: The NCAR Command Language (NCL) is a free interpreted language designed specifically for scientific data processing and visualization. In this Project, NCL was not used to incorporate the Project’s emissions model into the 2020 and 2031 Baseline emissions; rather the emissions were manually formatted and processed.
o BenMAP-CE: EPA Benefits Mapping and Analysis Program – Community Edition, BenMAP-CE is an open-source software that calculates the number and economic value of air pollution-related deaths and illnesses. A component of BenMAP-CE is a tool called PopGrid which is used to estimate the population within each grid cell by gender, race, ethnicity, and age range.

3.2 Modeling Domain
The modeling domain chosen for the Project study was obtained from the domain used in the 2018 SJVAPCD PM2.5 SIP modeling run completed by CARB. This modeling domain is comprised of three nested domains (i.e., 36 km, 12 km, and 4 km). For the current study, only the 4km domain was used for modeling. As the footprint of the Friant Ranch Project is wholly contained within the 4km domain, it was deemed unnecessary to conduct model simulations using the 36 km and 12 km domains.

Further, it is well known that when additional emissions of small magnitude are inserted into a handful of grid cells of the 4 km domain, the modelled impact to air quality in the larger 12 km and 36 km are negligible if not imperceptible. Data from the final modeling grid subset used by CARB was used for the Project CAMx runs, as boundary and initial conditions from CARB for CMAQ already incorporated data from coarser grids. This final grid covered an area of 348 by 412 kilometers, using a 4-km grid size and 87 by 103 cells. Figure 2 shows this grid layout, used by both CARB for CMAQ model runs, and by BlueScape for Project CAMx model runs. 3817
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3.3 WRF – Weather Research and Forecasting Model Data
The WRF model is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. The model generates three-dimensional (3D) wind fields for a specific domain, using telescopic grids to pass regional phenomena down to smaller areas. WRF is an integral part of both CMAQ and CAMx modeling systems.

CARB used WRF v3.8 to generate the meteorological fields needed for their 2020 CMAQ run used for the SJVAPCD PM2.5 SIP Study. BlueScape obtained output files from this WRF run that were post-processed by Ramboll using the wrfcamx postprocessing tool. Table 3 below contains the selected WRF modeling configuration used by CARB.

FIGURE 2. CARB CMAQ AND CAMX MODELING GRID
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TABLE 3

WRF MODELING CONFIGURATION
Grid Definition
CARB WRF data, final grid:
• 4-km grid cell, 189 x 189 grid cells in size, total area:
756 x 756 kms
Initial and Boundary
Conditions
NCEP North American Mesoscale (NAM) 12 km Analysis,
Grid 218
Vertical Layers 50 Layers with the lowest layer at 18 m above ground level (agl).

Data Assimilation
Analysis nudging at every 6 hours for the outermost
domain only. No temperature and moisture nudging
within the Planetary Boundary Layer (PBL).

3.4 CAMx Model Setup
The CAMx model was set up to use the same grid as the final grid from the CARB 2020 CMAQ model run. Table 4 contains the general CAMx model configuration used, while Table 5 contains the grid configuration.

TABLE 4

GENERAL CAMX CONFIGURATION
Chemical Mechanism SAPRC07 with version “c” toluene updates gas-phase mechanism
Advection Solver Piecewise Parabolic Method (PPM)
Chemical Solver Euler Backward Iterative solver (EBI)
Plume-In-Grid
Submodel
Greatly Reduced Execution and Simplified Dynamics
Submodel (GREASD)
Dry Deposition Model Zhang 2003
Photolysis In-line Calculation
TABLE 5
4-KM GRID CONFIGURATION
Grid Definition 4-km grid cell, 87 x 103 grid cells in size, total area: 348 x 412 kms
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3.5 Emissions Inventories
The CAMx model requires input of hourly, gridded criteria pollutant emissions of both anthropogenic and biogenic sources that have been spatially allocated to the appropriate grid cells and chemically speciated for the model’s applicable chemical mechanism.

Gridded area emissions, elevated point source emissions, boundary and initial conditions were converted by Ramboll from CARB CMAQ input files using the cmaq2camx conversion tool, changing the chemical mechanism from CB06 to SAPRC07. The input 2020 CMAQ files used 2013 emissions inventory files, that were updated by CARB for projected 2020 regional emissions. The gridded emission files include TOG, NOx, PM10 and PM2.5, to obtain the overall particulate concentration results. The TOG and particulate data were entered into CAMx by chemical species.

Project SOx and CO emissions were not included in the analysis, due to their small contribution to secondary PM2.5 and ozone formation.

The Future Baseline year 2031 CAMx ROG, NOx, PM10, and PM2.5 emissions were grown, or scaled, from 2020 emissions, using growth factors developed from the CEPAM 2016 SIP - Standard Emission Tool developed by CARB (CARB 2018). CEPAM lists emissions by source category: stationary sources, areawide sources, mobile sources, and natural sources. Each source category has subcategories, such as stationary source type, on-road and off-road subcategories. These CEPAM categories and subcategories were matched with the CARB CMAQ emission files source categories and subcategories. Note that should the Project development and full buildout be delayed past 2031, it is expected that of selection of 2031 emissions for CAMx modeling will be conservative.

The incremental Project emissions were incorporated into the Baseline year 2020 and Future year 2031 emissions. Tables 6 and 7 summarize the Project peak daily unmitigated and mitigated emissions in pounds per day (lb/day) by source category for any of the years between Project construction start in 2022 and the 2031 Buildout year. Note that for NOx, 2022 emissions were used to represent Project peak daily emissions. This is due to the fact that Community Plan NOx operational emissions and construction emissions are higher in 2022 than in 2031. This assumption for NOx is expected to provide a conservative analysis. Further detail on the development of Project emissions for HIA modeling is presented in the Air Emissions Report for the Friant Ranch Community Development Project (BlueScape 2021). 3820
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TABLE 6

PROJECT PEAK DAILY UNMITIGATED OPERATION

AND CONSTRUCTION EMISSIONS (LB/DAY)
Category ROG NOx
Fugitive
PM10
Exhaust
PM10
Fugitive
PM2.5
Exhaust
PM2.5
Construction
On-Road Mobile 0.773 3.13 2.59 0.014 0.699 0.013
Off-Road Mobile 9.36 111 15.6 4.10 4.34 3.79
Area/Energy 104 0 0 0 0 0
Operational (Specific Plan plus Community Plan)
On-Road Mobile 127 294 124 1.59 33.4 1.51
Off-Road Mobile 0 0 0 0 0 0
Area/Energy 101 28.9 0 27.0 0 27.0
Total 342 438 142 32.7 38.4 32.3
TABLE 7

PROJECT PEAK DAILY MITIGATED OPERATION

AND CONSTRUCTION EMISSIONS (LB/DAY)
Category ROG NOx
Fugitive
PM10
Exhaust
PM10
Fugitive
PM2.5
Exhaust
PM2.5
Construction
On-Road Mobile 0.773 2.63 2.59 0.014 0.699 0.013
Off-Road Mobile 3.83 60.4 6.08 3.49 1.69 3.49
Area/Energy 104 0 0 0 0 0
Operational (Specific Plan plus Community Plan)
On-Road Mobile 126 304 107 1.42 28.8 1.35
Off-Road Mobile 0 0 0 0 0 0
Area/Energy 81.8 22.9 0 4.82 0 4.82
Total 316 389 115 9.74 31.2 9.66
For CAMx modeling, Project ROG emissions were converted to TOG emissions using conversion factors developed based upon SMOKE speciation profiles (CMAS 2018).

For each Project scenario, in 2020 and 2031, in both unmitigated and mitigated scenarios, the following data was incorporated into the baseline gridded emission files:
• Temporal allocation of construction emissions by day-of-week and hour-ofday; 3821
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• Temporal profiles extracted by Ramboll from CARB CMAQ files for allocation of non-construction Project emissions, weekend-weekday and by time of day, for aggregated source categories;
• Spatial distribution of Project emissions from mobile sources, in ten grid cells near the Project site; and
• Speciation profiles of Project emissions for CAMx SAPRC07 specific gas and aerosol species.

Using this input data, new Project emissions were generated hour by hour for the entire simulation year. Per relevant CAMx species, incremental emissions were apportioned to the corresponding grid cells, and a complete Project emissions database was generated for each scenario. Figure 3 shows the Project site location and color-shading to represent emissions apportionment fractions by 4-km grid cell.

The darker blue shaded grid cells are closest to the Project site, and have the highest emissions apportionment. No Project emissions were apportioned outside the grid cells shown in Figure 3.

FIGURE 3. PROJECT EMISSIONS APPORTIONMENT TO GRID CELLS 3822
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3.6 Model Performance Evaluation
Air quality modeling studies are obliged to include efforts to assess the ability of an air quality modeling system to reliably recreate observed air quality events. In the current context of this study, the air quality modeling system is the combined meteorological modeling system (WRF), the photochemical modeling system (CAMx), and modeling input data sets. The air quality modeling system was derived from CARB’s 2013 air quality modeling system used for the SJVAPCD PM2.5 SIP Study to estimate regional ozone and PM2.5 concentration impacts in 2020.

CARB performed a model performance evaluation (MPE) for the 2013 period and deemed its CMAQ-based air quality modeling system adequate for use in additional studies (SJVAPCD 2018) for future years. Therefore, it is sufficient that if the CAMxbased system, which was based on the CARB databases, replicates the CMAQ-based air quality modeling predictions, then the CAMx-based system is also adequate for use in further air quality studies.

The Environmental Protection Agency (EPA) has issued guidance on how air quality simulation models are to be judged to be adequately performing in order to be used for studies such as conducted here (EPA 2018a). Two statistics that are used to judge such model performance are mean gross error and mean bias. If the gross error is less than 15% and bias is within +/-5%, it is an indicator that the model is adequately performing (Emery et. al. 2017).

The average daily gross error and average daily bias for ozone and PM2.5 between the CARB CMAQ-based predictions, and completed CAMx-based predictions that are the subject of this study were computed for each month of 2020 and each California County. The maximum CMAQ-to-CAMx difference for the ozone 8-hour average concentration in any grid cell was 0.24 ppbv. The maximum CMAQ-to-CAMx difference for the annual PM2.5 24-hour average was 0.091 µg/m3. As the gross error values were less than 5% and the bias values near zero relative to the maximum concentrations calculated for 2020 for the same cells by both the CMAQ and CAMx models, it was deemed that the CAMx-based system was suitable, and performed well for use in this HIA.

3.7 CAMx Modeling Results
The CAMx modeling results are presented in this section. Section 3.7.1 compares the impact of Project TOG and NOx emissions, unmitigated and mitigated, on regional ozone concentration changes, for the 2020 Existing Baseline and 2031 Future Baseline year scenarios. A comparison of the impact of Project PM2.5 and PM10 emissions, unmitigated and mitigated, on regional PM2.5 concentration changes is presented in Section 3.7.2.
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3.7.1 Ozone Concentration Change Results
Ozone modeling concentration change results are presented in Tables 8 to 11, for the CAMx 4-km grid in parts per billion by volume (ppbv), maximum daily 8-hour average (MDA8) concentrations.

For the 2020 Existing Baseline year scenario, Tables 8 and 9 compare the Project concentration changes at the grid cell with the highest change, both unmitigated and mitigated scenarios, to the baseline concentration results. The maximum MDA8 Project concentration change for either scenario is 0.021%, for the unmitigated emissions scenario. The unmitigated emissions impact for this scenario, 0.01 ppbv is well below (1% of) the 1 ppb SIL.

For the 2031 Future Baseline year scenario, Tables 10 and 11 compare the Project concentration changes at the grid cell with the highest change, both unmitigated and mitigated scenarios, to the baseline concentration results. The maximum MDA8 Project concentration change for either scenario is 0.029%, for the unmitigated emissions scenario. The unmitigated emissions impact for this scenario, 0.013 ppbv is well below (1.3% of) the 1 ppb SIL.

TABLE 8
2020 CAMX MODELING RESULTS FOR THE BASELINE AND UNMITIGATED PROJECT SCENARIOS, MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE,

AT THE GRID CELL WITH THE HIGHEST CHANGE
Baseline Scenario
(ppbv)
Project Scenario
(ppbv)
Maximum Project
Change (ppbv)
Maximum Project
Change (%)

48.417 48.427 0.01 0.021
TABLE 9
2020 CAMX MODELING RESULTS FOR THE BASELINE AND MITIGATED PROJECT SCENARIOS, MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE, AT THE GRID

CELL WITH THE HIGHEST CHANGE
Baseline Scenario
(ppbv)
Project Scenario
(ppbv)
Maximum Project
Change (ppbv)
Maximum Project
Change (%)

48.417 48.426 0.009 0.019
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TABLE 10
2031 CAMX MODELING RESULTS FOR THE FUTURE AND UNMITIGATED PROJECT SCENARIO, MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE, AT THE GRID

CELL WITH THE HIGHEST CHANGE
Future Scenario
(ppbv)
Project Scenario
(ppbv)
Maximum Project
Change (ppbv)
Maximum Project
Change (%)

44.357 44.370 0.013 0.029
TABLE 11
2031 CAMX MODELING RESULTS FOR THE FUTURE AND MITIGATED PROJECT SCENARIOUS, MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE, AT

THE GRID CELL WITH THE HIGHEST CHANGE
Future Scenario
(ppbv)
Project Scenario
(ppbv)
Maximum Project
Change (ppbv)
Maximum Project
Change (%)

44.357 44.369 0.012 0.027
Figures 4 and 5 display the modeled MDA8 ozone concentration results for the 2020 Existing Baseline year and 2031 Future Baseline year scenarios, comparing the difference from unmitigated (Figure 4 and 5 center panels) or mitigated (Figure 4 and 5 right panels) Project ozone impacts, to baseline concentrations, in units of ppbv.
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FIGURE 4. OZONE 2020 SCENARIO MAXIMUM DAILY 8-HOUR (MDA8) AVERAGE CONCENTRATIONS

COMPARISON OF BASELINE TO UNMITIGATED PROJECT EMISSIONS AND PROJECT MITIGATED EMISSIONS 2020 Baseline Ozone MDA8 (ppbv) 2020 Ozone MDA8 Unmitigated Project Emissions Difference from Baseline (ppbv)
2020 Ozone MDA8 Mitigated Project Emissions
Difference from Baseline (ppbv)
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FIGURE 5. OZONE 2031 SCENARIO MAXIMUM DAILY 8-HOUR (MDA8) AVERAGE CONCENTRATIONS

COMPARISON OF BASELINE TO UNMITIGATED PROJECT EMISSIONS AND PROJECT MITIGATED EMISSIONS 2031 Baseline Ozone MDA8 (ppbv) 2031 Ozone MDA8 Unmitigated Project Emissions Difference from Baseline (ppbv)
2031 Ozone MDA8 Mitigated Project
Emissions Difference from Baseline (ppbv)
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3.7.2 PM2.5 Concentration Change Results
The PM2.5 modeling concentration change results are presented in Tables 12 to 15, for the CAMx 4-km grid in micrograms per meters cubed (µg/m3), annual 24-hour average PM2.5 concentrations.

For the 2020 Existing Baseline year scenario, Tables 12 and 13 compare the Project concentration changes at the grid cell with the highest change, both unmitigated and mitigated scenarios, to the baseline concentration results. The maximum annual 24- hour PM2.5 Project concentration change for either scenario is 0.55%, for the unmitigated emissions scenario. The unmitigated emissions impact for this scenario,

0.051 µg/m3, is below (26% of) the 0.2 µg/m3 SIL.

TABLE 12
2020 CAMX MODELING RESULTS FOR THE BASELINE AND UNMITIGATED PROJECT SCENARIOS, ANNUAL AVERAGE 24-HOUR PM2.5, AT THE GRID CELL

WITH THE HIGHEST CHANGE
Baseline Scenario
(µg/m3)
Project Scenario
(µg/m3)
Maximum
Project Change
(µg/m3)
Maximum
Project Change
(%)

9.528 9.580 0.051 0.55
TABLE 13
2020 CAMX MODELING RESULTS FOR THE BASELINE AND MITIGATED PROJECT SCENARIOS, ANNUAL AVERAGE 24-HOUR PM2.5, AT THE GRID CELL WITH THE

HIGHEST CHANGE
Baseline Scenario
(µg/m3)
Project Scenario
(µg/m3)
Maximum
Project Change
(µg/m3)
Maximum
Project Change
(%)

9.528 9.567 0.038 0.41
For the 2031 Future Baseline year scenario, Tables 14 and 15 compare the Project concentration changes at the grid cell with the highest change, both unmitigated and mitigated scenarios, to the baseline concentration results. The maximum annual daily PM2.5 Project concentration change for either scenario is 0.56%, for the unmitigated emissions scenario. The unmitigated emissions impact for this scenario, 0.058 µg/m3, is below (29% of) the 0.2 µg/m3 SIL.
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TABLE 14
2031 CAMX MODELING RESULTS FOR THE BASELINE AND UNMITIGATED PROJECT SCENARIOS, ANNUAL 24-HOUR AVERAGE PM2.5, AT THE GRID CELL

WITH THE HIGHEST CHANGE
Baseline Scenario
(µg/m3)
Project Scenario
(µg/m3)
Maximum
Project Change
(µg/m3)
Maximum
Project Change
(%)

10.111 10.168 0.058 0.56
TABLE 15
2031 CAMX MODELING RESULTS FOR THE BASELINE AND MITIGATED PROJECT SCENARIOS, ANNUAL 24-HOUR AVERAGE PM2.5, AT THE GRID CELL WITH THE

HIGHEST CHANGE
Baseline Scenario
(µg/m3)
Project Scenario
(µg/m3)
Maximum
Project Change
(µg/m3)
Maximum
Project Change
(%)

10.111 10.155 0.044 0.44
Figures 6 and 7 display the modeled annual daily 24-hour PM2.5 for the 2020 Existing Baseline and 2031 Future Baseline year scenarios, comparing the difference from unmitigated (Figure 6 and 7 center panels) or mitigated (Figure 6 and 7 right panels) Project PM2.5 impacts, to the baseline concentrations, in units of (µg/m3). 3829
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FIGURE 6. PM2.5 2020 SCENARIO ANNUAL 24-HOUR AVERAGE CONCENTRATIONS

COMPARISON OF BASELINE TO UNMITIGATED PROJECT EMISSIONS AND PROJECT MITIGATED EMISSIONS 2020 Baseline Annual 24-hour Average PM2.5
(µg/m3)
2020 PM2.5 Unmitigated Project Emissions
Difference from Baseline (µg/m3)
2020 PM2.5 Mitigated Project Emissions Difference
from Baseline (µg/m3)
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FIGURE 7 . PM2.5 2031 SCENARIO ANNUAL 24-HOUR AVERAGE CONCENTRATIONS

COMPARISON OF BASELINE TO UNMITIGATED PROJECT EMISSIONS AND PROJECT MITIGATED EMISSIONS 2031 Baseline Annual 24-hour Average PM2.5
(µg/m3)
2031 PM2.5 Unmitigated Project Emissions
Difference from Baseline (µg/m3)
2031 PM2.5 Mitigated Project Emissions Difference
from Baseline (µg/m3)
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4.0 FRIANT RANCH REGIONAL HEALTH IMPACTS ANALYSIS
This section provides a discussion of the regional health impacts analysis for ozone and PM2.5 concentration changes due to Friant Ranch Project emissions. The HIA was completed using the air concentration data developed from CAMx, as described in Section 3.0, and using the EPA’s BenMAP-CE model. The results of the specific Project health impact analysis for ozone and PM2.5 are provided in this section.

4.1 BenMAP-CE Overview
The U.S. Environmental Protection Agency (EPA) Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE) tool is a publicly available, PC-based open-source software that can be configured to relate the human health impacts and/or benefits to air quality changes. The program includes a subset of air quality monitoring data, recent and projected demographic and baseline health data, concentration-response relationships drawn from the published epidemiological literature, and economic value estimates based on the published economics literature. The principal function of the software is to apply the findings of epidemiological and economics studies to estimate the health impacts and economic value of air pollution changes.

The latest version of BenMAP-CE is Version 1.5, which the EPA released in March 2019. For analyses that are within the Continental United States, BenMAP-CE includes preloaded databases containing the concentration-response (C-R) relationships, population files, and health data needed to quantify the number and economic value of human health impacts resulting from changes in air quality - specifically, groundlevel ozone and fine particles such as PM2.5. Figure 8 summarizes the process that BenMAP-CE uses to calculate human health impacts.

FIGURE 8. OVERVIEW OF THE PROCESS OF HEALTH IMPACT

CALCULATIONS IN BENMAP-CE
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To estimate human health effects, BenMAP-CE first determines the change in ambient air concentrations using user-specified air quality data for the baseline and controlled scenarios. The software then applies the relationship between the air concentrations with certain health effects (also known as health endpoints) using the health impact function or the C-R functions that were derived from epidemiology studies. BenMAPCE applies that relationship to the exposed population to calculate health impacts based on baseline health incidence data.

4.2 Application of BenMAP-CE to the Friant Ranch Project As part of an overall health impact analysis (HIA) for the proposed Friant Ranch Community Development Project, this analysis used BenMAP-CE (Version 1.5) to quantify the potential human health effects from the Project’s emissions of ozone precursors and PM2.5. The sections below summarize the data sources and specific BenMAP-CE input and method selections made for this Project-level analysis, covering five key pieces of information:

1. The ambient air quality data;

2. Population data representing the population exposed to the change in air pollution;

3. An analysis year;

4. Health endpoints (health effects) and health impact functions; and

5. The baseline rate of death and disease among the exposed population.

4.2.1 Air Quality Data
BenMAP-CE itself does not model changes in air pollutant emissions or the resultant concentrations. These data must be input into BenMAP-CE as dispersion modeling results or generated from air pollution monitoring data (some monitoring data is preloaded in BenMAP-CE). The potential ambient air concentrations of ozone and PM2.5 in the region where the Project is located were estimated using the CAMx as discussed in Section 3.0 of this report. As detailed, for each pollutant (ozone or PM2.5), each modeling year (Existing Baseline year 2020 or Future Baseline year 2031), and emissions for both unmitigated and mitigated conditions, each CAMx model run generated two sets of data:

1. The ambient air concentrations based on the 2020 emissions inventory provided by CARB, for the corresponding modeling year, 2020 or scaled to 2031 (referred to as the Existing Baseline year for 2020 and Future Baseline year for 2031); and

2. The resulting ambient air concentrations generated by adding the Project’s emissions (unmitigated or mitigated) to the corresponding concentrations developed from the 2013 regional air emission inventory extrapolated by CARB to 2020 (as described in Section 3.5) and scaled for this HIA to 2031 (referred 3833
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to as the Existing Baseline plus Project scenarios for 2020, and the Future Baseline plus Project scenarios for 2031).

As detailed in Section 3.7 of this report, due to the orders-of-magnitude smaller emissions from the Project as compared to the 2020 and 2031 Baseline emissions inventories, the difference between the two sets of data (air concentration delta or “delta”) was very small. This being the case, for informational purposes, even with the very small delta values between the two sets of concentration data and the inherent and unavoidable uncertainties associated with any CAMx modeling study, BenMAP-CE was used for this HIA to translate the potential Project-incurred changes in ambient air ozone and PM2.5 concentrations into potential human health outcomes of the Project. This approach is consistent with the EPA’s development and use of BenMAP-CE to understand potential regional health impact changes associated with concentration changes above baseline conditions.

For each BenMAP-CE run, the corresponding grid that matches the CAMx results were defined in the BenMAP-CE’s Grid Definition database first. With the combination of pollutant and grid, a total of eight (8) BenMAP-CE model runs were conducted to determine the Project’s potential health impacts, as summarized below:
• Ozone, Year 2020, Existing Baseline plus unmitigated Project emissions; • Ozone, Year 2020, Existing Baseline plus mitigated Project emissions; • Ozone, Year 2031, Future Baseline plus unmitigated Project emissions; • Ozone, Year 2031, Future Baseline plus mitigated Project emissions; • PM2.5, Year 2020, Existing Baseline plus unmitigated Project emissions; • PM2.5, Year 2020, Existing Baseline plus mitigated Project emissions; • PM2.5, Year 2031, Future Baseline plus unmitigated Project emissions; and • PM2.5, Year 2031, Future Baseline plus mitigated Project emissions.

The CAMx-generated 2020 Existing Baseline and 2031 Future Baseline concentration datasets were imported into the BenMAP-CE program as base case Air Quality Data, and then post-Project (Baseline or Future plus Project, unmitigated and mitigated) concentration datasets were imported as “Control” Air Quality Data. Then BenMAPCE calculates change (air concentration delta) using Control minus either the Existing Baseline or Future Baseline scenarios from the CAMx-generated input files.

4.2.2 Population Data
A customized population dataset matching the air quality grid definitions described above was generated using the EPA’s PopGrid software for use in BenMAP-CE (EPA 2021). The PopGrid program allocates the 2010 block-level U.S. Census data for the defined air quality grids. When importing the population datasets into BenMAP-CE, the “Use Population Growth Weights” checkbox was checked, so that BenMAP-CE can use the population weights file in forecasting population levels for future years up to 3834
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2050. The projected Friant Community Plan and Specific Plan population projections for 2020 and 2031 were added to the population for two grid cells, to ensure that the local population growth is not under-counted.

4.2.3 Analysis Years
Years 2020 and 2031 were selected for the BenMAP-CE runs, which represents the Project’s Existing Baseline year prior to start of construction in 2022, and the full build-out operational Future Baseline year 2031. In order to perform air quality modeling for this study, BlueScape obtained files processed from Ramboll, based upon air quality modeling from CARB, including the regional air quality emission inventory year 2020 from the SJVAPCD PM2.5 SIP Study. The regional emissions inventory for 2020 was then scaled to 2031 using growth factors. For the 2020 analysis, the highest daily contribution of Project-related emissions for each pollutant was used in the photochemical modeling, from the construction to the full build-out year. Further, the most conservative emissions (in lb/day) for any Project development year from 2022 to 2031 were used in the model.

4.2.4 Baseline Health Incidence Data
This analysis used the health incidence dataset preloaded within BenMAP-CE. This includes the Mortality Incidence dataset for the mortality endpoint group (year 2020 dataset for analysis year 2020, and year 2030 dataset for analysis year 2031), and the Other Incidence dataset for all the morbidity endpoint groups.

Among the baseline health incidence datasets used, the 2020 BenMAP-CE endpoint groups match the Baseline scenario year (2020 for Existing Baseline year). The 2030 BenMAP-CE endpoint groups for 2031 were used for the Future Baseline year 2031.

The mortality C-R functions offer datasets in 5-year increments, so the 2030 dataset preceding the 2031 Project year was assumed representative and conservative. From air quality related health effect perspective, since the ambient air quality is generally getting better (i.e., lower air pollutant concentrations) with more stringent regulations and technology advancement over time, the older years’ datasets (i.e., using the 2030 mortality dataset for Project year 2031) will likely have greater health incidences; therefore, the results generated by this analysis are expected to be adequately conservative.

4.2.5 Health Endpoints and Health Impact Functions
The EPA-provided health endpoints functions contained in the BenMAP-CE software were used for this analyses. For the same pollutant-health endpoint (like acute respiratory symptoms or asthma exacerbation, for example), BenMAP-CE contains many different functions developed from different epidemiologic studies. This analysis selected the functions that were either studies conducted in California, and/or studies that were conducted for multiple cities and counties in the United States.
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More detailed information regarding the BenMAP-CE setup for each model run can be found in the attached BenMAP-CE Audit Trial Reports (Appendix B).

4.3 BenMAP-CE Modeling Results and Results Interpretation For each of the modeled grid cells, BenMAP-CE generates the health incidence value resulting from the Project’s incremental increase in air pollutant concentrations that corresponds with each health endpoint and each C-R function. For many of the health endpoints (e.g., respiratory hospital admissions), the premade setups in BenMAP-CE contain many different C-R functions from different studies. To summarize the regional health incidence estimates, the incidence results were exported into an Access database and summed across all cells of the modeled geographic grid to generate one set of numerical values for each pollutant-health endpoint group combination.

Tables 16 through 23 summarize the BenMAP-CE results of ozone and PM2.5 from the Project, for both the Existing Baseline year 2020 and Future Baseline year 2031 and unmitigated and mitigated Project emission inventories. Each table contains the following columns:
• Group – General grouping of health impact outcomes;
• Health Endpoint – Specific health endpoint in the Health Endpoint Group.

Numbers in braces represent the age range for the specific health endpoints, and for specific health endpoints without a specified age range, the age range is for 0 to 99 years old;
• Mean – the mean number of health incidences estimated to occur due to Project-related emissions above the value reported in the column Baseline Incidence (# per year);
• 95% Confidence Interval – In statistics, a confidence interval (CI) provides a range of health incidences for which the true health incidence resides. The 95% CI is a common value reported by researchers. The interpretation of the 95% CI means that a researcher is 95% confident that the true health incidence falls within the associated range of health incidences; • Baseline Incidence (# per year) – The BenMAP-CE baseline health incidences for the Health Endpoint; and
• Incremental Incidence / Existing Baseline Incidence (%) – The percent increase of health incidences above the baseline calculated as the “Mean” divided by the “Baseline Incidence (# per year)” times 100.

As the data shown in these tables demonstrate, the maximum regional health impacts associated with the emissions of ozone precursors and corresponding formation of ozone in the atmosphere associated with the construction and operation of the Project included incidences of respiratory-related hospital admissions (0.0047 maximum 3836
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incidences/year), mortality (0.012 maximum incidences/year), and asthma-related emergency room visits (0.0765 maximum incidences/year) for all studied age groups combined. The Project’s incremental health incidences for ozone are less than

0.0003% for any of the baseline number of health endpoint occurrences. The baseline is the actual health effect occurrences measured in the regional population without the emissions produced by the Project.

The maximum regional health impacts associated with emissions of PM2.5 resulting from the construction and operation of the Project included incidences of acute myocardial infarction (0.0254 maximum incidences/year), mortality (0.35 maximum incidences/year), hospital admissions (0.114 maximum incidences/year) and asthma-related emergency room visits (0.20 maximum incidences/year) for all studied age groups combined. The Project’s incremental health incidences for PM2.5 are less than 0.00054% for any of the baseline number of health endpoint occurrences. The baseline is the actual health effect occurrences measured in the regional population without the emissions produced by the Project.

The very small Project-related changes in health endpoint occurrences, relative to the substantially larger number of baseline health endpoint occurrences, demonstrates that the Project provides a negligible contribution to regional health impacts; therefore, Fresno County can consider the Project-related health impacts to not be meaningful. In other words, the Project impact is not likely discernible as different than zero, as discussed below.

The health incidences estimated for modeled Project changes in ozone and PM2.5 concentrations are calculated within a range that is called the 95% confidence interval. The 95% confidence interval is a standard metric for statistical significance of an estimated unknown parameter from the observed data. It essentially means that 95% of the estimates of the health impact related to a change in concentration (estimated by 4 km grid cell) will include the most likely value within the distribution, but 5% won't (i.e., there is a 1‐in‐20 chance that the reported confidence intervals in Tables 16-23 do not include the true values). The health incidence values estimated for Tables 16-24 are the composite of values calculated for all the 4 km grid cells included within the modeling domain.

Based upon the wide range of Project incremental health incidence values calculated for the 95% CI shown in the tables, the reported positive mean Project incremental health incidence values are of high statistical uncertainty. In cases where there are negative health incidence values within the 95% confidence interval, meaning the interval includes zero, the result is highly uncertain and there is very low confidence that the result is statistically different than zero. In cases where the health incidence values within the 95% confidence interval are greater than zero but less than one, the values are also a negligible fraction of the background incidence values. In both cases, it is not expected that the Project will result in any meaningful health impact greater than zero that can be established with any certainty. 3837
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TABLE 16
BENMAP-CE MODELING RESULTS FOR MAXIMUM DAILY 8-HOUR (MDA8) AVERAGE OZONE, EXISTNG BASELINE YEAR 2020, UNMITIGATED
Human Health Endpoints Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health
Endpoint
Group Health Endpoint
Project Incremental
Incidence
(# per year)
Baseline
Incidence
(# per year)
Mean
95%
Confidence
Interval
Acute
Respiratory
Symptoms
Minor Restricted
Activity Days 10.959 4.5235 -

17.3852 28,359,911 0.000039%
Asthma
Exacerbation
One or More
Symptoms 1.6894 -8.6578 -

12.0214 12,205,190 0.000014%
Emergency
Room Visits
-Respiratory
Emergency Room
Visits Asthma
[0-17]

0.0354 0.0064 -

0.0645 14,828 0.000239%
Emergency
Room Visits
-Respiratory
Emergency Room
Visits Asthma
[18-99]

0.0239 0.0066 -

0.0412 20,037 0.000119%
Hospital
Admissions
Respiratory
HA All Respiratory 0.0027 -0.0006 -

0.0061 24,743 0.000011%
Mortality
All Cause 0.0076 0.0036 -

0.0115 51,215 0.000015%
Cardiopulmonary 0.0027 0.001 -

0.0044 17,114 0.000016%
Non-Accidental 0.0019 -0.0005 -

0.0043 38,717 0.000005%
Respiratory 0.0036 0.0012 -

0.006 4,213 0.000085%
School Loss
Days All Cause 9.0324 -1.0498 -

19.0997 7,988,290 0.000113%
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TABLE 17
BENMAP-CE MODELING RESUTS FOR MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE, EXISTING BASELINE YEAR 2020, MITIGATED
Human Health Endpoints Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health
Endpoint
Group
Health
Endpoint
Project
Incremental
Incidence
(# per year)
Baseline
Incidence
Mean (# per year)
95%
Confidence
Interval
Acute
Respiratory
Symptoms
Minor Restricted
Activity Days 10.301

4.252 -

16.3417 28,359,911 0.000036%
Asthma
Exacerbation
One or More
Symptoms 1.5930 -8.1641 -

11.3359 12,205,190 0.000013%
Emergency
Room Visits -
Respiratory
Emergency
Room Visits
Asthma [0-17]

0.0322

0.0058 -

0.0585 14,828 0.000217%
Emergency
Room Visits -
Respiratory
Emergency
Room Visits
Asthma [18-99]

0.0225

0.0062 -

0.0388 20,037 0.000112%
Hospital
Admissions
Respiratory
HA All
Respiratory 0.0026 -0.0006 -

0.0057 24,743 0.000010%
Mortality
All Cause 0.0070

0.0034 -

0.0107 51,215 0.000014%
Cardiopulmonary 0.0025

0.001 -

0.0041 17,114 0.000015%
Non-Accidental 0.0017 -0.0005 -

0.004 38,717 0.000005%
Respiratory 0.0033

0.0011 -

0.0055 4,213 0.000079%
School Loss
Days All Cause 8.5184 -0.99 -

18.0129 7,988,290 0.000107%
3839
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TABLE 18
BENMAP-CE MODELING RESULTS FOR MAXIMUM DAILY 8-HOUR (MDA8) AVERAGE OZONE, FUTURE BASELINE YEAR 2031, UNMITIGATED
Human Health Endpoints Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health
Endpoint
Group
Health
Endpoint
Project Incremental
Incidence
(# per year) Baseline
Incidence
(# per
Mean year)
95%
Confidence
Interval
Acute
Respiratory
Symptoms
Minor Restricted
Activity Days 14.5060 5.9875 -

23.0121 30,640,682 0.000047%
Asthma
Exacerbation
One or More
Symptoms 2.4333 -12.4705 -

17.3154 13,815,762 0.000018%
Emergency
Room Visits -
Respiratory
Emergency
Room Visits
Asthma [0-17]

0.0427 0.0077 -

0.0777 16,541 0.000258%
Emergency
Room Visits -
Respiratory
Emergency
Room Visits
Asthma [18-99]

0.0338 0.0093 -

0.0582 22,666 0.000149%
Hospital
Admissions
Respiratory
HA All
Respiratory 0.0047 -0.0011 -

0.0104 34,277 0.000014%
Mortality
All Cause 0.0120 0.0057 -

0.0182 67,316 0.000018%
Cardiopulmonary 0.0045 0.0017 -

0.0073 23,263 0.000019%
Non-Accidental 0.0030 -0.0008 -

0.0068 51,566 0.000006%
Respiratory 0.0058 0.002 -

0.0097 5,750 0.000101%
School Loss
Days All Cause 13.0657 -1.5185 -

27.6286 9,097,769 0.000144%
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TABLE 19
BENMAP-CE MODELING RESULTS FOR MAXIMUM DAILY 8-HOUR AVERAGE (MDA8) OZONE, FUTURE BASELINE YEAR 2031,

MITIGATED
Human Health Endpoints Aggregated Regional Results Incremental Incidence /
Existing Baseline
Incidence
Health Endpoint Group Health Endpoint (%)
Project Incremental Incidence
(# per year) Baseline
Incidence
Mean (# per year)
95% Confidence
Interval
Acute Respiratory Symptoms Minor Restricted Activity
Days 13.7277 5.6663 - 21.7775 30,640,682 0.000045%
Asthma Exacerbation One or More Symptoms 2.3068 -11.822 - 16.4149 13,815,762 0.000017% Emergency Room Visits -
Respiratory
Emergency Room Visits
Asthma [0-17] 0.0393 0.0071 - 0.0714 16,541 0.000237%
Emergency Room Visits -
Respiratory
Emergency Room Visits
Asthma [18-99] 0.0320 0.0088 - 0.0551 22,666 0.000141%
Hospital Admissions Respiratory HA All Respiratory 0.0044 -0.001 - 0.0098 34,277 0.000013% Mortality
All Cause 0.0112 0.0053 - 0.0171 67,316 0.000017%
Cardiopulmonary 0.0042 0.0016 - 0.0068 23,263 0.000018% Non-Accidental 0.0028 -0.0008 - 0.0064 51,566 0.000005% Respiratory 0.0055 0.0018 - 0.0091 5,750 0.000095%
School Loss Days All Cause 12.3866 -1.4396 - 26.1926 9,097,769 0.000136% 3841
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TABLE 20
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, EXISTING BASELINE YEAR 2020,

UNMITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence / Existing
Baseline Incidence
Health Endpoint Group Health Endpoint (%)
Project Incremental Incidence
(# per year) Baseline
Incidence
Mean (# per year)
95% Confidence
Interval
Acute Myocardial Infarction
Acute Myocardial
Infarction Nonfatal
[18-24]

0.00001 0.000006 - 0.000018 9 0.000142%
Acute Myocardial
Infarction Nonfatal
[25-44]

0.00085 0.000409 - 0.001281 462 0.000183%
Acute Myocardial
Infarction Nonfatal
[45-54]

0.00179 0.000866 - 0.002715 1,047 0.000171%
Acute Myocardial
Infarction Nonfatal
[55-64]

0.00396 0.001916 - 0.006004 2,152 0.000184%
Acute Myocardial
Infarction Nonfatal
[65-99]

0.01032 0.004989 - 0.015636 5,353 0.000193%
Emergency Room Visits -
Respiratory Asthma 0.14501 0.038122 - 0.251735 34,865 0.000416% Hospital Admissions
All Cardiovascular (less
Myocardial Infarctions) 0.01930 0.014227 - 0.024364 28,261 0.000068% All Respiratory 0.03885 0.022404 - 0.055276 24,743 0.000157% Asthma 0.01182 0.004526 - 0.019101 4,381 0.000270%
Mortality All Cause 0.21796 0.147276 - 0.288536 49,417 0.000441% 3842
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TABLE 21
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, EXISTING BASELINE YEAR 2020, MITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health Endpoint
Group
Health
Endpoint
Project Incremental
Incidence (# per year)
Baseline
Incidence
Mean (# per year)
95%
Confidence
Interval
Acute Myocardial
Infarction
Acute
Myocardial
Infarction
Nonfatal
[18-24]

0.00001 0.000005 -

0.000017 9 0.000127%
Acute
Myocardial
Infarction
Nonfatal
[25-44]

0.00075 0.000365 -

0.001144 462 0.000163%
Acute
Myocardial
Infarction
Nonfatal
[45-54]

0.00160 0.000774 -

0.002426 1,047 0.000153%
Acute
Myocardial
Infarction
Nonfatal
[55-64]

0.00354 0.001711 -

0.005362 2,152 0.000164%
Acute
Myocardial
Infarction
Nonfatal
[65-99]

0.00918 0.004441 -

0.013917 5,353 0.000172%
Emergency Room
Visits -
Respiratory
Asthma 0.12804 0.033662 -

0.222282 34,865 0.000367%
Hospital
Admissions
All
Cardiovascular
(less
Myocardial
Infarctions)

0.01718 0.012668 -

0.021695 28,261 0.000061%
All Respiratory 0.03461 0.019959 -

0.049244 24,743 0.000140%
3843
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TABLE 21
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, EXISTING BASELINE YEAR 2020, MITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health Endpoint
Group
Health
Endpoint
Project Incremental
Incidence (# per year)
Baseline
Incidence
Mean (# per year)
95%
Confidence
Interval
Asthma 0.01039 0.003977 -

0.016787 4,381 0.000237%
Mortality All Cause 0.19398 0.131076 -

0.256798 49,417 0.000393%
TABLE 22
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, FUTURE BASELINE YEAR 2031, UNMITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health Endpoint
Group
Health
Endpoint
Project Incremental
Incidence
(# per year)
Baseline
Incidence
(# per year)
Mean
95%
Confidence
Interval
Acute Myocardial
Infarction
Acute
Myocardial
Infarction
Nonfatal
[18-24]

0.00002 0.000008 -

0.000024 9 0.000174%
Acute
Myocardial
Infarction
Nonfatal
[25-44]

0.00120 0.00058 -

0.001817 548 0.000219%
Acute
Myocardial
Infarction
Nonfatal
[45-54]

0.00259 0.001254 -

0.003931 1,187 0.000218%
Acute
Myocardial 0.00448 0.002165 -0.006787 2,072 0.000216%
3844
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TABLE 22
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, FUTURE BASELINE YEAR 2031, UNMITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health Endpoint
Group
Health
Endpoint
Project Incremental
Incidence
(# per year)
Baseline
Incidence
(# per year)
Mean
95%
Confidence
Interval
Infarction
Nonfatal
[55-64]
Acute
Myocardial
Infarction
Nonfatal
[65-99]

0.01711 0.008276 -

0.025939 7,348 0.000233%
Emergency Room
Visits -
Respiratory
Asthma 0.20129 0.05292 -

0.34944 39,207 0.000513%
Hospital
Admissions
All
Cardiovascular
(less
Myocardial
Infarctions)

0.03239 0.023878 -

0.040894 39,203 0.000083%
All Respiratory 0.06536 0.037689 -

0.092987 34,277 0.000191%
Asthma 0.01597 0.006114 -

0.025805 4,762 0.000335%
Mortality All Cause 0.35149 0.237503 -

0.465302 65,401 0.000537%
3845
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TABLE 23
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, FUTURE BASELINE YEAR 2031, MITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health
Endpoint
Group
Health
Endpoint
Project Incremental
Incidence
(# per year)
Baseline
Incidence
Mean (# per year)
95%
Confidence
Interval
Acute Myocardial
Infarction
Acute
Myocardial
Infarction
Nonfatal
[18-24]

0.00001 0.000007 -

0.000022 9 0.000157%
Acute
Myocardial
Infarction
Nonfatal
[25-44]

0.00108 0.000523 -

0.001639 548 0.000197%
Acute
Myocardial
Infarction
Nonfatal
[45-54]

0.00234 0.001131 -

0.003546 1,187 0.000197%
Acute
Myocardial
Infarction
Nonfatal
[55-64]

0.00404 0.001955 -

0.006126 2,072 0.000195%
Acute
Myocardial
Infarction
Nonfatal
[65-99]

0.01539 0.007441 -

0.023321 7,348 0.000209%
Emergency
Room Visits -
Respiratory
Asthma 0.17935 0.047151 -

0.31135 39,207 0.000457%
3846
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BlueScape Environmental 39 August 4, 2021
TABLE 23
BENMAP-CE MODELING RESULTS FOR MAXIMUM ANNUAL 24-HOUR AVERAGE PM2.5, FUTURE BASELINE YEAR 2031, MITIGATED
Human Health Endpoints
Fine Modeling Grid
Aggregated Regional Results
Incremental
Incidence /
Existing
Baseline
Incidence
(%)
Health
Endpoint
Group
Health
Endpoint
Project Incremental
Incidence
(# per year)
Baseline
Incidence
Mean (# per year)
95%
Confidence
Interval
Hospital
Admissions
All
Cardiovascular
(less Myocardial
Infarctions)

0.02913 0.021476 -

0.036781 39,203 0.000074%
All Respiratory 0.05879 0.033903 -

0.083645 34,277 0.000172%
Asthma 0.01415 0.005419 -

0.02287 4,762 0.000297%
Mortality All Cause 0.31574 0.213348 -

0.41798 65,401 0.000483%

5.0 UNCERTAINTY IN PHOTOCHEMICAL MODELING AND HEALTH IMPACT

ANALYSIS
Air quality impact analysis and health impact studies and the results, as for any technical or scientific study, are subject to “uncertainty.” Uncertainty means that there is a range of possible values for the inputs to the modeling studies in which exist the true values, and therefore, there is uncertainty as well in the modeling study outputs. Conclusions from these studies should be made with consideration of the uncertainty. Section 5.1 focuses on uncertainty related to photochemical modeling for air concentration results. Section 5.2 discusses the uncertainty involved in relating concentration data to specific health impact results.

5.1 Uncertainty in Photochemical Modeling
Uncertainty addresses the potential variability in photochemical modeling concentration results. Variability in a value is due to the differences in the value among different members of a population (e.g., ozone or PM2.5 measurements from identical instruments over a common area, precipitation measured from identical rain gauges over a common area). Variability represents heterogeneity in a wellcharacterized population and is usually not reducible through further measurement. 3847
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Uncertainty in a value arises due to lack of knowledge regarding the true value of a quantity for a given member of a population (e.g., an emissions factor used to characterize emissions from automobiles, a speciation factor to split total organic gases into its constituent chemical species). Uncertainty represents lack of perfect knowledge about poorly characterized phenomena and is sometimes reducible through further measurement.

There has been much research on sensitivities and uncertainties regarding the evaluation of environmental models (Hanna et. al. 2001, Biswas and Rao 2001, Baker and Bash 2012). It is widely recognized that full and perfect validation of a chemical transport model against real, natural systems cannot be accomplished by computer modeling systems, because natural systems are never closed, and results are always unique. Thus, a model can be evaluated by comparisons with observations, but it can never be fully validated against natural systems because all possible scenarios cannot be anticipated nor included in modeling systems.

A computational photochemical model such as CAMx is used to describe through mathematical representation, the complex and dynamic physical and chemical processes occurring in the atmosphere, in order to calculate regional air pollutant concentration impacts due to anthropogenic and biogenic emissions (Leary 1976). As an attempt to mathematically represent physical reality, CAMx modeling is subject to uncertainty that can cause error in model outputs. These uncertainties must be understood to properly validate and interpret CAMx model results.

Uncertainty and error in CAMx predictions arise due to uncertainty and error in the data inputs to CAMx (e.g., emissions estimates, boundary conditions, meteorological predictions, chemistry, grid resolution, and model formulation) as well as the model formulation itself (Sathya et. al. 2000, Fine et. al. 2003).

Estimates of emissions are typically the most uncertain inputs to a photochemical model (NRC 1991). Emissions estimates include those from on- and off-road mobile sources (e.g., cars, trucks, agricultural equipment such as combines and tractors, airplanes and ocean-going vessels, lawnmowers), biogenics, stationary sources (e.g., power plants, large dairies), and area sources (e.g., restaurant charbroiling, gasoline stations), among others. A vast amount of data is necessary to estimate emissions from these sources (e.g., fuel consumption, number of lawnmowers, number of cows) utilizing other models (e.g., the Biogenic Emissions Inventory System (EPA 2018c), SMOKE (CMAS 2018) and tools. Given the suspect quality of some of these data, it is not surprising that there are large uncertainties in the resulting emissions estimates that are input into CAMx.

Observational data or attendant climate model predictions that are used to initialize the meteorological and photochemical models, provide boundary conditions, or evaluate model performance are uncertain due to limited characterization of their spatial and temporal variability. Observational data are variable and uncertain due to monitoring equipment, user error, monitoring network design, and issues with proper instrument calibration. Some pollutant species are easier to measure than others. 3848
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For example, measurements of NOx may actually capture NOy, which includes NOx plus products of NOx oxidation (CARB 1992).

WRF too is a mathematical representation of the atmospheric processes that drive weather; thus, it is also subject to uncertainties in model formulation and model inputs. The meteorological model relies on observations typically lacking in the spatial and temporal detail needed to initialize meteorological fields. The application of fourdimensional data assimilation dampens the temporal growth in errors by causing model results to conform to observations at regular intervals (Stauffer and Seaman 1990). Specifically, the three components of wind velocity calculated as a function of time are nudged toward measured values, and in so doing, reduce the amount of observational data remaining for performance evaluation. Thus, the WRF meteorological predictions that are input into CAMx are subject to uncertainty.

A complete understanding of atmospheric chemistry is unknown. The atmosphere has hundreds to thousands of chemical species that participate in thousands of chemical reactions. How fast these reactions take place and the products of these reactions are still not well known (EPRI 2000). Even if known completely, atmospheric chemistry cannot be represented in its entirety because it would impose excessive computational demands (Russell and Dennis 2000).

Additional uncertainty can be introduced due to the choice of grid resolution. It is well known that grid resolution can impact model predictions (Gillani and Pleim 1996).

Further, studies have shown that the use of air quality model predictions at various grid resolutions can impact predicted health outcomes (Thompson and Noelle 2012).

Uncertainties associated with model formulation (e.g., representing atmospheric turbulence, processes that remove chemicals from the atmosphere, representation of aerosol formation and portioning to aerosol sizes [PM2.5, PM10], and the numerical techniques used to solve the mathematical equations) in the models described above can cause generally incomplete representations. Simplified representations are necessary when knowledge is incomplete, or when a more precise formulation excessively increases computational requirements. Further, there typically exists more than one formulation to represent a process; thus, choosing one inevitably means accepting some uncertainties over another (NRC 1991, Barchet et. al. 1994).

5.2 Uncertainty in Health Impact Analysis
As many regional-scale health impact assessments and this project-level analysis demonstrate, performing a quantitative HIA is complex and difficult, but it is possible to perform such analyses. Nevertheless, the limits of such analyses should be noted.

The BenMAP-CE model outputs provide precise values. It would be inappropriate, however, to assume that these values, though seemingly precise, give an accurate understanding of the project’s actual impacts. The imprecision of such analyses is inherent and unavoidable. Uncertainties associated with air quality modeling have been discussed above. Air quality models, including CAMx and BenMAP-CE, rely on assumptions that may not fully or accurately capture the complexity or dynamism of the physical world. Each step in the modeling process, and each assumption 3849
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incorporated into each model, adds a degree of uncertainty to the reported results.

These inputs include air pollutant emission estimates, ambient air concentration modeling, and health impact calculations using various health impact functions.

The combination and compounding of the uncertainties from each step of the modeling analysis, in the context of the very small increments of change that are predicted associated with a single project, could result in large margins of error for the overall modeled outcomes. That does not necessarily mean the modeled results are invalid or meaningless. Rather, it means that one should not have undue confidence in the seeming precision of the reported outcome. In other words, the modeled results may be valid, but they should not be misinterpreted as an exact calculation of something as complex as criteria air pollutant dispersion modeling, or as correlating a given level of emissions with specific health effects. This is particularly true where, as in this study, regional models have been adapted for use at the Project-level. In this case, the calculated impact may be smaller than the reasonable margin of errors of such analyses.

Specific to the application of the BenMAP-CE model itself, although it can be a powerful tool, it has the following limitations which impact the precision of the generated results. These are especially relevant when applying this tool to a projectlevel analysis such as this study. • In its current form, BenMAP–CE cannot conduct source-specific modeling without inputs from other modeling programs, such as CAMx. The differences among the mathematical and/or statistical algorithms used by these additional air quality modeling programs and those used by BenMAP-CE to process the air quality inputs and then correlate these inputs to specific health effects introduces layers of uncertainty into the generated results. In addition, the air pollutant concentrations that BenMAP–CE uses represent overall ambient concentrations of ozone and PM2.5. Due to the fact that the estimated emissions from the Project are a very small fraction of the base case regional emission inventory, the incremental increase in concentrations and health incidences associated with the Project, as shown in this analysis, are very small, with very low confidence that the results represent any health impacts greater than zero.

Therefore, the small incremental increase in concentrations generated by the Project in the context of regional-scale ambient air modeling should not be taken as a precise or meaningful representation of the Project-specific contributions.
• There are a number of conservative assumptions built into the analysis, which include but are not limited to the following:
o Maximum annual average emissions were used in the modeling and were assumed to occur for the same year for each pollutant, as opposed to considering the potential variabilities in emissions from year to year; and 3850
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o Emissions from activities currently occurring on the project site were not removed from the model (although emissions from project-related VMT are net of existing mobile-source emissions in the traffic study area). • The health impact functions that BenMAP-CE uses are based on findings from population‐based epidemiological studies that develop statistical relationships between human health effects and air pollution exposures. Thus, each causeand-effect relationship used by BenMAP-CE has some inherent uncertainty due to important limitations associated with the epidemiological studies including but not limited to (Sacks et. al. 2018):
o The absence in the analysis of any linkage between predicted changes in ozone and PM2.5 concentrations associated with project operations and any specific individual health impact; instead, the analysis uses studies that report correlations between health effects and exposure to ozone and PM2.5, to estimate potential effects on the population in the modeling domain; o Difficulty in determining from epidemiological studies whether health effects are caused from exposure to the air pollutant of interest, or from other factors such as weather, other pollutants, or life style factors like smoking and diet;
o Inconsistencies across different epidemiological studies regarding the cause-and-effect relationship for the same pollutant and health endpoint combination, as demonstrated by the multiple health impact functions preloaded in BenMAP-CE for the same pollutant and health endpoint combinations;
o Limitations in application of concentration-response functions based on epidemiological studies. For example, estimates of all-cause mortality impacts from PM2.5 are based on a single epidemiological study that found an association between PM2.5 concentrations and mortality. Similar studies suggest that such an association exists, but uncertainty remains regarding a clear causal link. This uncertainty stems from the limitations of epidemiological studies, such as inadequate exposure estimates and the inability to control for many factors that could explain the association between PM2.5 and mortality, such as lifestyle factors like smoking or exposures to other air pollutants;
o The potential for overstating the overall impacts, since, for both the PM2.5 and ozone health effects calculated, each pollutant may confound the other and both air pollutants could contribute to the health effect outcomes evaluated; and
o The presumption in the estimate of health effects that impacts seen at large concentration differences can be linearly scaled down to small concentration differences, with no consideration of the potential thresholds 3851
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below which health effects may not occur. This method of linearly scaling impacts is broadly accepted for use in regulatory evaluations and is considered to be health protective.
• There is inherent uncertainty in the reporting of health effects. For example, asthma exacerbations may be reported as other conditions that fall under another endpoint group. This uncertainty in how health conditions are reported or categorized under various health studies not only increases the difficulty and uncertainty in the development of cause-and-effect relationships, but also increases the uncertainty of the reported baseline health incidence data. • The estimated health impact by BenMAP-CE is based on people’s assumed exposures to outdoor ambient air concentrations; however, these concentration values may be very different from the actual exposures that people experience. For example, ozone and PM2.5 exposures may be significantly lower for those who spend much of their day indoors.

6.0 CONCLUSIONS
This Health Impact Analysis (HIA) reviewed the potential regional impacts from increased emissions due to the Friant Ranch Project, including the Community Plan and Specific Plan developments. The study included construction and operational emissions of the ozone precursors NOx and ROG (or VOC, as a subset of TOG) and particulate matter (PM10 and PM2.5). The HIA reviewed the Project’s potential regional ozone and PM2.5 concentration impacts, and specific health impacts, above expected baseline conditions.

Existing Baseline year and Future Baseline year (2020 and 2031) regional air quality modeling scenario databases were simulated using the CAMx modeling system. CAMx was used to apportion pollutant emission increases to Project emissions, including unmitigated and mitigated scenarios. The CAMx modeling estimated that the Project's emissions will increase the regional maximum daily 8-hour (MDA8) average ozone concentration by a maximum of 0.013 ppbv for the 2031 unmitigated scenario. Thus, the ozone concentration changes above baseline conditions for all modeled scenarios are expected to be less than 1.3% of the 1 ppb Significant Impact Level (SIL). The ozone concentration changes due to the Friant Ranch Project can be considered by Fresno County to contribute little to regional ozone formation, such that the specific health impacts due to the Project-related ozone concentration changes are not expected to be meaningful.

The maximum regional annual 24-hour average PM2.5 concentration change due to the Project was estimated to be 0.058 µg/m3 for the 2031 unmitigated scenario. The PM2.5 concentration changes above baseline conditions for all modeled scenarios are expected to be less than 29% of the 0.2 µg/m3 SIL. Thus, the changes in emissions 3852
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due to the Friant Ranch Project can be considered by Fresno County to contribute little to regional PM2.5 concentrations, such that the specific health impacts due to the PM2.5 concentration changes are not expected to be meaningful.

While the CAMx modeling results show that ozone and PM2.5 concentration impacts from the Project above baseline conditions are not expected to be meaningful, BlueScape completed specific health impact modeling. The health impact modeling provides additional information that may be useful to Fresno County and the public, and is intended to meet the evidentiary requirements stated the Friant Ranch Case.

The specific health impacts for these pollutants were estimated using the BenMAPCE health assessment model. The ozone and PM2.5 concentration changes above baseline modeled by CAMx were converted to effects (or incidences) on various health endpoints including premature mortality, hospitalizations, and emergency room visits, and others, and then compared to the “background health incidence.” The background health incidence is the actual incidence of health effects as measured in the local population in the absence of additional emissions from the Project.

The results of the BenMAP-CE health incidence calculations for the Friant Ranch Project are shown in Section 4.3 of this report. For example, the analysis shows that for ozone-related health endpoints for the 2031 Project unmitigated scenario, asthma-related emergency room visits are: incidences/year = 0.0338 for adults ages 18 to 99, and incidences/year = 0.0427 for children ages 0 to 17. For the PM2.5- related health endpoints, for the Project 2031 unmitigated scenario, the health effect on mortality is: incidences/year = 0.35.

When taken into context, the Project-related change in health incidences/year is very small relative to background health incidences. Using the example provided, the largest Project-related ozone health incidence calculated for any scenario for asthma related emergency room visits by children age 0-17 represents 0.00026% of the total of all regional emergency room visits due to asthma. The largest Project-related PM2.5 health incidence for mortality calculated for any scenario represents 0.00054% of the total of all regional deaths. As the health incidence values estimated for Friant Ranch Project emissions are less than one, and a negligible fraction of the background incidence values, it is not expected that the Project will result in any meaningful health impact greater than zero, that can be established with any certainty.

7.0 REFERENCES
Baker and Bash 2012. Baker, K. R., and Bash, J. O., Regional Scale Photochemical Model Evaluation of Total Mercury Wet Deposition and Speciated Ambient Mercury, Atmospheric Environment. 49(3):1-424, 2012.

Barchet et. al. 1994. Barchet, W. R., Roth, P. M., and Tesche, T. W., Evaluating the Risks of Developing Inaccurate Emissions Control Recommendations Using Regional Photochemical Air Quality Simulation Models, prepared for the Electric Power 3853
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Research Institute, prepared by Batelle Pacific Northwest Laboratory, Envair, and Alpine Geophysics, LLC, 1994.

Biswas and Rao 2001. Biswas, J., and Rao, S. T., Uncertainties in Episodic Ozone Modeling Stemming from Uncertainties in the Meteorological Fields. J. Appl. Meteor., 40, 117–136, 2001.
https://doi.org/10.1175/1520-0450(2001)040<0117:UIEOMS>2.0.CO;2 BlueScape 2021. Air Emissions Report for the Friant Ranch Community Development Project, BlueScape Environmental, March 4, 2021.

CARB 1992. Technical Guidance Document: Photochemical Modeling. Calif. EPA, Calif.

Air Resources Board, Sacramento, CA 9. Morgan MG, Henrion M, 1992.

CARB 2018. CEPAM: 2016 SIP - Standard Emission Tool, last accessed April 5, 2020. www.arb.ca.gov/app/emsinv/fcemssumcat/fcemssumcat2016.php CARB 2021a. California Ambient Air Quality Standards, last accessed April 22, 2021. ww2.arb.ca.gov/resources/california-ambient-air-quality-standards CARB 2021b. California Ambient Air Quality Standards, last accessed April 22, 2021. ww2.arb.ca.gov/sites/default/files/2020-07/aaqs2.pdf
CMAS 2018. Sparse Matrix Operator Kerner Emissions (SMOKE) Modeling System, v.4.8.1, last accessed March 23, 2021.
www.cmascenter.org/smoke/index.cfm
Emery, C.; Liu, Z.; Russell, A. G.; Odman, M. T.; Yarwood, G.; Kumar, N. 2017 Recommendations on statistics and benchmarks to assess photochemical model performance. J. Air Waste Manage. Assoc. 2017, 67, 582– 598, DOI:

10.1080/10962247.2016.1265027.

EPA 2017. Modeling Guidance and Support for Regulatory Atmospheric Modeling (SCRAM), US EPA, last accessed March 23, 2021.
www.epa.gov/scram/modeling-guidance-and-support
EPA 2018a. Modeling Guidance for Demonstrating Air Quality Goals for Ozone, PM2.5 and Regional Haze. EPA 454/R-18-009. EPA Office of Air Quality Planning and Standards, Research Triangle Park, NC 27711. November 2018.

EPA 2018b. Guidance on Significant Impact Levels for Ozone and Fine Particles in the Prevention of Deterioration Permitting Program, US EPA, April 17, 2018. www.epa.gov/sites/production/files/2018-
04/documents/sils_policy_guidance_document_final_signed_4-17-18.pdf.

EPA 2018c. Biogenic Emissions Inventory System (BEIS), US EPA, last accessed August 20, 2019.
www.epa.gov/air-emissions-modeling/biogenic-emission-inventory-system-beis 3854
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EPA 2019a. Environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE), US EPA, page last updated 22-Aug-2019. www.epa.gov/benmap.

EPA 2019b. Ozone (O3) Air Quality Standards, Integrated Science Assessments (ISAs), US EPA, last accessed April 22, 2021.
www.epa.gov/naaqs/particulate-matter-pm-air-quality-standards EPA 2021. BenMap: Environmental Benefits Mapping and Analysis Program – Community Edition (User’s Manual). April. www.epa.gov/sites/default/files/2015- 04/documents/benmap-ce_user_manual_march_2015.pdf.

EPRI 2000. North American Research Strategy on Tropospheric Ozone, Chapter 4:

The air-quality modeling system. In an Assessment of Tropospheric Ozone Pollution:

A North American Perspective. Palo Alto, CA: Electric Power Res. Inst./NARST, 2000.

Fine et. al. 2003. Fine, J., Vuilleumier, L., Reynolds S., Roth P., and Brown, N., Evaluating Uncertainties in Regional Photochemical Air Quality Modeling. Atmos. Rev.

Environ. Resour. 28:59-106, 2003.

Gillani and Pleim 1996. Gillani, N., and Pleim, J. E., Sub-grid-scale features of anthropogenic emissions of NOx and VOC in the context of regional eulerian models.

Atmospheric Environment. 30(12):2043-2059, 1996.

Hanna et. al. 2001. Hanna, S. R., Lu, Z., Frey, H. C., Wheeler, N., Vukovich, J., Arunachalam, S., Fernau, M., and Hansen, D. A., Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical, grid model applied to the July 1995 OTAG domain. Atmospheric Environment. 35:891-903, 2001.

Leary 1976. Leary, R., Mathematical Models. In Boundaries of Analysis. Cambridge, MA: Ballinger, 1976.

NRC 1991. Rethinking the Ozone Problem in Urban and Regional Air Pollution, Committee on Tropospheric Ozone Formation and Measurement. National Research Council, National Academy Press, Washington, D.C, 1991.

Ramboll ENVIRON 2017. Comprehensive Air Quality Model with Extensions (CAMx) and CAMx Support Software 2020, last accessed March 23, 2021. www.camx.com/download/support-software/
Russell and Dennis 2000. Russell, A., and Dennis, R., NARSTO critical review of photochemical models and modeling. Atmospheric Environment. 34, 2283-2324, 2000.

Sacks et. al. 2018. The Environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP–CE): A tool to estimate the health and economic benefits of reducing air pollution. Environ Model Softw. 2018 Feb 11; 104: 118–129, 2018.

Sathya et. al. 2000. Sathya, V., Russel, A. G., Perego, S., Maignan M., Junier, M., Clappier, A., and Van den Bergh, H., Uncertainties in photochemical grid modeling of 3855
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ozone. In: Van Ham J., Baede A.P.M., Meyer L.A., Ybema R. (eds) Non-CO2 Greenhouse Gases: Scientific Understanding, Control and Implementation. Springer, Dordrecht, 2000.

SCAQMD 2015. Application of the South Coast Air Quality Management District for Leave to File Brief of Amicus Curiae in Support of Neither Party and [Proposed] Brief of Amicus Curiae. Case S219783 in the Supreme Court of California, Sierra Club et. al. v. County of Fresno and Friant Ranch, L.P. filed April 13, 2015.

SCAQMD 2017. Appendix V: Modeling & Attainment Demonstrations, 2016 Air Quality Management Plan, SCAQMD, March 2017, last accessed March 23, 2021. www.aqmd.gov/docs/default-source/clean-air-plans/air-quality-managementplans/2016-air-quality-management-plan/final-2016-aqmp/appendixv.pdf?sfvrsn=10 SJVAPCD 2015a. Application for Leave to File Amicus Curiae Brief of San Joaquin Valley Unified Air Pollution Control District in Support of Defendant and Respondent, County of Fresno and Real Party in Interest and Respondent Friant Ranch, L.P. Case S219783 in the Supreme Court of California, Sierra Club et. al. v. County of Fresno and Friant Ranch, L.P. filed April 13, 2015.

SJVAPCD 2015b. Air Quality Thresholds of Significance – Criteria Pollutants, March 19, 2015.
www.valleyair.org/transportation/0714-GAMAQI-Criteria-Pollutant-Thresholds-ofSignificance.pdf SJVAPCD 2015c. Guidance for Assessing and Mitigating Air Quality Impacts, March 19, 2015. www.valleyair.org/transportation/GAMAQI.pdf.

SJVAPCD 2018. Photochemical Modeling for the 2018 San Joaquin Valley Annual/24- Hour PM2.5 State Implementation Plan, prepared by the California Air Resources Board, October 2018.

SJVAPCD 2019. APR 1925 Policy for District Rule 2201 AAQA Modeling. San Joaquin Air Pollution Control District, Revised March 10, 2019. www.valleyair.org/policies_per/policies/apr-1925.pdf.

SJVAPCD 2020. Notice of Preparation of Subsequent Environmental Impact Report for the River Islands at Lathrop Phase 2 Project/Update to the West Lathrop Specific Plan, District CEQA Reference No: 20200208, San Joaquin Air Pollution Control District, May 25, 2020.

SMAQMD 2019. Friant Ranch Interim Recommendation, April 25, 2019. www.airquality.org/LandUseTransportation/Documents/FriantInterimRecommendati on.pdf.

SMAQMD 2020. Guidance to Address the Friant Ranch Ruling for CEQA Projects in the Sac Metro Air District, prepared by Ramboll, October 2020. www.airquality.org/LandUseTransportation/Documents/SMAQMDFriantRanchFinalOc t2020.pdf#search=Friant%20ranch
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Stauffer and Seaman 1990. Stauffer, D., and Seaman, N., Use of four-dimensional data assimilation in a limited area mesoscale, part I: experiments with synoptic data.

Mon. Weather Rev. 118:1250–77, 1990.

Thompson and Noelle 2012. Thompson, T., and Noelle, S., Influence of Air Quality Model Resolution on Uncertainty Associated with Health Impacts. Atmospheric Chemistry & Physics. 12. 9753-9762. 10.5194/acp-12-9753-2012, 2012. 3857
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APPENDIX A
CAMx Modeling Input and Output
(list of electronic files)
The input and output files used and generated by the CAMx models are detailed in the following table.

NOTE: An asterisk (*) indicates a wildcard, meant to indicate one or more files following that file naming pattern. Most filesets in this list contain one file per day for an entire year (modeled or meteorological).

TABLE A-1

MODELING INPUT AND OUTPUT FILES
Filename Description
Input files
input/camx/bcon/bc_gid555_vint.2013001.bin
…
input/camx/bcon/bc_gid555_vint.2013365.bin
CAMx boundary condition files. One
file per day.
input/camx/icon/ic.*.const.bin CAMx initial condition files. Monthly files.
input/camx/tuv/tuv.CEQA.FRanch.do_SAPRC07.20130101
…
input/camx/tuv/tuv.CEQA.FRanch.do_SAPRC07.20131231
CAMx photolysis input files. One file
per day.
input/camx/o3map/o3map.CEQA.FRanch.4km.2013001.txt
…
input/camx/o3map/o3map.CEQA.FRanch.4km.2013365.txt
CAMx ozone map input files. One
file per day.

Input/camx/chem/CAMx6.5.chemparam.SAPRC07_CF_SOAP_ISORROPIA CAMx SAPRC 2007 chemical parametrization file
input/camx/met/ camx.lu.4km.FRanch.bin CAMx static surface geophysical parameters (land use, terrain).
input/camx/met/camx.3d.4km.20130101.FRanch.bin
…
input/camx/met/camx.3d.4km.20131231.FRanch.bin
CAMx meteorological 3D data files.

One file per day.
input/camx/met/camx.2d.4km.20130101.FRanch.bin
…
input/camx/met/camx.2d.4km.20131231.FRanch.bin
CAMx meteorological 2D surface
data files. One file per day.
input/camx/met/camx.kvpatch.YSU.OB70.4km.20130101.FRanch.bin …
input/camx/met/camx.kvpatch.YSU.OB70.4km.20131231.FRanch.bin CAMx meteorological vertical
diffusivity data files. One file per
day.
input/camx/met/camx.3d.4km.20130101.FRanch.bin
…
input/camx/met/camx.3d.4km.20131231.FRanch.bin
CAMx meteorological cloud/rain
data files. One file per day.
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TABLE A-1

MODELING INPUT AND OUTPUT FILES
Filename Description
input/camx/met/camx.cr.2016010100
…
input/camx/met/camx.cr.2016123100
CAMx meteorological cloud/rain
data files. One file per day.
input/camx/emiss/2020/point/
point.camx.ceqa_FRanch.gid555.20130101.bin
…
input/camx/emiss/2020/point/
point.camx.ceqa_FRanch.gid555.20131231.bin
CAMx point source emissions files.

All scenarios 2020. One file per day.
input/camx/emiss/2031/point/point.bluescape.20130101.2031.bin …
input/camx/emiss/2031/point/point.bluescape.20130101.2031.bin CAMx point source emissions files.

All scenarios 2031. One file per day.
input/camx/emiss/2020/area/base/
camx_ar.ceqa_FRanch.wProj.2020proj.4km.20130101.bin
…
input/camx/emiss/2020/area/base/
camx_ar.ceqa_FRanch.wProj.2020proj.4km.20131231.bin
CAMx 3d gridded emissions files.

Base scenario 2020. One file per
day.
input/camx/emiss/2031/area/base/
area.bluescape.20130101.2031.base.bin
…
input/camx/emiss/2031/area/base/
area.bluescape.20131231.2031.base.bin
CAMx 3d gridded emissions files.

Base scenario 2031. One file per
day.
input/camx/emiss/2020/area/proj/
area.bluescape.20130101.2020.proj.bin
…
input/camx/emiss/2020/area/proj/
area.bluescape.20131231.2020.proj.bin
CAMx 3d gridded emissions files.

Unmitigated project scenario 2020.

One file per day.
input/camx/emiss/2020/area/pmit/
area.bluescape.20130101.2020.pmit.bin
…
input/camx/emiss/2020/area/pmit/
area.bluescape.20131231.2020.pmit.bin
CAMx 3d gridded emissions files.

Mitigated project scenario 2020.

One file per day.
input/camx/emiss/2031/area/proj/
area.bluescape.20130101.2031.proj.bin
…
input/camx/emiss/2031/area/proj/
area.bluescape.20131231.2031.proj.bin
CAMx 3d gridded emissions files.

Unmitigated project scenario 2031.

One file per day.
input/camx/emiss/2031/area/pmit/
area.bluescape.20130101.2031.pmit.bin
…
input/camx/emiss/2031/area/pmit/
area.bluescape.20131231.2031.pmit.bin
CAMx 3d gridded emissions files.

Mitigated project scenario 2031.

One file per day.

Output files
output/all/2020/base/
camx65_SAPRC07.FRanch.4km_SA.2020.base.20130101.avrg.grd01.nc …
output/all/2020/base/
camx65_SAPRC07.FRanch.4km_SA.2020.base.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Base scenario 2020. One file per
day.
output/all/2031/base/
camx65_SAPRC07.FRanch.4km_SA.2031.base.20130101.avrg.grd01.nc …
output/all/2031/base/
camx65_SAPRC07.FRanch.4km_SA.2031.base.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Base scenario 2031. One file per
day.
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TABLE A-1

MODELING INPUT AND OUTPUT FILES
Filename Description
output/all/2020/proj/
camx65_SAPRC07.FRanch.4km_SA.2020.proj.20130101.avrg.grd01.nc …
output/all/2020/proj/
camx65_SAPRC07.FRanch.4km_SA.2020.proj.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Unmitigated project scenario 2020.

One file per day.
output/all/2020/pmit/
camx65_SAPRC07.FRanch.4km_SA.2020.pmit.20130101.avrg.grd01.nc …
output/all/2020/pmit/
camx65_SAPRC07.FRanch.4km_SA.2020.pmit.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Mitigated project scenario 2020.

One file per day.
output/all/2031/proj/
camx65_SAPRC07.FRanch.4km_SA.2031.proj.20130101.avrg.grd01.nc …
output/all/2031/proj/
camx65_SAPRC07.FRanch.4km_SA.2031.proj.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Unmitigated project scenario 2031.

One file per day.
output/all/2031/pmit/
camx65_SAPRC07.FRanch.4km_SA.2031.pmit.20130101.avrg.grd01.nc …
output/all/2031/pmit/
camx65_SAPRC07.FRanch.4km_SA.2031.pmit.20131231.avrg.grd01.nc CAMx output files. NetCDF format.

Mitigated project scenario 2031.

One file per day.
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APPENDIX B
BenMAP-CE Audit Trial Reports
3861
ATTACHMENT B-1

BENMAP-CE AUDIT TRAIL REPORT
O3.2020.PROJ.TXT
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file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 02:59:08
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2020
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-15 07:28:36
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2020.proj.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-15 07:29:27
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2020.base.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
3863
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Qualifier:Metric adjusted using a ratio of 1.14 based on table 2 of Anderson & Bell Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:PM2.5
Reference:Jerrett, Michael, Burnett, Richard T, Pope, Arden C, et al. 2009. Long-Term Ozone Exposure and Mortality. New England Journal of Medicine.

Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.0>
<Health.impact.function.1>
Health impact function dataset:EPA Standard Health Functions Endpoint group:School Loss Days
Endpoint:School Loss Days, All Cause
Start age:5
End age:17
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
3864
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Author:Gilliland et al.

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
Other pollutants:

Variable dataset:

Percentile:0
</Health.impact.function.1>
<Health.impact.function.2>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Asthma Exacerbation
Endpoint:Asthma Exacerbation, One or More Symptoms
Start age:6
End age:18
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:O'Connor et al.

Qualifier:

Function:(1-EXP(-Beta*DELTAQ))*A*POP*Prevalence
Year:2008
Geographic area:Everywhere
Other pollutants:PM2.5,NO2
Reference:O'Connor, G. T., L. Neas, et al. (2008). "Acute respiratory health effects of air pollution on children with asthma in US inner cities." J Allergy Clin Immunol 121(5): 1133-1139 e1131 Baseline functional form:A*POP*Prevalence
Incidence dataset:

Prevalence dataset:Prevalence (2008)
Variable dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:
3865
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] C:0
NameC:

Percentile:0
</Health.impact.function.2>
<Health.impact.function.3>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Start age:65
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Katsouyanni et al.

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Reference:Katsouyanni, K., Samet, J. M., Anderson, H. R., Atkinson, R., Tertre, A. L., Medina, S., et al. (2009). Air Pollution and Health: A European and North American Approach (APHENA): Health Effects Institute.

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:17
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3866
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Geographic area:Everywhere
Other pollutants:

Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.4>
<Health.impact.function.5>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Respiratory Symptoms
Endpoint:Minor Restricted Activity Days
Start age:18
End age:64
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Ostro and Rothschild
Qualifier:8-hour max from 1-hour max.

Function:(1-(1/EXP(Beta*DELTAQ)))*A*POP
Year:1989
Geographic area:Everywhere
Other pollutants:PM2.5
Reference:Ostro, B.D. and S. Rothschild. Air Pollution and Acute Respiratory Morbidity - an Observational Study of Multiple Pollutants. Environ Res, 1989. 50(2): p. 238-247.

Baseline functional form:A*POP
Incidence dataset:

Prevalence dataset:

Variable dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.5>
3867
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] <Health.impact.function.6>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:18
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Adult model, 8 hour maximum metric, 4 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.6>
<Health.impact.function.7>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Start age:0
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

Qualifier:Ozone season
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:PM10
Reference:Richard L. Smith, Baowei Xu, and Paul Switzer (2009). Reassessing the relationship between ozone and short-term mortality in U.S. urban communities. Inhalation Toxicology, 2009; 21(S2): 37?65 3868
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.7>
<Health.impact.function.8>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Cardiopulmonary
Start age:0
End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
<Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Start age:0
3869
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Bell, M.L., F. Dominici, and J.M. Samet. A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology, 2005. 16(4): p. 436-45.

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2000)
Prevalence dataset:

Variable dataset:

Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.9>
</Selected.health.impact.functions>
<Log.And.Message>
Processing complete. HIF processing time: 0 hours 23 minutes 14 seconds. </Log.And.Message>
Sort Incidence LHPs:False
Default Advanced Pooling Method:Roundweightstotwodigits Default Monte Carlo Iterations:5000
Random Seed:1
<Inflation.Adjustment>
Dataset:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
3870
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<MortalityPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Year:2005
Location:US & non-US
Other pollutants:

Geographic area:Everywhere
Reference:Bell, M.L., F. Dominici, and J.M. Samet. A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology, 2005. 16(4): p. 436-45.

Start age:0
End age:99
Baseline functional form:

Incidence dataset:Mortality Incidence (2000)
Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Cardiopulmonary
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Year:2005
Location:19 US cities
Other pollutants:

Geographic area:Everywhere
Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Start age:0
End age:99
3871
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Baseline functional form:

Incidence dataset:Mortality Incidence (2020)
Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

Year:2009
Location:98 US cities
Other pollutants:PM10
Geographic area:Everywhere
Reference:Richard L. Smith, Baowei Xu, and Paul Switzer (2009). Reassessing the relationship between ozone and short-term mortality in U.S. urban communities. Inhalation Toxicology, 2009; 21(S2): 37?65 Start age:0
End age:99
Baseline functional form:PM10
Incidence dataset:Mortality Incidence (2020)
Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Year:2009
Location:86 urban areas
Other pollutants:PM2.5
3872
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Geographic area:Everywhere
Reference:Jerrett, Michael, Burnett, Richard T, Pope, Arden C, et al. 2009. Long-Term Ozone Exposure and Mortality. New England Journal of Medicine.

Start age:30
End age:99
Baseline functional form:PM2.5
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</MortalityPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Start age:

End age:99
Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2000)
Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
3873
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Endpoint:Mortality, Cardiopulmonary
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Start age:

End age:99
Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

End age:99
Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
3874
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Mortality>
Mortality
Bell:Pooling Method Type:None
Huang:Pooling Method Type:None
Smith:Pooling Method Type:None
Jerrett:Pooling Method Type:None
</Valuation.Pooling.Window.Name.Mortality>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:School Loss Days
Endpoint:School Loss Days, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Gilliland et al.

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
3875
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Other pollutants:

Reference:Gilliland, F.D., K. Berhane, E.B. Rappaport, D.C. Thomas, E. Avol, W.J. Gauderman, S.J. London, H.G. Margolis, R. McConnell, K.T. Islam and J.M. Peters. 2001. The effects of ambient air pollution on school absenteeism due to respiratory illnesses. Epidemi
Start age:5
End age:17
Baseline functional form:Incidence*POP*A*B
Incidence dataset:Other Incidence (2000)
Beta:0.007824
Beta distribution:Normal
P1Beta:0.004444898
P2Beta:0
A:0.3929
NameA:Scalar for % of school days in ozone season
B:0.945
NameB:Population of school children at-risk for a new absence C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.School.Loss.Days>
School Loss Days
</Valuation.Pooling.Window.Name.School.Loss.Days>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Asthma Exacerbation
Endpoint:Asthma Exacerbation, One or More Symptoms
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:O'Connor et al.

Qualifier:

Function:(1-EXP(-Beta*DELTAQ))*A*POP*Prevalence
Year:2008
Geographic area:Everywhere
Other pollutants:PM2.5,NO2
Reference:O'Connor, G. T., L. Neas, et al. (2008). "Acute respiratory health effects of air pollution on children with asthma in US inner cities." J Allergy Clin Immunol 121(5): 1133-1139 e1131 Start age:6
End age:18
Baseline functional form:A*POP*Prevalence
Incidence dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
3876
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] </Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Asthma.Exacerbation>
Asthma Exacerbation
</Valuation.Pooling.Window.Name.Asthma.Exacerbation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Katsouyanni et al.

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Hospital.Admissions.Respiratory> Hospital Admissions, Respiratory
</Valuation.Pooling.Window.Name.Hospital.Admissions.Respiratory> <Incidence.Pooling.And.Aggregation.>
<Emergency.Room.Visits.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Year:2009
Location:Seattle,WA
Other pollutants:

Geographic area:Everywhere
3877
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Start age:0
End age:17
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Year:2009
Location:Seattle,WA
Other pollutants:

Geographic area:Everywhere
Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Start age:18
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Emergency.Room.Visits.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
3878
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:

End age:17
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:
3879
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.proj.txt[5/5/2021 9:32:04 AM] C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Emergency.Room.Visits.Respiratory> Emergency Room Visits, Respiratory
Mar:Pooling Method Type:None
Mar:Pooling Method Type:None
</Valuation.Pooling.Window.Name.Emergency.Room.Visits.Respiratory> <Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Respiratory Symptoms
Endpoint:Minor Restricted Activity Days
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Ostro and Rothschild
Qualifier:8-hour max from 1-hour max.

Start age:18
End age:64
Baseline functional form:A*POP
Incidence dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Acute.Respiratory.Symptoms> Acute Respiratory Symptoms
</Valuation.Pooling.Window.Name.Acute.Respiratory.Symptoms> Processing complete. Valuation processing time: 0 hours 0 minutes 0 seconds. </Aggregate, Pool & Value>
3880
ATTACHMENT B-2

BENMAP-CE AUDIT TRAIL REPORT
O3.2020.PMIT.TXT
3881
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 02:59:08
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2020
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-15 07:28:36
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2020.pmit.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-15 07:29:27
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2020.base.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
3882
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
3883
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Author:Gilliland et al.

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
Other pollutants:

Variable dataset:

Qualifier:

Prevalence dataset:Prevalence (2008)
Variable dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:
3884
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] C:0
NameC:

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:17
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3885
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Ostro and Rothschild
Qualifier:8-hour max from 1-hour max.

Baseline functional form:A*POP
Incidence dataset:

Prevalence dataset:

Variable dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.5>
3886
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] <Health.impact.function.6>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:18
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Adult model, 8 hour maximum metric, 4 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:Ozone season
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:PM10
Reference:Richard L. Smith, Baowei Xu, and Paul Switzer (2009). Reassessing the relationship between ozone and short-term mortality in U.S. urban communities. Inhalation Toxicology, 2009; 21(S2): 37?65 3887
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
<Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Start age:0
3888
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2000)
Prevalence dataset:

Variable dataset:

Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
3889
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<MortalityPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Year:2005
Location:US & non-US
Other pollutants:

Start age:0
End age:99
Baseline functional form:

Incidence dataset:Mortality Incidence (2000)
Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Year:2005
Location:19 US cities
Other pollutants:

Start age:0
End age:99
3890
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Baseline functional form:

Incidence dataset:Mortality Incidence (2020)
Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Year:2009
Location:86 urban areas
Other pollutants:PM2.5
3891
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Geographic area:Everywhere
Reference:Jerrett, Michael, Burnett, Richard T, Pope, Arden C, et al. 2009. Long-Term Ozone Exposure and Mortality. New England Journal of Medicine.

Start age:30
End age:99
Baseline functional form:PM2.5
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
3892
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Endpoint:Mortality, Cardiopulmonary
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
3893
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
3894
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Other pollutants:

Qualifier:

Function:(1-EXP(-Beta*DELTAQ))*A*POP*Prevalence
Year:2008
Geographic area:Everywhere
Other pollutants:PM2.5,NO2
Reference:O'Connor, G. T., L. Neas, et al. (2008). "Acute respiratory health effects of air pollution on children with asthma in US inner cities." J Allergy Clin Immunol 121(5): 1133-1139 e1131 Start age:6
End age:18
Baseline functional form:A*POP*Prevalence
Incidence dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
3895
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] </Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Asthma.Exacerbation>
Asthma Exacerbation
</Valuation.Pooling.Window.Name.Asthma.Exacerbation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Katsouyanni et al.

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Geographic area:Everywhere
3896
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Start age:0
End age:17
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Emergency.Room.Visits.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
3897
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:

End age:17
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:
3898
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2020.pmit.txt[5/5/2021 9:32:03 AM] C:0
NameC:

Start age:18
End age:64
Baseline functional form:A*POP
Incidence dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

BENMAP-CE AUDIT TRAIL REPORT
O3.2031.PROJ.TXT
3900
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 04:30:00
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2031
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-19 04:07:41
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2031.proj.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-19 04:07:19
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2031.base.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
3901
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.0>
<Health.impact.function.1>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Asthma Exacerbation
Endpoint:Asthma Exacerbation, One or More Symptoms
Start age:6
End age:18
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
3902
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Author:O'Connor et al.

Qualifier:

Prevalence dataset:Prevalence (2008)
Variable dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.1>
<Health.impact.function.2>
Health impact function dataset:EPA Standard Health Functions Endpoint group:School Loss Days
Endpoint:School Loss Days, All Cause
Start age:5
End age:17
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Gilliland et al.

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
Other pollutants:

Variable dataset:

Beta:0.007824
Beta distribution:Normal
P1Beta:0.004444898
P2Beta:0
A:0.3929
NameA:Scalar for % of school days in ozone season
B:0.945
NameB:Population of school children at-risk for a new absence 3903
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] C:0
NameC:

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:17
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3904
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Ostro and Rothschild
Qualifier:8-hour max from 1-hour max.

Baseline functional form:A*POP
Incidence dataset:

Prevalence dataset:

Variable dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.5>
3905
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] <Health.impact.function.6>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:18
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Adult model, 8 hour maximum metric, 4 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562. 3906
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2000)
Prevalence dataset:

Variable dataset:

Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
<Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
3907
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Start age:0
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

Qualifier:Ozone season
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:PM10
Reference:Richard L. Smith, Baowei Xu, and Paul Switzer (2009). Reassessing the relationship between ozone and short-term mortality in U.S. urban communities. Inhalation Toxicology, 2009; 21(S2): 37?65 Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.9>
</Selected.health.impact.functions>
<Log.And.Message>
Processing complete. HIF processing time: 0 hours 21 minutes 34 seconds. </Log.And.Message>
Sort Incidence LHPs:False
Default Advanced Pooling Method:Roundweightstotwodigits Default Monte Carlo Iterations:5000
Random Seed:1
<Inflation.Adjustment>
Dataset:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
3908
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<MortalityPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Year:2005
Location:US & non-US
Other pollutants:

Start age:0
End age:99
Baseline functional form:

Incidence dataset:Mortality Incidence (2000)
Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Year:2005
Location:19 US cities
Other pollutants:

Start age:0
End age:99
3909
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Baseline functional form:

Incidence dataset:Mortality Incidence (2020)
Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Year:2009
Location:86 urban areas
Other pollutants:PM2.5
3910
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Geographic area:Everywhere
Reference:Jerrett, Michael, Burnett, Richard T, Pope, Arden C, et al. 2009. Long-Term Ozone Exposure and Mortality. New England Journal of Medicine.

Start age:30
End age:99
Baseline functional form:PM2.5
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
3911
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Endpoint:Mortality, Cardiopulmonary
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
3912
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
3913
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Other pollutants:

Qualifier:

Function:(1-EXP(-Beta*DELTAQ))*A*POP*Prevalence
Year:2008
Geographic area:Everywhere
Other pollutants:PM2.5,NO2
Reference:O'Connor, G. T., L. Neas, et al. (2008). "Acute respiratory health effects of air pollution on children with asthma in US inner cities." J Allergy Clin Immunol 121(5): 1133-1139 e1131 Start age:6
End age:18
Baseline functional form:A*POP*Prevalence
Incidence dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
3914
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] </Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Asthma.Exacerbation>
Asthma Exacerbation
</Valuation.Pooling.Window.Name.Asthma.Exacerbation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Katsouyanni et al.

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Geographic area:Everywhere
3915
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Start age:0
End age:17
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Emergency.Room.Visits.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
3916
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:

End age:17
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:
3917
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.proj.txt[5/5/2021 9:32:05 AM] C:0
NameC:

Start age:18
End age:64
Baseline functional form:A*POP
Incidence dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

BENMAP-CE AUDIT TRAIL REPORT
O3.2031.PMIT.TEXT
3919
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 05:35:30
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2031
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-19 05:14:11
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2031.pmit.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-19 04:07:19
Pollutant:Ozone
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\o3.2031.base.benmap.csv <Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:Ozone
Observation Type:Hourly
3920
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Season0:May 1-September 30
Metric0:D1HourMax
Metric1:D24HourMean
Metric2:D8HourMax
Metric3:D8HourMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.0>
<Health.impact.function.1>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Asthma Exacerbation
Endpoint:Asthma Exacerbation, One or More Symptoms
Start age:6
End age:18
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
3921
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Author:O'Connor et al.

Qualifier:

Prevalence dataset:Prevalence (2008)
Variable dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Gilliland et al.

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
Other pollutants:

Variable dataset:

Beta:0.007824
Beta distribution:Normal
P1Beta:0.004444898
P2Beta:0
A:0.3929
NameA:Scalar for % of school days in ozone season
B:0.945
NameB:Population of school children at-risk for a new absence 3922
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] C:0
NameC:

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:17
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3923
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Ostro and Rothschild
Qualifier:8-hour max from 1-hour max.

Baseline functional form:A*POP
Incidence dataset:

Prevalence dataset:

Variable dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.5>
3924
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] <Health.impact.function.6>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:18
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Adult model, 8 hour maximum metric, 4 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562. 3925
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2000)
Prevalence dataset:

Variable dataset:

Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
<Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
3926
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Start age:0
End age:99
Race:ALL
Ethnicity:ALL
Gender:ALL
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

Qualifier:Ozone season
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:PM10
Reference:Richard L. Smith, Baowei Xu, and Paul Switzer (2009). Reassessing the relationship between ozone and short-term mortality in U.S. urban communities. Inhalation Toxicology, 2009; 21(S2): 37?65 Baseline functional form:Incidence*POP*A
Incidence dataset:Mortality Incidence (2020)
Prevalence dataset:

Variable dataset:

Beta:0.000257743
Beta distribution:Normal
P1Beta:0.000167
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.9>
</Selected.health.impact.functions>
<Log.And.Message>
Processing complete. HIF processing time: 0 hours 20 minutes 54 seconds. </Log.And.Message>
Sort Incidence LHPs:False
Default Advanced Pooling Method:Roundweightstotwodigits Default Monte Carlo Iterations:5000
Random Seed:1
<Inflation.Adjustment>
Dataset:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
3927
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<MortalityPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Bell et al.

Year:2005
Location:US & non-US
Other pollutants:

Start age:0
End age:99
Baseline functional form:

Incidence dataset:Mortality Incidence (2000)
Beta:0.000795
Beta distribution:Normal
P1Beta:0.00021227
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Year:2005
Location:19 US cities
Other pollutants:

Start age:0
End age:99
3928
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Baseline functional form:

Incidence dataset:Mortality Incidence (2020)
Beta:0.0008125
Beta distribution:Normal
P1Beta:0.000258673
P2Beta:0
A:0.0027397
NameA:Scalar to convert annual mortality rate to daily rate B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Year:2009
Location:86 urban areas
Other pollutants:PM2.5
3929
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Geographic area:Everywhere
Reference:Jerrett, Michael, Burnett, Richard T, Pope, Arden C, et al. 2009. Long-Term Ozone Exposure and Mortality. New England Journal of Medicine.

Start age:30
End age:99
Baseline functional form:PM2.5
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
3930
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Endpoint:Mortality, Cardiopulmonary
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Huang et al.

Qualifier:Warm season. 8-hour max from 24-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2005
Geographic area:Everywhere
Other pollutants:

Reference:Huang, Y., F. Dominici and M. L. Bell. 2005. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. Vol. 16: 547?562.

Start age:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Non-Accidental
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Smith et al.

C:0
3931
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:Mean
Author:Jerrett et al.

Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2020)
Beta:0.004471161
Beta distribution:Normal
P1Beta:0.001510347
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Qualifier:All year. 8-hour max from 8-hour mean.

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A*B
Year:2001
Geographic area:Everywhere
3932
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Other pollutants:

Qualifier:

Function:(1-EXP(-Beta*DELTAQ))*A*POP*Prevalence
Year:2008
Geographic area:Everywhere
Other pollutants:PM2.5,NO2
Reference:O'Connor, G. T., L. Neas, et al. (2008). "Acute respiratory health effects of air pollution on children with asthma in US inner cities." J Allergy Clin Immunol 121(5): 1133-1139 e1131 Start age:6
End age:18
Baseline functional form:A*POP*Prevalence
Incidence dataset:

Beta:0.000966054
Beta distribution:Normal
P1Beta:0.002991454
P2Beta:0
A:0.207142857
NameA:Incidence rate
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
3933
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] </Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Asthma.Exacerbation>
Asthma Exacerbation
</Valuation.Pooling.Window.Name.Asthma.Exacerbation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Katsouyanni et al.

Qualifier:Summer, 1985-1994, penalized splines, 8 df
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.000636757
Beta distribution:Normal
P1Beta:0.000400294
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Geographic area:Everywhere
3934
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Reference:Mar, T. F., & Koenig, J. Q. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Annals of Allergy, Asthma, & Immunology, 103, 474-479.

Start age:0
End age:17
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Emergency.Room.Visits.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
3935
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] Pollutant:Ozone
Metric:D8HourMax
Metric statistic:None
Author:Mar and Koenig
Qualifier:Child model, 8 hour maximum metric, 3 day lag, May-September Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:

End age:17
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.010436002
Beta distribution:Normal
P1Beta:0.004357595
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Start age:18
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.007696104
Beta distribution:Normal
P1Beta:0.002837389
P2Beta:0
A:0
NameA:

B:0
NameB:
3936
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/o3.2031.pmit.txt[5/5/2021 9:32:05 AM] C:0
NameC:

Start age:18
End age:64
Baseline functional form:A*POP
Incidence dataset:

Beta:0.002596
Beta distribution:Normal
P1Beta:0.00077644
P2Beta:0
A:0.02137
NameA:mRAD18to64; Ostro and Rothschild, 1989, p 243.

B:0
NameB:

C:0
NameC:

BENMAP-CE AUDIT TRAIL REPORT
PM25.2020.PROJ.TXT
3938
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 09:06:49
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2020
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-19 08:43:47
Pollutant:PM2.5
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\pm25.2020.proj.benmap.csv
<Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-19 08:43:19
Pollutant:PM2.5
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\pm25.2020.base.benmap.csv
<Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 3939
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] </Grid.Definition>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:Mean
Seasonal metric:QuarterlyMean
Author:Krewski et al.

Qualifier:Random effects cox; 44 individual and 7 ecologic co-variates; 1999--2000 follow-up (Commentary table 4)
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:TSP, O3, SO4, SO2
Reference:Krewski D, Jerrett M, Burnett R, et al. 2009. Extended Follow-Up and Spatial analysis of the American Cancer Society Linking Particulate Air Pollution and Mortality. Health Effects Institute, Cambridge MA
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2000)
Prevalence dataset:

Variable dataset:

Beta:0.005826891
Beta distribution:Normal
P1Beta:0.000962763
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.0>
<Health.impact.function.1>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
3940
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Start age:18
End age:24
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:

Reference:Zanobetti, A., M. Franklin and J. Schwartz. 2009. Fine particulate air pollution and its components in association with cause-specific emergency admissions. Environmental Health Vol. 8: 58-60.

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.1>
<Health.impact.function.2>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Start age:45
End age:54
Race:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
3941
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.2>
<Health.impact.function.3>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Start age:55
End age:64
Race:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Start age:25
End age:44
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
3942
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.4>
<Health.impact.function.5>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Cardiovascular
Endpoint:HA, All Cardiovascular (less Myocardial Infarctions) Start age:65
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Bell
Qualifier:National; Yearly
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2012
Geographic area:Everywhere
Other pollutants:

Reference:Bell, M.L. 2012. Assessment of the Health Impacts of Particulate Matter Characteristics. Research Report 161. Health Effects Institute. Research Report 161: 1-59.

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.0008
Beta distribution:Normal
P1Beta:0.000107143
P2Beta:0
A:0
NameA:

B:0
3943
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.5>
<Health.impact.function.6>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Start age:65
End age:99
Race:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.925661
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.6>
<Health.impact.function.7>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Start age:65
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3944
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00207
Beta distribution:Normal
P1Beta:0.000446429
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.7>
<Health.impact.function.8>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, Asthma
Start age:0
End age:64
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Sheppard
Qualifier:

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2003
Geographic area:Everywhere
Other pollutants:

Reference:Sheppard, L. Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. 2003, Health Effects Institute: Boston, MA. p. 227-230.

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.003323789
Beta distribution:Normal
P1Beta:0.00104459
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
3945
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] <Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Mar et al.

Qualifier:Entire study period (January 3, 1999 - May 30, 2002) Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2010
Geographic area:Everywhere
Other pollutants:

Reference:Mar, T. F., J. Q. Koenig and J. Primomo. 2010. Associations between asthma emergency visits and particulate matter sources, including diesel emissions from stationary generators in Tacoma, Washington.

Inhal Toxicol. Vol. 22 (6): 445-8. http://www.ncbi.nlm
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.005602959
Beta distribution:Normal
P1Beta:0.002103073
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.9>
</Selected.health.impact.functions>
<Log.And.Message>
Processing complete. HIF processing time: 0 hours 16 minutes 11 seconds. </Log.And.Message>
Sort Incidence LHPs:False
Default Advanced Pooling Method:Roundweightstotwodigits Default Monte Carlo Iterations:5000
Random Seed:1
<Inflation.Adjustment>
Dataset:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
3946
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:Mean
Seasonal metric:QuarterlyMean
Author:Krewski et al.

Qualifier:Random effects cox; 44 individual and 7 ecologic co-variates; 1999--2000 follow-up (Commentary table 4)
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:TSP, O3, SO4, SO2
Reference:Krewski D, Jerrett M, Burnett R, et al. 2009. Extended Follow-Up and Spatial analysis of the American Cancer Society Linking Particulate Air Pollution and Mortality. Health Effects Institute, Cambridge MA
Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2000)
Beta:0.005826891
Beta distribution:Normal
P1Beta:0.000962763
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Mortality>
Mortality
</Valuation.Pooling.Window.Name.Mortality>
<Incidence.Pooling.And.Aggregation.>
<Acute.Myocardial.InfarctionPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
3947
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Year:2009
Location:26 U.S. Communities
Other pollutants:

Geographic area:Everywhere
Reference:Zanobetti, A., M. Franklin and J. Schwartz. 2009. Fine particulate air pollution and its components in association with cause-specific emergency admissions. Environmental Health Vol. 8: 58-60.

Start age:18
End age:24
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:45
End age:54
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
3948
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Year:2009
Location:26 U.S. Communities
Other pollutants:

Start age:55
End age:64
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:25
End age:44
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
3949
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] B:0
NameB:

C:0
NameC:

Start age:65
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.925661
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Acute.Myocardial.InfarctionPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:18
End age:24
Baseline functional form:Incidence*POP*A
3950
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:45
End age:54
Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:55
End age:64
Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:25
End age:44
Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
3952
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP*A
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.925661
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Acute.Myocardial.Infarction> Acute Myocardial Infarction
Zanobetti:Pooling Method Type:None
Zanobetti:Pooling Method Type:None
Zanobetti:Pooling Method Type:None
Zanobetti:Pooling Method Type:None
Zanobetti:Pooling Method Type:None
</Valuation.Pooling.Window.Name.Acute.Myocardial.Infarction> <Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Cardiovascular
Endpoint:HA, All Cardiovascular (less Myocardial Infarctions) Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Bell
Qualifier:National; Yearly
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2012
Geographic area:Everywhere
Other pollutants:

Reference:Bell, M.L. 2012. Assessment of the Health Impacts of Particulate Matter Characteristics. Research Report 161. Health Effects Institute. Research Report 161: 1-59.

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
3953
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Beta:0.0008
Beta distribution:Normal
P1Beta:0.000107143
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Hospital.Admissions.Cardiovascular> Hospital Admissions, Cardiovascular
</Valuation.Pooling.Window.Name.Hospital.Admissions.Cardiovascular> <Incidence.Pooling.And.Aggregation.>
<Hospital.Admissions.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Year:2009
Location:26 U.S. Communities
Other pollutants:

Start age:65
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00207
Beta distribution:Normal
P1Beta:0.000446429
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, Asthma
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
3954
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Author:Sheppard
Year:2003
Location:Seattle, WA
Other pollutants:

Geographic area:Everywhere
Reference:Sheppard, L. Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. 2003, Health Effects Institute: Boston, MA. p. 227-230.

Start age:0
End age:64
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.003323789
Beta distribution:Normal
P1Beta:0.00104459
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Hospital.Admissions.RespiratoryPooling.Method.TypeNone> <Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, All Respiratory
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:

Start age:65
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.00207
Beta distribution:Normal
P1Beta:0.000446429
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
3955
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] </Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Respiratory
Endpoint:HA, Asthma
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Sheppard
Qualifier:

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2003
Geographic area:Everywhere
Other pollutants:

Start age:0
End age:64
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.003323789
Beta distribution:Normal
P1Beta:0.00104459
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Hospital.Admissions.Respiratory> Hospital Admissions, Respiratory
Zanobetti:Pooling Method Type:None
Sheppard:Pooling Method Type:None
</Valuation.Pooling.Window.Name.Hospital.Admissions.Respiratory> <Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Mar et al.

Qualifier:Entire study period (January 3, 1999 - May 30, 2002) Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2010
Geographic area:Everywhere
Other pollutants:

Inhal Toxicol. Vol. 22 (6): 445-8. http://www.ncbi.nlm
3956
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.proj.txt[5/5/2021 9:32:07 AM] Start age:0
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Beta:0.005602959
Beta distribution:Normal
P1Beta:0.002103073
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Emergency.Room.Visits.Respiratory> Emergency Room Visits, Respiratory
</Valuation.Pooling.Window.Name.Emergency.Room.Visits.Respiratory> Processing complete. Valuation processing time: 0 hours 0 minutes 0 seconds. </Aggregate, Pool & Value>
3957
ATTACHMENT B-6

BENMAP-CE AUDIT TRAIL REPORT
PM25.2020.PMIT.TXT
3958
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] BenMAP-CE 1.5.0
<Aggregate, Pool & Value>
Create Datetime:2021-04-19 10:12:52
IsRunInPointMode:False
Latin Hypercube Points:20
Population Dataset:PopFriantUpdated-Friant
Year:2020
Threshold:0
Incidence averaging:All
<Baseline.And.Control.Group0>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
<Baseline.Air.Quality.Surfaces>
Create Datetime:2021-04-19 09:57:07
Pollutant:PM2.5
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\pm25.2020.pmit.benmap.csv
<Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Grid.Definition>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
</Baseline.Air.Quality.Surfaces>
<Control.Air.Quality.Surfaces>
Create Datetime:2021-04-19 08:43:19
Pollutant:PM2.5
Model Database File:C:\Users\Frank\Documents\My BenMAP-CE Files\Friant\Data\pm25.2020.base.benmap.csv
<Grid.Definition>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 3959
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] </Grid.Definition>
<Pollutant>
Name:PM2.5
Observation Type:Daily
Season0:January 1-March 31
Season1:April 1-June 30
Season2:July 1-September 30
Season3:October 1-December 31
Metric0:D24HourMean
Seasonal Metric0:QuarterlyMean
</Pollutant>
</Control.Air.Quality.Surfaces>
</Baseline.And.Control.Group0>
<Selected.health.impact.functions>
<Health.impact.function.0>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Start age:30
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:Mean
Seasonal metric:QuarterlyMean
Author:Krewski et al.

Variable dataset:

Beta:0.005826891
Beta distribution:Normal
P1Beta:0.000962763
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.0>
<Health.impact.function.1>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
3960
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Start age:18
End age:24
Race:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
3961
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.3>
<Health.impact.function.4>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Hospital Admissions, Cardiovascular
Endpoint:HA, All Cardiovascular (less Myocardial Infarctions) Start age:65
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
3962
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Metric:D24HourMean
Metric statistic:None
Author:Bell
Qualifier:National; Yearly
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2012
Geographic area:Everywhere
Other pollutants:

Reference:Bell, M.L. 2012. Assessment of the Health Impacts of Particulate Matter Characteristics. Research Report 161. Health Effects Institute. Research Report 161: 1-59.

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.0008
Beta distribution:Normal
P1Beta:0.000107143
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.4>
<Health.impact.function.5>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Start age:25
End age:44
Race:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
3963
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] NameB:

C:0
NameC:

Ethnicity:

Gender:

Baseline functional form:Incidence*POP*A
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.925661
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Qualifier:All Seasons
Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2009
3964
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.00207
Beta distribution:Normal
P1Beta:0.000446429
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Sheppard
Qualifier:

Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2003
Geographic area:Everywhere
Other pollutants:

Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.003323789
Beta distribution:Normal
P1Beta:0.00104459
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.8>
3965
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] <Health.impact.function.9>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Emergency Room Visits, Respiratory
Endpoint:Emergency Room Visits, Asthma
Start age:0
End age:99
Race:

Ethnicity:

Gender:

Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Mar et al.

Qualifier:Entire study period (January 3, 1999 - May 30, 2002) Function:(1-EXP(-Beta*DELTAQ))*Incidence*POP
Year:2010
Geographic area:Everywhere
Other pollutants:

Inhal Toxicol. Vol. 22 (6): 445-8. http://www.ncbi.nlm
Baseline functional form:Incidence*POP
Incidence dataset:Other Incidence (2014)
Prevalence dataset:

Variable dataset:

Beta:0.005602959
Beta distribution:Normal
P1Beta:0.002103073
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
</Health.impact.function.9>
</Selected.health.impact.functions>
<Log.And.Message>
Processing complete. HIF processing time: 0 hours 15 minutes 20 seconds. </Log.And.Message>
Sort Incidence LHPs:False
Default Advanced Pooling Method:Roundweightstotwodigits Default Monte Carlo Iterations:5000
Random Seed:1
<Inflation.Adjustment>
Dataset:

Year:-1
</Inflation.Adjustment>
<Income.Growth.Adjustment>
Dataset:

Year : -1
</Income.Growth.Adjustment>
<Incidence.Aggregation>
Name:Friant
ID:33
Columns:87
3966
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Incidence.Aggregation>
<Valuation.Aggregation>
Name:Friant
ID:33
Columns:87
Rows:103
Grid Type:Shapefile
Shapefile Name:CEQA_FRanch_lc_87_103_4000_4000_-108_-256_WGS1984 </Valuation.Aggregation>
<Incidence.Pooling.And.Aggregation.>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Mortality
Endpoint:Mortality, All Cause
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:Mean
Seasonal metric:QuarterlyMean
Author:Krewski et al.

Qualifier:Random effects cox; 44 individual and 7 ecologic co-variates; 1999--2000 follow-up (Commentary table 4)
Function:(1-(1/EXP(Beta*DELTAQ)))*Incidence*POP
Year:2009
Geographic area:Everywhere
Other pollutants:TSP, O3, SO4, SO2
Reference:Krewski D, Jerrett M, Burnett R, et al. 2009. Extended Follow-Up and Spatial analysis of the American Cancer Society Linking Particulate Air Pollution and Mortality. Health Effects Institute, Cambridge MA
Start age:30
End age:99
Baseline functional form:Incidence*POP
Incidence dataset:Mortality Incidence (2000)
Beta:0.005826891
Beta distribution:Normal
P1Beta:0.000962763
P2Beta:0
A:0
NameA:

B:0
NameB:

C:0
NameC:

Percentile:0
Weight:0
</Health.impact.function>
</Incidence.Pooling.And.Aggregation.>
<Valuation.Pooling.Window.Name.Mortality>
Mortality
</Valuation.Pooling.Window.Name.Mortality>
<Incidence.Pooling.And.Aggregation.>
<Acute.Myocardial.InfarctionPooling.Method.TypeNone>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
3967
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Year:2009
Location:26 U.S. Communities
Other pollutants:

Start age:18
End age:24
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:45
End age:54
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Percentile:0
3968
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Weight:0
</Health.impact.function>
<Health.impact.function>
Health impact function dataset:EPA Standard Health Functions Endpoint group:Acute Myocardial Infarction
Endpoint:Acute Myocardial Infarction, Nonfatal
Pollutant:PM2.5
Metric:D24HourMean
Metric statistic:None
Author:Zanobetti et al
Year:2009
Location:26 U.S. Communities
Other pollutants:

Start age:55
End age:64
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.971812
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:25
End age:44
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
3969
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] B:0
NameB:

C:0
NameC:

Start age:65
End age:99
Baseline functional form:

Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.925661
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Start age:18
End age:24
Baseline functional form:Incidence*POP*A
3970
file:///C/Users/lmoel/Downloads/BenMAP%20Audit%20Trails/BenMAP%20Audit%20Trails/pm25.2020.pmit.txt[5/5/2021 9:32:06 AM] Incidence dataset:Other Incidence (2014)
Beta:0.00225
Beta distribution:Normal
P1Beta:0.000591837
P2Beta:0
A:0.98148
NameA:% of hospMI surviving 28 days
B:0
NameB:

C:0
NameC:

Ontario City CouncilJune 16, 2026

Ontario City Council
June 16, 2026