Public Comment Packet 9 20 AM #1 -ID 24-1571 _Redacted — Fresno City Council, December 5, 2024 (part 2 of 3)

Altamont Corridor Expressway (ACE/Forward) Project EIR/EIS. Prepared a program‐ and project‐ level Hazardous Materials analysis for over 120 miles of railroad corridor from San Jose to Merced. Hazardous materials concerns, such as release sites, petroleum pipelines, agricultural pesticides, and nearby school sites were evaluated in GIS.

Stonegate Residential Subdivision EIR. Prepared a project‐level Hydrology and Water Quality analysis for a residential development lo
cated within the 100‐year floodplain. The proposed project included modifications to existing levees and flood channels.

BART Silicon Valley Extension Project. Prepared Initial Site Assessment and Hazardous Materials EIS/EIR section for extending 6 miles of proposed BART service through the Cities of San Jose and Santa Clara.

EXHIBIT B

Pilot Study of High
Performance Air Filtration
for Classrooms Applications

Draft repor
t: October 2009

Prepared by:

South Coast Air Quality Management District (AQMD)
Diamond Bar, CA 91765

IQAir North America
,
Santa Fe Springs, CA 90670

ABSTRACT

A pilot study was conducted between April and December 2008 to investigate the effectiveness of three different air purification systems in reducing the exposure of children to air contaminants inside nine classrooms at three Southern California schools (three classrooms per school). Two of them, Del Amo Elementary and Dominguez Elementary, are part of the Los Angeles Unified School District ( LAUSD), while the third school , Hudson Elementary, is part of the Long Beach Unified School District (LBUSD). Continuous and integrated measurements were conducted to monitor the indoor and outdoor concentrations of the following species: ultrafine particl es (UFP), particulate matter mass (both PM2.5 and PM 10), black carbon (BC), and volatile organic compounds (VOCs). An HVAC-based high-performance panel filter (HP -PF), a register- based air purifier ( RS), and a standalone system (SA ) were tested alone and in different combinations for their ability to remove the monitored pollutants from the indoor air.

Overall, the coupling between a register system and a high-performance panel filter (RS + HP-PF) was the most effective solution for reducing the indoor concentrations of BC, UFP, and PM 2.5, with study average removal efficiencies varying from 87 to 96%. When using a HP-PF alone, reductions close to 90% were also obtained .

Due to re-suspension of dust and other relatively large particles from common indoor activities such as walking and cleaning, the removal performance of PM 10 was lower than that of other particle measurements (68% when using a RS + HP-PF combination).

In all
cases, air quality conditions were improved substantially with respect to the corresponding baseline (pre-existing) conditions, when removal efficiencies for the different particulate pollutants varied between 20% and 50%. Data obtained from the analysis of canister samples collected at Dominguez elementary showed that the total VOC removal performance of the register system (RS) was 28%. These values were substantially higher for the standalone unit (SA) operated with and without the use of the HVAC system (58 and 86%, respectively) . Because gas-absorbing media may be subject to saturation after experiencing high short-term concentrations, the effectiveness, lifetime, costs, benefits, and maintenance of the gas removal systems tested in this pilot study
must be further assessed before conclusions and recommendations can be made.

TABLE OF CONTENTS

 INTRODUCTION
o Background 1
o Objectives and Study Design 1

 METHODS
o Schools and Classrooms Characteristics 3 o Air Purification Solutions 5
 High-performance panel filter (HP-PF) 6  Register system (RS) 8  Standalone system (SA) 9 o In-classroom configurations 10 o Indoor and outdoor measurements 12

 RESULTS AND DISCUSSION
o Removal of PM and other particle species 14 o Impact on the HVAC system airflow 23 o Removal of VOCs 24

 ACKNOWLEDGEMENTS 26

 REFERENCES 27

 APPENDIX A 28

 APPENDIX B 46

1 INTRODUCTION

Background
Numerous epidemiological and toxicological studies have found positive associations between exposure to atmospheric particulate matter (PM) and adverse health effects (Pope and Dockery, 2006; Environmental Protection Agency Integrated Science Assessments, 2009). Although air quality standards have been established for outdoor ambient environments, a significant portion of human exposures to PM occurs indoors, where people spend around 85-90% of their time. Hence, it is important to understand and reduce the sources of both indoor and outdoor PM. Indoor PM consists of outdoor particles that have infiltrated indoors, particles emitted indoors (primary), and particles formed indoors (secondary) from precursors emitted both indoors and outdoors.

Children are regarded as particularly susceptible to potential health hazards related to PM exposure, which include asthma, lung inflammation, allergies and other types of respiratory and cardiovascular problems. School-aged children spend approximately 30% of their day in classrooms. For this reason, minimizing the concentration of PM (as well as that of other air contaminants) inside classrooms is important, especially at schools located in close proximity to roadways and other substantial sources of air pollution. One approach i s the installation of panel filters inside the Heating, Ventilating, and Air Conditioning (HVAC) system. Common medium performance filters with a Minimum Performance Reporting Value (MERV) of 7 (those installed in most commercial buildings) remove only a small fraction of the particles with aerodynamic diameters lower than 0.3 µm, although higher removal efficiencies are generally achieved for larger particles. Diesel particulate matter, which is considered an air toxic, generally consists of particles less than 0.3 µm. New evidence also suggests that ultrafine particles, less than 0.1 µm by definition, have harmful health effects beyond those caused by particle mass.

Filtration in classrooms presents some unique challenges. The older HVAC systems that exist in older schools were not designed with air filtration in mind. The classroom is a noise sensitive environment, so filtration systems must meet strict decibel li
mits when in operation. Classrooms often have high ventilation rates with doors and windows that are frequently open to outside air. Finally, classrooms are large, densely occupied spaces with a lot of activity that can lead to indoor generation of particles and other pollutants.

Objectives and Study Design
The objective of this pilot study was to investigate the effectiveness of three different air purification systems/solutions in reducing the exposure of children to outdoor-infiltrated and indoor-generated air contaminants inside nine classrooms at three Southern California schools. To this end, the South Coast Air Quality Management District (SCAQMD; 21865 Copley Dr, Diamond Bar, CA 91765) worked in close collaboration with IQAir (IQAir North America, 10440 Ontiveros Place, Santa Fe Springs, CA 90670), a company that specializes in air purification solutions, and Thermal Comfort Systems (Thermal Comfort Systems Inc., 8038 Andasol Ave., Northridge, CA 91325), an HVAC contractor. Of particular interest was the removal of various sizes and types of particulate matter, especially the smaller sizes associated with diesel engine 2 exhaust. Solutions for removing gaseous air contaminants that may be air toxics or cause odors were also examined. The types of pollutants for which the performance of the installed systems were tested are described below:

- Ultra-fine particles (UFPs; particles with an aerodynamic diameter less than 0.1 µm) :

UFP are primarily produced from the combustion of fossil fuels (e.g. motor-vehicle emissions)
. Recent health studies suggest that UFPs are more toxic than fine particles, possibly due to their chemical composition and their ability to penetrate cell walls, enter the blood stream, and translocate to organs throughout the body. UFP are currently unregulated in the United States.

- Fine PM (PM 2.5; particles with an aerodynamic diameter less than 2.5 µm) : Sources of PM 2.5 include emissions from motor vehicles, power plants, residential wood burning, forest fires, agricultural burning, and other combustion activities. Fine particles have well established health effects, including multiple adverse respiratory and cardiovascular outcomes. PM2.5 is a U.S. Environmental Protection Agency (U.S.

EPA) criteria pollutant for which there exist National Ambient Air Quality Standards (NAAQS).

- PM10 (p articles with an aerodynamic diameter less than 10 µm) : PM10 includes all PM2.5 particles, but also larger particles between 2.5 and 10 µm in diameter. So urces of these coarse particles include crushing or grinding operations, re-suspension of dust from vehicles traveling on roads, and other mechanical processes. PM10 is also a U.S. Environmental Protection Agency (U.S. EPA) criteria pollutant and also has associated National Ambient Air Quality Standards (NAAQS).

- Black Carbon (BC ; sometimes referred to as soot; related closely to elemental carbon): BC is a component of PM and is formed through the incomplete combustion of fossil fuels and biomass, and is emitted from both natural and anthropogenic sources. Most atmospheric BC is in the fine or ultra-fine particle size ranges. The majority of BC in Southern California comes from diesel particulate matter (DPM) emissions. DPM is considered an air toxic by the State of California, and the SCAQMD has recently estimated that DPM accounts for more than 80% of the total cancer risk from air toxics in the South Coast Air Basin (MATES III Study, 2008).

- Volatile Organic Compounds (VOCs): these gases are emitted by a variety of evaporative processes and combustion sources, including paints, cleaning supplies, pesticides,
building materials, household products, refineries, and mobile sources.

Given some of the indoor sources, concentrations of many VOCs may be much higher indoors than outdoors (Jia et al., 2007; Bruno et al., 2008). Gasoline and diesel fuels are also important sources of VOCs. Exposure to many of these organic contaminants
has also been associated with a wide array of toxic health effects.

3 METHODS

Schools and Classrooms Characteristics
Three elementary schools (all located in Southern Los Angeles County in the Carson-Long Beach area) were selected for th is pilot study. Two of them, Del Amo Elementary and Dominguez Elementary, are part of the Los Angeles Unified School District (LAUSD), while the third school, Hudson Elementary, is part of the Long Beach Unified School District (LBUSD). All three schools are in close proximity to at least three large refineries and several heavily trafficked highways and freeways including the I-110, I-405, I-710
, and CA-103 (Figure 1). The Los Angeles and Long Beach Port complexes and the Union Pacific Railroad Intermodal Container Transfer Facility (UPRR ICTF) are other major emissions sources in the area . The presence of these important emissions sources has lead to local concerns about the air quality in the surrounding communities.

Figure 1. Map of the study area as obtained from Google Earth (Google Inc. 1400 Amphitheatre Pkwy, Mountain View, CA 94043 ). The yellow circles indicate the locations of the three elementary schools participating in this pilot study: Del Amo (A), Hudson (B), and Dominguez (C). The Union Pacific Railroad Intermodal Container Transfer Facility is marked by the black rectangle
C
B
A
4 mi
CC
BB
AA
4 mi4 mi
4 At each of the three elementary schools, three classrooms with similar structural characteristics and ventilation conditions were selected to provide reproducible test conditions for the various air purification systems deployed. All classrooms (varying between 7533 and 9196 ft 3 in size) already included forced-air HVAC systems, although windows and doors were regularly used for additional ventilation. The most relevant characteristics of all nine classrooms are listed in Table 1, along with their respectiv e identification numbers.

Table 1.

Structural characteristics and ventilation conditions of the nine classrooms selected for this pilot study

Prior to beginning this study, none of the selected classrooms featured any
specific air purification device other than one or more medium performance panel filters (MERV 7) installed inside the respective HVAC systems. The typical replacement interval for these air filters is approximately three months according to schools schedules.

The primary purpose of this panel filter is to remove coarser particles and dust to protect the HVAC system's heating and cooling coils. These filters generally provide little or no removal of smaller particles or gaseous pollutants.

SCHOOL

DEL AMO HUDSON DOMINGUEZ
Classroom ID DA-6 DA-7 DA-8 H-11 H-15 H-52 DZ-7 DZ-9 DZ-11 Total Number of Occupants 18 19 22 21 11 17 28 28 29
Room Size (ft) 38×24×10 38×24×10 38×24×10 30×30×9 30×30×9 31×27×9 38×22×11 38×22×11 38×22×11 Room Volume (ft3) 9120 9120 9120 8100 8100 7533 9196 9196 9196 HVAC System Type DW-M* DW-M* DW-M* DM-ZR** DM-ZR** DR*** DR*** DR*** DR*** HVAC Panel Filter Type 2" Pleated 2" Pleated 2" Pleated 2" Pleated 2" Pleated 2" Fiberglass 1" Pleated 1" Pleated 1" Pleated Filter Rating MERV 7 MERV 7 MERV 7 MERV 7 MERV 7 Unclassified MERV 7 MERV 7 MERV 7 HVAC Operation Manual Manual Manual Automatic Automatic Manual Manual Manual Manual Number of Supply Vents 1 1 1 1 1 4 3 3 3
Supplied Airflow# (cfm) 1200 1200 1250 840 903 1236 1642 1681 1772 Air Exchange Rate 7.9 7.9 8.2 6.2 6.7 9.8 10.7 11.0 11.5 *DW-M = Ducted Wall-Mount
**DM-ZR = Ducted Multi-Zone Rooftop
***DR = Ducted Rooftop
#With existing panel filter
5 Air Purification Solutions
Three different air purification solutions were tested for their ability to remove UFP, PM2.5, PM10, BC and, where possible, VOCs from the air stream:

a) an HVAC-based high-performance panel filter (HP-PF),

b) a register
-based air purifier (here referred to as register system or RS), and

c) a standalone system (SA).

All air purification solutions were provided, installed, and maintained by IQAir, and their primary features are summarized in Table 2.

Table 2. Summary of the primary features of the three air purification devices adopted for this pilot study: high-performance panel filter (HP-PF), register system (RS), and standalone system (SA)

High-performance Register Standalone
Panel Filter Sysyem System
(PF) (RS) (SA)
High UFP and PM2.5 Filtration Efficiency √ √ √
High Gas Phase Filtration Efficiency 0 √ √
Low Pressure Drop / High Air Flow √ √ √
Low Noise √ √ √
Low Maintenance √ √ √
High Classroom Compatibility √ √ √
No HVAC System Retrofit √ 0 √
Minimal Impact on Classroom Space √ √ √
Low Power Consumption N/A N/A √
Tamper-Resistant Design N/A N/A √
√ = featured
0 = not featured
6 High-performance panel filter (HP-PF)
In most classrooms, the existing medium performance panel filters were replaced with one or more HP-PFs as shown to in Figure 2.

Figure 2.

Schematic of a typical HVAC system. The picture on the right-hand side shows a typical high-performance panel filter (HP-PF) after several months of usage

Compared to standard/conventional medium performance MERV filters, the high-
performance panel filters used for this pilot study are twice as thick (2” in depth) and have a much larger filter surface area (five to nine times larger). Due to the increased surface area and the special filter material used, they generally have similar air resistance properties as conventional filters and,
thus, do not act to reduce the air flow through the
HVAC system. Also, due to the increased surface area and specific design, these media have the potential to last longer than conventional filters before replacement is required.

Because these filters are manufactured using a proprietary “nano-fiber” technology, their ability to remove UFPs and BC from the air stream is also higher. Table 3 shows a comparison between the characteristics of several conventional MERV filters available for residential and commercial applications and the HP-PF employed in this pilot study.

7 Table 3. Comparison between the main characteristics of several conventional MERV filters and the high-performance panel filters (HP-PF) tested in this study

Panel Filter Filter Pressure Media Filter Filter Annual Annual Total Annual Type Rating Drop (in w.g.)2 Area (ft2) Life (months) Cost ($) Filter Cost ($) Maintenance Cost ($)3 Cost ($) at 0.3 µm at 1.0 µm

CONVENTIONAL PANEL FILTERS
Low Efficiency
2" Fiberglass
Medium Efficiency
1" Pleated
Medium Efficiency
2" Pleated
High Efficiency
2" Pleated
High Efficiency
2" Pleated
High Efficiency
2" Mini-Pleat

PILOT STUDY HIGH-PERFORMANCE PANEL FILTER
High-performance
2" Mini-Pleat
Data are based on a nominal 24" × 24" filter size
1Typical minimum efficiency at rated face velocity of 492 fpm 2Typical pressure drop of a new filter; based on a face velocity of 492 fpm 3Based on an estimated maintenance time of 15 min per filter change (at $50/hr) MERV 16
1 30
90 Efficiency (%)1
(unrated)
MERV 7
MERV 7
10 0.28 4.0 3 3 to 5
MERV 11
MERV 13
12 to 20 50 62 to 70
3 25 0.48 7.5 3 5 to 7 20 to 28 50 70 to 78
5 35 0.30 11.8 3 7 to 10 28 to 40 50 78 to 90
15 58 0.39 17.8 3 13 to 20 52 to 80 50 102 to 130
100 to 160 50 150 to 21085 0.41 21.1 3
MERV 16 93 99 0.38
80 32099 2.00 55.0 3
13 to 25 133 to 245
Filter

60.0 6 to 12 120 120 to 240
50 370
25 to 40
8 Register system (RS)
This device is installed directly on the HVAC register, where the air supply enters the room. The unit is equipped with a “nano-technology” filter media for the removal of PM and high-capacity gas phase filter cartridges to eliminate certain gaseous pollutants from the air stream (e.g. VOCs ) (Figure 3). This particular design allows for a longer contact time between the filtration media and the gaseous pollutants than would be permitted by using an activated carbon panel filter in the HVAC system. Nevertheless, the RS does not reduce the overall HVAC system airflow if installed by a trained specialist.

Figure 3
. Schematic of the register system (RS) as installed in one of the study classrooms. A high-performance panel filter (HP-PF) may also be installed in the HVAC air handler to
provide additional particle filtration

9 Standalone system (SA)
A standalone system ( SA) is a self-contained air cleaning device that operates independently of a classroom’s HVAC system. This air filtration system is 6 f eet tall and has a footprint of about 4 ft2 (Figure 4). The SA is tamper proof, runs on a standard power circuit, and is built with an energy efficient fan, located inside a specially designed box for ultra quiet operation (<45 db(A) at high airflow). Indoor air enters from the lower part of the system (about 6 inches off the ground) and passes, sequentially, through a large “nano-technology” filter media, for the removal of PM,
and 12 high -capacity gas phase
filter cartridges, for removal of the gaseous pollutants commonly found indoors (VOCs) (Figure 4).

Figure 4.

Schematic of the standalone system (SA) as installed in one of the classrooms

The main characteristics of the SA tested in this pilot study have been summarized in Table 4 and compared to those of other typical “residential” and “commercial” standalone units available on the market. A major design consideration for the SA was low noise. Many school districts have set a 45db(A) noise threshold for new in classroom equipment. At this noise level, available residential and commercial air purification devices offer less than two air changes per hour (ACH) in a typical classroom. This SA unit offers more than five ACH.

10 Table 4. Comparison between the main features of the standalone system used for this pilot study and those of other commercially available standalone air purifiers

In-classroom configurations
Different combinations of the standalone system, HVAC-based high-performance panel filter, and register-based air purifier were u sed inside the studied classrooms to evaluate the performance of these air filtration devices:

1. High-performance panel filter alone: HP-PF

2. Register-based air purifier alone ( RS). It should be noted that in some cases a conventional / medium performance panel filter (PF) was already installed inside the HVAC system prior to the beginning of the study: RS+PF

3. Register-based air purifier in conjunction with a h igh-performance panel filter:

RS + HP-PF

4. Standalone system in classrooms with no HVAC running: SA

5. Standalone system in classrooms with a HVAC running, in which case a conventional / medium performance panel filter ( PF) was already installed inside the HVAC system prior to the beginning of the study: SA + PF

6. Standalone system in conjunction with a high-performance panel filter : SA + HP- PF

A schematic representation of these
six configurations is shown below (Figure 5).

Residential Commercial Pilot Study
Air Purifier Standalone Standalone
Particle Filtration Technology Electronic / Media Electronic / Media Media Removal Efficiency at 0.3 µm (%) 40 to 99 60 to 99 > 99 Maximum Airflow (cfm) 150 to 400 400 to 1200 1200
Airflow at 45 dB(A) (cfm) 25 to 100 100 to 200 800
Gas-phase Filtration Media (lb) 0.5 to 18 10 to 80 100
Price ($) 200 to 1,000 1,500 to 12,000 8,500
Price / CFM at 45 db(A) ($) 8 to 10 15 to 60 11
Classroom ACH at 45 db(A)* 0.2 to 0.7 0.7 to 1.3 5.3
*Air Changes per Hour (ACH) based on a 9000 ft3 room
11 Figure 5
. Schematic representation of the six air purification solutions tested in this pilot study

Register-
based air
purifier (RS)
Standalone
system
(SA)
HVAC-based high-
performance panel
filter (HP-PF)
Conventional /
medium efficiency
panel filter (PF)
12 Indoor and outdoor measurements
Four mobile air quality monitoring stations were used to measure the indoor and outdoor concentrations of the targeted air pollutants. Each of these stations was comprised of a mobile cart supporting the following instruments (Figure 6):

 A portable Aethalometer (model AE42, Magee Scientific, 2800 Adeline St., Berkeley CA 94703) to provide continuous measurements of BC concentrations
(ng/m3)
 A water-based condensation particle counter (CPC model 3781 , TSI, 500 Cardigan Road, Shoreview, MN 55126) to provide continuous measurements of the particle number concentration (#/cm 3), an indicator of
UFPs
 A laser particle counter (IQAir ParticleScan Pro): for determining the number concentration (#/cm 3) of particles down to 0.3 μm in diameter .

Since the PM2.5 particle mass concentration in urban areas tends to be dominated by particles in the 0.3 – 1.0 μm range, this instrument provides a rough estimate of the PM2.5 mass.
 A laser-based particle mass monitor (Aerocet 531 Aerosol Particulate Profiler, MetOne; 1600 Washington Blvd., Grants Pass, Oregon 97526): to provide continuous measurements of the mass concentration (µg/m 3) of both PM2.5 and PM10
 A low volume filter sampler (SKC Leland Legacy Sample Pump with SKC DPS Impactor, 863 Valley View Road Eighty Four, PA 15330): to collect time-integrated filter-based PM10 samples. Samples were collected at 10L/min on 47mm Teflon filters for the duration of a typical school day.

These substrates were weighed before and after collection using a microbalance, and the PM 10 concentration (µg/m 3) was calculated by dividing the difference in PM10 mass by the corresponding sampling volume. These gravimetric measurements were considered as primary indicators of the PM10 mass.
 6L EPA TO-15 SUMMA canisters: to collect time-integrated air samples over the course of a typical school day. Samples were then analyzed by gas chromatography-mass spectrometry (GC-MS) to measure the concentrations
of 61 specific VOCs (ppbv).

13 Figure 6. One of the four mobile stations used to monitor the indoor and outdoor concentrations of the targeted air pollutants

At each school, one air quality monitoring cart was set-up outside to sample outdoor air. The remaining three stations were placed indoors, one in each classroom, near one of the walls and just a few meters away from the students. Measurements were made away from all air conditioning vents to better represent mixed indoor air quality conditions as experienced by students and teachers. All sensors and inlets were approximately three f eet above the floor, or about the height of a child’s head when seated.

The effectiveness of each of the tested air purification solutions was then evaluated by comparing the indoor concentrations of the targeted air pollutants to the corresponding outdoor levels. Baseline measurements were taken b efore installing any of
the air purification solutions to estimate the pre-existing removal efficiencies of the classrooms before modification
. Measurements that were found to be inaccurate or
unrepresentative due to meteorological conditions (e.g. rain), improper cart placement, or instrument malfunction were not considered in the
data analysis.

Before and after school hours, the four measurement stations were collocated in a storage room and the continuous instruments were run “side- by-side” to provide quality assurance of the measurements, to estimate the precision characteristics, and to identify any potential problems. Table 5 shows
the specific air purification solutions that were
tested inside each of the nine classrooms, along with the dates when all baseline and actual measurements were taken.

14 Table 5. Summary of the air purification solutions tested in each of the nine classrooms.

The dates when all baseline and actual measurements were taken are also included

The three schools were tested one at a time from April to December 2008 for a total of over 15 0 valid measurement days across all schools and classrooms. The period of sampling was during regularly scheduled school hours, with minor adjustments for school schedule changes.

RESULTS AND DISCUSSION

Removal of PM and other particle species
Figure 7a summarizes the study average particle removal efficiencies (here defined as the percentage reduction in the indoor concentration of a particular pollutant relative to its concurrent outdoor concentration) achieved by the six air purification solutions. Indoor and outdoor mass and particle number concentrations wer e averaged
over the duration of a typical school day and across all days, classrooms and schools. The corresponding study average particle removal efficiencies for each elementary school are shown in Figures 7b
, 7c, and 7d for Del Amo, Hudson and Dominguez, respectively.

Daily and weekly average indoor and outdoor concentrations of BC, UFP, PM 2.5 and PM10 at all schools and classrooms are provided in APPENDIX A, along with the corresponding average indoor/outdoor ratios and removal efficiencies.

Overall, the combination of a register system and a high-performance panel filter (RS + HP-PF) was the most effective solution for reducing the indoor concentrations of BC, UFP, and PM 2.5 (both mass and particle count), with average removal efficiencies varying from 87 to 96% (Figure 7a) . Replacing a conventional HVAC-based panel filter (PF) with a HP-PF resulted in a substantial reduction in the indoor levels of all particulate pollutants inside all classrooms, especially when this high-performance panel filter was operated
in conjunction with other air filtration devices. When using the HP-PF alone, the study average removal efficiencies were also close to 90% (88, 86, 91, and 88%, for BC, School / Class ID
04 / 07-11 / 08 04 / 14-18 / 08 04 / 21-25 / 08 04 / 28 / 08 to 05 / 02 / 08 Del Amo / DA-6 Baseline SA + PF SA + PF SA + HP-PF
Del Amo / DA-7 Baseline RS RS RS
Del Amo / DA-8 Baseline HP-PF HP-PF HP-PF
05 / 12-16 / 08 05 / 19-23 / 08 05 / 26-30 / 08 06 / 02-06 / 08 Hudson / H-11 Baseline HP-PF RS + HP-PF RS + HP-PF
Hudson / H-15 Baseline HP-PF RS + HP-PF RS + HP-PF
Hudson / H-52 Baseline HP-PF RS + HP-PF RS + HP-PF
11 / 18-26 / 08 12 / 01-05 / 08 12 / 08-12 / 08 12 / 15-19 / 08 Dominguez / DZ-7 Baseline SA / SA + PF SA + HP-PF SA + HP-PF Dominguez / DZ-9 Baseline HP-PF HP-PF HP-PF
Dominguez / DZ-11 Baseline HP-PF RS + HP-PF RS + HP-PF
HP-PF = HVAC-based high-performance panel filter
RS = register-based air purifier
SA = stand alone system
PF = conventional / medium efficiency panel filter
Configurations Used
15 UFP, PM2.5 count, and PM 2.5 mass, respectively). These average values are significantly higher than baseline (pre-existing) conditions, when removal efficiencies for the different pollutants were only about 20-50%.

ALL CLASSROOMS AT ALL SCHOOLS
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%Removal Efficiency
Baseline
SA + PF
SA + HP-PF
RS
RS + HP-PF
HP-PF
BC (µg/m3)
UFP (#/cm3)
PM2.5 (#/cm3)
PM2.5 (µg/m3)
PM10 (µg/m3)*
* From gravimetric / filter measurements
The PM10 concentration was higher indoors than outdoors due to indoor sources

16 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Removal Efficiency

DEL AMO ELEMENTARY SCHOOL
Baseline
SA + PF
SA + HP-PF
RS
HP-PF
BC (µg/m3)
UFP (#/cm3)
PM2.5 (#/cm3)
PM2.5 (µg/m3)
PM10 (µg/m3)*
**
* From gravimetric / filter measurements ** With HVAC The PM10 concentration was higher indoors than outdoors due to indoor sources

b)
17 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Removal Efficiency

HUDSON ELEMENTARY SCHOOL
Baseline
RS + HP-PF
HP-PF
BC (µg/m3)
UFP (#/cm3)
PM2.5 (#/cm3)
PM2.5 (µg/m3)
PM10 (µg/m3)*
* From gravimetric / filter measurements
c)
18 Figure 7
. Particle removal efficiencies (%) achieved by the six air purification solutions.

Bars indicate data averaged a) at all schools and in all classrooms, b) at Del Amo, c) at Hudson, and d) at Dominguez

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Removal Efficiency

DOMINGUEZ ELEMENTARY SCHOOL
Baseline
SA
SA + PF
SA + HP-PF
RS + HP-PF
HP-PF
BC (µg/m3)
UFP (#/cm3)
PM2.5 (#/cm3)
PM2.5 (µg/m3)
PM10 (µg/m3)*
**
***
* From gravimetric / filter measurements ** Without HVAC *** With HVAC The PM10 concentration was higher indoors than outdoors because of indoor sources

d)
19 In all cases, air quality conditions were improved substantially with respect to the corresponding baseline measurements. The intra-classroom variability of the measured removal efficiencies was low , as indicated by the low standard deviations given in Table 6a. This reflects the fact that all air purification solutions were highly effective at all schools and in all classrooms, as confirmed by the particle removal performance data for each of the three elementary schools in Tables 6b (Del Amo), 6c (Hudson) and 6d (Dominguez).

The stand-alone system (SA) is well suited for indoor environments not equipped with an HVAC. In order to simulate conditions similar to those encountered in older classrooms not equipped with a forced air climate control device, the HVAC in room DZ- 7 (at Dominguez) was intentionally turned off for part of the study. When the SA unit was running with the HVAC off,
removal efficiencies were close to 90% for BC, UFP
and PM 2.5 (count) (Table 6d). For BC and UFP, these percentages were slightly lower when the HVAC was running since more of the small er particles (mostly unfiltered by the existing conventional panel filter) were entering the classrooms from outdoors.

Overall, our results confirmed that conventional HVAC panel filters are not particularly effective in removing UFP,
although they can be effective in removing coarser particles.

20 Table 6. Particle removal efficiencies (%) achieved by the six air purification solutions.

Data represent averages a) at all schools and in all classrooms, b) at Del Amo, c) at Hudson, and d) at Dominguez

It should be noted that the negative removal efficiencies associated with several baseline PM10 measurements indicate conditions where indoor concentrations were higher than the corresponding outdoor levels. This is likely due to re-suspension of dust and other relatively large particles caused by in-classroom activities such as walking and cleaning. Due to the presence of these indoor sources, the removal performance of PM 10 was
lower than that of other particle measurements.
a)
Study
days (#)
Baseline 48 22 ± 13 52 ± 17 45 ± 14 37 ± 26 -67 ± 156 13 ± 36 SA + PF** 14 67 ± 6 77 ± 6 79 ± 5 75 ± 5 17 ± 71 59 ± 9 SA + HP-PF 11 91 ± 6 93 ± 4 90 ± 3 82 ± 12 49 ± 16 53 ± 33 RS + PF 15 74 ± 20 81 ± 10 79 ± 17 69 ± 24 31 ± 55 22 ± 46 RS + HP-PF 35 95 ± 2 96 ± 3 93 ± 5 87 ± 11 68 ± 11 42 ± 28 HP-PF 35 88 ± 5 86 ± 7 91 ± 4 88 ± 8 54 ± 25 53 ± 31
b)
Study
days (#)
Baseline 15 8 ± 9 45 ± 16 18 ± 20 27 ± 17 -224 ± 278 26 ± 26 SA* N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A SA + PF** 10 52 ± 7 68 ± 6 60 ± 7 64 ± 5 29 ± 102 51 ± 9 SA + HP-PF 5 90 ± 5 92 ± 3 93 ± 1 91 ± 4 84 ± 11 74 ± 11 RS + PF 15 74 ± 20 81 ± 10 79 ± 17 69 ± 24 31 ± 55 22 ± 46 RS + HP-PF N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A HP-PF 15 88 ± 4 87 ± 4 89 ± 5 89 ± 5 58 ± 28 62 ± 13
c)
Study
days (#)
Baseline 15 33 ± 9 56 ± 18 46 ± 11 74 ± 5 64 ± 28 54 ± 23 SA* N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A SA + PF** N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A SA + HP-PF N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A RS + PF N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A RS + HP-PF 27 96 ± 2 98 ± 2 94 ± 4 94 ± 5 67 ± 8 51 ± 30 HP-PF 15 92 ± 2 91 ± 4 93 ± 2 93 ± 4 68 ± 19 59 ± 33
d)
Study
days (#)
Baseline 18 24 ± 21 54 ± 16 70 ± 11 11 ± 55 -40 ± 161 -42 ± 60 SA* 3 90 ± 4 94 ± 2 92 ± 6 75 ± 10 0 ± 34 31 ± 42
SA + PF** 4 82 ± 5 86 ± 5 97 ± 2 86 ± 4 4 ± 40 66 ± 8
SA + HP-PF 6 91 ± 6 94 ± 4 87 ± 5 72 ± 20 13 ± 20 32 ± 55 RS + PF N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A N/A ± N/A RS + HP-PF 8 94 ± 2 94 ± 3 91 ± 6 80 ± 17 69 ± 14 33 ± 25 HP-PF 18 85 ± 8 81 ± 13 91 ± 5 81 ± 16 35 ± 28 39 ± 48
Note: Negative removal efficiencies indicate the presence of an indoor source of PM10 1From gravimetric / filter measurements
2Using a particle mass monitor
*The HVAC system was turned off
**Operated in conjunction with a standard (MERV 7) panel filter installed in the HVAC system

HUDSON ELEMENTARY SCHOOL

DOMINGUEZ ELEMENTARY SCHOOL
BC (%) UFP (%)

ALL CLASSROOMS AND ALL SCHOOLS

DEL AMO ELEMENTARY SCHOOL
BC (%) UFP (%) PM2.5 count (%) PM2.5 mass (%)
PM10 PM10
mass monitor (%)2
PM10
UFP (%)BC (%) PM2.5 count (%) PM2.5 mass (%)
gravimetric mass (%)1
PM10
gravimetric mass (%)1 mass monitor (%)2
BC (%) UFP (%) PM2.5 count (%) PM2.5 mass (%)
PM10 PM10
gravimetric mass (%)1 mass monitor (%)2
PM2.5 count (%) PM2.5 mass (%)
PM10 PM10
gravimetric mass (%)1 mass monitor (%)2
21 Figure 8 illustrates the effect of indoor activities on in-classroom PM10 levels at Hudson Elementary School (Room H -15) on May 21, 2008. On this day removal efficiencies approached 100% before the school day started and during lunchtime (when students and staff members were outside the classroom) and were substantially lower when classes were in session.

Figure 8
. Effect of indoor activities on the removal performance of PM 10 at Hudson elementary school (Room H-15) on May 21, 2008

Activities occurring immediately outside the school boundaries were observed to influence the indoor concentrations of some pollutants and, thus, the ir corresponding removal efficiencies. Figure 9 shows the effect of increased motor-vehicle emissions due to the morning drop-off of students (grey areas) on the outdoor concentrations of BC, and the associated spikes in indoor BC levels occurring just before the beginning of the school day, when the classroom doors were left open. Overall, these indoor peaks caused a relatively small
decrease in the calculated removal performance when averaged over the c
ourse of the entire school day.

0 +50
+100
-50
-100
-150
-200
0 +50
+100
-50
-100
-150
-200
Removal Efficiency (%)
Recess
Students out of class
Lunch
Recess
Carts
moving
Time
Class duration

EFFECT OF INDOOR ACTIVITIES ON THE
REMOVAL EFFICIENCY OF PM10
22 Figure 9
. Effect of before scho ol activities on BC concentrations. Grey areas show an increase in both indoor and outdoor levels due to morning drop-off traffic

0 500
1000
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4.5 4.0

3.5 3.0

2.5 2.0

1.5 1.0

0.5 0.0
BC (µg/m3)

EFFECT OF PRE-SCHOOL ACTIVITIES ON

BC CONCENTRATIONS
Outdoors H-11 H-15 H-52
23 Impact on the HVAC system airflow
As discussed earlier, the high-performance panel filters (HP-PF) used for this pilot study are thicker than standard/conventional medium performance MERV filters.

However, due to their increased surface area and proprietary “nano-fiber” design, they generally have similar air resistance properties as conventional filters and, thus, do not
reduce the airflow through the HVAC system.

As shown in Table 7 , replacing a conventional panel filter (PF; typically 1” in depth) with a thicker high-performance panel filter (HP-PF; 2” deep) did not alter the measured airflow in any of the studied classrooms. Adding a register system without upgrading to a high-performance panel filter (see the RS-PF configuration data below) reduced the HVAC system airflow by an average of 9%. This small reduction is due to the increased pressure drop resulting from the addition of a gas-phase filtration media.

Using a register system while also upgrading to a high-performance panel filter (RS + HP-PF configuration in Table 7
) altered the airflow by only 1-3%. At Hudson elementary school, installation of the register system in classrooms H-11 and H -15 required a widening of the connection to the supply duct. This caused an airflow increase between 17 and 24%.

Table 7.

Effect of a high-performance panel filter (HP-PF) and/or a register system (RS) on the HVAC system airflow

Airflow (cfm) Change (%) Airflow (cfm) Change (%) Airflow (cfm) Change (%) Baseline 1200 1200 1250
HP-PF 1210 1 N/A N/A 1250 0
RS + PF N/A N/A 1090 -9 N/A N/A
Airflow (cfm) Change (%) Airflow (cfm) Change (%) Airflow (cfm) Change (%) Baseline 840 903 1236
HP-PF 844 0 913 1 1246 1
RS + HP-PF 1039 24 1054 17 1194 -3
Airflow (cfm) Change (%) Airflow (cfm) Change (%) Airflow (cfm) Change (%) Baseline 1642 1681 1722
HP-PF 1661 1 1664 -1 1771 3
RS + HP-PF N/A N/A N/A N/A 1742 1

DEL AMO ELEMENTARY SCHOOL

HUDSON ELEMENTARY SCHOOL

DOMINGUEZ ELEMENTARY SCHOOL
DA-6 DA-7 DA-8
H-11 H-15 H-52
DZ-7 DZ-9 DZ-11
24 Removal of VOCs
Although canister samples were collected at all schools and classrooms, and all samples were analyzed for VOCs, the data recovery at Del Amo and Hudson was insufficient to guarantee an adequate interpretation of the results. The detection limits of the analysis method used at those schools were not low enough to quantify most of the VOCs of interest. After the analysis methods were modified to correct for this problem, reliable VOC data were obtained for Dominguez elementary. Therefore, only VOC data from Dominguez are discussed in this section. Table 8
summarizes the removal
efficiencies for:

 Total VOCs : expressed as the sum of 61 individual compounds and 53 unspeciated organic compounds
 Ethanol: a chemical emitted from both indoor and outdoor evaporative sources  Benzene: a species mostly emitted from gasoline-powered vehicles. This compound was used here as an indicator of VOCs of outdoor origin

Daily average concentrations of individual VOCs measured at Dominguez elementary school (i.e. DZ-7, DZ-9, and DZ-11) are given in APPENDIX B.

Table 8
. Average removal efficiencies of total VOCs, ethanol, and benzene at Dominguez elementary school

Large standard deviations reflect the wide concentration ranges for the different chemicals. As expected, existing and high -performance panel filters (PF and HP-PF, respectively) had virtually no effect on the VOC levels measured indoors, since these air filtration media did not include gas removal capabilities. The standalone system (SA) demonstrated a 52 to 73% removal performance for benzene.

At all three schools, the indoor concentrations of ethanol were consistently the highest among all measured VOCs and higher than outdoor levels. This organic Study
Days (#)
Baseline 18 -114 ± 731 -1230 ± 982 -11 ± 22
SA (HVAC off)* 3 15 ± 132 -349 ± 276 52 ± 35
SA + PF (HVAC on)** 4 19 ± 198 -587 ± 903 58 ± 33
SA + HP-PF 6 -6 ± 280 -929 ± 853 73 ± 11
RS N/A N/A ± N/A N/A ± N/A N/A ± N/A
RS + HP-PF 8 -3 ± 345 -534 ± 502 58 ± 49
HP-PF 18 -64 ± 404 -1111 ± 1164 1 ± 38
1Sum of 61 known VOCs and 53 unspeciated organic compounds *Operated with the HVAC system turned off
**Operated with the HVAC system turned on

DOMINGUEZ ELEMENTARY SCHOOL
Total VOCs (%)1 Ethanol (%) Benzene (%)
25 compound is a common solvent used in whiteboard markers, detergents and other cleaning products, and has several potential indoor sources. The negative removal efficiencies shown in Table 8 indicate that the indoor concentrations of some VOCs were often higher tha n the corresponding outdoor levels. Our findings are in line with those from previous research studies (Jia et al., 2007; Bruno et al., 2008), and con firm that several measured indoor VOCs are mostly of indoor origin. For this reason, a direct comparison of indoor and outdoor total VOC concentrations is not appropriate when significant indoor sources exist.

Therefore, classroom DZ-9, whose air conditioning system was equipped with a HP-PF and no gas phase filtration device,
was used as the “baseline” (rather than the
outdoor monitoring site) to better evaluate the actual effectiveness of the standalone unit (SA) and the register system (RS) installed in classrooms DZ-7 and DZ-11, respectively (Table 9
). When compared to the control classroom (DZ-9), the removal efficiencies for total VOCs in classrooms DZ-7 and DZ-11 showed a reduction in gaseous pollutants with respect to
baseline conditions.

Table 9.

Average removal efficiencies of total VOCs with respect to a control classroom (DZ-9) not equipped with any gas phase filtration device. All data refer to measurements taken at Dominguez elementary school

Removal efficiencies corresponding to baseline measurements indicate that the total VOC concentration inside the two
test rooms (DZ-7 and DZ-11) were, on average,
31% higher than that in the control classroom (DZ-9), probably because of differences in indoor activities (e.g. cleaning). Assuming this difference persisted throughout the entire duration of the study, the actual VOC removal performance of the register system (RS) was
about 28% (-3% + 31%). Similarly, when normalizing for the initial conditions in the control classroom, the removal efficiencies of the standalone (SA) unit operated with and without the use of the HVAC system were about 58% (27% + 31%) and 86% (55% + 31%), respectively.

Classroom Study
Comparison1 Days (#)
Baseline DZ-7 & DZ-11 vs DZ-9 14 -31 ± 367
RS DZ-11 vs DZ-9 10 -3 ± 521
SA (HVAC off)* DZ-7 vs DZ-9 2 55 ± 50
SA + PF (HVAC on)** DZ-7 vs DZ-9 8 27 ± 198
1DZ-9 = "control classroom" (HP-PF but no gas-phase filtration) 2Sum of 61 known VOCs and 53 unspeciated organic compounds *Operated with the HVAC system turned off
**Operated with the HVAC system turned on
Total VOCs (%)2
(removal efficiency with respect to classroom DZ-9)

DOMINGUEZ ELEMENTARY SCHOOL
26 Overall, these solutions demonstrated some ability to reduce VOCs indoors , although not as consistently or effectively as the particle filtration. This may be due to the presence of one or more indoor sources of gaseous pollutants. The removal performance of gas-absorbing media (as opposed to filtration substrates) is dependent on media history and may be subject to saturation after experiencing high short-term concentrations or after longer-term use. Therefore, the
lifetime, cost, benefits, and maintenance of the gas
removal media must be further assessed before conclusions and recommendations can be made.

ACKNOWLEDGEMENTS
This pilot study was funded through the use of mitigation fees collected by the South Coast Air Quality Management District (SCAQMD) under Rule 1172 for VOC releases by local refineries. AQMD is the air pollution control agency for all of Orange County and urban portions of Los Angeles, Riverside and San Bernardino counties, the smoggiest region of the United States. IQAir North America, Inc., a leading specialist in air filtration solutions for homes, hospitals and schools, and Thermal Comfort Systems, specialist in HVAC system design, were
selected by SCAQMD through a competitive bid
process to provide for the design, engineering and installation of the air filtration devices used for this work
.

27 REFERENCES

Bruno, P., Caselli, M., De Gennaro, G., Iacobellis, S., and Tutino, M. (2008) “Monitoring of volatile organic compounds in non-residential indoor environments”, Indoor Air, 18 (3): 250-256

Environmental Protection Agency Integrated Science Assessments (External Review Draft; 2009).
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201805

Jia, C., Batterman, S. and Godwin, C. (2007) “VOCs in industrial, urban and suburban neighborhoods, Part 1: Indoor and outdoor concentrations, variation, and risk drivers”, Atmospheric Environment, 42 (9): 2083-2100

MATES III Study, 2008 (http://www.aqmd.gov/prdas/matesIII/matesIII.html)

Pope, C. A., and Dockery, D. W. (2006) "Health effects of fine particulate air pollution:

Lines that connect", Journal of the Air & Waste Management Association, 56:
709-742

28 APPENDIX A. Daily and weekly average indoor and outdoor concentrations of black carbon (BC), ultra-fine particles (UFP), fine particulate matter (PM2.5) and coarse PM (PM10) at all schools and classrooms. The corresponding average indoor / outdoor ratios and removal efficiencies are also included. Missing data (mostly due to instrument malfunction) and periods affected by rain have been highlighted in yellow. The air purification solutions adopted in each classroom have been summarized below each Table

Del Amo Elementary School - Black Carbon
Average Outdoor Average Indoor Concentration (ng/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (ng/m3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 1,611 1,392 1,490 1,465 0.86 0.92 0.91 14% 8% 9% 4/8/2008 948 902 1,094 887 0.95 1.15 0.94 5% -15% 6%
4/9/2008 1,119 1,166 1,147 1,044 1.04 1.03 0.93 -4% -3% 7% 4/10/2008 1,692 1,518 1,495 1,500 0.90 0.88 0.89 10% 12% 11% 4/11/2008 4,451 3,547 3,665 3,651 0.80 0.82 0.82 20% 18% 18% Average (Week 1) 1,964 1,705 1,778 1,709 0.91 0.96 0.90 9% 4% 10% Standard Deviation 1,426 1,056 1,071 1,117 0.09 0.13 0.05 9% 13% 5% 4/14/2008 3,688 1,802 383 410 0.49 0.10 0.11 51% 90% 89% 4/15/2008 1,128 595 851 93 0.53 0.75 0.08 47% 25% 92%
4/16/2008 1,353 824 703 333 0.61 0.52 0.25 39% 48% 75%
4/17/2008 4,392 2,301 1,656 435 0.52 0.38 0.10 48% 62% 90% 4/18/2008 3,387 1,752 1,061 254 0.52 0.31 0.07 48% 69% 93% Average (Week 2) 2,789 1,455 931 305 0.53 0.41 0.12 47% 59% 88% Standard Deviation 1,462 718 475 138 0.04 0.24 0.07 4% 24% 7% 4/21/2008 1,409 537 171 105 0.38 0.12 0.07 62% 88% 93%
4/22/2008 2,396 1,097 414 265 0.46 0.17 0.11 54% 83% 89% 4/23/2008 1,180 498 226 125 0.42 0.19 0.11 58% 81% 89%
4/24/2008 1,691 734 362 193 0.43 0.21 0.11 57% 79% 89%
4/25/2008 3,261 1,377 455 278 0.42 0.14 0.09 58% 86% 91% Average (Week 3) 1,987 848 326 193 0.42 0.17 0.10 58% 83% 90% Standard Deviation 846 379 122 79 0.03 0.04 0.02 3% 4% 2% 4/28/2008 3,789 209 349 375 0.06 0.09 0.10 94% 91% 90%
4/29/2008 1,908 135 269 279 0.07 0.14 0.15 93% 86% 85%
4/30/2008 1,077 104 108 127 0.10 0.10 0.12 90% 90% 88%
5/1/2008 1,055 191 156 160 0.18 0.15 0.15 82% 85% 85%
5/2/2008 3,338 292 1,899 505 0.09 0.57 0.15 91% 43% 85% Average (Week 4) 2,233 186 556 289 0.10 0.21 0.13 90% 79% 87% Standard Deviation 1,272 73 756 156 0.05 0.20 0.02 5% 20% 2% Date

School / Class ID
04 / 07-11 / 08 04 / 14-18 / 08 04 / 21-25 / 08 04 / 28 / 08 to 05 / 02 / 08 Del Amo / DA-6 Baseline SA + PF SA + PF SA + HP-PF
Del Amo / DA-7 Baseline RS RS RS
Del Amo / DA-8 Baseline HP-PF HP-PF HP-PF
HP-PF = HVAC-based high-performance panel filter
RS = register-based air purifier
SA = stand alone system
PF = conventional / medium efficiency panel filter
Configurations Used

29 Del Amo Elementary School - Ultra Fine Particles
Average Outdoor Average Indoor Concentration (particles/cm3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/cm3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 34,674 25,215 0.73 27%
4/8/2008 39,291 15,555 17,984 14,386 0.40 0.46 0.37 60% 54% 63% 4/9/2008 19,124 11,354 18,384 12,960 0.59 0.96 0.68 41% 4% 32% 4/10/2008 41,814 19,800 21,327 19,463 0.47 0.51 0.47 53% 49% 53% 4/11/2008 42,613 19,833 22,452 19,935 0.47 0.53 0.47 53% 47% 53% Average (Week 1) 35,503 16,635 21,072 16,686 0.48 0.64 0.49 52% 36% 51% Standard Deviation 9,665 4,054 2,996 3,533 0.08 0.21 0.13 8% 21% 13% 4/14/2008 53,086 16,017 6,724 7,303 0.30 0.13 0.14 70% 87% 86% 4/15/2008 7,878 8,865 10,233
4/16/2008 35,591 14,757 14,140 8,932 0.41 0.40 0.25 59% 60% 75% 4/17/2008 55,384 13,945 12,367 5,628 0.25 0.22 0.10 75% 78% 90% 4/18/2008 35,185 14,434 11,992 4,979 0.41 0.34 0.14 59% 66% 86% Average (Week 2) 44,812 13,406 10,818 7,415 0.34 0.27 0.16 66% 73% 84% Standard Deviation 10,923 3,184 2,974 2,201 0.08 0.12 0.06 8% 12% 6% 4/21/2008 57,526 20,259 6,007 5,267 0.35 0.10 0.09 65% 90% 91% 4/22/2008 46,241 13,552 8,158 7,011 0.29 0.18 0.15 71% 82% 85% 4/23/2008 34,366 9,741 5,891 3,039 0.28 0.17 0.09 72% 83% 91% 4/24/2008 38,854 10,831 7,090 5,171 0.28 0.18 0.13 72% 82% 87% 4/25/2008 33,004 8,965 4,695 2,794 0.27 0.14 0.08 73% 86% 92% Average (Week 3) 41,998 12,670 6,368 4,656 0.30 0.16 0.11 70% 84% 89% Standard Deviation 10,101 4,585 1,312 1,751 0.03 0.03 0.03 3% 3% 3% 4/28/2008 40,429 2,179 4,967 5,287 0.05 0.12 0.13 95% 88% 87% 4/29/2008 57,136 3,963 7,457 7,819 0.07 0.13 0.14 93% 87% 86% 4/30/2008 30,692 1,909 2,347 3,136 0.06 0.08 0.10 94% 92% 90% 5/1/2008 37,507 4,076 4,677 3,640 0.11 0.12 0.10 89% 88% 90% 5/2/2008 34,214 3,845 12,424 4,961 0.11 0.36 0.14 89% 64% 86% Average (Week 4) 39,996 3,194 6,374 4,968 0.08 0.16 0.12 92% 84% 88% Standard Deviation 10,249 1,058 3,836 1,827 0.03 0.11 0.02 3% 11% 2% Date

30 Del Amo Elementary School - PM2.5 (count)
Average Outdoor Average Indoor Concentration (particles/ft3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/ft3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 1,425,737
4/8/2008 1,129,234 860,458 818,779 859,380 0.76 0.73 0.76 24% 27% 24% 4/9/2008 606,772 589,396 761,753 0.97 1.26 3% -26%
4/10/2008 878,513 621,944 654,570 0.71 0.75 29% 25%
4/11/2008 1,657,318 1,007,993 1,686,712 1,092,355 0.61 1.02 0.66 39% -2% 34% Average (Week 1) 1,067,959 1,098,063 929,208 842,014 0.69 0.86 0.86 31% 14% 14% Standard Deviation 447,095 293,205 515,070 186,681 0.11 0.16 0.27 11% 16% 27% 4/14/2008 1,510,925 608,865 153,470 164,656 0.40 0.10 0.11 60% 90% 89% 4/15/2008 1,448,473 675,560 901,449 87,792 0.47 0.62 0.06 53% 38% 94% 4/16/2008 1,448,590 823,550 755,949 363,943 0.57 0.52 0.25 43% 48% 75% 4/17/2008 2,375,182 935,700 625,960 216,222 0.39 0.26 0.09 61% 74% 91% 4/18/2008 3,303,699 1,068,499 835,426 206,160 0.32 0.25 0.06 68% 75% 94% Average (Week 2) 2,017,374 822,435 654,451 207,755 0.43 0.35 0.11 57% 65% 89% Standard Deviation 819,499 187,458 298,268 100,893 0.09 0.21 0.08 9% 21% 8% 4/21/2008 1,117,692 445,613 132,034 89,866 0.40 0.12 0.08 60% 88% 92% 4/22/2008 1,962,746 721,027 258,062 184,328 0.37 0.13 0.09 63% 87% 91% 4/23/2008 1,677,902 639,840 235,809 136,928 0.38 0.14 0.08 62% 86% 92% 4/24/2008 1,606,064 565,163 258,425 167,180 0.35 0.16 0.10 65% 84% 90% 4/25/2008 1,649,781 558,423 189,268 127,409 0.34 0.11 0.08 66% 89% 92% Average (Week 3) 1,602,837 586,013 214,719 141,142 0.37 0.13 0.09 63% 87% 91% Standard Deviation 305,266 102,511 54,125 36,669 0.02 0.02 0.01 2% 2% 1% 4/28/2008 1,284,388 94,732 159,555 165,842 0.07 0.12 0.13 93% 88% 87% 4/29/2008 2,011,522 121,487 205,529 272,593 0.06 0.10 0.14 94% 90% 86% 4/30/2008 1,367,829 108,012 120,466 153,098 0.08 0.09 0.11 92% 91% 89% 5/1/2008 143,394 155,073
5/2/2008 791,947 275,170
Average (Week 4) 1,554,580 108,077 284,178 204,355 0.07 0.10 0.13 93% 90% 87% Standard Deviation 397,917 13,378 285,555 63,660 0.01 0.02 0.01 1% 2% 1% Date

31 Del Amo Elementary School - PM2.5 (mass)
Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 8.74 10.50 6.68 6.51 1.20 0.76 74% -20% 24% 26% 4/8/2008 14.25 10.54 7.04 0.74 0.49 26% 51%
4/9/2008 8.61 5.07 6.43 6.05 0.59 0.75 0.70 41% 25% 30% 4/10/2008 7.48 6.53 5.73 4.55 0.87 0.77 0.61 13% 23% 39% 4/11/2008 7.24 3.89 5.94 4.73 0.54 0.82 0.65 46% 18% 35% Average (Week 1) 9.27 6.50 7.06 5.77 0.80 0.77 0.64 20% 23% 36% Standard Deviation 2.86 2.88 1.98 1.10 0.31 0.03 0.10 31% 3% 10% 4/14/2008 10.14 3.61 1.93 0.90 0.36 0.19 0.09 64% 81% 91% 4/15/2008 15.06 4.75 12.40 1.15 0.32 0.82 0.08 68% 18% 92% 4/16/2008 12.49 5.44 9.75 3.31 0.44 0.78 0.26 56% 22% 74% 4/17/2008 10.60 5.21 0.62 0.49 0.06 51% 94%
4/18/2008 8.25 3.76 3.68 1.05 0.46 0.45 0.13 54% 55% 87% Average (Week 2) 11.31 4.39 6.59 1.41 0.39 0.55 0.12 61% 45% 88% Standard Deviation 2.58 0.86 4.35 1.08 0.07 0.26 0.08 7% 26% 8% 4/21/2008 10.99 3.56 2.28 0.81 0.32 0.21 0.07 68% 79% 93% 4/22/2008 11.03 3.65 1.82 1.31 0.33 0.17 0.12 67% 83% 88% 4/23/2008 8.59 2.81 2.54 1.18 0.33 0.30 0.14 67% 70% 86% 4/24/2008 12.72 4.27 2.74 1.70 0.34 0.22 0.13 66% 78% 87% 4/25/2008 7.09 2.44 1.31 0.52 0.34 0.19 0.07 66% 81% 93% Average (Week 3) 10.08 3.35 2.14 1.10 0.33 0.21 0.11 67% 79% 89% Standard Deviation 2.23 0.73 0.58 0.45 0.01 0.05 0.03 1% 5% 3% 4/28/2008 5.61 0.69 1.05 0.34 0.12 0.19 0.06 88% 81% 94% 4/29/2008 17.88 0.87 1.61 1.79 0.05 0.09 0.10 95% 91% 90% 4/30/2008 14.50 1.35 1.25 1.87 0.09 0.09 0.13 91% 91% 87% 5/1/2008 12.95 1.78 1.17 1.62 0.14 0.09 0.13 86% 91% 88% 5/2/2008 14.08 0.75 5.79 2.10 0.05 0.41 0.15 95% 59% 85% Average (Week 4) 13.00 1.09 2.17 1.55 0.09 0.17 0.11 91% 83% 89% Standard Deviation 4.52 0.46 2.03 0.69 0.04 0.14 0.03 4% 14% 3% Date

32 Del Amo Elementary School - PM10 (from particle mass monitor measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 35.78 35.22 28.42 21.97 0.98 0.79 61% 2% 21% 39% 4/8/2008 45.40 55.07 23.04 1.21 0.51 -21% 49%
4/9/2008 28.99 20.88 24.58 23.40 0.72 0.85 0.81 28% 15% 19% 4/10/2008 29.55 34.72 22.50 18.44 1.18 0.76 0.62 -18% 24% 38% 4/11/2008 41.44 11.06 25.65 20.46 0.27 0.62 0.49 73% 38% 51% Average (Week 1) 36.23 25.47 31.25 21.46 0.79 0.85 0.61 21% 15% 39% Standard Deviation 7.22 11.68 13.49 2.04 0.39 0.22 0.13 39% 22% 13% 4/14/2008 37.29 16.79 31.87 11.31 0.45 0.85 0.30 55% 15% 70% 4/15/2008 49.49 18.09 81.10 17.11 0.37 1.64 0.35 63% -64% 65% 4/16/2008 43.89 27.44 75.71 22.75 0.63 1.72 0.52 37% -72% 48% 4/17/2008 43.78 48.13 11.29 1.10 0.26 -10% 74%
4/18/2008 33.84 19.71 38.90 16.90 0.58 1.15 0.50 42% -15% 50% Average (Week 2) 41.66 20.51 55.14 15.87 0.51 1.29 0.38 49% -29% 62% Standard Deviation 6.14 4.77 22.09 4.79 0.12 0.37 0.12 12% 37% 12% 4/21/2008 45.13 17.94 31.35 14.58 0.40 0.69 0.32 60% 31% 68% 4/22/2008 39.96 23.99 20.92 18.86 0.60 0.52 0.47 40% 48% 53% 4/23/2008 33.54 14.53 32.46 18.54 0.43 0.97 0.55 57% 3% 45% 4/24/2008 40.68 21.02 26.43 21.74 0.52 0.65 0.53 48% 35% 47% 4/25/2008 35.52 15.42 18.02 10.66 0.43 0.51 0.30 57% 49% 70% Average (Week 3) 38.96 18.58 25.83 16.88 0.48 0.67 0.44 52% 33% 56% Standard Deviation 4.56 3.94 6.33 4.31 0.08 0.19 0.12 8% 19% 12% 4/28/2008 5.61 0.69 1.05 0.34 0.12 0.19 0.06 88% 81% 94% 4/29/2008 49.89 12.13 17.43 13.57 0.24 0.35 0.27 76% 65% 73% 4/30/2008 55.49 19.75 18.17 23.04 0.36 0.33 0.42 64% 67% 58% 5/1/2008 44.69 17.16 17.06 21.40 0.38 0.38 0.48 62% 62% 52% 5/2/2008 52.56 9.90 32.02 19.66 0.19 0.61 0.37 81% 39% 63% Average (Week 4) 41.65 11.93 17.14 15.60 0.26 0.37 0.32 74% 63% 68% Standard Deviation 20.53 7.40 10.97 9.25 0.11 0.15 0.16 11% 15% 16% Date

33 Del Amo Elementary School - PM10 (from filter-based measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 DA-6 DA-7 DA-8 4/7/2008 11 72 44 80 6.55 4.00 727% -555% -300% -627%
4/8/2008 15 72 22 4.80 1.47 -380% -47%
4/9/2008 66 60 11
4/10/2008 53 29 65
4/11/2008 64 47 29 43 0.73 0.45 0.67 27% 55% 33%
Average (Week 1) 30 60 47 44 3.64 3.08 3.14 -264% -208% -214% Standard Deviation 30 12 19 29 4.11 2.31 3.60 411% 231% 360% 4/14/2008 83 87 150 11 1.05 1.81 0.13 -5% -81% 87%
4/15/2008 61 25 76 52 0.41 1.25 0.85 59% -25% 15%
4/16/2008 84 41 71 42 0.49 0.85 0.50 51% 15% 50%
4/17/2008 85 20 61 32 0.24 0.72 0.38 76% 28% 62%
4/18/2008 73 11 29 53 0.15 0.40 0.73 85% 60% 27%
Average (Week 2) 77 37 77 38 0.47 1.00 0.52 53% 0% 48%
Standard Deviation 10 30 45 17 0.35 0.54 0.28 35% 54% 28% 4/21/2008 100 71 31 11 0.71 0.31 0.11 29% 69% 89%
4/22/2008 14 49 26 14 3.50 1.86 1.00 -250% -86% 0%
4/23/2008 110 11 46 21 0.10 0.42 0.19 90% 58% 81%
4/24/2008 61 20 33 43 0.33 0.54 0.70 67% 46% 30%
4/25/2008 73 12 29 21 0.16 0.40 0.29 84% 60% 71%
Average (Week 3) 72 33 33 22 0.96 0.70 0.46 4% 30% 54%
Standard Deviation 38 26 8 13 1.44 0.65 0.38 144% 65% 38% 4/28/2008 88 11 59 30 0.13 0.67 0.34 88% 33% 66%
4/29/2008 780 14 25 36 0.02 0.03 0.05 98% 97% 95%
4/30/2008 67 12 12 22 0.18 0.18 0.33 82% 82% 67%
5/1/2008 63 21 42 28 0.33 0.67 0.44 67% 33% 56%
5/2/2008 86 12 28 28 0.14 0.33 0.33 86% 67% 67%
Average (Week 4) 217 14 33 29 0.16 0.37 0.30 84% 63% 70% Standard Deviation 315 4 18 5 0.11 0.29 0.15 11% 29% 15% Date

34 Hudson Elementary School - Black Carbon
Average Outdoor Average Indoor Concentration (ng/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (ng/m3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 889 557 643 666 0.63 0.72 0.75 37% 28% 25%
5/13/2008 1,765 983 1,155 1,208 0.56 0.65 0.68 44% 35% 32% 5/14/2008 1,906 1,031 1,297 1,616 0.54 0.68 0.85 46% 32% 15% 5/15/2008 3,632 2,026 2,597 2,903 0.56 0.72 0.80 44% 28% 20% 5/16/2008 3,756 2,163 2,486 2,771 0.58 0.66 0.74 42% 34% 26% Average (Week 1) 2,390 1,352 1,636 1,833 0.57 0.69 0.76 43% 31% 24% Standard Deviation 1,253 704 863 978 0.03 0.03 0.06 3% 3% 6% 5/19/2008 2,007 78 97 194 0.04 0.05 0.10 96% 95% 90%
5/20/2008 1,066 74 71 96 0.07 0.07 0.09 93% 93% 91%
5/21/2008 1,344 104 75 111 0.08 0.06 0.08 92% 94% 92%
5/22/2008 903 95 67 82 0.11 0.07 0.09 89% 93% 91%
5/23/2008 731 68 71 73 0.09 0.10 0.10 91% 90% 90%
Average (Week 2) 1,210 84 76 111 0.08 0.07 0.09 92% 93% 91% Standard Deviation 499 15 12 49 0.03 0.02 0.01 3% 2% 1% 5/26/2008
5/27/2008 1,028 63 72 26 0.06 0.07 0.03 94% 93% 97%
5/28/2008 778 59 58 19 0.08 0.07 0.02 92% 93% 98%
5/29/2008 1,098 37 53 23 0.03 0.05 0.02 97% 95% 98%
5/30/2008 1,140 35 41 27 0.03 0.04 0.02 97% 96% 98%
Average (Week 3) 1,011 48 56 24 0.05 0.06 0.02 95% 94% 98% Standard Deviation 162 15 13 4 0.02 0.02 0.00 2% 2% 0%
6/2/2008 1,128 45 36 0.04 0.03 96% 97%
6/3/2008 1,495 50 69 33 0.03 0.05 0.02 97% 95% 98%
6/4/2008 1,106 55 63 18 0.05 0.06 0.02 95% 94% 98%
6/5/2008 1,320 60 58 65 0.05 0.04 0.05 95% 96% 95%
6/6/2008 2,046 51 52 38 0.03 0.03 0.02 97% 97% 98%
Average (Week 4) 1,419 54 57 38 0.04 0.04 0.03 96% 96% 97% Standard Deviation 384 5 9 17 0.01 0.01 0.01 1% 1% 1%
Date

School / Class ID
05 / 12-16 / 08 05 / 19-23 / 08 05 / 26-30 / 08 06 / 02-06 / 08 Hudson / H-11 Baseline HP-PF RS + HP-PF RS + HP-PF
Hudson / H-15 Baseline HP-PF RS + HP-PF RS + HP-PF
Hudson / H-52 Baseline HP-PF RS + HP-PF RS + HP-PF
HP-PF = HVAC-based high-performance panel filter
RS = register-based air purifier
SA = stand alone system
PF = conventional / medium efficiency panel filter
Configurations Used

35 Hudson Elementary School - Ultra Fine Particles
Average Outdoor Average Indoor Concentration (particles/cm3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/cm3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 14,643 4,083 5,190 8,433 0.28 0.35 0.58 72% 65% 42% 5/13/2008 40,865 10,619 9,421 19,956 0.26 0.23 0.49 74% 77% 51% 5/14/2008 47,145 11,563 14,730 19,466 0.25 0.31 0.41 75% 69% 59% 5/15/2008 44,862 18,397 27,694 29,422 0.41 0.62 0.66 59% 38% 34% 5/16/2008 38,322 10,435 24,291 29,818 0.27 0.63 0.78 73% 37% 22% Average (Week 1) 37,167 11,019 16,265 21,419 0.29 0.43 0.58 71% 57% 42% Standard Deviation 13,049 5,083 9,577 8,792 0.07 0.18 0.14 7% 18% 14% 5/19/2008 38,368 1,835 2,732 5,456 0.05 0.07 0.14 95% 93% 86% 5/20/2008 17,442 755 2,099 2,033 0.04 0.12 0.12 96% 88% 88% 5/21/2008 80,163 6,255 4,714 10,681 0.08 0.06 0.13 92% 94% 87% 5/22/2008 27,886 1,381 1,291 3,353 0.05 0.05 0.12 95% 95% 88% 5/23/2008 20,524 1,792 1,367 3,214 0.09 0.07 0.16 91% 93% 84% Average (Week 2) 36,877 2,404 2,440 4,947 0.06 0.07 0.13 94% 93% 87% Standard Deviation 25,505 2,197 1,401 3,434 0.02 0.03 0.02 2% 3% 2% 5/26/2008
5/27/2008 50,891 763 1,295 793 0.01 0.03 0.02 99% 97% 98% 5/28/2008 36,964 452 458 594 0.01 0.01 0.02 99% 99% 98% 5/29/2008 40,035 367 435 572 0.01 0.01 0.01 99% 99% 99% 5/30/2008 57,760 456 566 1,006 0.01 0.01 0.02 99% 99% 98% Average (Week 3) 46,413 510 689 741 0.01 0.01 0.02 99% 99% 98% Standard Deviation 9,639 174 408 203 0.00 0.01 0.00 0% 1% 0% 6/2/2008 35,495 430 426 0.01 0.01 99% 99%
6/3/2008 32,336 700 432 0.02 0.01 98% 99%
6/4/2008 18,941 1,656 393 346 0.09 0.02 0.02 91% 98% 98% 6/5/2008 39,083 53 570 3,727 0.00 0.01 0.10 100% 99% 90% 6/6/2008 43,572 609 607 950 0.01 0.01 0.02 99% 99% 98%
Average (Week 4) 33,885 773 540 1,176 0.03 0.02 0.03 97% 98% 97% Standard Deviation 9,343 814 127 1,446 0.05 0.00 0.04 5% 0% 4% Date

36 Hudson Elementary School - PM2.5 (count)
Average Outdoor Average Indoor Concentration (particles/ft3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/ft3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 1,109,627 576,204 511,734 718,130 0.52 0.46 0.65 48% 54% 35% 5/13/2008 1,850,803 757,977 897,986 1,156,730 0.41 0.49 0.62 59% 51% 38% 5/14/2008 1,760,128 682,498 838,534 1,199,580 0.39 0.48 0.68 61% 52% 32% 5/15/2008 1,839,611 767,375 1,011,002 1,191,460 0.42 0.55 0.65 58% 45% 35% 5/16/2008 1,128,564 525,461 723,482 781,768 0.47 0.64 0.69 53% 36% 31% Average (Week 1) 1,537,747 661,903 796,548 1,009,534 0.44 0.52 0.66 56% 48% 34% Standard Deviation 383,828 108,096 190,066 238,576 0.05 0.07 0.03 5% 7% 3% 5/19/2008 660,607 44,930 61,168 59,352 0.07 0.09 0.09 93% 91% 91% 5/20/2008 1,477,586 68,443 70,820 131,786 0.05 0.05 0.09 95% 95% 91% 5/21/2008 1,613,826 103,883 73,924 125,633 0.06 0.05 0.08 94% 95% 92% 5/22/2008 1,530,791 106,300 69,737 94,795 0.07 0.05 0.06 93% 95% 94% 5/23/2008 987,855 77,470 71,330 70,589 0.08 0.07 0.07 92% 93% 93% Average (Week 2) 1,254,133 80,205 69,396 96,431 0.07 0.06 0.08 93% 94% 92% Standard Deviation 412,015 25,650 4,850 32,203 0.01 0.02 0.01 1% 2% 1% 5/26/2008
5/27/2008 440,181 52,467 51,042 26,303 0.12 0.12 0.06 88% 88% 94% 5/28/2008 362,533 66,769 46,541 18,906 0.18 0.13 0.05 82% 87% 95% 5/29/2008 369,467 33,173 50,616 17,735 0.09 0.14 0.05 91% 86% 95% 5/30/2008 529,995 35,119 28,628 23,916 0.07 0.05 0.05 93% 95% 95% Average (Week 3) 425,544 46,882 44,207 21,715 0.11 0.11 0.05 89% 89% 95% Standard Deviation 77,973 15,843 10,582 4,067 0.05 0.04 0.01 5% 4% 1% 6/2/2008 1,472,339 38,432 36,347 0.03 0.02 97% 98%
6/3/2008 2,102,152 56,800 81,009 39,991 0.03 0.04 0.02 97% 96% 98% 6/4/2008 1,346,575 64,055 66,975 19,669 0.05 0.05 0.01 95% 95% 99% 6/5/2008 1,167,940 77,692 56,657 29,352 0.07 0.05 0.03 93% 95% 97% 6/6/2008 998,499 67,890 40,091 24,490 0.07 0.04 0.02 93% 96% 98% Average (Week 4) 1,417,501 66,609 56,633 29,970 0.05 0.04 0.02 95% 96% 98% Standard Deviation 422,678 8,703 18,070 8,331 0.02 0.01 0.00 2% 1% 0% Date

37 Hudson Elementary School - PM2.5 (mass)
Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 8.19 2.68 2.55 1.82 0.33 0.31 0.22 67% 69% 78% 5/13/2008 13.38 3.36 3.62 3.02 0.25 0.27 0.23 75% 73% 77% 5/14/2008 14.65 2.55 3.74 4.09 0.17 0.25 0.28 83% 75% 72% 5/15/2008 20.11 3.60 5.52 4.92 0.18 0.27 0.24 82% 73% 76% 5/16/2008 14.66 3.13 4.81 4.41 0.21 0.33 0.30 79% 67% 70% Average (Week 1) 14.20 3.06 4.05 3.65 0.23 0.29 0.25 77% 71% 75% Standard Deviation 4.25 0.45 1.15 1.23 0.06 0.03 0.03 6% 3% 3% 5/19/2008 7.86 0.56 0.84 0.04 0.07 0.11 0.01 93% 89% 99% 5/20/2008 6.19 1.00 0.82 0.28 0.16 0.13 0.05 84% 87% 95% 5/21/2008 14.18 1.37 0.79 0.28 0.10 0.06 0.02 90% 94% 98% 5/22/2008 29.03 2.09 1.00 0.93 0.07 0.03 0.03 93% 97% 97% 5/23/2008 17.46 1.32 1.20 0.29 0.08 0.07 0.02 92% 93% 98% Average (Week 2) 14.95 1.27 0.93 0.37 0.10 0.08 0.02 90% 92% 98% Standard Deviation 9.11 0.56 0.17 0.33 0.04 0.04 0.02 4% 4% 2% 5/26/2008
5/27/2008 3.12 0.92 0.91 0.10 0.30 0.29 0.03 70% 71% 97% 5/28/2008 2.34 1.48 0.88 0.63 0.38 37% 62%
5/29/2008 3.36 0.47 0.84 0.07 0.14 0.25 0.02 86% 75% 98% 5/30/2008 4.65 0.54 0.59 0.17 0.12 0.13 0.04 88% 87% 96% Average (Week 3) 3.37 0.86 0.81 1.64 0.30 0.26 0.03 70% 74% 97% Standard Deviation 0.96 0.46 0.15 3.05 0.24 0.10 0.01 24% 10% 1% 6/2/2008 12.73 0.49 0.45 0.04 0.04 96% 96%
6/3/2008 15.40 1.09 1.07 0.31 0.07 0.07 0.02 93% 93% 98% 6/4/2008 9.62 1.43 1.10 0.00 0.15 0.11 0.00 85% 89% 100% 6/5/2008 12.63 1.72 1.16 0.22 0.14 0.09 0.02 86% 91% 98% 6/6/2008 16.85 1.25 0.79 0.17 0.07 0.05 0.01 93% 95% 99% Average (Week 4) 13.45 1.38 0.92 0.23 0.11 0.07 0.02 89% 93% 98% Standard Deviation 2.80 0.27 0.28 0.17 0.04 0.03 0.01 4% 3% 1% Date

38 Hudson Elementary School - PM10 (from particle mass monitor measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 34.99 35.13 29.94 9.35 1.00 0.86 0.27 0% 14% 73% 5/13/2008 72.87 36.97 35.03 14.02 0.51 0.48 0.19 49% 52% 81% 5/14/2008 57.56 23.93 32.32 13.91 0.42 0.56 0.24 58% 44% 76% 5/15/2008 73.56 33.55 33.75 11.95 0.46 0.46 0.16 54% 54% 84% 5/16/2008 65.32 28.13 36.48 18.64 0.43 0.56 0.29 57% 44% 71% Average (Week 1) 60.86 31.54 33.50 13.57 0.56 0.58 0.23 44% 42% 77% Standard Deviation 15.86 5.38 2.52 3.41 0.25 0.16 0.05 25% 16% 5% 5/19/2008 44.62 22.67 31.37 3.21 0.51 0.70 0.07 49% 30% 93% 5/20/2008 29.90 32.03 32.45 7.32 1.07 1.09 0.24 -7% -9% 76% 5/21/2008 99.04 30.71 30.14 5.48 0.31 0.30 0.06 69% 70% 94% 5/22/2008 97.14 48.80 26.72 6.28 0.50 0.28 0.06 50% 72% 94% 5/23/2008 62.98 28.55 30.00 3.31 0.45 0.48 0.05 55% 52% 95% Average (Week 2) 66.74 32.55 30.14 5.12 0.57 0.57 0.10 43% 43% 90% Standard Deviation 30.94 9.77 2.15 1.82 0.29 0.34 0.08 29% 34% 8% 5/26/2008
5/27/2008 21.70 31.71 35.38 3.04 1.46 1.63 0.14 -46% -63% 86% 5/28/2008 18.66 37.53 29.44 2.01 1.58 -101% -58%
5/29/2008 24.67 20.97 32.20 3.62 0.85 1.31 0.15 15% -31% 85% 5/30/2008 28.26 22.01 18.07 6.19 0.78 0.64 0.22 22% 36% 78% Average (Week 3) 23.32 28.06 28.77 5.26 1.28 1.29 0.17 -28% -29% 83% Standard Deviation 4.10 7.96 7.54 2.38 0.58 0.46 0.04 58% 46% 4% 6/2/2008 47.55 23.85 10.14 0.50 0.21 50% 79%
6/3/2008 55.46 33.55 38.16 8.32 0.61 0.69 0.15 39% 31% 85% 6/4/2008 42.76 37.37 37.82 3.91 0.87 0.88 0.09 13% 12% 91% 6/5/2008 48.79 40.38 34.67 7.35 0.83 0.71 0.15 17% 29% 85% 6/6/2008 58.51 34.10 23.89 6.60 0.58 0.41 0.11 42% 59% 89% Average (Week 4) 50.61 36.35 31.68 7.27 0.72 0.64 0.14 28% 36% 86% Standard Deviation 6.33 3.17 7.26 2.30 0.15 0.19 0.05 15% 19% 5% Date

39 Hudson Elementary School - PM10 (from filter-based measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) H-11 H-15 H-52 H-11 H-15 H-52 H-11 H-15 H-52 5/12/2008 120 68 92 0.57 0.77 43% 23%
5/13/2008 110 46 20 0.42 0.18 58% 82%
5/14/2008 45 43 24
5/15/2008 130 42 26 0.00 0.20 100% 80%
5/16/2008 26 27
Average (Week 1) 120 46 35 42 0.33 0.18 0.48 67% 82% 52% Standard Deviation 10 17 13 33 0.29 0.40 29% 40%
5/19/2008 120 35 24 0.29 0.20 71% 80%
5/20/2008 78 29 61 0.37 0.78 63% 22%
5/21/2008 60 11 28 0.18 0.47 82% 53%
5/22/2008 130 39 29 0.30 0.22 70% 78%
5/23/2008 99 20 15 0.20 0.15 80% 85%
Average (Week 2) 97 27 46 31 0.27 0.36 73% 64%
Standard Deviation 29 11 19 17 0.08 0.26 8% 26%
5/26/2008
5/27/2008 210 31 53 0.15 0.25 85% 75%
5/28/2008 120 34 23 0.28 0.19 72% 81%
5/29/2008 99 11 35 0.11 0.35 89% 65%
5/30/2008 87 20 19 0.23 0.22 77% 78%
Average (Week 3) 129 24 90 33 0.19 0.25 81% 75%
Standard Deviation 56 11 34 15 0.08 0.07 8% 7%
6/2/2008 120 41 0.34 66%
6/3/2008 110 30 30 0.27 0.27 73% 73%
6/4/2008 82 27 41 0.33 0.50 67% 50%
6/5/2008 100 35 36 0.35 0.36 65% 64%
6/6/2008 120 27 39 0.23 0.33 78% 68%
Average (Week 4) 106 30 37 0.29 0.36 71% 64%
Standard Deviation 16 4 5 0.06 0.08 6% 8%
Date

40 Dominguez Elementary School - Black Carbon
Average Outdoor Average Indoor Concentration (ng/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (ng/m3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008 5,137 2,149 4,384 5,096 0.42 0.85 0.99 58% 15% 1% 11/19/2008 8,787 3,951 5,935 6,332 0.45 0.68 0.72 55% 32% 28% 11/20/2008 9,243 3,932 7,292 6,616 0.43 0.79 0.72 57% 21% 28% 11/21/2008 11,210 6,967 8,270 8,928 0.62 0.74 0.80 38% 26% 20% Average (Week 1) 8,594 4,250 6,470 6,743 0.48 0.76 0.81 52% 24% 19% Standard Deviation 2,533 1,999 1,688 1,599 0.10 0.08 0.13 10% 8% 13% 11/24/2008 4,474 2,918 4,828 4,903 0.65 1.08 1.10 35% -8% -10% 11/25/2008 5,234 4,005 4,944 5,166 0.77 0.94 0.99 23% 6% 1% 11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 4,854 3,462 4,886 5,035 0.71 1.01 1.04 29% -1% -4% Standard Deviation 538 768 82 186 0.08 0.10 0.08 8% 10% 8% 12/1/2008 8,642 2,023 2,996 2,744 0.23 0.35 0.32 77% 65% 68% 12/2/2008 2,434 268 217 187 0.11 0.09 0.08 89% 91% 92%
12/3/2008 4,351 557 1,024 444 0.13 0.24 0.10 87% 76% 90% 12/4/2008 3,953 819 354 267 0.21 0.09 0.07 79% 91% 93%
12/5/2008 5,734 766 856 346 0.13 0.15 0.06 87% 85% 94%
Average (Week 3) 5,023 887 1,089 798 0.16 0.18 0.12 84% 82% 88% Standard Deviation 2,340 671 1,117 1,092 0.05 0.11 0.11 5% 11% 11% 12/8/2008 2,112 290 269 105 0.14 0.13 0.05 86% 87% 95%
12/9/2008 5,452 549 816 311 0.10 0.15 0.06 90% 85% 94%
12/10/2008 2,819 136 351 249 0.05 0.12 0.09 95% 88% 91% 12/11/2008 9,169 515 892 222 0.06 0.10 0.02 94% 90% 98% 12/12/2008 4,670 342 687 444 0.07 0.15 0.10 93% 85% 90% Average (Week 4) 4,844 366 603 266 0.08 0.13 0.06 92% 87% 94% Standard Deviation 2,769 169 279 124 0.04 0.02 0.03 4% 2% 3% 12/15/2008
12/16/2008 3,274 209 432 187 0.06 0.13 0.06 94% 87% 94% 12/17/2008
12/18/2008 1,976 397 325 109 0.20 0.16 0.05 80% 84% 95% 12/19/2008 4,558 320 904 234 0.07 0.20 0.05 93% 80% 95% Average (Week 5) 3,269 308 554 177 0.11 0.16 0.05 89% 84% 95% Standard Deviation 1,291 95 308 63 0.08 0.03 0.00 8% 3% 0% Date

School / Class ID
11 / 18-26 / 08 12 / 01-05 / 08 12 / 08-12 / 08 12 / 15-19 / 08 Dominguez / DZ-7 Baseline SA / SA + PF SA + HP-PF SA + HP-PF Dominguez / DZ-9 Baseline HP-PF HP-PF HP-PF
Dominguez / DZ-11 Baseline HP-PF RS + HP-PF RS + HP-PF
HP-PF = HVAC-based high-performance panel filter
RS = register-based air purifier
SA = stand alone system
PF = conventional / medium efficiency panel filter
Configurations Used

41 Dominguez Elementary School - Ultra Fine Particles
Average Outdoor Average Indoor Concentration (particles/cm3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/cm3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008 25,476 5,422 12,344 12,550 0.21 0.48 0.49 79% 52% 51% 11/19/2008 42,651 9,488 19,540 24,676 0.22 0.46 0.58 78% 54% 42% 11/20/2008 39,794 10,924 24,111 21,528 0.27 0.61 0.54 73% 39% 46% 11/21/2008 38,976 12,874 18,814 22,150 0.33 0.48 0.57 67% 52% 43% Average (Week 1) 36,724 9,677 18,702 20,226 0.26 0.51 0.55 74% 49% 45% Standard Deviation 7,663 3,158 4,844 5,295 0.05 0.07 0.04 5% 7% 4% 11/24/2008 34,386 8,720 18,565 24,049 0.25 0.54 0.70 75% 46% 30% 11/25/2008 31,838 10,383 17,528 23,812 0.33 0.55 0.75 67% 45% 25% 11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 33,112 9,552 18,047 23,930 0.29 0.55 0.72 71% 45% 28% Standard Deviation 1,802 1,176 733 168 0.05 0.01 0.03 5% 1% 3% 12/1/2008 41,439 8,349 9,891 13,190 0.20 0.24 0.32 80% 76% 68% 12/2/2008 17,370 1,000 1,962 1,613 0.06 0.11 0.09 94% 89% 91% 12/3/2008 16,420 1,805 3,508 2,413 0.11 0.21 0.15 89% 79% 85% 12/4/2008 16,970 2,768 2,214 1,469 0.16 0.13 0.09 84% 87% 91% 12/5/2008 29,061 2,522 5,130 2,277 0.09 0.18 0.08 91% 82% 92% Average (Week 3) 24,252 3,289 4,541 4,192 0.12 0.17 0.14 88% 83% 86% Standard Deviation 10,957 2,911 3,244 5,046 0.06 0.05 0.10 6% 5% 10% 12/8/2008 16,048 1,148 9,995 440 0.07 0.62 0.03 93% 38% 97% 12/9/2008 34,610 2,241 4,785 1,755 0.06 0.14 0.05 94% 86% 95% 12/10/2008 32,657 817 5,299 2,790 0.03 0.16 0.09 97% 84% 91% 12/11/2008 29,250 1,146 3,203 812 0.04 0.11 0.03 96% 89% 97% 12/12/2008 23,839 1,262 4,040 2,750 0.05 0.17 0.12 95% 83% 88% Average (Week 4) 27,281 1,323 5,464 1,709 0.05 0.24 0.06 95% 76% 94% Standard Deviation 7,492 539 2,653 1,080 0.02 0.22 0.04 2% 22% 4% 12/15/2008
12/16/2008 26,441 1,066 4,072 1,209 0.04 0.15 0.05 96% 85% 95% 12/17/2008
12/18/2008 23,513 3,355 2,871 1,283 0.14 0.12 0.05 86% 88% 95% 12/19/2008 28,783 1,247 7,747 2,101 0.04 0.27 0.07 96% 73% 93% Average (Week 5) 26,246 1,889 4,897 1,531 0.08 0.18 0.06 92% 82% 94% Standard Deviation 2,641 1,272 2,540 495 0.06 0.08 0.01 6% 8% 1% Date

42 Dominguez Elementary School - PM2.5 (count)
Average Outdoor Average Indoor Concentration (particles/ft3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (particles/ft3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008
11/19/2008 8,866,226 1,411,242 2,600,108 3,571,960 0.16 0.29 0.40 84% 71% 60% 11/20/2008 8,237,390 1,286,754 3,015,586 2,734,509 0.16 0.37 0.33 84% 63% 67% 11/21/2008 7,625,250 1,471,170 2,634,126 3,393,425 0.19 0.35 0.45 81% 65% 55% Average (Week 1) 8,242,955 1,389,722 2,749,940 3,233,298 0.17 0.33 0.39 87% 75% 71% Standard Deviation 620,507 94,073 230,684 441,091 0.02 0.04 0.06 9% 17% 20% 11/24/2008 8,511,436 1,151,144 3,000,744 3,943,011 0.14 0.35 0.46 86% 65% 54% 11/25/2008 6,035,358 949,498 1,853,996 2,542,878 0.16 0.31 0.42 84% 69% 58% 11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 7,273,397 1,050,321 2,427,370 3,242,944 0.15 0.33 0.44 85% 67% 56% Standard Deviation 1,750,852 142,585 810,873 990,043 0.02 0.03 0.03 2% 3% 3% 12/1/2008 7,115,843 375,673 1,197,684 1,210,391 0.05 0.17 0.17 95% 83% 83% 12/2/2008 6,472,443 120,635 344,786 289,496 0.02 0.05 0.04 98% 95% 96% 12/3/2008 10,298,411 190,597 1,628,370 753,983 0.02 0.16 0.07 98% 84% 93% 12/4/2008 10,129,374 294,693 520,489 458,324 0.03 0.05 0.05 97% 95% 95% 12/5/2008 5,018,869 103,392 368,715 228,546 0.02 0.07 0.05 98% 93% 95% Average (Week 3) 7,806,988 216,998 812,009 588,148 0.03 0.10 0.08 97% 90% 92% Standard Deviation 2,325,563 116,301 573,363 403,128 0.01 0.06 0.05 1% 6% 5% 12/8/2008 1,539,967 207,455 112,870 0.13 0.07 87% 93%
12/9/2008 2,540,284 271,444 165,653 85,637 0.11 0.07 0.03 89% 93% 97% 12/10/2008 425,792 74,899 59,053 73,412 0.18 0.14 0.17 82% 86% 83% 12/11/2008 2,040,036 147,307 136,505 62,538 0.07 0.07 0.03 93% 93% 97% 12/12/2008 2,259,506 157,821 151,593 190,241 0.07 0.07 0.08 93% 93% 92% Average (Week 4) 1,761,117 171,785 125,135 102,957 0.11 0.08 0.08 89% 92% 92% Standard Deviation 831,458 73,119 41,798 58,950 0.04 0.03 0.07 4% 3% 7% 12/15/2008
12/16/2008 518,108 83,088 73,452 54,664 0.16 0.14 0.11 84% 86% 89% 12/17/2008
12/18/2008 148,282 83,984 1,067
12/19/2008 108,951 203,523 2,678
Average (Week 5) 518,108 113,440 120,320 19,470 0.16 0.14 0.11 84% 86% 89% Standard Deviation 32,828 72,249 30,490
Date

43 Dominguez Elementary School - PM2.5 (mass)
Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008 4.11 4.66 3.70 11.47 1.13 0.90 2.79 -13% 10% -179% 11/19/2008 13.52 9.93 6.52 10.89 0.73 0.48 0.81 27% 52% 19% 11/20/2008 30.59 10.79 14.26 12.27 0.35 0.47 0.40 65% 53% 60% 11/21/2008 16.15 11.71 8.78 11.07 0.72 0.54 0.69 28% 46% 31% Average (Week 1) 16.09 9.27 8.31 11.43 0.74 0.60 1.17 26% 40% -17% Standard Deviation 10.96 3.16 4.48 0.61 0.32 0.20 1.09 32% 20% 109% 11/24/2008 17.12 11.75 12.16 18.94 0.69 0.71 1.11 31% 29% -11% 11/25/2008 12.77 15.62 11.33 17.13 1.22 0.89 1.34 -22% 11% -34% 11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 14.95 13.69 11.74 18.04 0.95 0.80 1.22 5% 20% -22% Standard Deviation 3.07 2.73 0.58 1.28 0.38 0.13 0.17 38% 13% 17% 12/1/2008 17.46 3.27 5.87 3.81 0.19 0.34 0.22 81% 66% 78% 12/2/2008 9.45 2.08 0.87 0.87 0.22 0.09 0.09 78% 91% 91% 12/3/2008 35.25 3.24 8.29 2.21 0.09 0.24 0.06 91% 76% 94% 12/4/2008 27.93 4.50 1.85 1.27 0.16 0.07 0.05 84% 93% 95% 12/5/2008 13.35 1.84 1.71 1.22 0.14 0.13 0.09 86% 87% 91% Average (Week 3) 20.69 2.99 3.72 1.87 0.16 0.17 0.10 84% 83% 90% Standard Deviation 10.67 1.07 3.21 1.19 0.05 0.11 0.07 5% 11% 7% 12/8/2008 5.77 2.08 1.10 0.79 0.36 0.19 0.14 64% 81% 86% 12/9/2008 7.96 2.55 0.87 0.87 0.32 0.11 0.11 68% 89% 89% 12/10/2008 1.80 0.84 1.02 1.10 0.47 0.57 0.61 53% 43% 39% 12/11/2008 5.82 1.01 0.79 0.88 0.17 0.14 0.15 83% 86% 85% 12/12/2008 10.99 0.94 1.35 1.91 0.09 0.12 0.17 91% 88% 83% Average (Week 4) 6.47 1.49 1.03 1.11 0.28 0.23 0.24 72% 77% 76% Standard Deviation 3.37 0.78 0.22 0.46 0.15 0.20 0.21 15% 20% 21% 12/15/2008
12/16/2008 3.31 0.55 0.41 0.57 0.17 0.12 0.17 83% 88% 83% 12/17/2008
12/18/2008 1.29 0.73 0.75 0.14 0.57 0.58 0.11 43% 42% 89% 12/19/2008 6.18 0.59 1.48 0.68 0.10 0.24 0.11 90% 76% 89% Average (Week 5) 3.59 0.63 0.88 0.46 0.28 0.31 0.13 72% 69% 87% Standard Deviation 2.46 0.09 0.55 0.29 0.25 0.24 0.04 25% 24% 4% Date

44 Dominguez Elementary School - PM10 (from particle mass monitor measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008 51.98 64.34 34.38 91.83 1.24 0.66 1.77 -24% 34% -77% 11/19/2008 52.85 73.95 47.85 79.93 1.40 0.91 1.51 -40% 9% -51% 11/20/2008 84.85 62.75 69.89 85.40 0.74 0.82 1.01 26% 18% -1% 11/21/2008 61.53 69.08 58.16 72.86 1.12 0.95 1.18 -12% 5% -18% Average (Week 1) 62.81 67.53 52.57 82.51 1.12 0.83 1.37 -12% 17% -37% Standard Deviation 15.32 5.06 15.11 8.06 0.28 0.13 0.34 28% 13% 34% 11/24/2008 50.13 74.55 76.61 135.45 1.49 1.53 2.70 -49% -53% -170% 11/25/2008 48.79 98.76 88.63 128.96 2.02 1.82 2.64 -102% -82% -164% 11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 49.46 86.66 82.62 132.21 1.76 1.67 2.67 -76% -67% -167% Standard Deviation 0.95 17.12 8.49 4.59 0.38 0.20 0.04 38% 20% 4% 12/1/2008 86.09 27.81 54.38 37.73 0.32 0.63 0.44 68% 37% 56% 12/2/2008 25.06 26.79 16.38 15.78 1.07 0.65 0.63 -7% 35% 37% 12/3/2008 57.34 13.40 43.07 18.36 0.23 0.75 0.32 77% 25% 68% 12/4/2008 52.74 23.08 13.42 12.93 0.44 0.25 0.25 56% 75% 75% 12/5/2008 41.97 14.59 17.31 18.03 0.35 0.41 0.43 65% 59% 57% Average (Week 3) 52.64 21.13 28.91 20.56 0.48 0.54 0.41 52% 46% 59% Standard Deviation 22.45 6.76 18.58 9.84 0.34 0.20 0.15 34% 20% 15% 12/8/2008 28.57 21.91 19.27 18.93 0.77 0.67 0.66 23% 33% 34% 12/9/2008 39.04 23.09 11.40 17.01 0.59 0.29 0.44 41% 71% 56% 12/10/2008 21.52 11.43 16.69 18.02 0.53 0.78 0.84 47% 22% 16% 12/11/2008 44.75 11.02 12.31 15.96 0.25 0.28 0.36 75% 72% 64% 12/12/2008 46.70 11.24 18.51 24.48 0.24 0.40 0.52 76% 60% 48% Average (Week 4) 36.12 15.74 15.63 18.88 0.48 0.48 0.56 52% 52% 44% Standard Deviation 10.78 6.19 3.59 3.32 0.23 0.23 0.19 23% 23% 19% 12/15/2008
12/16/2008 15.49 8.83 8.94 14.83 0.57 0.58 0.96 43% 42% 4% 12/17/2008
12/18/2008 7.99 14.10 19.02 8.39 1.76 2.38 1.05 -76% -138% -5% 12/19/2008 27.12 10.76 23.70 14.98 0.40 0.87 0.55 60% 13% 45% Average (Week 5) 16.87 11.23 17.22 12.73 0.91 1.28 0.85 9% -28% 15% Standard Deviation 9.64 2.66 7.54 3.76 0.74 0.97 0.26 74% 97% 26% Date

45 Dominguez Elementary School - PM10 (from filter-based measurements) Average Outdoor Average Indoor Concentration (µg/m3) Average Indoor/Outdoor Ratio Average Removal Efficiency Concentration (µg/m3) DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 DZ-7 DZ-9 DZ-11 11/17/2008
11/18/2008 720 940 740 310 1.31 1.03 0.43 -31% -3% 57%
11/19/2008 290 100 250 160 0.34 0.86 0.55 66% 14% 45%
11/20/2008 33 220 150 38 6.67 4.55 1.15 -567% -355% -15% 11/21/2008 200 120 180 25 0.60 0.90 0.13 40% 10% 88%
Average (Week 1) 311 345 330 133 2.23 1.83 0.56 -123% -83% 44% Standard Deviation 293 400 277 133 2.99 1.81 0.43 299% 181% 43% 11/24/2008 180 220 200 180 1.22 1.11 1.00 -22% -11% 0%
11/25/2008 150 220 140 140 1.47 0.93 0.93 -47% 7% 7%
11/26/2008
11/27/2008
11/28/2008
Average (Week 2) 165 220 170 160 1.34 1.02 0.97 -34% -2% 3% Standard Deviation 21 0 42 28 0.17 0.13 0.05 17% 13% 5% 12/1/2008 180 280 180 82 1.56 1.00 0.46 -56% 0% 54%
12/2/2008 130 170 110 40 1.31 0.85 0.31 -31% 15% 69%
12/3/2008 150 110 120 36 0.73 0.80 0.24 27% 20% 76%
12/4/2008 140 120 90 36 0.86 0.64 0.26 14% 36% 74%
12/5/2008 130 91 87 41 0.70 0.67 0.32 30% 33% 68%
Average (Week 3) 146 154 117 47 1.03 0.79 0.32 -3% 21% 68% Standard Deviation 21 76 38 20 0.38 0.14 0.08 38% 14% 8% 12/8/2008 140 150 120 25 1.07 0.86 0.18 -7% 14% 82%
12/9/2008 130 100 100 30 0.77 0.77 0.23 23% 23% 77%
12/10/2008 91 95 100 34 1.04 1.10 0.37 -4% -10% 63%
12/11/2008 130 82 77 33 0.63 0.59 0.25 37% 41% 75%
12/12/2008 140 78 36 87 0.56 0.26 0.62 44% 74% 38%
Average (Week 4) 126 101 87 42 0.81 0.71 0.33 19% 29% 67% Standard Deviation 20 29 32 26 0.23 0.31 0.18 23% 31% 18% 12/15/2008
12/16/2008 91 84 70 30 0.92 0.77 0.33 8% 23% 67%
12/17/2008
12/18/2008 71 79 68 17 1.11 0.96 0.24 -11% 4% 76%
12/19/2008 93 75 73 23 0.81 0.78 0.25 19% 22% 75%
Average (Week 5) 85 79 70 23 0.95 0.84 0.27 5% 16% 73%
Standard Deviation 12 5 3 7 0.15 0.10 0.05 15% 10% 5%
Date

46 APPENDIX B. Daily average concentrations of individual VOCs measured outside Dominguez elementary school and inside three of its classrooms (here referred to as DZ- 7, DZ-9, and DZ-11)

Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 7.7 5.7 4.5 4.7 3.2
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.7 0.7 0.7 0.7 0.6 0.6 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.03 0.02 0.02 0.02 Chloromethane 0.5 0.08 0.6 0.7 0.6 0.6 0.6 0.6
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05 0.07 0.03
Ethanol 1.5 0.30 47 36 24 24 15 14
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 0.2
Isopropyl alcohol(2-Propanol) 1.5 0.03 7.7 5.4 2.1 4.9 2 1.9 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.07 0.06 0.06 0.07 0.06 0.06 Acetone 3.0 0.08 25 23 16 20 17 11
Carbon disulfide 1.0 0.04 0.04 0.06 0.03 0.04 0.04 0.01 Methylene chloride 1.5 0.04 1.2 1 0.6 1.1 0.6 0.4
Methyl-tert-butyl ether(MTBE) 0.5 0.05
trans-1,2-Dichloroethene 0.5 0.04
n-Hexane 0.5 0.03 1 1 1.2 1.2 0.5 0.4
1,-Dichloroethane 0.5 0.04
Vinyl acetate 0.5 0.05
2-Butanone(MEK) 1.5 0.07 1.8 3 3 1.9 1.8 0.9
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07 0.5 0.6 0.2 0.4 0.2 0.2
Chloroform 0.5 0.03 0.08 0.06 0.08 0.03 0.03
Tetrahydrofuran 0.5 0.07 0.2 0.2 0.3 0.3 0.2
1,1,1-Trichloroethane 0.5 0.04 0.02
Cyclohexane 0.5 0.04 0.7 0.6 0.6 0.7 0.2 0.2
Carbon tetrachloride 0.5 0.04 0.08 0.07 0.07 0.07 0.07 0.06 n-Heptane 0.5 0.04 0.9 0.7 0.7 0.8 0.4 0.3
1,2-Dichlroethane 0.5 0.02
Benzene 0.5 0.02 1.2 1.2 1 1.4 0.6 0.5
Trichloroethene 0.5 0.04 0.1 0.09 0.05 0.06 0.03 0.05
1,2-Dichloropropane 0.5 0.05
Bromodichloromethane 0.5 0.04 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.2 0.2 0.2 0.2 0.09 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 5.2 4.8 3.8 5.5 2.2 1.8
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.2 0.4 0.1
Tetrachloroethene 0.5 0.04 0.3 0.2 0.09 0.2 0.08 0.08
Dibromochloromethane 0.5 0.06 0.02 0.01
1,2-Dibromoethane 0.5 0.06
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.7 0.8 0.6 0.8 0.4 0.3
Xylene (para & meta) 1.0 0.10 2.3 2.2 1.9 2.6 1.2 1
Xylene (Ortho) 0.5 0.05 0.9 0.9 0.8 1 0.5 0.4
Styrene 0.5 0.05 1.4 0.7 0.8 0.4 0.9 0.2
Bromoform 0.5 0.05 0.01
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.8 0.8 0.6 0.9 0.4 0.3
1,3,5-Trimethylbenzene 0.5 0.05 0.2 0.2 0.3 0.1 0.09
1,2,4-Trimethylbenzene 0.5 0.08 0.8 0.7 0.6 0.9 0.4 0.3 1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.09 0.1 0.09 0.07 0.04
Benzyl chloride 0.5 0.06
1,2-Dichlorobenzene 0.5 0.10 0.01
1,2,4-Trichlorobenzene 1.0 0.10
Hexachloro-1,3-butadiene 0.5 0.30 0.03 0.02
VOC concentration (ppbv)
11/19/08 11/20/08 11/21/0811/18/08 11/24/08 11/25/08SPECIATED ORGANIC COMPOUNDS

47 Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 4.6 1.5 1.4 2.6
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 1 0.5 0.5 0.4 0.6 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02 0.01 0.02 0.05 0.04 0.05 Chloromethane 0.5 0.08 0.6 0.5 0.5 0.5 0.7 0.5
Vinyl chloride 0.5 0.04 0.06
Bromomethane 0.5 0.05 0.03 0.07 0.07 0.04
Ethanol 1.5 0.30 21 14 6.4 7 11 13
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.4 0.2 0.2 0.2 0.4 Isopropyl alcohol(2-Propanol) 1.5 0.03 4.8 1.4 1.1 0.6 1.7 1.5 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.07 0.07 0.1 0.07 0.08 Acetone 3.0 0.08 15 18 20 13 17 19
Carbon disulfide 1.0 0.04 0.03 0.03 0.08 0.07 0.1
Methylene chloride 1.5 0.04 0.2 0.4 0.1 0.3 0.4 0.5
Methyl-tert-butyl ether(MTBE) 0.5 0.05 0.04
trans-1,2-Dichloroethene 0.5 0.04 0.03
n-Hexane 0.5 0.03 0.2 1.4 0.3 0.3 0.3 0.5
1,-Dichloroethane 0.5 0.04 0.03
Vinyl acetate 0.5 0.05 0.06
2-Butanone(MEK) 1.5 0.07 1.6 2.5 5.4 2.3 4.4 1.5
cis-1,2-Dichloroethene 0.5 0.05 0.03
Ethyl acetate 0.5 0.07 0.2 0.2
Chloroform 0.5 0.03 0.06 0.08 0.05 0.08
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04 0.03
Cyclohexane 0.5 0.04 0.7 0.2 0.2 0.3
Carbon tetrachloride 0.5 0.04 0.07 0.07 0.07 0.1 0.07 0.06 n-Heptane 0.5 0.04 0.3 0.2 0.2 0.4
1,2-Dichlroethane 0.5 0.02 0.05
Benzene 0.5 0.02 0.3 1.2 0.2 0.4 0.3 0.8
Trichloroethene 0.5 0.04 0.03 0.05 0.06 0.09 0.05
1,2-Dichloropropane 0.5 0.05 0.04
Bromodichloromethane 0.5 0.04 0.04 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.08 0.08 0.1 0.1 0.2 0.09 cis-1,3-Dichloropropene 0.5 0.04 0.03
Toluene 0.5 0.05 0.9 4.2 0.6 0.9 1.2 2.4
trans-1,3-Dichloropropene 0.5 0.04 0.03 0.03 0.03
1,1,2-Trichloroethane 0.5 0.06 0.04
2-Hexanone(MBK) 1.5 0.05 0.3 0.2 0.4 0.3 0.9 0.1
Tetrachloroethene 0.5 0.04 0.02 0.09 0.07 0.07 0.09
Dibromochloromethane 0.5 0.06 0.04 0.02
1,2-Dibromoethane 0.5 0.06 0.03
Chlorobenzene 0.5 0.04 0.04 0.03
Ethylbenzene 0.5 0.05 0.2 0.6 0.08 0.2 0.2 0.3
Xylene (para & meta) 1.0 0.10 0.4 1.9 0.2 0.4 0.6 1
Xylene (Ortho) 0.5 0.05 0.2 0.8 0.1 0.2 0.3 0.4
Styrene 0.5 0.05 0.09 0.8 0.06 0.1 0.1 1
Bromoform 0.5 0.05 0.02
1,1,2,2-Tetrachloroethane 0.5 0.08 0.06 0.06 0.04
4-Ethyltoluene 0.5 0.06 0.2 0.6 0.08 0.1 0.2 0.3
1,3,5-Trimethylbenzene 0.5 0.05 0.05 0.1 0.02 0.06 0.1
1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.6 0.08 0.1 0.2 0.3 1,3-Dichlorobenzene 0.5 0.06 0.04 0.04 0.03
1,4-Dichlorobenzene 0.5 0.05 0.05 0.06 0.05 0.05 0.04
Benzyl chloride 0.5 0.06 0.04 0.05
1,2-Dichlorobenzene 0.5 0.10 0.02 0.04 0.04 0.03
1,2,4-Trichlorobenzene 1.0 0.10 0.2 0.07 0.06 0.04
Hexachloro-1,3-butadiene 0.5 0.30 0.04 0.05 0.05 0.04
VOC concentration (ppbv)
12/5/0812/1/08 12/2/08 12/3/08 12/4/0811/26/08SPECIATED ORGANIC COMPOUNDS

48 Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.2 1.6 2.3 2.6 1.1
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.6 0.5 0.4 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.03 0.03 0.03 0.02 0.03 Chloromethane 0.5 0.08 0.4 0.04 0.4 0.5 0.4 0.5
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 3.1 13 6.3 9.4 9.1 14
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 0.3 0.3 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 0.7 3.3 1 1.6 4.6 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.08 0.08 0.08 0.05 0.06 0.05 Acetone 3.0 0.08 4.3 23 9.8 13 11 8
Carbon disulfide 1.0 0.04 0.03 0.05 0.04 0.04 0.04 0.01 Methylene chloride 1.5 0.04 0.2 0.5 0.3 0.5 0.5
Methyl-tert-butyl ether(MTBE) 0.5 0.05
trans-1,2-Dichloroethene 0.5 0.04
n-Hexane 0.5 0.03 0.2 0.8 0.2 0.7 0.8 0.2
1,-Dichloroethane 0.5 0.04
Vinyl acetate 0.5 0.05
2-Butanone(MEK) 1.5 0.07 0.4 1.3 0.8 1.8 1.5 0.8
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07 0.4 0.1 0.2
Chloroform 0.5 0.03 0.04 0.07
Tetrahydrofuran 0.5 0.07 1.4
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.1 0.5 0.2 0.3 0.4 0.1
Carbon tetrachloride 0.5 0.04 0.08 0.07 0.07 0.05 0.06 0.06 n-Heptane 0.5 0.04 0.1 0.5 0.4 0.4 0.2
1,2-Dichlroethane 0.5 0.02
Benzene 0.5 0.02 0.3 1.5 0.4 0.6 0.7 0.3
Trichloroethene 0.5 0.04 0.02 0.05 0.02 0.03 0.03 0.02
1,2-Dichloropropane 0.5 0.05
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.04 0.1 0.05 0.08 0.2 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 0.7 4.5 1.3 2.2 2.2 0.8
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.03 0.1 0.3 0.3 0.1
Tetrachloroethene 0.5 0.04 0.03 0.1 0.04 0.08 0.08 0.04 Dibromochloromethane 0.5 0.06
1,2-Dibromoethane 0.5 0.06
Chlorobenzene 0.5 0.04 0.01
Ethylbenzene 0.5 0.05 0.1 0.6 0.2 0.4 0.4 0.2
Xylene (para & meta) 1.0 0.10 0.3 2.1 0.6 1.1 1.2 0.5
Xylene (Ortho) 0.5 0.05 0.1 0.8 0.2 0.4 0.4 0.2
Styrene 0.5 0.05 0.05 1.7 0.5 0.2 0.3 0.1
Bromoform 0.5 0.05
1,1,2,2-Tetrachloroethane 0.5 0.08 0.06
4-Ethyltoluene 0.5 0.06 0.1 0.6 0.2 0.3 0.4 0.2
1,3,5-Trimethylbenzene 0.5 0.05 0.03 0.2 0.05 0.09 0.1 0.05 1,2,4-Trimethylbenzene 0.5 0.08 0.1 0.6 0.2 0.3 0.4 0.2 1,3-Dichlorobenzene 0.5 0.06 0.01 0.03
1,4-Dichlorobenzene 0.5 0.05 0.02 0.04 0.01 0.04 0.05 0.02 Benzyl chloride 0.5 0.06 0.02
1,2-Dichlorobenzene 0.5 0.10 0.01 0.01 0.03
1,2,4-Trichlorobenzene 1.0 0.10 0.02 0.02 0.05 0.08 0.03 Hexachloro-1,3-butadiene 0.5 0.30 0.02
VOC concentration (ppbv)
12/10/08 12/11/08 12/12/08 12/15/0812/8/08 12/9/08SPECIATED ORGANIC COMPOUNDS

49 Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 2.4 1.4 2.6
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.4 0.4 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03
Chloromethane 0.5 0.08 0.5 0.5 0.4 0.4
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 7.6 1.7 4.3 8.4
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 1.7 1.4
Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.06 0.05 Acetone 3.0 0.08 10 1.2 6.8 13
Carbon disulfide 1.0 0.04 0.02
Methylene chloride 1.5 0.04 0.3 0.08 0.1 0.3
Methyl-tert-butyl ether(MTBE) 0.5 0.05
trans-1,2-Dichloroethene 0.5 0.04
n-Hexane 0.5 0.03 0.3 0.03 0.2 0.3
1,-Dichloroethane 0.5 0.04
Vinyl acetate 0.5 0.05
2-Butanone(MEK) 1.5 0.07 0.9 0.1 1.1 1.3
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07
Chloroform 0.5 0.03
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.2 0.1 0.2
Carbon tetrachloride 0.5 0.04 0.06 0.05 0.05 0.05
n-Heptane 0.5 0.04 0.5 0.4
1,2-Dichlroethane 0.5 0.02
Benzene 0.5 0.02 0.5 0.1 0.3 0.6
Trichloroethene 0.5 0.04 0.02 0.02
1,2-Dichloropropane 0.5 0.05
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06
cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 1.4 0.2 0.8 1.8
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.1 0.2 0.3
Tetrachloroethene 0.5 0.04 0.06 0.04
Dibromochloromethane 0.5 0.06
1,2-Dibromoethane 0.5 0.06
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.2 0.03 0.1 0.3
Xylene (para & meta) 1.0 0.10 0.7 0.09 0.4 0.9
Xylene (Ortho) 0.5 0.05 0.3 0.04 0.2 0.4
Styrene 0.5 0.05 0.2 0.04 0.7
Bromoform 0.5 0.05
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.2 0.03 0.1 0.3
1,3,5-Trimethylbenzene 0.5 0.05 0.01 0.04 0.08
1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.03 0.1 0.3
1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.01 0.01 0.02
Benzyl chloride 0.5 0.06
1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10
Hexachloro-1,3-butadiene 0.5 0.30
VOC concentration (ppbv)
12/17/08 12/18/08 12/19/0812/16/08SPECIATED ORGANIC COMPOUNDS

50 Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A 2.1 2.1 2.1 2.5
2,3-Dimethyl pentane N/A N/A 1.4 1.2 1 1.5
2,4-Dimethyl hexane N/A N/A
2-Methyl butane N/A N/A 7.8 7.9 9.2 9 4.2 3.6
2-Methyl hexane N/A N/A 1.2 1.2
2-Methyl pentane N/A N/A 3.2 3.3 3.5 3.9 1.6 1.3
2-Pentene N/A N/A
3-Methyl hexane N/A N/A 1.5 1.2 1.1 1.5
3-Methyl pentane N/A N/A 1.7 1.8 2 2.1
Acetaldehyde N/A N/A 1.6 2.1 1.8
Butanal N/A N/A
Butane N/A N/A 5.5 6.8 8.6 6.5 3.5 2.9
Difluorochloromethane N/A N/A 5.6
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A 2.6
Isobutane N/A N/A 4.4 5.1 4.5 4.6 3.1 2.6
Limonene N/A N/A
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 1.4 1.5 1.8 1.8
Octamethyl cyclotrisiloxane N/A N/A
Pentane N/A N/A 3.5 3.7 4.1 2.3
Trimethyl silanol N/A N/A
Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A 1.2
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A 1
2,4-Dimethyl hexane N/A N/A
2-Methyl butane N/A N/A 2.6 8.8 1.8 2.2 1.8 3.2
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A 3.3 1.6
2-Pentene N/A N/A
3-Methyl hexane N/A N/A 1.1
3-Methyl pentane N/A N/A 2
Acetaldehyde N/A N/A 2 2.1 4 2.8 2.4 1
Butanal N/A N/A 1.3 1.1 1.7
Butane N/A N/A 2.4 8.6 2.6 2.4 1.6 2.4
Difluorochloromethane N/A N/A 2.6
Heptanal N/A N/A 1.1
Hexamethyl cyclotrisiloxane N/A N/A 2.3
Isobutane N/A N/A 2.4 5.8 1.7 1.2 1.9
Limonene N/A N/A
Methyl cyclohexane N/A N/A 1
Methyl cyclopentane N/A N/A 2.2
Octamethyl cyclotrisiloxane N/A N/A 1.1
Pentane N/A N/A 4.8
Trimethyl silanol N/A N/A
VOC concentration (ppbv)
VOC concentration (ppbv)
11/21/08 11/24/08 11/25/08
12/3/08 12/4/08 12/5/08
TENTATIVELY IDENTIFIED COMPOUNDS 11/18/08 11/19/08 11/20/08 TENTATIVELY IDENTIFIED COMPOUNDS 11/26/08 12/1/08 12/2/08

51 Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
2,2,3,3-Tetramethyl butane N/A N/A 1.1 1.2
2,2,4-Trimethyl pentane N/A N/A 1.9
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2,4-Dimethyl hexane N/A N/A
2-Methyl butane N/A N/A 1.2 4.8 1.5 4 3.9
2-Methyl hexane N/A N/A 1.1
2-Methyl pentane N/A N/A 2.5 2.6
2-Pentene N/A N/A 1.4
3-Methyl hexane N/A N/A 1.4
3-Methyl pentane N/A N/A 1.4 1.5
Acetaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 3.5 1 2.5 2.6
Difluorochloromethane N/A N/A
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Isobutane N/A N/A 2.1 1.4 1.4
Limonene N/A N/A
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 1.5 1.2 1.2
Octamethyl cyclotrisiloxane N/A N/A
Pentane N/A N/A 1.7 1.7
Trimethyl silanol N/A N/A
Dominguez Elementary School - Outdoor VOC data
Reporting Limit Method Detection
(ppbv) limit (ppbv)
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2,4-Dimethyl hexane N/A N/A 1.1
2-Methyl butane N/A N/A 1.3 1.5
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A 1.1
2-Pentene N/A N/A
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A
Acetaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 1.1
Difluorochloromethane N/A N/A
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Isobutane N/A N/A
Limonene N/A N/A
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A
Octamethyl cyclotrisiloxane N/A N/A
Pentane N/A N/A
Trimethyl silanol N/A N/A 1.4
VOC concentration (ppbv)
VOC concentration (ppbv)
12/10/08 12/11/08 12/12/08 12/15/08TENTATIVELY IDENTIFIED COMPOUNDS TENTATIVELY IDENTIFIED COMPOUNDS 12/16/08 12/17/08 12/18/08 12/19/08 12/8/08 12/9/08

52 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 9.5 4.4 3.4 5.9 5.3 2.8
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.7 0.6 0.6 0.6 0.7 0.5 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.01 0.02 0.02 Chloromethane 0.5 0.08 0.8 0.9 0.7 0.8 0.6 0.92
Bromomethane 0.5 0.05 0.02 0.02 0.02
Ethanol 1.5 0.30 140 310 480 380 59 480
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3
Isopropyl alcohol(2-Propanol) 1.5 0.03 14 35 39 31 6.9 32 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.08 0.08 0.06 0.06 0.06 0.06 Acetone 3.0 0.08 37 44 22 35 16 33
1,1-Dichloroethene 0.5 0.03
Carbon disulfide 1.0 0.04 0.08 0.08 0.08 0.07 0.06 0.1
Methylene chloride 1.5 0.04 1.8 1 0.6 2.2 0.7 0.5
n-Hexane 0.5 0.03 2.2 1 0.9 1.8 0.8 0.5
2-Butanone(MEK) 1.5 0.07 2.5 2.3 1.4 2.5 1.2 2.7
Ethyl acetate 0.5 0.07 1 0.7 0.3 0.5 0.3 0.3
Chloroform 0.5 0.03 0.1 0.07 0.06 0.1 0.06 0.05
Tetrahydrofuran 0.5 0.07 1.7 0.4 0.2 0.6 0.4 0.4
1,1,1-Trichloroethane 0.5 0.04 0.02
Cyclohexane 0.5 0.04 1.5 0.8 0.5 3.3 0.5 0.4
Carbon tetrachloride 0.5 0.04 0.07 0.07 0.07 0.07 0.06 0.06 n-Heptane 0.5 0.04 2.6 1.9 1.1 0.8
Benzene 0.5 0.02 2.1 1 0.8 1.6 1 0.6
Trichloroethene 0.5 0.04 0.05 0.05 0.04 0.06 0.03 0.06
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.4 0.4 0.4 4 0.2 0.9 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 8.3 4.2 2.8 7.2 4.5 3.2
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.1 0.2 0.2 0.5
Tetrachloroethene 0.5 0.04 0.3 0.1 0.08 0.4 0.1 0.1
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 1.1 0.6 0.5 1.1 0.7 0.5
Xylene (para & meta) 1.0 0.10 3.9 1.8 1.5 3.7 2.3 1.4
Xylene (Ortho) 0.5 0.05 1.4 0.7 0.7 1.4 0.8 0.6
Styrene 0.5 0.05 1.3 0.5 0.4 0.7 0.4 0.6
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 1.3 0.7 0.6 1.2 0.7 0.5
1,3,5-Trimethylbenzene 0.5 0.05 0.4 0.2 0.4 0.2 0.2
1,2,4-Trimethylbenzene 0.5 0.08 1.4 0.8 0.6 1.3 0.7 0.6 1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.5 0.7 0.5 0.7 0.2 0.8
1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10
Hexachloro-1,3-butadiene 0.5 0.30 0.02
VOC concentration (ppbv)
11/18/08 11/19/08 11/20/08 11/21/08 11/24/08 11/25/08SPECIATED ORGANIC COMPOUNDS

53 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.1 3
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.4 0.5 0.4 3.6 1.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02 0.03 0.04 0.05 Chloromethane 0.5 0.08 0.9 0.7 0.8 1 0.7 0.7
Bromomethane 0.5 0.05 0.02 0.04
Ethanol 1.5 0.30 130 32 40 160 49 49
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.1 0.7 0.3 Isopropyl alcohol(2-Propanol) 1.5 0.03 16 3.9 6.6 5.9 2.5 7.6 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.06 0.05 0.05 Acetone 3.0 0.08 17 19 6.8 10 12 9
1,1-Dichloroethene 0.5 0.03 0.02
Carbon disulfide 1.0 0.04 0.04 0.02 0.02 0.08 0.07 0.09 Methylene chloride 1.5 0.04 0.2 0.2 0.2 0.2 0.2 0.4
n-Hexane 0.5 0.03 0.2 0.4 0.07 0.1 0.1 0.2
2-Butanone(MEK) 1.5 0.07 0.5 3.3 0.2 1.4 1.4 0.4
Ethyl acetate 0.5 0.07 0.3
Chloroform 0.5 0.03 0.04 0.04
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.2 0.2 0.06 0.08 0.1 0.1
Carbon tetrachloride 0.5 0.04 0.06 0.04 0.04 0.03
n-Heptane 0.5 0.04 0.4 0.3
Benzene 0.5 0.02 0.3 0.3 0.05 0.1 0.1 0.2
Trichloroethene 0.5 0.04 0.02 0.03 0.03 0.04
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.2 0.1 0.4 0.1 0.1 0.1 cis-1,3-Dichloropropene 0.5 0.04 0.02
Toluene 0.5 0.05 1.3 0.9 0.1 0.2 0.3 0.7
trans-1,3-Dichloropropene 0.5 0.04 0.02 0.02
1,1,2-Trichloroethane 0.5 0.06 0.03
2-Hexanone(MBK) 1.5 0.05 0.2 0.2 0.2
Tetrachloroethene 0.5 0.04 0.03 0.02 0.03 0.04 0.05
Chlorobenzene 0.5 0.04 0.03 0.03 0.03
Ethylbenzene 0.5 0.05 0.2 0.1 0.03 0.06 0.08 0.1
Xylene (para & meta) 1.0 0.10 0.6 0.4 0.06 0.1 0.2 0.3
Xylene (Ortho) 0.5 0.05 0.3 0.2 0.03 0.07 0.08 0.2
Styrene 0.5 0.05 0.2 0.1 0.02 0.04 0.05 0.1
1,1,2,2-Tetrachloroethane 0.5 0.08 0.03 0.03
4-Ethyltoluene 0.5 0.06 0.3 0.2 0.04 0.05 0.1 0.2
1,3,5-Trimethylbenzene 0.5 0.05 0.08 0.04 0.01 0.03 0.05 0.07 1,2,4-Trimethylbenzene 0.5 0.08 0.3 0.2 0.04 0.05 0.1 0.2 1,3-Dichlorobenzene 0.5 0.06 0.03 0.03 0.03
1,4-Dichlorobenzene 0.5 0.05 0.8 0.1 0.08 0.06 0.06 0.05 1,2-Dichlorobenzene 0.5 0.10 0.02 0.03 0.03 0.03
1,2,4-Trichlorobenzene 1.0 0.10 0.04 0.04 0.04
Hexachloro-1,3-butadiene 0.5 0.30 0.03 0.03 0.04 0.03
VOC concentration (ppbv)
12/5/0812/1/08 12/2/08 12/3/08 12/4/0811/26/08SPECIATED ORGANIC COMPOUNDS

54 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.8 1.7
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 1 6.3 0.6 0.8 0.5 0.5 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.03 0.02 Chloromethane 0.5 0.08 0.7 0.6 0.6 0.5 0.6
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 22 26 35 34 68 27
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 1.1 0.3 0.2 0.2 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 3.6 3.7 4.6 3.6 14 3.6 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.03 0.03 0.03 0.02 Acetone 3.0 0.08 11 14 9.1 9.2 20 10
1,1-Dichloroethene 0.5 0.03 0.3
Carbon disulfide 1.0 0.04 0.07 0.05 0.04 0.04 0.02 0.02 Methylene chloride 1.5 0.04 0.3 0.5 0.3 0.4 0.6 0.3
n-Hexane 0.5 0.03 0.08 0.1 0.09 0.1 0.2 0.09
2-Butanone(MEK) 1.5 0.07 1 1.3 0.2 0.6 0.7 0.5
Ethyl acetate 0.5 0.07 0.1
Chloroform 0.5 0.03 0.02
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.06 0.1 0.1 0.1 0.3 0.06
Carbon tetrachloride 0.5 0.04 0.03 0.03 0.03 0.02
n-Heptane 0.5 0.04 0.1 0.5
Benzene 0.5 0.02 0.1 0.2 0.1 0.2 0.1 0.1
Trichloroethene 0.5 0.04 0.02 0.02
Bromodichloromethane 0.5 0.04 0.01
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.04 0.08 0.03 3 0.07 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 0.3 0.5 0.4 0.7 0.4 0.4
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.1 0.2 0.08 0.07
Tetrachloroethene 0.5 0.04 0.02 0.04 0.02 0.02
Chlorobenzene 0.5 0.04 0.01
Ethylbenzene 0.5 0.05 0.05 0.08 0.06 0.1 0.07 0.07
Xylene (para & meta) 1.0 0.10 0.1 0.2 0.2 0.3 0.2 0.2
Xylene (Ortho) 0.5 0.05 0.06 0.1 0.08 0.1 0.08 0.1
Styrene 0.5 0.05 0.02 0.07 0.1 0.08 0.05 0.06
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.05 0.08 0.07 0.1 0.07 0.1
1,3,5-Trimethylbenzene 0.5 0.05 0.02 0.03 0.03 0.05 0.04 1,2,4-Trimethylbenzene 0.5 0.08 0.05 0.08 0.07 0.2 0.07 0.1 1,3-Dichlorobenzene 0.5 0.06 0.01 0.01
1,4-Dichlorobenzene 0.5 0.05 0.04 0.05 0.02 0.02 0.01 0.07 1,2-Dichlorobenzene 0.5 0.10 0.01 0.01
1,2,4-Trichlorobenzene 1.0 0.10 0.02 0.02 0.02 0.03
Hexachloro-1,3-butadiene 0.5 0.30 0.02 0.02 0.01
VOC concentration (ppbv)
12/11/08 12/12/08 12/15/0812/8/08 12/9/08 12/10/08SPECIATED ORGANIC COMPOUNDS

55 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1 1.5
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.4 0.4 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03
Chloromethane 0.5 0.08 0.6 0.5 0.5 0.6
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 110 54 110 56
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 5.4 4.6 5.6 3.6
Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.02 0.04 0.02 Acetone 3.0 0.08 7.9 6.5 11 14
1,1-Dichloroethene 0.5 0.03
Carbon disulfide 1.0 0.04 0.06 0.01 0.03
Methylene chloride 1.5 0.04 0.4 0.2 0.2 0.3
n-Hexane 0.5 0.03 0.1 0.09 0.1
2-Butanone(MEK) 1.5 0.07 0.4 0.8 0.8
Ethyl acetate 0.5 0.07 0.4 0.8
Chloroform 0.5 0.03
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.08 0.1 0.1
Carbon tetrachloride 0.5 0.04 0.04 0.02
n-Heptane 0.5 0.04 0.3
Benzene 0.5 0.02 0.2 0.2 0.1 0.2
Trichloroethene 0.5 0.04
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.05 0.05 0.04
cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 0.5 0.6 1.5 0.9
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05
Tetrachloroethene 0.5 0.04 0.02
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.09 0.1 0.05 0.1
Xylene (para & meta) 1.0 0.10 0.3 0.3 0.2 0.4
Xylene (Ortho) 0.5 0.05 0.4 0.1 0.06 0.2
Styrene 0.5 0.05 0.07 0.07 0.02 0.2
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.2 0.1 0.06 0.2
1,3,5-Trimethylbenzene 0.5 0.05 0.06 0.04 0.02 0.05
1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.1 0.06 0.2
1,3-Dichlorobenzene 0.5 0.06 0.02
1,4-Dichlorobenzene 0.5 0.05 0.05 0.04 0.04 0.04
1,2-Dichlorobenzene 0.5 0.10 0.02
1,2,4-Trichlorobenzene 1.0 0.10 0.04
Hexachloro-1,3-butadiene 0.5 0.30
12/17/08 12/18/08 12/19/08
VOC concentration (ppbv)
12/16/08SPECIATED ORGANIC COMPOUNDS

56 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
.alpha.-Pinene N/A N/A
1,3-Pentadiene N/A N/A
1-Butanol N/A N/A 2.9
1R-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 1.8 1.5 1.7
2,2,4-Trimethyl pentane N/A N/A 4
2,2-Dimethyl hexane N/A N/A 2.9
2,3-Dimethyl pentane N/A N/A 2.7 1.9 1.2
2,4-bis(trimethylsiloxane) Benzaldehyde N/A N/A 28
2-Methyl butane N/A N/A 16 6.9 6 12 7.3 4.4
2-Methyl hexane N/A N/A 2.1
2-Methyl pentane N/A N/A 6.4 2.8 2.4 4.9 2.8
2-Methyl-1,3-butadiene N/A N/A 3.2 1.3 2.6 2.5
3-Methyl hexane N/A N/A 2.5 2 1.2
3-Methyl pentane N/A N/A 3.5 1.5 1.4 2.6 1.5
4-Ethyl-2,2,6,6-tetramethyl heptane N/A N/A 1.8
Acetaldehyde N/A N/A 1.6 1.3 1.5 3.1
Benzaldehyde N/A N/A 2.1 2 2.4 2.5 1.9
Butanal N/A N/A
Butane N/A N/A 11 5.3 5.4 8.4 5.3 3.6
Butyl ester acetic acid N/A N/A 8.8
Decanal N/A N/A
Difluorochloromethane N/A N/A
D-Limonene N/A N/A 1.7 5.5 1.5 2.6
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A 1
Hexanal N/A N/A 1.2 1.8
Isobutane N/A N/A 7 3.3 12 5 3.8 2.2
Methyl cyclopentane N/A N/A 3 1.3 1.3 2.3 1.3
Nonanal N/A N/A 1.2 2.3
Ocatanal N/A N/A 1.8
Octamethyl cyclotrisiloxane N/A N/A 68
Pentane N/A N/A 6.1 3.1 5.4 3.1 2.8
Tridecane N/A N/A
11/25/08TENTATIVELY IDENTIFIED COMPOUNDS 11/18/08 11/19/08 11/20/08 11/21/08 VOC concentration (ppbv)
11/24/08

57 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
.alpha.-Pinene N/A N/A 1.2
1,3-Pentadiene N/A N/A 2.2
1-Butanol N/A N/A 1.8
1R-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2,4-bis(trimethylsiloxane) Benzaldehyde N/A N/A
2-Methyl butane N/A N/A 2.6 2.8 2.1 1.3
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A
2-Methyl-1,3-butadiene N/A N/A
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A
4-Ethyl-2,2,6,6-tetramethyl heptane N/A N/A
Acetaldehyde N/A N/A 2.7 2.7 1.5
Benzaldehyde N/A N/A
Butanal N/A N/A 1.1 1
Butane N/A N/A 2 3.4 1.8 1.9 1.1 1.6
Butyl ester acetic acid N/A N/A
Decanal N/A N/A
Difluorochloromethane N/A N/A 1.5
D-Limonene N/A N/A 37
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1.7 2.6 1.9 1.6 1.3 1.8
Methyl cyclopentane N/A N/A
Nonanal N/A N/A 1.1
Ocatanal N/A N/A
Octamethyl cyclotrisiloxane N/A N/A 1
Pentane N/A N/A 1.5
Tridecane N/A N/A 1.1
VOC concentration (ppbv)
TENTATIVELY IDENTIFIED COMPOUNDS 11/26/08 12/1/08 12/2/08 12/3/08 12/4/08 12/5/08

58 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
.alpha.-Pinene N/A N/A
1,3-Pentadiene N/A N/A
1-Butanol N/A N/A
1R-.alpha.-Pinene N/A N/A 1.9 4.1
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A 2.4
2,3-Dimethyl pentane N/A N/A
2,4-bis(trimethylsiloxane) Benzaldehyde N/A N/A
2-Methyl butane N/A N/A 1.1
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A
2-Methyl-1,3-butadiene N/A N/A
3-Methyl hexane N/A N/A 4.3
3-Methyl pentane N/A N/A
4-Ethyl-2,2,6,6-tetramethyl heptane N/A N/A
Acetaldehyde N/A N/A
Benzaldehyde N/A N/A 2.4
Butanal N/A N/A 2
Butane N/A N/A 1 1.8
Butyl ester acetic acid N/A N/A
Decanal N/A N/A 1.9
Difluorochloromethane N/A N/A 1.9
D-Limonene N/A N/A
Heptanal N/A N/A 1.9
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1.3 1.3
Methyl cyclopentane N/A N/A
Nonanal N/A N/A
Ocatanal N/A N/A 1.8
Octamethyl cyclotrisiloxane N/A N/A
Pentane N/A N/A
Tridecane N/A N/A
12/12/08 12/15/0812/8/08 12/9/08 12/10/08 12/11/08TENTATIVELY IDENTIFIED COMPOUNDS VOC concentration (ppbv)

59 Dominguez Elementary School - Room 7 (DZ-7) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
.alpha.-Pinene N/A N/A
1,3-Pentadiene N/A N/A
1-Butanol N/A N/A
1R-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2,4-bis(trimethylsiloxane) Benzaldehyde N/A N/A
2-Methyl butane N/A N/A
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A
2-Methyl-1,3-butadiene N/A N/A
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A
4-Ethyl-2,2,6,6-tetramethyl heptane N/A N/A
Acetaldehyde N/A N/A
Benzaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 1.2
Butyl ester acetic acid N/A N/A
Decanal N/A N/A
Difluorochloromethane N/A N/A
D-Limonene N/A N/A
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1
Methyl cyclopentane N/A N/A
Nonanal N/A N/A
Ocatanal N/A N/A
Octamethyl cyclotrisiloxane N/A N/A
Pentane N/A N/A
Tridecane N/A N/A

TENTATIVELY IDENTIFIED COMPOUNDS
VOC concentration (ppbv)
12/16/08 12/17/08 12/18/08 12/19/08

60 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 4.1 4.4 2.8
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.6 0.6 0.6 0.5 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02 0.02 0.02 0.02 Chloromethane 0.5 0.08 0.7 0.7 0.7 0.7 0.8 0.8
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05 0.05 0.02
Ethanol 1.5 0.30 200 580 150 120 520 180
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 0.2
Isopropyl alcohol(2-Propanol) 1.5 0.03 17 160 20 92 270 200 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.06 0.06 0.06 0.06 0.06 Acetone 3.0 0.08 25 27 17 27 31 26
Carbon disulfide 1.0 0.04 0.09 0.06 0.04 0.09 0.05 0.04 Methylene chloride 1.5 0.04 1.2 1 0.6 1.4 0.6 0.4
n-Hexane 0.5 0.03 1.1 1.3 1.4 1.2 0.7 0.5
2-Butanone(MEK) 1.5 0.07 1.8 2.2 2.5 2.3 2.3 2.5
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07 1.6 0.7 0.3 0.8 0.4 0.3
Chloroform 0.5 0.03 0.08 0.07 0.07 0.09 0.05 0.05
Tetrahydrofuran 0.5 0.07 0.5 0.2 0.1 0.4 0.3 0.2
1,1,1-Trichloroethane 0.5 0.04 0.02 0.02
Cyclohexane 0.5 0.04 0.8 1.7 0.8 1.7 0.5 0.3
Carbon tetrachloride 0.5 0.04 0.07 0.07 0.07 0.07 0.06 0.06 n-Heptane 0.5 0.04 1.2 4.7 0.9 0.9 0.6
Benzene 0.5 0.02 1.2 1.1 1.1 1.3 0.8 0.6
Trichloroethene 0.5 0.04 0.09 0.07 0.05 0.06 0.03 0.06
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.5 1.3 0.3 0.4 0.4 0.4 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 5.4 5.1 4.3 6.4 3.8 3
trans-1,3-Dichloropropene 0.5 0.04
2-Hexanone(MBK) 1.5 0.05 0.1 0.2 0.2 0.4 0.4
Tetrachloroethene 0.5 0.04 0.3 0.2 0.1 0.2 0.1 0.09
Dibromochloromethane 0.5 0.06
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.7 0.7 0.6 0.9 0.5 0.4
Xylene (para & meta) 1.0 0.10 2.4 2 1.8 2.9 1.7 1.1
Xylene (Ortho) 0.5 0.05 0.9 0.9 0.7 1.1 0.7 0.5
Styrene 0.5 0.05 1.1 0.6 0.6 0.5 1.1 0.4
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.8 0.7 0.6 0.9 0.6 0.4
1,3,5-Trimethylbenzene 0.5 0.05 0.2 0.2 0.3 0.2 0.1
1,2,4-Trimethylbenzene 0.5 0.08 0.9 0.7 0.6 1 0.7 0.5
1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.08 0.09 0.09 0.1 0.1 0.08 1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10
Hexachloro-1,3-butadiene 0.5 0.30 0.03 0.03

SPECIATED ORGANIC COMPOUNDS
VOC concentration (ppbv)
11/20/08 11/21/08 11/24/08 11/25/0811/18/08 11/19/08

61 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 0.9 4 1.1 0.7 1.1 2.6
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.5 0.4 0.4 0.5 0.6 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02 0.02 0.04 0.05 0.05 Chloromethane 0.5 0.08 0.7 0.7 0.6 0.8 0.6 0.6
Vinyl chloride 0.5 0.04 0.04
Bromomethane 0.5 0.05 0.05 0.05
Ethanol 1.5 0.30 110 210 82 59 61 84
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.3 0.3 0.4 Isopropyl alcohol(2-Propanol) 1.5 0.03 41 15 5.4 8.7 4.2 39 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.06 0.06 0.07 0.09 0.08 0.1 Acetone 3.0 0.08 13 23 16 7.8 23 16
Carbon disulfide 1.0 0.04 0.03 0.04 0.05 0.07 0.09 0.1
Methylene chloride 1.5 0.04 0.2 0.4 0.1 0.2 0.4 0.5
n-Hexane 0.5 0.03 0.2 1.4 0.2 0.2 0.3 0.5
2-Butanone(MEK) 1.5 0.07 0.4 2.2 2.8 0.8 4 1
cis-1,2-Dichloroethene 0.5 0.05 0.02
Ethyl acetate 0.5 0.07 0.4 0.3 0.2
Chloroform 0.5 0.03 0.06 0.08
Tetrahydrofuran 0.5 0.07 0.4
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.1 0.7 0.1 0.1 0.2 0.4
Carbon tetrachloride 0.5 0.04 0.06 0.07 0.07 0.08 0.07 0.07 n-Heptane 0.5 0.04 0.6 0.2 0.4
Benzene 0.5 0.02 0.2 1.2 0.2 0.2 0.4 0.9
Trichloroethene 0.5 0.04 0.02 0.04 0.03 0.07 0.05
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.1 0.2 0.2 0.2 0.2 0.08 cis-1,3-Dichloropropene 0.5 0.04 0.02 0.02
Toluene 0.5 0.05 1.2 4.9 0.8 0.6 1.2 2.6
trans-1,3-Dichloropropene 0.5 0.04 0.02 0.02
2-Hexanone(MBK) 1.5 0.05 0.1 0.3 0.4
Tetrachloroethene 0.5 0.04 0.02 0.06 0.05 0.07 0.1
Dibromochloromethane 0.5 0.06 0.02 0.02
Chlorobenzene 0.5 0.04 0.03
Ethylbenzene 0.5 0.05 0.2 0.6 0.09 0.1 0.2 0.4
Xylene (para & meta) 1.0 0.10 0.4 1.9 0.2 0.3 0.4 1.1
Xylene (Ortho) 0.5 0.05 0.2 0.8 0.1 0.2 0.2 0.4
Styrene 0.5 0.05 0.1 0.9 0.07 0.08 0.09 0.9
1,1,2,2-Tetrachloroethane 0.5 0.08 0.04
4-Ethyltoluene 0.5 0.06 0.2 0.7 0.1 0.2 0.2 0.4
1,3,5-Trimethylbenzene 0.5 0.05 0.05 0.2 0.03 0.09 0.06 0.1 1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.7 0.1 0.3 0.2 0.4 1,3-Dichlorobenzene 0.5 0.06 0.03 0.03 0.03
1,4-Dichlorobenzene 0.5 0.05 0.06 0.1 0.03 0.04 0.04 0.05 1,2-Dichlorobenzene 0.5 0.10 0.02 0.03 0.03 0.03
1,2,4-Trichlorobenzene 1.0 0.10 0.04 0.04 0.03
Hexachloro-1,3-butadiene 0.5 0.30 0.03 0.03 0.03 0.03
VOC concentration (ppbv)
12/4/08 12/5/0811/26/08 12/1/08 12/2/08 12/3/08SPECIATED ORGANIC COMPOUNDS

62 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.4 4.9 2.6 1.1
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.6 0.5 0.7 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.03 0.03 0.03 Chloromethane 0.5 0.08 0.5 0.5 0.4 0.5 0.4 0.5
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 68 30 110 68 21 110
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 0.3 0.4 0.2 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 4.9 43 53 6.2 36 20 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.09 0.09 0.08 0.06 0.06 0.05 Acetone 3.0 0.08 14 15 15 25 17 12
Carbon disulfide 1.0 0.04 0.03 0.05 0.05 0.03
Methylene chloride 1.5 0.04 0.3 0.6 0.2 0.8 0.6 0.2
n-Hexane 0.5 0.03 0.2 0.5 0.6 0.9 0.6 0.2
2-Butanone(MEK) 1.5 0.07 1.7 0.9 1.2 2 2.4 1.3
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07 0.1 0.3 0.3 0.2
Chloroform 0.5 0.03 0.06 0.06 0.07
Tetrahydrofuran 0.5 0.07 0.7
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.2 0.3 1.1 0.6 0.4 0.2
Carbon tetrachloride 0.5 0.04 0.08 0.08 0.07 0.06 0.06 0.05 n-Heptane 0.5 0.04 0.5 1.6 0.8 0.6 0.6
Benzene 0.5 0.02 0.3 0.8 0.4 1.1 0.6 0.3
Trichloroethene 0.5 0.04 0.02 0.04 0.02 0.06 0.02 0.02
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.08 0.1 0.3 0.1 0.1 0.2 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 1 2.2 1.4 4.4 2.3 0.8
trans-1,3-Dichloropropene 0.5 0.04
2-Hexanone(MBK) 1.5 0.05 0.1 0.2 0.1 0.4 0.2
Tetrachloroethene 0.5 0.04 0.03 0.08 0.04 0.1 0.09 0.03 Dibromochloromethane 0.5 0.06
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.1 0.3 0.2 0.6 0.3 0.1
Xylene (para & meta) 1.0 0.10 0.4 1 0.8 1.9 1.1 0.4
Xylene (Ortho) 0.5 0.05 0.2 0.4 0.2 0.7 0.4 0.2
Styrene 0.5 0.05 0.07 0.4 0.6 0.7 0.3 0.1
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.1 0.3 0.2 0.6 0.3 0.1
1,3,5-Trimethylbenzene 0.5 0.05 0.04 0.1 0.06 0.2 0.1 0.05 1,2,4-Trimethylbenzene 0.5 0.08 0.1 0.3 0.2 0.6 0.4 0.2 1,3-Dichlorobenzene 0.5 0.06 0.01 0.01
1,4-Dichlorobenzene 0.5 0.05 0.02 0.02 0.02 0.04 0.03 0.02 1,2-Dichlorobenzene 0.5 0.10 0.01 0.01 0.02 0.01
1,2,4-Trichlorobenzene 1.0 0.10 0.02 0.02 0.02 0.04 0.04 0.03 Hexachloro-1,3-butadiene 0.5 0.30 0.01 0.01 0.02
VOC concentration (ppbv)
12/11/08 12/12/08 12/15/0812/8/08 12/9/08 12/10/08SPECIATED ORGANIC COMPOUNDS

63 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 0.4 1.7 2.6
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.4 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02
Chloromethane 0.5 0.08 0.4 0.5 0.4 0.4
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 54 76 52 40
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 2.4 2.7 9.6 31
Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.05 0.05 0.05 Acetone 3.0 0.08 19 5.8 9.9 16
Carbon disulfide 1.0 0.04 0.02 0.01 0.02 0.03
Methylene chloride 1.5 0.04 0.4 0.1 0.1 0.3
n-Hexane 0.5 0.03 0.3 0.1 0.3 0.4
2-Butanone(MEK) 1.5 0.07 2 0.2 0.9 1
cis-1,2-Dichloroethene 0.5 0.05
Ethyl acetate 0.5 0.07 0.5
Chloroform 0.5 0.03
Tetrahydrofuran 0.5 0.07
1,1,1-Trichloroethane 0.5 0.04
Cyclohexane 0.5 0.04 0.3 0.2 0.3 0.3
Carbon tetrachloride 0.5 0.04 0.05 0.06 0.05 0.05
n-Heptane 0.5 0.04 3.7 3.9 1.3 1.4
Benzene 0.5 0.02 0.6 0.1 0.6
Trichloroethene 0.5 0.04 0.02 0.02
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.1 0.05 0.1 0.06
cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 1.7 0.4 6.5 2
trans-1,3-Dichloropropene 0.5 0.04
2-Hexanone(MBK) 1.5 0.05 0.3
Tetrachloroethene 0.5 0.04 0.06 0.02 0.04
Dibromochloromethane 0.5 0.06
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.2 0.05 0.2 0.3
Xylene (para & meta) 1.0 0.10 0.8 0.2 0.6 1
Xylene (Ortho) 0.5 0.05 0.3 0.08 0.3 0.4
Styrene 0.5 0.05 0.25 0.03 0.06 0.8
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.3 0.06 0.2 0.3
1,3,5-Trimethylbenzene 0.5 0.05 0.09 0.02 0.3 0.09
1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.07 0.2 0.3
1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.02 0.02 0.02 0.03
1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10 0.02
Hexachloro-1,3-butadiene 0.5 0.30
12/16/08 12/17/08 12/18/08 12/19/08
VOC concentration (ppbv)

SPECIATED ORGANIC COMPOUNDS

64 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
4-Bromofluorobenzene N/A N/A 9.84 10.13 10.69 11.36 11.97 11.86 1,3-Dimethyl cyclohexane N/A N/A
1,3-Pentadiene N/A N/A 1.4
1,4-Pentadiene N/A N/A 1.4
1-Butanol N/A N/A 1.2 1.9
1-Dodecene N/A N/A
1S-.alpha.-Pinene N/A N/A 1.5
2,2,3,3-Tetramethyl butane N/A N/A 2.1
2,2,4,6,6-Pentamethyl heptane N/A N/A 9.4 5.3 13
2,2,4-Trimethyl pentane N/A N/A 2.3 2.1 1.2
2,2-Dimethyl hexane N/A N/A 2.4
2,3,3-Trimethyl pentane N/A N/A
2,3-Dimethyl pentane N/A N/A 1.5 1.1
2-Methyl butane N/A N/A 8.2 7.4 9 8.9 5.3 3.8
2-Methyl hexane N/A N/A 1.3 1.4
2-Methyl pentane N/A N/A 3.5 3.3 3.6 3.7 2 1.5
2-Methyl-1,3-butadiene N/A N/A 1.6 1.5
3-Methyl butanal N/A N/A
3-Methyl hexane N/A N/A 1.5 1.5 1.3 1.5
3-Methyl pentane N/A N/A 1.9 1.8 2.1 2
Acetaldehyde N/A N/A 1.6 2.4 2.4
Benzaldehyde N/A N/A 1.9 2 3
Butanal N/A N/A
Butane N/A N/A 5.9 6.1 8.3 6.7 4.3 3.1
Difluorochloromethane N/A N/A 26 3.6 19
D-Limonene N/A N/A 1.1 1.7 1.9
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 4.6 4 5.1 4.6 3.5 2.2
Methyl cyclohexane N/A N/A 1.1
Methyl cyclopentane N/A N/A 1.6 1.6 1.9 1.7
Nonanal N/A N/A 2 2
Ocatanal N/A N/A 2
Octamethyl cyclotrisiloxane N/A N/A 10 16
Octane N/A N/A
Pentane N/A N/A 3.6 4 4.1 3
trans-1,4-Dimethyl cyclohexane N/A N/A
11/24/08 11/25/08TENTATIVELY IDENTIFIED COMPOUNDS 11/18/08 11/19/08 11/20/08 11/21/08 VOC concentration (ppbv)

65 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
4-Bromofluorobenzene N/A N/A 11.53 9.44 9.66 8.08 9.07 8.76 1,3-Dimethyl cyclohexane N/A N/A
1,3-Pentadiene N/A N/A
1,4-Pentadiene N/A N/A
1-Butanol N/A N/A
1-Dodecene N/A N/A 1
1S-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 1.3
2,2,4,6,6-Pentamethyl heptane N/A N/A 1.1
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3,3-Trimethyl pentane N/A N/A
2,3-Dimethyl pentane N/A N/A
2-Methyl butane N/A N/A 2 8.5 2.1 1.3 1.9 3.5
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A 3.1 1.9
2-Methyl-1,3-butadiene N/A N/A 1.6
3-Methyl butanal N/A N/A 1 1.1
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A 1.9
Acetaldehyde N/A N/A 1.6 2.6 1.2 3
Benzaldehyde N/A N/A 1.4
Butanal N/A N/A 1.1 2
Butane N/A N/A 1.6 8.1 2.9 1.7 1.8 2.6
Difluorochloromethane N/A N/A
D-Limonene N/A N/A 2.2
Heptanal N/A N/A 1.1
Hexamethyl cyclotrisiloxane N/A N/A 2.5
Hexanal N/A N/A 1
Isobutane N/A N/A 1.6 5.5 2.1 7 1.4 2.1
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 2
Nonanal N/A N/A 1.6
Ocatanal N/A N/A 1.3
Octamethyl cyclotrisiloxane N/A N/A 1.3 3.4
Octane N/A N/A
Pentane N/A N/A 4.8
trans-1,4-Dimethyl cyclohexane N/A N/A
VOC concentration (ppbv)
12/4/08 12/5/0811/26/08 12/1/08 12/2/08 12/3/08TENTATIVELY IDENTIFIED COMPOUNDS

66 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
4-Bromofluorobenzene N/A N/A 8.61 8.41 8.22 9.55 9.52 10.21 1,3-Dimethyl cyclohexane N/A N/A
1,3-Pentadiene N/A N/A
1,4-Pentadiene N/A N/A
1-Butanol N/A N/A 1.9
1-Dodecene N/A N/A
1S-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 1 1.9 1.1
2,2,4,6,6-Pentamethyl heptane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3,3-Trimethyl pentane N/A N/A
2,3-Dimethyl pentane N/A N/A 1.6
2-Methyl butane N/A N/A 1.2 2.7 1.4 4.5 3.2
2-Methyl hexane N/A N/A 1.6 1.2
2-Methyl pentane N/A N/A 1.7 3.2
2-Methyl-1,3-butadiene N/A N/A
3-Methyl butanal N/A N/A
3-Methyl hexane N/A N/A 2.4 1.4
3-Methyl pentane N/A N/A 1.8 1.2
Acetaldehyde N/A N/A
Benzaldehyde N/A N/A
Butanal N/A N/A 1.1
Butane N/A N/A 1 2.1 1.2 3.5 2.3
Difluorochloromethane N/A N/A
D-Limonene N/A N/A 2.3
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1.3 1.8 1.6
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 1.5 1.1
Nonanal N/A N/A
Ocatanal N/A N/A 1.3
Octamethyl cyclotrisiloxane N/A N/A
Octane N/A N/A
Pentane N/A N/A
trans-1,4-Dimethyl cyclohexane N/A N/A
12/10/08 12/11/08 12/12/0812/8/08 12/9/08TENTATIVELY IDENTIFIED COMPOUNDS VOC concentration (ppbv)
12/15/08

67 Dominguez Elementary School - Room 9 (DZ-9) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
4-Bromofluorobenzene N/A N/A 9.66 9.52 9.74 10.01
1,3-Dimethyl cyclohexane N/A N/A 1.8
1,3-Pentadiene N/A N/A
1,4-Pentadiene N/A N/A
1-Butanol N/A N/A
1-Dodecene N/A N/A
1S-.alpha.-Pinene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A
2,2,4,6,6-Pentamethyl heptane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3,3-Trimethyl pentane N/A N/A 1.2
2,3-Dimethyl pentane N/A N/A 1.4 1.1
2-Methyl butane N/A N/A 1.6 1.1 1.5
2-Methyl hexane N/A N/A 2.4 2.2
2-Methyl pentane N/A N/A 1.1 1.2
2-Methyl-1,3-butadiene N/A N/A
3-Methyl butanal N/A N/A
3-Methyl hexane N/A N/A 5 5.1 1.3 1.5
3-Methyl pentane N/A N/A
Acetaldehyde N/A N/A 1.1
Benzaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 1.2 1 1.2
Difluorochloromethane N/A N/A
D-Limonene N/A N/A
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A
Nonanal N/A N/A
Ocatanal N/A N/A
Octamethyl cyclotrisiloxane N/A N/A
Octane N/A N/A 2.7
Pentane N/A N/A
trans-1,4-Dimethyl cyclohexane N/A N/A 1
VOC concentration (ppbv)
TENTATIVELY IDENTIFIED COMPOUNDS 12/16/08 12/17/08 12/18/08 12/19/08

68 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 5.4 3.7 6 4.5 2.9
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.7 0.5 0.7 0.6 0.6 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.01 0.01 0.01 Chloromethane 0.5 0.08 0.9 0.8 0.7 0.8 0.8 0.8
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05 0.04 0.03 0.02
Ethanol 1.5 0.30 530 150 180 170 270 200
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.3 0.2
Isopropyl alcohol(2-Propanol) 1.5 0.03 70 22 15 12 11 16 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.07 0.06 0.06 0.06 0.06 Acetone 3.0 0.08 34 30 20 28 29 21
Carbon disulfide 1.0 0.04 0.08 0.07 0.07 0.05 0.06 0.06 Methylene chloride 1.5 0.04 1.3 1 0.6 1.8 0.6 0.4
n-Hexane 0.5 0.03 1.3 1.1 1.9 1.5 0.6 0.4
2-Butanone(MEK) 1.5 0.07 2.4 2.8 1.8 2.4 2.5 1.9
Ethyl acetate 0.5 0.07 0.9 0.5 0.3 0.5 0.2 0.2
Chloroform 0.5 0.03 0.1 0.08 0.06 0.09 0.05 0.05
Tetrahydrofuran 0.5 0.07 0.7 0.6 0.1 0.5 0.3 0.2
Cyclohexane 0.5 0.04 1 0.7 2.2 9.6 0.5 0.3
Carbon tetrachloride 0.5 0.04 0.07 0.07 0.07 0.07 0.06 0.07 n-Heptane 0.5 0.04 3.6 0.7 1.7 1.1 0.6
Benzene 0.5 0.02 1.5 1.2 1 1.6 0.8 0.6
Trichloroethene 0.5 0.04 0.07 0.07 0.03 0.07 0.02 0.05
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 42 0.4 0.7 0.5 0.3 0.7 cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 6.8 4.9 3.3 9.6 3.2 2.4
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.2 0.1 0.4 0.2
Tetrachloroethene 0.5 0.04 0.3 0.2 0.08 0.3 0.1 0.09
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.9 0.8 0.5 1.7 0.5 0.4
Xylene (para & meta) 1.0 0.10 3.1 2.3 1.7 5.7 1.7 1.2
Xylene (Ortho) 0.5 0.05 1.1 0.9 0.7 1.7 0.7 0.6
Styrene 0.5 0.05 1.5 0.6 0.4 0.7 1.3 0.4
Bromoform 0.5 0.05 0.01
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 1.1 0.8 0.6 1.2 0.6 0.5
1,3,5-Trimethylbenzene 0.5 0.05 0.3 0.2 0.3 0.2 0.1
1,2,4-Trimethylbenzene 0.5 0.08 1.1 0.8 0.6 1.2 0.6 0.5 1,3-Dichlorobenzene 0.5 0.06
1,4-Dichlorobenzene 0.5 0.05 0.2 0.1 0.09 0.2 0.1 0.08
1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10
Hexachloro-1,3-butadiene 0.5 0.30 0.02
VOC concentration (ppbv)
11/24/08 11/25/0811/18/08 11/19/08 11/20/08 11/21/08SPECIATED ORGANIC COMPOUNDS

69 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.2 4.4 2.7
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.4 0.5 0.4 0.5 0.6 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.01 0.01 0.01 0.04 0.04 0.06 Chloromethane 0.5 0.08 0.8 0.6 0.6 0.9 0.6 0.6
Vinyl chloride 0.5 0.04 0.05
Bromomethane 0.5 0.05 0.06 0.05
Ethanol 1.5 0.30 260 100 310 210 63 41
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.2 0.3 0.4 Isopropyl alcohol(2-Propanol) 1.5 0.03 160 52 32 13 4.4 5.4 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.06 0.06 0.06 0.09 0.09 0.1 Acetone 3.0 0.08 26 28 14 17 13 22
Carbon disulfide 1.0 0.04 0.03 0.06 0.07 0.04 0.1
Methylene chloride 1.5 0.04 0.2 0.4 0.2 0.3 0.4 0.6
n-Hexane 0.5 0.03 0.2 1.5 0.3 0.3 0.3 0.6
2-Butanone(MEK) 1.5 0.07 2.1 3.9 1.4 2 1.2 1.6
Ethyl acetate 0.5 0.07 0.2 0.3 0.3 0.2 0.3
Chloroform 0.5 0.03 0.03 0.06 0.06 0.09
Tetrahydrofuran 0.5 0.07
Cyclohexane 0.5 0.04 0.7 0.2 0.2 0.4
Carbon tetrachloride 0.5 0.04 0.06 0.07 0.07 0.08 0.07 0.07 n-Heptane 0.5 0.04 1.4 1.3 0.2 0.5
Benzene 0.5 0.02 0.2 1.2 0.2 0.4 0.4 0.9
Trichloroethene 0.5 0.04 0.02 0.03 0.04 0.06 0.05
Bromodichloromethane 0.5 0.04 0.03
4-Methyl-2-pentanone(MIBK) 0.5 0.06 2 0.4 0.1 0.2 0.2 0.2 cis-1,3-Dichloropropene 0.5 0.04 0.02
Toluene 0.5 0.05 1 4.6 0.8 0.9 1 2.7
trans-1,3-Dichloropropene 0.5 0.04 0.02 0.02
1,1,2-Trichloroethane 0.5 0.06 0.03
2-Hexanone(MBK) 1.5 0.05 0.2 0.4 0.2 0.2 0.1 0.1
Tetrachloroethene 0.5 0.04 0.03 0.08 0.06 0.07 0.1
Chlorobenzene 0.5 0.04 0.03 0.04
Ethylbenzene 0.5 0.05 0.2 0.6 0.1 0.2 0.2 0.4
Xylene (para & meta) 1.0 0.10 0.5 2 0.3 0.4 0.4 1.2
Xylene (Ortho) 0.5 0.05 0.3 0.9 0.2 0.2 0.2 0.5
Styrene 0.5 0.05 0.1 0.8 0.1 0.1 0.1 0.9
Bromoform 0.5 0.05
1,1,2,2-Tetrachloroethane 0.5 0.08 0.03 0.04
4-Ethyltoluene 0.5 0.06 0.2 0.7 0.2 0.1 0.2 0.5
1,3,5-Trimethylbenzene 0.5 0.05 0.05 0.2 0.04 0.06 0.06 0.2 1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.8 0.2 0.1 0.2 0.5 1,3-Dichlorobenzene 0.5 0.06 0.03 0.03 0.03
1,4-Dichlorobenzene 0.5 0.05 0.05 0.1 0.03 0.04 0.04 0.06 1,2-Dichlorobenzene 0.5 0.10 0.03 0.03 0.04
1,2,4-Trichlorobenzene 1.0 0.10 0.05 0.03 0.05
Hexachloro-1,3-butadiene 0.5 0.30 0.04 0.03 0.04
VOC concentration (ppbv)
SPECIATED ORGANIC COMPOUNDS 11/26/08 12/1/08 12/2/08 12/3/08 12/4/08 12/5/08

70 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1 4.7 0.7
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.5 0.5 0.6 0.6 0.6 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.03 0.02 Chloromethane 0.5 0.08 0.5 0.5 0.4 0.6 0.5 0.5
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 18 42 6.4 60 23 99
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.2 0.4 0.2 Isopropyl alcohol(2-Propanol) 1.5 0.03 35 8.4 4.2 7.9 3.2 13 Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.04 0.04 0.06 0.03 Acetone 3.0 0.08 14 15 5.9 19 19 15
Carbon disulfide 1.0 0.04 0.03 0.06 0.04 0.02 0.02
Methylene chloride 1.5 0.04 0.2 0.5 0.2 0.5 0.8 0.3
n-Hexane 0.5 0.03 0.05 0.1 0.07 0.2 0.8 0.1
2-Butanone(MEK) 1.5 0.07 0.8 0.7 0.3 1.4 1.3
Ethyl acetate 0.5 0.07 0.2
Chloroform 0.5 0.03
Tetrahydrofuran 0.5 0.07
Cyclohexane 0.5 0.04 0.04 0.08 0.2 0.5 0.2
Carbon tetrachloride 0.5 0.04 0.03 0.03 0.06
n-Heptane 0.5 0.04 0.1 0.2 0.6
Benzene 0.5 0.02 0.07 0.2 0.2 0.1 1.1 0.06
Trichloroethene 0.5 0.04 0.02 0.06
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 0.04 0.09 2.9
cis-1,3-Dichloropropene 0.5 0.04 2.9
Toluene 0.5 0.05 0.1 0.4 0.5 0.5 3.8
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.1 0.2
Tetrachloroethene 0.5 0.04 0.01 0.03 0.02 0.02 0.1
Chlorobenzene 0.5 0.04 0.01
Ethylbenzene 0.5 0.05 0.03 0.07 0.07 0.08 0.6 0.03
Xylene (para & meta) 1.0 0.10 0.08 0.2 0.2 0.2 1.7 0.08 Xylene (Ortho) 0.5 0.05 0.04 0.1 0.1 0.1 0.6 0.03
Styrene 0.5 0.05 0.02 0.06 0.2 0.06 0.7 0.02
Bromoform 0.5 0.05
1,1,2,2-Tetrachloroethane 0.5 0.08 0.03
4-Ethyltoluene 0.5 0.06 0.04 0.07 0.1 0.08 0.5 0.03
1,3,5-Trimethylbenzene 0.5 0.05 0.02 0.03 0.05 0.03 0.1 0.01 1,2,4-Trimethylbenzene 0.5 0.08 0.04 0.08 0.2 0.08 0.5 0.04 1,3-Dichlorobenzene 0.5 0.06 0.01 0.01
1,4-Dichlorobenzene 0.5 0.05 0.01 0.02 0.01 0.01 0.04 0.01 1,2-Dichlorobenzene 0.5 0.10
1,2,4-Trichlorobenzene 1.0 0.10 0.02 0.02 0.02 0.02 0.02 Hexachloro-1,3-butadiene 0.5 0.30 0.01
VOC concentration (ppbv)
12/11/08 12/12/08 12/15/0812/8/08 12/9/08 12/10/08SPECIATED ORGANIC COMPOUNDS

71 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
Propylene 1.0 0.04 1.1 1.8
Freon 12 (Dichlorodifluoromethane) 0.5 0.04 0.4 0.4 0.4 0.4 Freon 114(1,2-Dichlorotetrafluoroethan) 0.5 0.03 0.02
Chloromethane 0.5 0.08 0.5 0.6 0.5 0.5
Vinyl chloride 0.5 0.04
Bromomethane 0.5 0.05
Ethanol 1.5 0.30 110 150 58 32
Freon 11(Trichlorofluoromethane) 0.5 0.04 0.2 0.2 0.2 0.1 Isopropyl alcohol(2-Propanol) 1.5 0.03 18 20 10 5.4
Freon 113(1,1,2-Trichlorotrifluoroethan) 0.5 0.06 0.02
Acetone 3.0 0.08 14 17 19 15
Carbon disulfide 1.0 0.04 0.04 0.01 0.03
Methylene chloride 1.5 0.04 0.4 0.2 0.2 0.3
n-Hexane 0.5 0.03 0.1 0.05
2-Butanone(MEK) 1.5 0.07 0.3 0.6 1
Ethyl acetate 0.5 0.07 0.2
Chloroform 0.5 0.03
Tetrahydrofuran 0.5 0.07
Cyclohexane 0.5 0.04 0.1 0.05
Carbon tetrachloride 0.5 0.04 0.03 0.02 0.02
n-Heptane 0.5 0.04
Benzene 0.5 0.02 0.2 0.03 0.05 0.09
Trichloroethene 0.5 0.04 0.02
Bromodichloromethane 0.5 0.04
4-Methyl-2-pentanone(MIBK) 0.5 0.06 6.6 3.5 0.2 0.8
cis-1,3-Dichloropropene 0.5 0.04
Toluene 0.5 0.05 0.6 0.07 0.3 0.2
trans-1,3-Dichloropropene 0.5 0.04
1,1,2-Trichloroethane 0.5 0.06
2-Hexanone(MBK) 1.5 0.05 0.08 0.2
Tetrachloroethene 0.5 0.04 0.03
Chlorobenzene 0.5 0.04
Ethylbenzene 0.5 0.05 0.1 0.02 0.03 0.05
Xylene (para & meta) 1.0 0.10 0.4 0.05 0.07 0.2
Xylene (Ortho) 0.5 0.05 0.2 0.04 0.06
Styrene 0.5 0.05 0.06 0.05
Bromoform 0.5 0.05
1,1,2,2-Tetrachloroethane 0.5 0.08
4-Ethyltoluene 0.5 0.06 0.2 0.02 0.03 0.05
1,3,5-Trimethylbenzene 0.5 0.05 0.01 0.02
1,2,4-Trimethylbenzene 0.5 0.08 0.2 0.02 0.03 0.05
1,3-Dichlorobenzene 0.5 0.06 0.01
1,4-Dichlorobenzene 0.5 0.05 0.03
1,2-Dichlorobenzene 0.5 0.10 0.01
1,2,4-Trichlorobenzene 1.0 0.10 0.05
Hexachloro-1,3-butadiene 0.5 0.30
VOC concentration (ppbv)
12/16/08 12/17/08 12/18/08 12/19/08SPECIATED ORGANIC COMPOUNDS

72 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
1,1-Difluoroethane N/A N/A
1-Butanol N/A N/A 1.7
1-Dodecene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 2.1
2,2,4,6,6-Pentamethyl heptane N/A N/A 1.2
2,2,4-Trimethyl pentane N/A N/A 2.8
2,2-Dimethyl hexane N/A N/A 2.1 3
2,3-Dimethyl pentane N/A N/A 1.8 1.3 1.3 1.8
2-Methyl butane N/A N/A 9.2 7.7 8.2 11 4.7 3.8
2-Methyl hexane N/A N/A 1.5 1.6 1.4
2-Methyl pentane N/A N/A 4.1 3.2 3.6 4.6 2 1.4
2-Methyl-1,3-butadiene N/A N/A 2.6 1.8 1.3 1.7 1.4 1.1
3-Methyl butanal N/A N/A
3-Methyl butane N/A N/A
3-Methyl hexane N/A N/A 1.9 1.7 1.8 1.1
3-Methyl pentane N/A N/A 2.1 1.8 2.2 2.5 1.1
3-Methyl-1,2-butadiene N/A N/A
Acetaldehyde N/A N/A 1.3 1.7 1.5 1.9 2.1
Benzaldehyde N/A N/A 1.4 1 1.5 1.8 1.6
Butanal N/A N/A
Butane N/A N/A 6.4 6.4 7.5 7.9 3.9 3.1
Butyl ester acetic acid N/A N/A 8.8
Difluorochloromethane N/A N/A 12
D-Limonene N/A N/A 1.4
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A 3.5
Hexanal N/A N/A
Isobutane N/A N/A 4.6 4.9 4.5 4.7 2.9 2.3
Methyl cyclohexane N/A N/A 1.2
Methyl cyclopentane N/A N/A 1.8 1.4 2.3 2
Naphthalene N/A N/A
Nonanal N/A N/A 1.4
Ocatanal N/A N/A 1.1 1.3
Octamethyl cyclotrisiloxane N/A N/A 5.7 5.3
Pentanal N/A N/A
Pentane N/A N/A 4.1 3.7 4.9 2.5
11/24/08 11/25/08TENTATIVELY IDENTIFIED COMPOUNDS 11/18/08 11/19/08 11/20/08 11/21/08 VOC concentration (ppbv)

73 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
1,1-Difluoroethane N/A N/A
1-Butanol N/A N/A
1-Dodecene N/A N/A 1
2,2,3,3-Tetramethyl butane N/A N/A 1.6
2,2,4,6,6-Pentamethyl heptane N/A N/A
2,2,4-Trimethyl pentane N/A N/A 1.3
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2-Methyl butane N/A N/A 2.1 8.7 2.2 2.1 2 3.9
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A 3.3 2
2-Methyl-1,3-butadiene N/A N/A 1.2
3-Methyl butanal N/A N/A 1.1
3-Methyl butane N/A N/A 1
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A 2.1
3-Methyl-1,2-butadiene N/A N/A 1.2
Acetaldehyde N/A N/A 2.8 2.7 2.1 2.7
Benzaldehyde N/A N/A 1.8 1.6
Butanal N/A N/A 1.6 1.1
Butane N/A N/A 2 8.5 2.6 2.5 1.8 2.9
Butyl ester acetic acid N/A N/A
Difluorochloromethane N/A N/A
D-Limonene N/A N/A 2.5 8
Heptanal N/A N/A 2.4
Hexamethyl cyclotrisiloxane N/A N/A 3.3 1.2
Hexanal N/A N/A 1.8
Isobutane N/A N/A 5.6 2.3 1.8 1.3 2.6
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 2.1
Naphthalene N/A N/A
Nonanal N/A N/A 1.7 1.7
Ocatanal N/A N/A 1.6 1.8
Octamethyl cyclotrisiloxane N/A N/A 6.9
Pentanal N/A N/A 1.5
Pentane N/A N/A
12/4/08 12/5/0811/26/08 12/1/08 12/2/08 12/3/08TENTATIVELY IDENTIFIED COMPOUNDS VOC concentration (ppbv)

74 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
1,1-Difluoroethane N/A N/A 2.6
1-Butanol N/A N/A
1-Dodecene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 1 1.8
2,2,4,6,6-Pentamethyl heptane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2-Methyl butane N/A N/A 2.7 4.4
2-Methyl hexane N/A N/A 1.1
2-Methyl pentane N/A N/A 1.7 3.2
2-Methyl-1,3-butadiene N/A N/A
3-Methyl butanal N/A N/A
3-Methyl butane N/A N/A
3-Methyl hexane N/A N/A 1.3
3-Methyl pentane N/A N/A 1.7
3-Methyl-1,2-butadiene N/A N/A
Acetaldehyde N/A N/A
Benzaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 1.3 2.1 3.4 1.1
Butyl ester acetic acid N/A N/A
Difluorochloromethane N/A N/A
D-Limonene N/A N/A 2.3
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1.3 2.4 1.2 1.7
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A 1.4
Naphthalene N/A N/A
Nonanal N/A N/A
Ocatanal N/A N/A
Octamethyl cyclotrisiloxane N/A N/A
Pentanal N/A N/A
Pentane N/A N/A
12/10/08 12/11/08 12/12/0812/8/08 12/9/08TENTATIVELY IDENTIFIED COMPOUNDS VOC concentration (ppbv)
12/15/08

75 Dominguez Elementary School - Room 11 (DZ-11) VOC data Reporting Limit Method Detection
(ppbv) limit (ppbv)
1,1-Difluoroethane N/A N/A
1-Butanol N/A N/A
1-Dodecene N/A N/A
2,2,3,3-Tetramethyl butane N/A N/A 1.1
2,2,4,6,6-Pentamethyl heptane N/A N/A
2,2,4-Trimethyl pentane N/A N/A
2,2-Dimethyl hexane N/A N/A
2,3-Dimethyl pentane N/A N/A
2-Methyl butane N/A N/A
2-Methyl hexane N/A N/A
2-Methyl pentane N/A N/A
2-Methyl-1,3-butadiene N/A N/A
3-Methyl butanal N/A N/A
3-Methyl butane N/A N/A
3-Methyl hexane N/A N/A
3-Methyl pentane N/A N/A
3-Methyl-1,2-butadiene N/A N/A
Acetaldehyde N/A N/A
Benzaldehyde N/A N/A
Butanal N/A N/A
Butane N/A N/A 1.1
Butyl ester acetic acid N/A N/A 1.6
Difluorochloromethane N/A N/A
D-Limonene N/A N/A
Heptanal N/A N/A
Hexamethyl cyclotrisiloxane N/A N/A
Hexanal N/A N/A
Isobutane N/A N/A 1.4
Methyl cyclohexane N/A N/A
Methyl cyclopentane N/A N/A
Naphthalene N/A N/A 1.2
Nonanal N/A N/A
Ocatanal N/A N/A
Octamethyl cyclotrisiloxane N/A N/A
Pentanal N/A N/A
Pentane N/A N/A

TENTATIVELY IDENTIFIED COMPOUNDS
VOC concentration (ppbv)
12/16/08 12/17/08 12/18/08 12/19/08

EXHIBIT C
Draft
Planning
HealtHy
Places
a guiDebook
for
aDDressing
l
ocal sources of
air Poll
utants in
community Planning
may
2016
bay area air Qualit y management Distr ict
1 Planning Healthy Places
A Guidebook for Addressing Local Sources of Air Pollutants in Community Planning This report is for information purposes only. Recommendations are advisory and should be followed by local governments at their own discretion. This report may inform local land use planning in the Bay Area, but does not commit local governments or the Air District to any specific course of regulatory action.
i
planning healthy places | 2016 | Bay area air Quality Management District Planning Healthy Places 1
eff
orts to reduce air Pollution & exposur
e
2 on-going challenges
2 clima
te change
& Public Health
3 Health impacts
4 sensitive
Populations & land uses
6 P
lanning stra
tegies
8 best P
ractices to reduce emissions
8 best P
ractices to reduce exposur
e
10 further study areas
12 applying the
Planning stra
tegies
13 implementa
tion
14 P
lanning for construction
25 refer
ences
27 glossar
y
31 appendix a: best Pr
actices to reduce emissions
33 appendix b: best Pr
actices to reduce exposur
e
37 appendix c: technical not
es 42
table of contents
ii
planning healthy places | 2016 | Bay area air Quality Management District WHat is Planning HealtHy Pla ces?
Planning Healthy Places is a guidebook that provides important air quality and public health information, and is intended to assist local governments in addressing and minimizing potential air quality issues by providing tools and recommended best practices that can be implemented to reduce exposure and emissions from local sources of air pollutants. The Bay Area Air Quality Management District (Air District) provides this information to be considered by land use planners, elected officials, developers, community groups, public health advocates, and anyone interested in integrating land use, air quality and public health. The Air District intends that the information and recommendations in this guidebook be incorporated into city or county General Plans, neighborhood or specific plans, land use development ordinances, or into single projects. The Air District’s primary goal in providing this guidebook is to support and promote infill development - which is vital to reducing vehicle miles traveled (VMT) and the associated air pollutant and greenhouse gas (GHG) emissions - while minimizing local exposure to air pollution and promoting clean, healthy air for existing and future residents.

The Air District acknowledges that many factors other than solely air quality play a role in public health and healthy communities, including adequate housing, access to food and healthcare services, opportunities for active transportation and exercise, water quality, outdoor space, and more.

There are many elements to consider and balance when planning for healthy communities, and the Air District encourages local governments and other decision-makers to use this guidebook in conjunction with resources on other aspects of public health. iii
planning healthy places | 2016 | Bay area air Quality Management District Planning HealtHy Pla ces
Protecting Bay Area public health, air quality and the climate is the core mission of the Bay Area Air Quality Management District (Air District). Clean air is fundamental to public health and the high quality of life that makes the Bay Area a desirable place to live, work and visit. There are millions of emission sources in our region - oil refineries, industrial manufacturers, gas stations, cars and trucks, construction equipment, lawn mowers, fireplaces, consumer products, and more - that collectively emit many different types of air pollutants that are harmful to public health and the global climate.

Through Air District and state level regulations and incentive programs, tremendous progress has been made in improving air quality. However, despite this progress, the quest for clean air continues and the challenges ahead seem daunting in our motor vehicle driven society. Transportation related emissions are significant sources of air pollutants such as fine particulate matter (fine PM) and toxic air contaminants (TACs) that have adverse health effects; further reductions in transportation emissions will result in health benefits. Additionally, cars and trucks represent the single largest source of greenhouse gas (GHG) emissions in the Bay Area; reducing these transportation related emissions is critical to achieving GHG reduction goals to stabilize the climate.

New health studies are continually providing evidence that air pollutants are harmful to our health at lower levels than previously thought. Additionally, some communities and neighborhoods in the region experience relatively higher air pollution levels and corresponding negative health impacts than others. Levels of local air pollutants such as fine PM and TACs are highest near air pollution sources, such as freeways, heavily trafficked seaports, and large industrial facilities. In addition, there are many smaller, more discrete sources of air pollution - including gas stations and back-up diesel generators - that exacerbate conditions in communities with already elevated levels of air pollution that can be harmful to people’s health.

Placing residences in infill locations near jobs, transit and other services is increasingly important to help to reduce vehicle miles traveled, which will in turn improve overall air quality and reduce GHGs.

However, careful planning is needed in areas that may have high localized levels of air pollution.

Development in locations near major sources of air pollution could also result in increased local exposure to unhealthy levels of air pollutants to the people living there unless steps are taken to minimize exposure and reduce emissions. To assist local governments in addressing and minimizing potential air quality issues, the Air District is releasing this guidebook which provides recommended best practices that can be implemented to reduce exposure and emissions from local sources of air pollutants. Local governments, developers, and other interested stakeholders are encouraged to utilize this guidebook to implement these air quality solutions. 1 planning healthy places | 2016 | Bay area air Quality Management District efforts to reDuce
air
Pollution
& exPosure
For over sixty years the Air District has
been implementing programs to reduce air
pollution and public exposure. Air District
actions include: conducting air monitoring
and modeling to identify locations of
elevated pollution concentrations and
to assess potential health impacts (see
Figure 1); adopting regulations, plans
and guidelines to reduce emissions from
stationary (i.e. industrial) and mobile (i.e.
cars) pollution sources; enforcing existing
Air District regulations and the state’s
mobile source regulations; providing
grants and incentives to reduce emissions
from mobile sources (targeted in the Bay
Area’s most impacted communities);
and outreach and education to Bay Area
residents on air quality issues and trends.

These efforts, in combination with the
California Air Resources Board’s (ARB)
vast array of regulations to reduce criteria pollutants, and toxic air contaminant (TAC’s) including diesel particulate matter emissions from cars, trucks and industrial facilities, have been successful. Levels of criteria pollutants including fine particulates (fine PM), and TACs have been reduced dramatically in the Bay Area. The region has seen a fourfold reduction in cancer risk due to air toxics since 1990.
on-going cHallenges
However, despite these accomplishments, some communities in the Bay Area are still disproportionately impacted by unhealthy levels of air pollution. The Air District’s Community Air Risk Evaluation (CARE) program, which was initiated to identify, evaluate, and reduce health risks associated with exposure to air pollution, has conducted extensive research into identifying where disproportionately impacted communities are located. The CARE program examines TAC and fine PM emissions data from stationary sources, area sources, and on-road and off-road mobile sources, as well as ozone standard exceedance data, and health data for communities throughout the Bay Area to assess the potential exposure and health risks to sensitive populations such as children and the elderly. Identifying impacted communities and the significant air pollution sources within communities has helped the Air District to target emission reduction strategies for specific sources, and identify Figure 1: 2013 Impacted Communities
2 planning healthy places | 2016 | Bay area air Quality Management District potential land use mitigation strategies to further reduce exposure in these disproportionately impacted areas. Figure 1 shows the most impacted communities in the Bay Area, as identified by the CARE program. It is particularly important for local governments within these CARE communities to implement the recommendations in this guidebook, because existing residents in these areas are exposed to higher concentrations of air pollution than other areas throughout the region.

The information presented in this guidebook builds upon the work conducted under the CARE program. The maps produced in conjunction with this guidebook are based upon local modeling conducted to identify potential impacts of air pollution at a fine grained, smaller scale (down to a 20m X 20m grid), as opposed to the region-wide maps conducted by the CARE program to identify communities which are, overall, more impacted by air quality than others. climate cHange & Public HealtH
As described previously, despite progress in reducing air pollution, some Bay Area residents are disproportionately impacted from exposure to air pollutants, and climate change threatens to further exacerbate air pollution. Longer and more severe heat waves will increase emissions of ozone precursors, accelerate ozone formation, and reduce wind and vertical mixing that disperse pollutants.

Higher t
emperatures and drought conditions will create the conditions that lead to more frequent and more severe wildfires. As a conseq
uence of climate change, Bay Area residents will
be susceptible to increased respiratory and cardiovascular disease, as well as heat stroke and heat exhaustion.

And the Ba
y Area communities that are already most impacted by air pollution will also be most vulnerable to the negative health impacts related to climate change. Theref ore, it is
more important than ever that we plan our communities to safeguard public health and minimize exposure to air pollution.
3 planning healthy places | 2016 | Bay area air Quality Management District HealtH imPac ts
It is important to understand the potential health outcomes from exposure to certain types of air pollutants. Fine particulate matter and toxic air contaminants are the air pollutants which pose the greatest risk to people’s health in the Bay Area.

Toxic Air Contaminants (TACs): The California Air Resources Board (ARB) is responsible for identifying TACs, which are defined as pollutants that “may cause or contribute to an increase in deaths or in serious illness, or which may pose a present or potential hazard to human health”. TACs are emitted from a wide range of sources in the Bay Area including diesel engines, cars, trucks, industrial processes, and gas stations. Types of TACs include diesel particulates, lead, benzene, formaldehyde, and hexavalent chromium, to name a few. These TACs, and others, are present in Bay Area air. Diesel particulate matter is the most significant toxic air contaminant, accounting for roughly 85% of the cancer risk from air toxics in the region. Exposure to TACs can cause serious health effects, including cancer and birth defects. Other adverse health effects can include damage to the immune system, neurological, reproductive (reduced fertility), developmental, and respiratory problems.

Fine Particulate Matter (PM): Epidemiological studies have established that exposure to fine particulate matter has serious adverse health impacts. “Fine” particulate matter refers to very small particles (less than 2.5 microns in diameter) that can travel deep into the lungs and enter the bloodstream. Fine PM originates from a variety of sources, including fossil fuel combustion, residential wood burning and cooking, and natural sources such as wildfires and dust. Researchers established long ago that exposure to PM has negative effects on the respiratory system, such as triggering asthma attacks, aggravating bronchitis, and diminishing lung function. More recent studies have found that fine PM can also harm the cardiovascular system, and may cause atherosclerosis (hardening of the arteries), ischemic strokes (caused by an obstruction of the blood supply to the brain), and heart attacks. Because of the serious cardiovascular effects of exposure to PM, studies have found a clear correlation between PM levels and exposure, and mortality.

Studies also indicate that exposure to PM may be related to other negative health effects, including impacts on the brain such as reduced cognitive function, as well as an increased risk of diabetes.

Recent research in the United States and internationally has begun to examine the potential health effects of even smaller particles known as ultrafine particles (UFP), which are particles less than

1.0 microns in diameter. Findings to date demonstrate that UFP can evade the body’s defense mechanisms and penetrate deeply into lungs, bloodstream and organs. Exposure to fine PM remains the leading public health risk and contributor to premature death from air pollution in the Bay Area.

For more information on fine PM and associated health effects, see the Air District’s informational report entitled, “Understanding Particulate Matter: Protecting Public Health in the San Francisco Bay Area” (2012).

As discussed previously, air pollution control programs and strategies in the state and the Bay Area have helped improve region-wide air quality significantly, despite growth in population and vehicle-miles traveled. However, these regional strategies are not always sufficient in protecting the 4 planning healthy places | 2016 | Bay area air Quality Management District health of people who live nearby sources of localized fine PM and toxic air contaminants. Research and epidemiological findings confirm that harmful TAC and fine PM pollutants are found in higher concentrations closer to their source of origin.

A number of health studies have shown that increased pollutant levels occur near busy roadways.

For example, according to ARB, a study conducted in the Bay Area found concentrations of traffic- related fine PM and TACs to be highest within 300 meters downwind of freeways. Accordingly, the associated adverse health impacts are elevated in these areas. Evidence from recent studies is rapidly accumulating that indicates that people who live near busy roadways/freeways and other major sources of pollution are more likely to suffer from adverse health effects, including respiratory ailments such as reduced lung function and asthma, cardiovascular disease, low birth weight and pre-term birth, and have higher mortality rates than people who do not live in close proximity to such pollution sources. For instance, a Los Angeles County study found that pregnant women who live within 750 feet of high-volume roads have a 10-20% higher risk of early birth and low-birth weight babies. Health impacts to children living near roadways have been well documented and include wheezing, reduced lung function, and asthma. Other key health findings from health studies include:
•
Increased premature death from near
-roadway exposure of fine PM (Caiazzo,
et al. 2013);
• Emerging consensus that exposure t
o near-roadway traffic-related pollution
causes the development of asthma in children (Perez, et al. 2012); • Increased non-a
sthma respiratory symptoms, impaired lung function, all-cause mortality, cardiovascular mortality, and cardiovascular morbility from exposure to traffic emissions (Boehmer, et al. 2013);
•
Exposure to fine PM and o
ther traffic-related particles were associated with
decreased birth weight in California (Basu, 2013).

In response to earlier findings from the research, ARB developed recommendations for restricting sensitive land uses near heavily trafficked freeways/roadways and other types of air pollution sources (ARB’s Air Quality and Land Use Handbook: A Community Health Perspective, April 2005). The U.S. EPA and Federal Highway Administration recently adopted new rules requiring agencies to demonstrate that transportation projects involving significant increases in diesel traffic do not create hazardous “hot spots”. The U.S. EPA has also established new air monitoring requirements for locations near busy freeways in order to characterize local air pollutant concentrations, as well as associated exposures to sensitive populations. The implications of localized air pollutant concentrations and associated adverse health impacts make it important that local planners and policy makers take into account the local effects of air pollution on new development, as well as the effect of existing and new sources of air pollution on existing communities.
5 planning healthy places | 2016 | Bay area air Quality Management District sensitive PoPulations & lanD uses
Children and infants are among the most
susceptible to air pollution due to their
developing lungs, higher inhalation rates,
narrower airways, and less mature immune
systems. Children with allergies may also have
an enhanced allergic response when exposed
to particulate matter pollution. Other sensitive
populations include the elderly, pregnant
women, and those with respiratory or
cardiovascular illnesses affected by air
pollution. In recent years, the scientific
understanding of the range of health effects
of air pollution has increased, and numerous
studies are finding adverse health effects from air pollution at levels once considered safe.

Sensitive land uses are places where sensitive populations are most likely to spend their time, such as schools, playgrounds, day care centers, nursing homes, medical facilities, and residential communities. Employment centers and commercial areas (that do not include residential or day care facilities) are generally not considered to be sensitive land uses, although local governments may apply recommendations in this document to such land uses if they so choose. location, location, location
The Air District has a long history of supporting land use strategies that will reduce automobile use and emissions. Steps taken by the Air District to promote such land use strategies include the provision of tools such as CEQA guidelines for land use development projects, a transportation demand management tool, and control strategies in the 2010 Clean Air Plan (and prior clean air plans) on transportation and land use. The Air District collaborated with the Metropolitan Transportation Commission in 2014 to develop and jointly adopt the Bay Area Commuter Benefits Program which promotes the use of alternative transportation modes such as bicycling, walking, and taking transit. The Air District also provides grants for bicycling, ridesharing and shuttle programs, for example Bay Area BikeShare, to reduce on-road vehicle emissions and promote sustainable transportation modes.

Accordingly, the Air District strongly supports local and regional efforts to reduce vehicle miles traveled and promote “focused growth”, i.e. infill, transit-oriented, and mixed-use development throughout the region. Building such communities is critical to achieving reduced vehicle miles traveled, which will assist the Bay Area in attaining and maintaining health-based ambient air quality standards; in achieving continued reductions in TACs and fine PM from mobile sources; and in meeting GHG reduction goals. Focused growth strategies have the long-term benefit of improving 6 planning healthy places | 2016 | Bay area air Quality Management District overall air quality while also providing many other benefits to the Bay Area environment, including the preservation of natural land and open space, improved water quality, and protection of habitat and native wildlife species. Focused growth also provides important economic and equity benefits, including reduced traffic congestion and lower transportation costs, more housing options, and better access to jobs. Plan Bay Area, approved in July 2013, is the Bay Area’s long-range plan to meet the requirements of SB 375 and advance focused growth initiatives which will reduce greenhouse gas emissions, improve regional air quality, expand housing and transportation choices, and build a strong regional economy. Plan Bay Area is an important step in creating healthier communities in our region, and the Air District strongly supports its initiatives.

However, despite the many long-term benefits of focused growth, the Air District cautions that locating sensitive populations in close proximity to major sources of air pollution (such as freeways and large industrial facilities) can expose people to harmful air pollution. As noted, concentrations of TACs and fine PM can be substantially elevated adjacent to and downwind of these sources, putting people who live there at risk of developing adverse health effects. Fortunately, negative health effects can be greatly reduced when distance is increased between the source of air pollution and sensitive land uses, and/or when measures are taken to reduce to remove air pollution (for example, through the use of air filtration). Accordingly, the Air District has provided a list of best practices that should be applied when placing sensitive land uses in areas with high levels if air pollution or in close proximity to local sources of air pollution.

The Air District acknowledges that local governments consider and balance many factors when making local land use decisions. This guidebook provides public health and air quality information to be considered along with other issues, such as housing needs, economic development priorities, and other quality of life issues. As previously stated, the overarching goal of this guidebook is to support and encourage infill development while promoting clean, healthy air for existing and future residents. Careful community planning can address the competing issues created by the need for infill development, while also protecting public health. This guidebook provides local governments with the information and tools needed to make their communities as health-protective as can be (from an air quality perspective).
7 planning healthy places | 2016 | Bay area air Quality Management District Planning strategies
Planning Healthy Places recommends three primary strategies: (1) reduce or prevent emissions from pollution source(s) when possible; (2) implement best practices where appropriate to reduce exposure to harmful pollutants; and (3) perform a more detailed study of an area when necessary.

These recommendations are all described in detail in the following section. A flowchart (pg. 13) and an interactive map are available to assist in identifying where best practices and further study should be applied. The locations shown in purple on the Air District’s mapping tool represent where the Air District recommends implementing best practices. The locations shown in blue on the mapping tool represent where the Air District recommends conducting further study. See Figure 2 on pg. 10 for an example of the map, and visit www.baaqmd.gov/planninghealthyplaces for the complete map. imPlement best Practices to reDuce emissions
One of the most effective ways to reduce the public’s exposure to harmful air pollution is to reduce emissions of TACs and fine PM released into the Bay Area air basin. Several agencies at various levels of government work to reduce air pollution. Air quality is regulated at the federal level by the U.S. EPA, at the state level by ARB, and by regional air districts. The Air District implements many programs to reduce the amount of air pollution emitted from stationary and mobile sources of air pollution.

However, with over ~19,000 permitted stationary sources of emissions, 5 oil refineries, 150 million vehicle miles driven per day, and numerous sea ports and airports, the cumulative effects of all these sources cannot be completely eliminated.

Local governments can complement federal, state, and regional air quality programs to help protect residents of the Bay Area by implementing strategies that reduce emissions, and therefore the public’s exposure to TACs and fine PM, through their land use authority and adoption of local ordinances. Examples include policies that limit the use of diesel generators, or control their emissions; limit the idling of trucks to 2 minutes or less; require the electrification of loading docks in new and existing commercial land uses; transportation demand management strategies; traffic management strategies, and stipulations on development projects to use only the cleanest equipment, vehicles and fuel during construction (a complete list of construction measures, which can be adopted as standard conditions of approval, begins on pg. 25). Local action to reduce air pollutant emissions has the benefit of protecting both existing and future residents from the potential adverse health effects from exposure to air pollution.

The Air District recommends that local governments adopt, as policies and/or enforceable ordinances, the following “best practices to reduce emissions”. Implementing as many “best practices to reduce emissions” as is feasible will reduce potential health risks to the greatest extent. Best practices to reduce exposure to air pollution are discussed beginning on pg. 10.
8 planning healthy places | 2016 | Bay area air Quality Management District best Practices to reduce emissions of local air Pollution Retrofit Generators to Low or Zero Emitting Technology
Encourage or require existing uses to retrofit generators with Best Available Control Technology to meet ARB’s Tier 4 emission standards. Encourage the use of zero emission back-up power.

Electrify Loading Docks
Require the electrification of all loading docks to facilitate plug-in capability, and encourage or require trucks to utilize grid power in order to deliver goods.

Limit Idling Times
Prohibit trucks from idling for more than two minutes, or prohibit idling altogether.

Promote Zero Emission Vehicles and Alternative Fuels
Promote the use of zero emission vehicles and equipment, as well as renewable fuels (such as biogas).

Promote or Require the Use of Transportation Refrigeration Units (TRU) The use of TRU’s allows delivery trucks to maintain refrigeration in lieu of running/idling the main engine, thereby reduces emissions of diesel PM and TACs.

Transportation Demand Management (TDM) Strategies
Require the implementation of as many TDM strategies as feasible into projects. Examples include, but are not limited to, parking pricing strategies; parking maximums; mandated parking spaces for car-sharing programs; the provision of transit passes in residential, commercial and office developments; charging stations for electric vehicles; bicycle lockers or racks; teleworking policies; bicycling improvements; and more. For a recommended list of TDM strategies, consult the Air District’s TDM tool: http://www.baaqmd.gov/plans-and-climate/air-quality-plans/smart-growth.

Traffic Management Strategies
Implement traffic circles at intersections, and lower speed limits. Consistent findings from multiple studies indicate that stop-and-go driving, vehicle idling, and deceleration/acceleration create hot spots. Additionally, many studies show that there are optimal speed limit ranges that result in lower emissions. As a co-benefit, these actions can enhance the pedestrian and bicycling environment.

A full description and detail on each best practice to reduce emissions is located in Appendix A.
9 planning healthy places | 2016 | Bay area air Quality Management District imPlement best
Pra
ctices to
reDuce
exPosure
In recent years, communities
throughout California have been
investigating and implementing best
practices to reduce local exposure to
air pollution. Reducing exposure to
harmful air pollutants is not the same
as reducing actual emissions. However,
there are a variety of practices that are
effective, technically feasible, relatively
low cost, and have demonstrated the
ability to reduce people’s exposure to
air pollution, and therefore minimize
the potential adverse health effects.

Many such best practices can be easily
replicated from one jurisdiction to
another.

The best practices to reduce exposure are generally oriented for new development.

However, many of the best practices to reduce exposure, such as installing air filters, can also be implemented at existing development, though implementation may be more difficult or costly.

The Air District recommends implementing all “best practices to reduce exposure” that are feasible and applicable to a project or plan in locations identified by the Air District as likely to experience elevated levels of air pollution, which are depicted in purple on the Air District’s mapping tool (see FIgure 2 for an example, visit www.baaqmd.gov/ planninghealthyplaces to access the tool).

Figure 2
10 planning healthy places | 2016 | Bay area air Quality Management District summary of best Pr actices to reduce exposur e to local air Pollution Health Protective Distances
Plan sensitive land uses as far from local sources of air pollution such as freeways as is feasible.

Install Air Filters
Install air filters rated at a minimum efficiency reporting value (MERV) 13 or higher in buildings associated with sensitive land uses (e.g. schools, residences, hospitals).

Project Phasing
When applicable, and when development is being phased over time (i.e. being built over several years), build residential units and/or sensitive land uses that are closest to the emissions source at the latest date in the future (e.g. in year 5 vs. year 1).

Building Site Design and Operations
When designing a project site or developing a plan area, place sensitive land uses as far away from emission sources (including loading docks, busy roads, etc.) as is feasible. Place open space, commercial buildings, or parking garages between sensitive land uses and air pollution sources. This will help to create a “buffer” separating housing and other sensitive land uses away from air pollutants. Locate operable windows, balconies, and building air intakes as far away from any emission source as is feasible.

Incorporating open space (i.e. parks) between buildings can improve air flow and air pollution movement.

Barriers (sound walls)
Consider incorporating solid barriers into site design, similar to a sound wall, between buildings and sources of air pollution (for example, a freeway).

Vegetation
Plant dense rows of trees and other vegetation between sensitive land uses and emission source(s).

Large, evergreen trees with long life spans work best in trapping air pollution, including: Pine, Cypress, Hybrid Poplar, and Redwoods.

Consider Limiting Ground Floor Uses
Consider limiting sensitive land uses on the ground floor units of buildings near non-elevated sources, e.g. ground level heavily traveled roadways and freeways.

Alternative Truck Routes
Truck routes can be planned or re-rerouted through non-residential neighborhoods, and to avoid other sensitive land uses such as daycare centers, schools, and elderly facilities.

A full description and detail on each Best Practice to Reduce Exposure to air pollution is located in Appendix B.
11 planning healthy places | 2016 | Bay area air Quality Management District furtHer stuDy areas
The Air District has identified a number of areas within the Bay Area where additional analysis (i.e. further study) is recommended to assess the local concentrations of TACs and fine PM, and therefore the health risks from air pollution. These areas are characterized by “large and complex” industrial facilties such as oil refineries, large airports, and seaports, etc., and the Air District recommends using caution when considering sensitive land uses in these areas. More information on “large and complex” sources is below.

Conducting “further study” would entail air quality modeling to more precisely determine fine PM concentrations and/or to estimate increased health risks from air toxics to determine if there is an unacceptable level of health risk, and to identify measures that can be implemented to reduce the health risks to acceptable levels. Air District staff can provide assistance in conducting “further study”, including providing emissions data and information on specific air pollution sources. Once further study is complete, Air District staff can assist in identifying the best measures to reduce health risks. Local jurisdictions or project applicants can request Air District assistance with the “further study” process by contacting the Air District. Contact information is available at www. baaqmd.gov/planninghealthyplaces.

In conjunction with this guidebook is a mapping tool produced by the Air District, which shows where the “large and complex” sources are located. Visit www.baaqmd.gov/planninghealthyplaces to view the maps. The locations depicted in blue show the location of the “large and complex” sources, and are designated as further study. The flowchart on the next page provides an explanation on determining if a project or plan area may be in a location with elevated levels of air pollutants, and where the Air District recommends conducting further study versus implementing best practices. large and/or complex sourc es
Large and complex sources, for example oil refineries or seaports, can emit relatively high levels of TACs and fine PM. There are typically numerous emission sources within each of these facilities, making it difficult to characterize the specific local variations of concentrations of TACS and fine PM within the surrounding community.

Larger gas stations with higher volume throughput are considered complex sources due to the type of emissions they release. Gas stations emit TACs that are primarily gaseous in nature. Because some of the best practices discussed previously (e.g. air filters) solely reduce fine (and coarse) PM, a more detailed local analysis is necessary to determine potential impacts of gaseous air pollutants and to identify appropriate health protective measures. Gas stations are required to install best available control technology as part of their permit from the Air District. The control technologies reduce upwards of 95% of their emissions, but not all of them. Therefore, aside from increasing the distance between these sources and sensitive land uses, there are fewer options to reduce exposure from these source types. Carbon filters can be used in building ventilation systems to remove odors, gases and vapors; however they are not commonly used in residential buildings due to cost and maintenance requirements.
12 planning healthy places | 2016 | Bay area air Quality Management District aPPlying tHe Planning
str
ategies
The flowchart below provides a general overview for determining if a particular planning area or project site may be located in an area with elevated concentrations of air pollution, and how to address such situations during the planning process. To accompany the flow chart, the Air District provides an interactive mapping tool of Bay Area communities which identifies the locations that are characterized by elevated air pollution levels or the presence of “large and complex” sources. The interactive mapping tool depicts areas where the Air District recommends implementing best practices, and where the Air District recommends conducting further study. The mapping tool also quickly shows where no additional analysis or best practices are recommended (from an air quality perspective). Visit www.baaqmd.gov/planninghealthyplaces to access the mapping tool.

Instructions: Open the Air District’s interactive mapping tool. Find your project or plan area by using the search function in the map. Consult the flow chart below.

1. is your Project or Plan in a blue area?
--> yes: conduct further study (see pg. 12).
--> no: go to step 2.

2. is your Project or Plan in a PurPle area?
--> yes: implement best pr actices to reduce exposure (see pg. 10). --> no: no fur
ther analysis is recommended.
13 planning healthy places | 2016 | Bay area air Quality Management District imPlementation
Local government agencies can utilize a variety of strategies to reduce exposure to, and emissions of, air pollution, including the adoption of air quality-specific ordinances (e.g. San Francisco’s Article 38); standard conditions of approval relating to air quality (e.g. Oakland’s Standard Conditions of Approval); and the incorporation of air quality-related policies and measures into general plans and other planning documents (e.g. Richmond, San Jose, Hayward, and Santa Clara County General Plan updates). Several examples of local government actions are described below. The examples are offered to demonstrate that there are ways in which local government agencies can pro-actively address local sources of air pollution within their communities. The Air District recommends that local governments implement policies and/or ordinances that are clear and enforceable, and include a mechanism for monitoring. Strong language in policies and/or ordinances will increase effectiveness of the action.

These examples may change or be updated over time. Visit the Air District’s website, www.baaqmd.gov/planninghealthyplaces to view any additions or revisions to the list of case studies highlighted below. The Air District also encourages readers to visit each jurisdiction’s website to determine the most up-to-date policies and requirements. city of san francisco, article 38
Article 38 (originally adopted in 2008 and updated in 2014), intended to protect health and welfare in San Francisco, established Air Pollutant Exposure Zones (Zones) and requires enhanced ventilation systems to be installed for all urban infill sensitive land use development within those Zones. San Francisco collaborated with the Air District to create a map of the Air Pollutant Exposure Zones based on air quality modeling. These Zones depict all locations within San Francisco where the estimated cumulative PM2.5 concentration is greater than 10 micrograms/m
3 or where the cumulative excess cancer risk of cancer from air pollutants is greater than 100 in a million. Additionally, the Zones include all locations within 500 feet of any freeway, even if those locations were not otherwise captured by modeling estimates. The Zones also incorporate additional areas of concern, which include zip codes with high hospitalization rates and emergency room visits for air pollution-related conditions (such as asthma, pneumonia, etc.) and concentrations of PM2.5 greater than 9 micrograms/m
3 or cumulative excess cancer risk is greater than 90 in a million.

Article 38 requires enhanced ventilation systems “capable of achieving the protection from particulate matter (PM2.5) equivalent to that associated with MERV 13 filtration (as defined by ASHRAE standard 52.2)” to be installed in sensitive use buildings that are identified within the Air Pollutant Exposure Zones that are either a) newly constructed; b) undergoing a “major alteration to existing building”; or c) subject of an application for a Planning Department-permitted Change of Use.

Additional information, including a map of the Air Pollutant Exposure Zones, is located on the City of San Francisco’s Article 38 webpage.
14 planning healthy places | 2016 | Bay area air Quality Management District san francisco clean construction orDinance
In April 2007, the City and County of San Francisco (City) adopted an Ordinance requiring public projects to reduce emissions at construction sites starting in 2009. In March 2015, the City expanded the existing Ordinance to require public projects to further reduce emissions at construction sites in certain areas with high levels of background concentrations of air pollutants. The revised Clean Construction Ordinance became operative on September 6, 2015 and contains the following requirements:
•
Use Tier 2 or higher engines and the most eff
ective Verified Diesel Emission
Control Strategies (VDECS) available for the engine type (Tier 4 engines automatically meet this requirement) as certified by the California Air Resources Board (ARB).
•
Prohibit por
table diesel engines where access to alternative sources of power are available.
• Restrict idling t
o two minutes.
• Properly maintain and tune eq
uipment in accordance with manufacturer
specifications.

A Construction Emissions Minimization Plan is required for all construction projects within an Air Pollution Exposure Zone, which must include the following:
•
An equipment in
ventory which shall include estimates of the construction timeline by phase with description of each piece of off- road equipment required for each phase.
•
Signage indicating idling limits and engine/Verified Diesel Emission Contr ol
Strategies requirements.
• Certification Stat
ement.

Monitoring of all construction activities including:
• An equipment in
ventory which shall include estimates of the construction timeline by phase with description of each piece of off- road equipment required for each phase.
•
Quart
erly reports documenting compliance with the Emissions Plan which shall be maintained at the project site.
15 planning healthy places | 2016 | Bay area air Quality Management District • Final report summarizing construction activities.
city of san francisco, community
risk
reDuc
tion Plan
The City and County of San Francisco is developing a Community Risk Reduction Plan (CRRP). The purpose of the CRRP is to protect human health through the reduction of emissions and exposure to ambient air pollution in the City and County of San Francisco. The CRRP is expected to establish citywide objectives and targets for air quality improvement and a set of local actions to reduce health impacts for disproportionately exposed communities in San Francisco. city of oaklanD, st anDarD
conDitions
of aPPr
oval
To help clarify and standardize analysis and decision-making in the environmental review process in the City of Oakland, the City established standard conditions of approvals (SCAs) that apply to all development projects, depending upon the specific circumstances of each project. The SCAs are designed to substantially mitigate environmental effects. There are a number of SCAs on environmental issues ranging from aesthetics, to air quality, to transportation. The SCAs are part of the municipal code, formally adopted by the Oakland City Council in 2008. They were most recently updated in July 2015.

Oakland’s SCA’s for air quality relate to construction and operations. The SCA’s for construction require that “all projects involving construction activities shall implement all of the following applicable air pollution control measures during construction of the project”. The SCAs include a number of “basic controls” for dust and exhaust related construction emissions. There are also “enhanced controls” for construction that apply to projects of certain criteria, such as large residential projects, demolition projects, etc. These projects must apply all “basic” and “enhanced” controls (which include additional measures addressing dust and exhaust related emissions).

The City of Oakland also requires conditions to all projects that meet the following criteria:
a. The project involves any of the following sensitive land uses: residential uses; new or expanded daycares, schools, parks, nursing homes, or medical facilities; AND b. The project is located within 1,000 (or other distance as specified below) of one or more of the following sources of air pollution:
•
Free
way;
• Roadw
ay with significant traffic (at least 10,000 vehicles/day); 16 planning healthy places | 2016 | Bay area air Quality Management District • Rail line (ex cept BART) with over 30 trains per day; • Distribution center that accommodat
es more than 100 trucks per day, more
than 40 trucks with operating TRU units per day, or where the TRU unit operations exceed 300 hours per work week.
• Major rail or truck yar
d (such as the Union Pacific rail yard adjacent to the
Port of Oakland);
• Ferr
y Terminal;
• Stationary pollutant sour
ce requiring permit from BAAQMD (such as a diesel
generator);
• Within 0.5 miles of the Por
t of Oakland or Oakland Airport;
• Within 300 fee
t of a gas station;
• Within 300 fee
t of a dry cleaner with a machine using PERC (or within 500 feet of a dry cleaner with two or more machines using PERC); AND c. The project exceeds the health risk screening criteria after a screening analysis is conducted in accordance with the BAAQMD CEQA Guidelines.

Health Risk Reduction Measures
Requirement: The project applicant shall incorporate appropriate measures into the project design in order to reduce the potential health risk due to exposure to toxic air contaminants.

The project applicant shall choose one of the following methods:

1. The project applicant shall retain a qualified air quality consultant to prepare a Health Risk Assessment (HRA) in accordance with California Air Resources Board (CARB) and Office of Environmental Health and Hazard Assessment requirements to determine the health risk of exposure of project residents/occupants/users to air pollutants. The HRA shall be submitted to the City for review and approval. If the HRA concludes that the health risk is at or below acceptable levels, then health risk reduction measures are not required. If the HRA concludes that the health risk exceeds acceptable levels, health risk reduction measures shall be identified to reduce the health risk to acceptable levels. Identified risk reduction measures shall be submitted to the City for review and approval and be included on the project drawings submitted for the construction-related permit or on other documentation submitted to the City;
OR

2. The project applicant shall incorporate the following health risk reduction measures into the project. These features shall be submitted to the City for review and approval and be included on the 17 planning healthy places | 2016 | Bay area air Quality Management District project drawings submitted for the construction-related permit or on other documentation submitted to the City:
• Installation of air filtration to reduce cancer risks and P articulate Matter (PM)
exposure for residents and other sensitive populations in the project that are in close proximity to sources of air pollution. Air filter devices shall be rated MERV-13 [MERV-16 for projects located in the West Oakland Specific Plan area] or higher. As part of implementing this measure, an ongoing maintenance plan for the building’s HVAC air filtration system shall be required. •
Where appropriat
e, install passive electrostatic filtering systems, especially those with low air velocities (i.e., 1 mph).
• Phasing of residential dev
elopments when proposed within 500 feet of
freeways such that homes nearest the freeway are built last, if feasible. • The project shall be designed t
o locate sensitive receptors as far away as
feasible from the source(s) of air pollution. Operable windows, balconies, and building air intakes shall be located as far away from these sources as feasible.

If near a distribution center, residents shall be located as far away as feasible from a loading dock or where trucks concentrate to deliver goods. •
Sensitive recept
ors shall be located on the upper floors of buildings, if feasible. • Planting trees and/or vege
tation between sensitive receptors and pollution
source, if feasible. Trees that are best suited to trapping PM shall be planted, including one or more of the following: Pine (Pinus nigra var. maritima), Cypress (X Cupressocyparis leylandii), Hybrid popular (Populus deltoids X trichocarpa), and Redwood (Sequoia sempervirens).
•
Sensitive recept
ors shall be located as far away from truck activity areas, such as loading docks and delivery areas, as feasible.
• Existing and new diesel generat
ors shall meet CARB’s Tier 4 emission
standards, if feasible.
• Emissions from diesel trucks shall be reduced thr
ough implementing the
following measures, if feasible: Installing electrical hook-ups for diesel trucks at loading docks; Requiring trucks to use Transportation Refrigeration Units (TRU) that meet Tier 4 emission standards; Requiring truck-intensive projects to use advanced exhaust technology (e.g., hybrid) or alternative fuels; Prohibiting trucks from idling for more than two minutes; Establishing truck routes to avoid sensitive receptors in the project. A truck route program, along with truck calming, parking, and delivery restrictions, shall be implemented. 18 planning healthy places | 2016 | Bay area air Quality Management District Maintenance of Health Risk Reduction Measures
Requirement: The project applicant shall maintain, repair, and/or replace installed health risk reduction measures, including but not limited to the HVAC system (if applicable), on an ongoing and as-needed basis. Prior to occupancy, the project applicant shall prepare and then distribute to the building manager/operator an operation and maintenance manual for the HVAC system and filter including the maintenance and replacement schedule for the filter.

Stationary Sources of Air Pollution (Toxic Air Contaminants): The following condition applies to all projects that involve a stationary pollutant source requiring a permit from BAAQMD, including but not limited to back-up diesel generators. The California Building Code requires back-up diesel generators for all buildings over 70 feet tall.

Requirement: The project applicant shall incorporate appropriate measures into the project design in order to reduce the potential health risk due to on-site stationary sources of toxic air contaminants.

The project applicant shall choose one of the following methods:
•
The project applicant shall re
tain a qualified air quality consultant to prepare
a Health Risk Assessment (HRA) in accordance with California Air Resources Board (CARB) and Office of Environmental Health and Hazard Assessment requirements to determine the health risk associated with proposed stationary sources of pollution in the project. The HRA shall be submitted to the City for review and approval. If the HRA concludes that the health risk is at or below acceptable levels, then health risk reduction measures are not required. If the HRA concludes the health risk exceeds acceptable levels, health risk reduction measures shall be identified to reduce the health risk to acceptable levels. Identified risk reduction measures shall be submitted to the City for review and approval and be included on the project drawings submitted for the construction-related permit or on other documentation submitted to the City.

OR
•
The project applicant shall incor
porate the following health risk reduction
measures into the project. These features shall be submitted to the City for review and approval and be included on the project drawings submitted for the construction-related permit or on other documentation submitted to the City: Installation of non-diesel fueled generators, if feasible, or; Installation of diesel generators with an EPA-certified Tier 4 engine or engines that are retrofitted with a CARB Level 3 Verified Diesel Emissions Control Strategy, if feasible.

Truck-Related Risk Reduction Measures (Toxic Air Contaminants): The following condition applies to all projects that involve new truck loading docks or a truck fleet of any size registered to the project applicant/operator.
19 planning healthy places | 2016 | Bay area air Quality Management District Truck Loading Docks
Requirement: The project applicant shall locate proposed truck loading docks as far from nearby sensitive receptors as feasible.

Truck Fleet Emission Standards
Requirement: The project applicant shall comply with all applicable California Air Resources Board (CARB) requirements to control emissions from diesel engines and demonstrate compliance to the satisfaction of the City. Methods to comply include, but are not limited to, new clean diesel trucks, lower-tier diesel engine trucks with added Particulate Matter (PM) filters, hybrid trucks, alternative energy trucks, or other methods that achieve the applicable CARB emission standard. Compliance with this requirement shall be verified through CARB’s Verification. city of san jose, 2040 general Plan
The City of San Jose (City) updated its general plan in 2012. The City’s 2040 General Plan includes a number of environmentally sustainable and environmental justice goals and initiatives, including reducing residents’ exposure to toxic air contaminants. To promote implementation of these policies, City staff has identified measurements and tracking tools to monitor the City’s progress, as well as specific policies and action statements.

Policies on Toxic Air Contaminants
•
Req
uire completion of air quality modeling for sensitive land uses such as new residential developments that are located near sources of pollution, such as freeways and industrial uses.
• Req
uire new residential development projects and projects categorized as sensitive receptors to incorporate effective mitigation into project designs or be located an adequate distance from sources of toxic air contaminants (TACs) to avoid significant risks to health and safety. •
For pr
ojects that emit toxic air contaminants, require project proponents to prepare health risk assessments in accordance with Air District recommended procedures as part of environmental review and employ effective mitigation to reduce possible health risks to a less than significant level. Alternatively, require new projects (such as but not limited to industrial, manufacturing, and processing facilities) that are sources of TACs to be located an adequate distance from residential areas and other sensitive populations.
•
Re
view projects generating significant heavy duty truck traffic to designate truck routes that minimize exposure of sensitive populations to TACs and particulate matter.
20 planning healthy places | 2016 | Bay area air Quality Management District • Encourage the installation of appropriat e air filtration at existing schools, residences, and other sensitive land uses adversely affected by pollution sources.
• Encourage the use of pollution absorbing trees and vege tation in buffer
areas between substantial sources of TACs and sensitive land uses.

Actions on Toxic Air Contaminants
• Dev
elop and adopt a comprehensive Community Risk Reduction Plan that includes: baseline inventory of toxic air contaminants and particulate matter smaller than 2.5 microns (PM2.5) emissions from all sources, emissions reduction targets, and enforceable emission reduction strategies and performance measures. The Community Risk Reduction Plan will include enforcement and monitoring tools to ensure regular review of progress toward the emission reduction targets, progress reporting to the public and responsible agencies, and periodic updates of the plan, as appropriate. •
Consult with the Air District to identify stationar
y and mobile TAC sources and
determine the need for and requirements of a health risk assessment for proposed developments.
•
For ne
w projects that generate truck traffic, require signs which remind drivers that the State truck idling law limits truck idling to five minutes. city of ricHmonD, gener al Plan
The City of Richmond (City) updated its General Plan in 2012, and it includes a voluntary Community Health and Wellness element. The purpose of the new element is to “establish a strong policy framework for developing conditions that will improve the physical health and emotional well-being of Richmond residents.” The element also seeks to make the connection between community and environmental health and compact, sustainable development. Richmond’s General Plan states that “…many residents and workers are impacted by air, water, soil and noise pollution. Richmond has many heavy industrial land uses including a seaport, major refinery, and significant railroad terminal that contribute to local air and noise pollution.” To address these impacts, the City adopted policies in the General Plan to reduce emissions of, and exposure to, air pollutants (see below).

City staff also recommends improvements to air quality by working with the Air District and industrial operators to reduce emissions from industry, ships, trucks and automobiles; especially to reduce exposure to children and seniors. Specific air quality-related policies within the Community Health and Wellness element include:
•
Support regional policies and ef
forts that improve air quality to protect
human and environmental health and minimize disproportionate impacts on 21 planning healthy places | 2016 | Bay area air Quality Management District sensitive population groups. Work with businesses and industry, residents and regulatory agencies to reduce the impact of direct, indirect and cumulative impacts of stationary and non-stationary sources of pollution such as industry, the Port, railroads, diesel trucks and busy roadways.

Ensure that sensitive uses such as schools, childcare centers, parks and playgrounds, housing and community gathering places are protected from adverse impacts of emissions.
•
Continue to w
ork with stakeholders to reduce impacts associated with air quality on disadvantaged neighborhoods and continue to participate in regional planning efforts with nearby jurisdictions and the Bay Area Air Quality Management District to meet or exceed air quality standards.

Support regional, state and federal efforts to enforce existing pollution control laws and strengthen regulations.
city of HayWarD, gener al Plan
The 2040 Hayward General Plan (Approved July 2014) integrates the typical elements of a community risk reduction plan into the policy framework of the General Plan. The policy framework includes specific long-term goals, policies, and implementation programs to reduce communitywide exposure to TACs and PM2.5. This integrated approach allows the City to incorporate the analysis and components of a “stand-alone” community risk reduction plan into appropriate section of the General Plan. One of the long-term goals of the Plan (NR-2) is to improve the health and sustainability of the community through continued local efforts to improve regional air quality, reduce greenhouse gas emissions, and reduce community exposure to health risks associated with toxic air contaminants and fine particulate matter. Notable policies and programs under this goal include:
•
NR-2.
13 Wood Stove and Fireplace Replacement: The City shall promote the replacement of non-EPA certified fireplaces and woodstoves and encourage city residents to participate in Bay Area Air Quality Management District programs, such as the Wood Stove Rebate Program.
•
NR-2.
15 Community Risk Reduction Strategy: The City shall maintain and implement the General Plan as Hayward’s community risk reduction strategy to reduce health risks associated with toxic air contaminants (TACs) and fine particulate matter (PM2.5) in both existing and new development. •
NR-2.
16 Sensitive Uses: The City shall minimize exposure of sensitive receptors to toxic air contaminants (TAC), fine particulate matter (PM2.5), and odors to the extent possible, and consider distance, orientation, and wind direction when siting sensitive land uses in proximity to TAC- and PM2.5-emitting sources and odor sources in order to minimize health risk. 22 planning healthy places | 2016 | Bay area air Quality Management District • NR-2.17 Source Reduction Measures: The City shall coordinate with and suppor
t the efforts of the Bay Area Air Quality Management District, the California Air Resources Board, the U.S. Environmental Protection Agency, and other agencies as appropriate to implement source reduction measures and best management practices that address both existing and new sources of toxic air contaminants (TAC), fine particulate matter (PM2.5), and odors. •
NR-2.
18 Exposure Reduction Measures for New Receptors: The City shall require development projects to implement all applicable best management practices that will reduce exposure of new sensitive receptors (e.g., hospitals, schools, daycare facilities, elderly housing and convalescent facilities) to odors, toxic air contaminants (TAC) and fine particulate matter (PM2.5). •
NR-2.
19 Exposure Reduction Measures for both Existing and New Receptors:

The City shall work with area businesses, residents and partnering organizations to provide information about best management practices that can be implemented on a voluntary basis to reduce exposure of sensitive receptors to toxic air contaminants (TAC) and fine particulate matter (PM2.5). santa clara county , gener al Plan
The Health Element of the Santa Clara County General Plan has been prepared at the direction of the Santa Clara County Board of Supervisors as a new element, incorporating and updating certain existing subject matter and policies from the existing Health and Safety Chapters, and building a renewed emphasis on collaborative, comprehensive approaches to planning for community health.

Under the “Air Quality and Climate Change,” section there are major strategies and policies intended to convey a comprehensive approach for improving air quality, protecting the climate, and protecting public health. Examples include:
•
HE-G.4 Off-r
oad source: Encourage mobile source emission reduction from off-road equipment such as construction, farming, lawn and garden, and recreational vehicles by retrofitting, retiring and replacing equipment and by using alternate fuel vehicles.
•
HE-G.7 Sensitive recept
or uses: Promote measures to protect sensitive
receptor uses, such as residential areas, schools, day care centers, recreational playfields and trails, and medical facilities by locating uses away from major roadways and stationary area sources of pollution, where possible, or incorporating feasible, effective mitigation measures. •
HE-G.8 CARE Communities focus: Pr
omote awareness of geographic
areas subject to persistently poorer air quality and assist the Air District in monitoring and reducing emissions from all sources in CARE communities 23 planning healthy places | 2016 | Bay area air Quality Management District • HE-G.9 Healthy infill de velopment: Promote measures and mitigations for infill development to protect residents from air and noise pollution, such as more stringent building performance standards, proper siting criteria, development and environmental review processes, and enhanced air filtration.
24 planning healthy places | 2016 | Bay area air Quality Management District Planning for
construction
While construction activities are typically short-term or temporary in duration, they can generate a substantial amount of particulate matter and other criteria pollutants, toxic air contaminants, fugitive dust, and greenhouse gases. Therefore the emissions associated with construction activity can have regional implications to the attainment status of state and federal ambient air quality standards, but more importantly may adversely impact the health of
nearby sensitive populations.

Emissions from construction equipment are regulated
by both the US EPA and ARB. The emission standards for
new engines vary according to the rated horsepower of
the engine and model year of the equipment, and are set forth in a series of tiers (1-4), with each tier becoming progressively cleaner for either nitrogen oxides (NOx)
and/or PM emissions. In addition, ARB’s In-Use Off-Road Diesel Vehicle Regulation (Off-Road rule) generally applies to all self-propelled off-road diesel vehicles over 25
horsepower used in California. The Off-Road rule requires off-road fleet owners subject to the rule to meet fleet wide emission limits based on the size of their fleet and to reduce their emissions by retiring, replacing, or repowering older engines or installing Verified Diesel Emission Control Strategy, or VDECS. Compliance dates range from 2014 for larger fleets to 2019 for the smallest fleets.

The overall purpose of the Off-Road rule is to encourage turnover of older, higher-emitting equipment to cleaner, lower-emitting equipment in construction fleets. This turnover will help to further reduce emissions of NOx and fine PM within California communities.

While such programs and regulations will gradually reduce air pollution from the construction fleet, best practices are still needed to reduce air pollutants at the local level, which will help to protect sensitive populations that may be in close proximity to construction activity. construction best Pr actices
Table 1 presents a current list of best practices for construction equipment identified by the Air District. The best practices address both dust generated by construction activity as well as exhaust from construction equipment. This list will be updated as new technologies or strategies become available to further reduce the air quality and health impacts associated with construction activity.

All of the best practices applicable to a project should be required at the time grading permits are issued.
all of the best prac tices for
construction should be required
at the time grading permits are
issued. implementa
tion of these
best practices, or others that achieve
the same or greater emission
reductions, should ensure that
regional or local air quality impacts
from construction are minimized
to the maximum extent feasible.
25 planning healthy places | 2016 | Bay area air Quality Management District Table 1: Best Practices for Construction
For Dust
All exposed surfaces (e.g., parking areas, staging areas, soil piles, graded areas, and unpaved access roads) shall be watered two times per day. Maintain minimum soil moisture of 12 percent. Moisture content can be verified by lab samples or moisture probe.

All haul trucks transporting soil, sand, or other loose material off-site shall be covered.

All visible mud or dirt track-out onto adjacent public roads shall be removed using wet power vacuum street sweepers at least once per day. The use of dry power sweeping should be done in conjunction with thorough watering of the subject roads.

All vehicle speeds on unpaved roads shall be limited to 15 mph.

All roadway, driveway and sidewalk paving shall be completed as soon as possible. Building pads shall be paved as soon as possible after grading.

All construction sites shall provide a posted sign visible to the public with the telephone number and person to contact at the Lead Agency regarding dust complaints. The recommended response time for corrective action shall be within 48 hours. The Air District’s Complaint Line (1-800-334-6367) shall also be included on posted signs to ensure compliance with applicable regulations.

All excavation, grading, and/or demolition activities shall be suspended when average wind speeds exceed 20 mph.

Wind breaks (e.g. trees, fences) shall be installed on the windward side(s) of actively disturbed areas of construction. Wind breaks should have maximum 50 percent air porosity.

Vegetative ground cover (e.g. fast germinating native grass seed) shall be planted in disturbed areas as soon as possible and watered appropriately until vegetation is established.` The simultaneous occurrence of excavation, grading, and ground-disturbing construction activities on the same area at any one time shall be limited. Activities shall be phased to reduce the amount of disturbed surfaces at any one time).

All trucks and equipment, including their tires, shall be washed off prior to leaving the site.

Site accesses to a distance of 100 feet from the paved road shall be treated with a six- to 12-inch compacted layer of wood chips, mulch, or gravel.

Sandbags or other erosion control measures shall be installed to prevent silt runoff to public roadways from sites with a slope greater than one percent.

For Exhaust
The applicant/general contractor for the project shall demonstrate to the local jurisdiction that all off-road equipment greater than 25 hp that will be operating for more than 20 hours over the entire duration of the construction activities at the site, including equipment from subcontractors meets the following requirement:
1) Be Zero Emissions OR 2) have engines that meet for exceed either US EPA or ARB Tier 2 off-road emission standards; and 3) have engines are retrofitted with an ARB Level 3 Verified Diesel Emissions Control Strategy (VDECS), if one is available for the equipment being used (equipment with engines meeting Tier 4 Interim or Tier 4 Final emission standards automatically meet this requirement, therefore a VDECS would not be required).

Idling time of diesel powered construction equipment, trucks and generators shall be limited to no more than 2 minutes. Clear signage shall be provided for construction workers at all access points.

All construction equipment shall be maintained and properly tuned in accordance with the manufacturers’ specifications.

Portable diesel generators shall be prohibited. Grid power electricity should be used to provide power at construction sites; or propane and natural gas generators may be used when grid power electricity is not feasible.
26 planning healthy places | 2016 | Bay area air Quality Management District references
• Air Resources Board (2005), “Air Quality and Land Use Handbook: A Community Health P
erspective.”
• Air Resour
ces Board, “Overview of the Airborne Toxic Control Measure for Chromium Plating.” http://www.arb.ca.gov/toxics/chrome/chromefs.pdf • Air Resour
ces Board, “Reducing TACs in California’s Communities.” http:// www.arb.ca.gov/toxics/brochure.pdf
• Bay Area Air Quality Management District (20
14), “Improving Air Quality and
Health in Bay Area Communities: Community Air Risk Evaluation Program Retrospective and Path Forward.”
•
Bay Area Air Quality Management District (20
12). “Understanding Particulate
Matter: Protecting Public Health in the San Francisco Bay Area” http://www. baaqmd.gov/~/media/Files/Planning%20and%20Research/Plans/PM%20 Planning/ParticulatesMatter_Nov%207.ashx?la=en
•
Becke
tt, K. P., P. H. Freer-Smith and G. Taylor (1998). “Urban woodlands: their role in reducing the effects of particulate pollution.” Environmental Pollution 99(3): 347-360.
•
Becke
tt, K. P., P. H. Freer-Smith and G. Taylor (2000). “Particulate pollution capture by urban trees: Effect of species and windspeed.” Global Change Biology 6(8): 995-1003.
•
Brantley
, H. L., G. S. W. Hagler, P. J. Deshmukh and R. W. Baldauf (2014). “Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter.” Science of The Total Environment 468– 469(0): 120-129.
•
Brunekreef, B., N. Janssen, J. deHart
og, H. Harssema, M. Knape and P.
vanVliet (1997). “Air pollution from truck traffic and lung function in children living near motorways.” Epidemiology 8: 298 - 303.
•
Cahill, T. A
., P. J. Feeney, R. J. Flocchini and T. Dunn (1973). “Contribution of Freeway Traffic to Airborne Particulate Matter.” Final Report to the California Air Resources Board on Contract ARB - 502 http://www.arb.ca.gov/research/ apr/past/arb-502.pdf.
27 planning healthy places | 2016 | Bay area air Quality Management District • California Air Pollution Control Officer’s Association (2014), “California’s Pr
ogress Toward Clean Air.”
• Chen, K. and
L. Yu (2007). “Microscopic Traffic-Emission Simulation and Case Study for Evaluation of Traffic Control Strategies.” Journal of Transportation Systems Engineering and Information Technology 7(1): 93-99. •
City and County of San Francisco, “Clean Construction Or dinance.”: http://
www.sfenvironment.org/policy/resolution-in-support-of-amendments-to-the- clean-construction-ordinance
•
City and County of San Francisco, Depar
tment of Public Health. “Article 38.”
https://www.sfdph.org/dph/EH/Air/Article38.asp
• City of Hayw
ard, General Plan: http://www.hayward-ca.gov/GENERALPLAN/ • City of Oakland, Department of Planning and Building Bureau of Planning (July 20
15), “Standard Conditions of Approval.”
• City of Richmond, General Plan: http://www.ci.richmond.ca.us/2608/ General-Plan-2030
•
City of San Jose, General Plan: https://www.sanjoseca.go v/index.
aspx?nid=1737
• De Coensel, B., A. Can, B. Degraeuw
e, I. De Vlieger and D. Botteldooren
(2012). “Effects of traffic signal coordination on noise and air pollutant emissions.” Environmental Modelling & Software 35(0): 74-83. •
De Vlieger, I., D. De K
eukeleere and J. G. Kretzschmar (2000).
“Environmental effects of driving behaviour and congestion related to passenger cars.” Atmospheric Environment 34(27): 4649-4655. •
Dijkema, M. B. A
., S. C. van der Zee, B. Brunekreef and R. T. van Strien (2008). “Air quality effects of an urban highway speed limit reduction.” Atmospheric Environment 42(40): 9098-9105.
•
Doug Brugge1*, John L Durant2 and Christine Rioux3. “Near-highw ay
pollutants in motor vehicle exhaust: A review of epidemiologic evidence of cardiac and pulmonary health risks.” Environmental Health 2007, 6:23 •
El-Shaw
arby, I., K. Ahn and H. Rakha (2005). “Comparative field evaluation of vehicle cruise speed and acceleration level impacts on hot stabilized 28 planning healthy places | 2016 | Bay area air Quality Management District emissions.” Transportation Research Part D: Transport and Environment 10(1): 13-30.
• Fabio Caiazzo, Aksha
y Ashok, Ian A. Waitz, Steve H.L. Yim, Steven R.H.

Barrett (2013). “Air pollution and early deaths in the United States. Part 1:

Quantifying the impact of major sectors in 2005.” Atmospheric Environment 79 (2013) 198-208.
•
Finn, D., K. L. Clawson, R. G. Car
ter, J. D. Rich, R. M. Eckman, S. G. Perry,
V. Isakov and D. K. Heist (2010). “Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions.” Atmospheric Environment 44(2): 204-214. •
Gallagher, J., L. W
. Gill and A. McNabola (2011). “Optimizing the use of on- street car parking system as a passive control of air pollution exposure in street canyons by large eddy simulation.” Atmospheric Environment 45(9):
1684-1694.
•
Gauderman, W. J., E. A
vol, F. Lurmann, N. Kuenzli, F. Gilliland, J. Peters and R.

McConnell (2005). “Childhood Asthma and Exposure to Traffic and Nitrogen Dioxide.” Epidemiology 16(6): 737-743 710.1097/1001.ede.0000181308.0 000151440.0000181375.
•
Hagler, G. S. W
., W. Tang, M. J. Freeman, D. K. Heist, S. G. Perry and A. F.

Vette (2011). “Model evaluation of roadside barrier impact on near-road air pollution.” Atmospheric Environment 45(15): 2522-2530.
•
HEI (201
0). “Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects.” Health Effects Institute. • Höglund, P. G. and J. Niittymäki (1
999). Estimating vehicle emissions and
air pollution related to driving patterns and traffic calming. Urban Transport Systems Conference Sweden.
•
Janice J. Kim, Karen Huen, Sara A
dams, Svetlana Smorodinsky, Abby
Hoats,4Brian Malig, Michael Lipsett, and Bart Ostro (2008), “Residential Traffic and Children’s Respiratory Health.” Environmental Health Perspective; 116(9): 1274-1279.
•
Janssen, N. A. H., P
. H. N. van Vliet, F. Aarts, H. Harssema and B. Brunekreef (2001). “Assessment of exposure to traffic related air pollution of children attending schools near motorways.” Atmospheric Environment 35(22): 3875- 3884.
29 planning healthy places | 2016 | Bay area air Quality Management District • Karner, A. A., D. S. Eisinger and D. A. Niemeier (2010). “Near-Roadway Air Quality: Synthesizing the Findings fr
om Real-World Data.” Environmental
science & technology 44(14): 5334-5344.
• Litman, T. (1
999). “Traffic calming: benefits, costs and equity impacts.” Victoria, BC,, Canada: Victoria Transport Policy Institute. • Laura P
erez, Fred Lurmann, John Wilson, Manual Pastor, Sylvia J. Brandt, Nino Kunzli, and Rob McConnell (2012). “Near-Roadway Pollution and Childhood Asthma: Implications for Developing “Win-Win” Compact Urban Development and Clean Vehicle Strategies.” Environmental Health Perspectives, Volume 120 Num 11, November 2012.
•
Santa Clara County, Health Element: https://www
.sccgov.org/sites/planning/
PlansPrograms/GeneralPlan/Health/Pages/HealthElement.aspx • Tegan K. Boehmer
, Stephanie L. Foster, Jeffrey R. Henry, Efomo L. Woghiren- Akinnifesi, Fuyuen Y. Yip (2013). “Residential Proximity to Major Highways - United States 2010.” Centers for Disease Control and Prevention, Morbidity and Mortality Weekly Report, 62 (03); 46-50 November 22, 2013. •
US EPA: R
esidential Air Cleaners, A Summary of Available Information. August 2009. http://www.epa.gov/iaq/pdfs/residential_air_cleaners.pdf • (40 CFR 1508.7) Nor
thwest Envtl. Def. Ctr. v. Nat’l Marine Fisheries Serv., 647 F. Supp. 2d 1221, 1244 (D. Or. 2009)
30 planning healthy places | 2016 | Bay area air Quality Management District glossary
Bay Area Air Quality Management District (Air District) A regional air pollution control agency with jurisdiction o ver the nine counties that surround the Bay
(excepting northeastern Solano and northern Sonoma counties); the Air District oversees policies and adopts regulations for the control of air pollution from stationary sources.

Best Practices to Reduce Emissions
Measures that reduce actual emissions, and therefore reduce health risks fr om air pollution. The
Air District recommends that local governments adopt best practices as community-wide policies or ordinances. See pg. 9 for a complete list of best practices to reduce emissions.

Best Practices to Reduce Exposure
Measures that do not reduce actual emissions, but reduce people’s e xposure to pollutants and
therefore reduce health risks. Examples include air filters, vegetation, and alternative truck routes.

The Air District recommends implementing these types of measures in areas with elevated health risks (purple areas on Air District maps). See pg. 10 for the map, and for a complete list of best practices to reduce exposure.

California Air Resources Board (ARB)

A state agency
, whose mission is to promote and protect public health, welfare and ecological resources through the reduction of air pollutants; the ARB oversees policies and adopts regulations for the control of air pollution from primarily mobile sources.

Cumulative Impact
The impact on the environment and the public which results from the incremental impact of the action when added to other past, present, and reasonably foreseeable future actions regardless of what agency or person undertakes such other actions. Cumulative impacts can result from individually minor but collectively significant actions taking place over a period of time Fine Particulate Matter (PM)

Includes tiny par
ticles with a diameter less than or equal to 2.5 microns. This fraction of particulate matter penetrates more deeply into the lungs than larger particles.

Further Study

Conducting further study w
ould entail air quality modeling of fine PM concentrations, and/or estimating increased health risks from air toxics to determine if there is an unacceptable level of health risk, and to identify if measures can be implemented to reduce health risks to acceptable levels.

Mobile Sources of Air Pollution

Sources of air pollution such as aut
omobiles, motorcycles, trucks, off-road vehicles, boats and airplanes.
31 planning healthy places | 2016 | Bay area air Quality Management District Sensitive Land uses
Places where sensitive populations are most lik
ely to spend their time, such as schools, playgrounds,
daycare centers, nursing homes, medical facilities, and residential communities.

Sensitive Populations
People, including inf
ants, children, the elderly, and those with pre-existing conditions (such as asthma) that are at greater risk than the general population to the adverse health effects of air pollutants.

Stationary Sources of Air Pollution
Non-mobile sources such as po
wer plants, refineries and manufacturing facilities which emit air pollutants.

Toxic Air Contaminants (TACs) or Air Toxics

TA
Cs are air pollutants, identified by the ARB, which may cause or contribute to an increase in deaths or in serious illness, or which may pose a present or potential health hazard. Health effects may occur at extremely low levels of TACs.
32 planning healthy places | 2016 | Bay area air Quality Management District aPPenDix a: best Practices
to reDuce
emissions
of
local
air
Pollution
The Air District recommends that local government agencies adopt the following “best practices to reduce emissions” as enforceable ordinances or standard conditions of approval, and/or as community-wide policies. Implementing all of the “best practices to reduce emissions” will likely result in the greatest reduction in local levels of air pollutants. However, the Air District acknowledges that implementing all of the following “best practices to reduce emissions” may not be feasible or appropriate in every community.

The research regarding the availability and effectiveness of “best practices to reduce exposure” is continually evolving. Air District staff will update the recommended measures as new information becomes available.
retrofit generators (to loW or zer o emissions)
Many buildings in developed areas include back-up diesel generators to provide emergency power in the event of power failure. Even if such engines are not used for emergency purposes, they are still operated periodically for maintenance and testing. Diesel backup generators, specifically older ones, can have significant diesel particulate matter emissions. As part of its diesel risk reduction program, ARB adopted an air toxic control measure for stationary engines, or generators. The measure requires that new generators, including back-up generators and generators used in construction, be certified to meet emission standards set by ARB and US EPA (ARB and US EPA have identical emission standards for generators). ARB/US EPA emission standards apply to generators larger than 50 horse power and are set forth as Tiers 1 through 4, with Tier 4 engines being the cleanest. Generator engines certified as Tier 4 reduce PM emissions 85 to 90 percent over a non-tiered engine, whereas Tier 1 only reduces PM emissions by 25 percent. By 2015, all new generator engines must have met Tier 4 emission standards. But since these regulations apply only to new engines, older existing generators can continue contributing to local air pollution. Local governments can require, via a local ordinance, development agreement, or other means, that existing older generators not subject to ARB limits be replaced with a new low or zero emitting generator or be retrofitted with control technologies such as diesel particulate filters, resulting in significant reductions in diesel PM emissions. New, zero emission back-up power technologies are also becoming available, including fuel cell back-up power (example: Bloom Energy).
electrify loaDing Docks
Heavy duty diesel trucks are the predominant means to deliver goods to grocery stores, shopping malls, and other commercial and retail land uses. Diesel trucks normally need to idle their main diesel engine during loading and unloading operations to operate mechanical lift equipment or 33 planning healthy places | 2016 | Bay area air Quality Management District to run the air conditioner or heater in the cab of the truck. This idling of the main diesel engine produces a substantial amount of diesel particulate matter emissions and can impact the health of nearby people. The particulate matter emissions can be reduced or eliminated by requiring the electrification of all loading docks. Trucks that are equipped to utilize grid power can significantly reduce their emissions. Installing electrical outlets at all loading docks and promoting or requiring only trucks capable of plugging-in to deliver goods will lead to localized reductions in diesel emissions, thereby decreasing the potential for health risks to those that live and work in the area. limit iDling times
Prohibiting trucks from idling for more than two minutes can reduce emissions by limiting the amount of time that trucks run their engines. Idling limits could apply to all types and sizes of trucks, and/or buses, that spend extended periods of time at idle when loading and unloading, staging or when not in active use. ARB regulations limit idling time to no more than five continuous minutes (for commercial motor vehicles with gross vehicular weight ratings of greater than 10,000 pounds).

Local governments may, and often do, pass local ordinances that further limit allowable idling time to no more than two continuous minutes. In addition, local enforcement of ARB or local idling limits increases their effectiveness. Strict local limits on idling diesel engines, combined with local enforcement, can reduce local exposure to diesel exhaust.

ARB’s idling regulation contains a number of exemptions that allow for longer idling periods when safety or power needs for equipment are required. Communities should consider if similar exemptions are appropriate when adopting a local ordinance on idling time limits. zero emission tecHnology & alternative fuels
Zero emission (i.e. plug-in electric or hydrogen powered) vehicles have become more commonplace but will need the necessary infrastructure to continue to grow. Local governments can promote this infrastructure by requiring it at new or existing development (for example, required plug-in stations for electric vehicles). Diesel powered on-road and off-road equipment manufacturers are constantly developing new technologies and strategies to reduce diesel particulate matter emissions in order to comply with increasingly stringent ARB regulations. In addition, fuel providers are also developing lower emission and renewable fuels, such as biodiesel, to comply with ARB fuels regulations.

Promoting the use of these new technologies and fuels within our communities, either through requirements or incentives, can reduce or eliminate the adverse health impacts from local sources of TACs and PM air pollution.

For example, truck manufacturers have begun offering diesel electric hybrids for all but the heaviest trucks. Gasoline hybrids are available for lighter weight trucks. The availability of propane and natural gas powered trucks is somewhat limited in terms of weight class and usage, although there are some well-established markets for natural and/or bio gas buses and garbage trucks. Trucks powered by battery or fuel cell hybrid electric are currently limited to demonstration projects, but when commercialized will present the lowest emission option. 34 planning healthy places | 2016 | Bay area air Quality Management District Promote or reQuire tHe use of
tr
ansPor
tation refriger
ation unit
s (tru)
Trucks delivering goods often need to keep perishable items refrigerated or at a constant temperature. The use of Transportation Refrigeration Units (TRUs) in lieu of running the main engine on delivery trucks maintains refrigeration while minimizing diesel emissions. TRUs are refrigeration systems powered by diesel internal combustion engines designed to refrigerate perishable products that are transported in various containers, including semi-trailers, truck vans, shipping containers, and rail cars. Local policies or programs that promote the use of transportation refrigeration units, especially if they meet the federal Environmental Protection Agency’s (US EPA) Tier 4 emission standards, can reduce emissions of diesel particulate matter and toxic air contaminants by 50 to 80 percent. It should be noted that while TRU engines are relatively small, ranging from 9 to 36 horsepower, significant numbers of these engines congregating at distribution centers, truck stops, and other facilities, could still result in the potential for adverse health risks to sensitive populations nearby.
transPortation DemanD
management
(tDm)
str
ategies
As previously mentioned in this guidebook, the Air District strongly supports local and regional efforts to reduce vehicle miles traveled and promote “focused growth”, i.e. infill, transit-oriented, and mixed-use development throughout the region. Building such communities is critical to achieving reduced vehicle miles traveled, which will: reduce criteria pollutants, greenhouse gases, and toxic air contaminant and fine PM emissions from passenger vehicles, as well as assist the Bay Area in attaining and maintaining health-based ambient air quality standards. Focused growth strategies have the long-term benefit of improving overall air quality while also providing many other benefits to the Bay Area environment, including the preservation of natural land and open space, improved water quality, and protection of habitat and native wildlife species. Focused growth also provides important economic and equity benefits, including reduced traffic congestion and lower transportation costs, more housing options, and better access to jobs.

The Air District recommends requiring the implementation of as many TDM strategies as is feasible into projects and plans. Examples include, but are not limited to, parking pricing strategies; parking maximums; mandated parking spaces for car-sharing programs; the provision of transit passes in residential, commercial and office developments; charging stations for electric vehicles; bicycle lockers or racks; teleworking policies; bicycling improvements; and more. For a recommended list of TDM strategies, consult the Air District’s TDM tool: http://www.baaqmd.gov/plans-and-climate/air- quality-plans/smart-growth.
traffic management str ategies
Studies demonstrate that managing how traffic flows is a strategy to reduce the amount of air pollution emitted from vehicles.
35 planning healthy places | 2016 | Bay area air Quality Management District Traffic Smoothing
Reducing acceleration and deceleration can reduce fuel consumption and emissions. Creating a more constant traffic speed (i.e. traffic smoothing) can reduce emissions fairly significantly (up to ~50%, according to several studies). Strategies to smooth traffic include installing roundabouts at stop- controlled intersections.

Speed Limits
Driving speed is one of the most important factors that determine vehicle emissions, according to ARB. A study by El-Shawarby et al (2005) found that fuel consumption and emission rates are optimum in the range of 38-55 mph. Outside of this range, both fuel consumption and emission rates increase considerably.
36 planning healthy places | 2016 | Bay area air Quality Management District aPPenDix b: best Practices
to reDuce
exPosure
to
local
air
Pollution
The Air District recommends that local government agencies adopt the following “best practices to reduce exposure” as enforceable ordinances or standard conditions of approval, and/or as community-wide policies. Implementing all of the “best practices to reduce exposure” will likely result in the greatest reduction in potential health risks from air pollution. However, the Air District acknowledges that implementing all of the following “best practices to reduce exposure” may not be feasible or appropriate in every community. Of particular importance is the best practice related to air filtration, which is one of the most effective strategies to reduce exposure.

HealtH Protective Distance
As stated, from an air quality standpoint, reducing vehicle miles travled (VMT) is crucial. Reducing VMT will reduce criteria pollutants, greenhouse gases, and toxic air contaminants. Cars and trucks represent the single largest source of greenhouse gas emissions in the Bay Area; reducing these transportation-related emissions through integrated land use and transportation planning and infill development is critical to achieving GHG reduction goals to stabilize the climate. Transportation is also a significant source of fine PM and TACs. Therefore, reducing VMT is a high priority for air quality and the climate. However, increased development in certain locations near major sources of air pollution may result in increased local exposure to unhealthy levels of air pollutants to the people living there unless steps are taken to reduce exposure and reduce emissions. This guidebook includes many strategies to reduce both emissions and exposure. One strategy for reducing exposure is to plan sensitive land uses farther from localized air pollution sources (such as freeways) as is feasible and appropriate. This is one of the most effective health protective strategies that can be implemented to protect children and other vulnerable populations from the harmful effects of air pollution. In general, as the distance from a local source of air pollution increases, the level of air pollution and associated health risk decreases.

A means to implement or consider proximity to air pollution sources is zoning. For example, when updating or making revisions to a zoning code in an area characterized by elevated levels of air pollution (such as immediately adjacent to a freeway), local government may choose to designate the land use as commercial, office, or parking instead of residential, if that is feasible or appropriate given the context. The Air District aknowledges that local land use decisions are complicated and many factors need to be considered and balanced. The Air District simply encourages local 37 planning healthy places | 2016 | Bay area air Quality Management District governments to consider air quality along with other public health elements when making land use decisions.

The Air District recognizes that in dense urban communities, implementing a health protective distance between sensitive land uses and sources of air pollution may not always be feasible. If it is not possible to implement health protective distances, then the additional best practices to reduce exposure to local air pollution will help to reduce health risks, if fully implemented. air filters
Because many people spend a majority of their time indoors, reducing the entry of air pollutants into a home (or school, daycare, etc.) is a viable option to mitigate the adverse health impacts related to air pollutant exposures, particularly fine PM. Heating, ventilating, and air conditioning (HVAC) systems control the air flow in buildings by circulating outside air through, and eventually out of a building.

The use of high efficiency filtration in central HVAC systems and in portable air cleaners has been shown to be effective in most circumstances. Depending on the particle size, high efficiency filters can remove 50% - 98% of particles in the air, and portable air cleaners (designed for homes without a central HVAC) can remove 30% to 90% of particles.

The American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) uses a Minimum Efficiency Reporting Value (MERV) measurement scale to rate the effectiveness of air filters on a scale of 1 to 16. For example, MERV-13 air filtration devices installed on an HVAC air intake system can remove 80-90% of indoor particulate matter (greater than 0.3 microns in diameter).

High Efficiency Particle Filters, or HEPA filters, are effective at removing mold, pollen and ultrafine particles. HEPA filters have a particle size removal efficiency of > 99.999% for particles 0.3 - 1 micron in diameter which is roughly equivalent to a MERV 20 rating (US EPA, Residential Air Cleaners August 2009). However, only a few HEPA filters are designed for use in residential applications.

Studies conducted in California (Bhangar et al 2011, Less et al., 2015) have shown that particulate levels in homes with high efficiency filtration systems were 50% to 74% lower than those without filtration systems. Modeling simulations (Brown et al 2014) showed similar findings. The effectiveness of air filters in reducing health risks depends heavily on properly sealed ducting and maintenance.

Higher MERV rated filters also require increased air pressure, which requires more energy use and can cause ducts to fail if not properly installed and sealed. However, while air filtration systems do result in additional energy use, a well-sealed building envelope will help reduce energy use and will also increase the effectiveness of air filtration. An ongoing maintenance plan for a building’s HVAC air filtration system should therefore be included in any air filtration best practice adopted by a local government. For additional information on air filters, see the U.S. EPA’s document, “Residential Air Cleaners: A Summary of Available Information August 2009”.

HVAC filtration is an effective and feasible air quality mitigation strategy. It is becoming increasingly common in Bay Area jurisdictions. For example, San Francisco requires MERV 13 air filters in new residential buildings located within designated “air pollutant exposure zones” (locations where toxic risk or fine PM levels exceed designated thresholds).

38 planning healthy places | 2016 | Bay area air Quality Management District The Air District recommends requiring the installation and implementation of an air filtration system in sensitive land uses (minimum of MERV 13) along with a maintenance plan detailing how the filtration system will be maintained.

Project PHasing
In 2008, the California Air Resources Board adopted the On-Road Heavy Duty Diesel Vehicle (in use) regulation to dramatically reduce diesel particulate matter emissions from trucks and buses.

The regulation requires owners of diesel trucks to retrofit or replace their engines so that by 2016, nearly all trucks would have diesel particulate matter emissions equal to a 2010 or newer model year engine. The regulation went into effect in 2012, and will result in significant reductions in diesel particulate matter emissions from on-road diesel trucks and buses as truck and bus owners comply with the regulation. ARB estimates there should be up to an 80 percent reduction in diesel particulate matter by 2023 from on-road trucks and buses. Accordingly, it is expected that the geographic scope of areas with unhealthy levels of diesel exhaust will decrease in future years as this truck and bus fleet becomes cleaner.

The ARB regulation makes project phasing an effective strategy for reducing people’s exposure to fine PM and TAC emissions when the project or plan area is impacted from a source of emissions that includes on-road trucks and buses, such as a freeway or distribution center. When it is feasible to do so, such as on a relatively large project site, buildings that will be closest to the source of diesel particulate matter from on-road trucks or buses could be built last, so that air pollution from nearby highways or roadways will have time to decline based on the turnover of older diesel trucks and buses resulting from the ARB regulation. Phasing development near highways and major roadways can reduce exposure to fine PM concentrations and TACs.
builDing anD site Design
Designing residential buildings and sites to locate people away from emission sources is an effective way to protect people’s health.

Building Design
Building design can be an important factor in reducing exposure to PM and TACs by improving indoor air quality, especially when considering the location of the air intake for building ventilation. Generally, air pollution decreases with distance and with height, therefore air intake locations should be located as far as is feasible away from emission sources to provide the cleanest air to building occupants.

Other beneficial design features may further improve indoor air quality. Operable windows and balconies could be installed away from high volume roadways or other sources of air pollution, if feasible. For example, if local sources of air pollution are located on the west of the building, operable windows and balconies could be installed on the east side of the building (if feasible) where the concentrations of fine PM and TACs are likely to be lower. 39 planning healthy places | 2016 | Bay area air Quality Management District Site Design
When designing a plan or project that includes sensitive land uses near local sources of fine PM and TACs, buildings within the development that do not house people, such as parking garages, commercial buildings or open space, could be located closest to the local source of emissions (such as a freeway), and act as a barrier between the pollution source and residential or other sensitive land uses. Also, implementing open space such as parks (that do not have recreational amenities such as basketball or tennis courts, soccer fields, playgrounds, etc.) between buildings can improve air flow and air pollution movement. This strategy can help to reduce build up of air pollution, or air pollution “hot spots”.
soliD barriers
Consider incorporating solid barriers, similar to sound walls, between buildings and sources of air pollution. Studies have demonstrated that barriers can reduce air pollutant levels, while also reducing noise (co-benefit). Recent research indicates that sound walls, in conjunction with vegetation (see below) is more effective than either strategy implemented on it’s own to reduce air pollutant levels. vegetation
Planting certain trees can be an effective strategy for reducing exposure to air pollution. Some trees and vegetation type may trap and filter coarse and fine particulates in the leaves, stems, and twigs. Trapped particles are eventually washed to the ground by rainfall. Trees also lower the air temperature by providing shade over streets and parking lots, thereby reducing evaporative emissions from vehicles and energy consumed on air conditioning during summer months.

The effectiveness of fine PM removal depends on the tree species planted. Large, evergreen trees (those with foliage year-round) with long-life spans are best. In addition, trees with branches and leaves that have a sticky surface are best at trapping fine PM. Trees with a fine, complex foliage structure that allows significant in-canopy airflow will also perform better at trapping particulate matter. Pines, Cypress, Hybrid Popular, and Redwoods are an example of trees that do well in trapping pollution.

In addition to the type of tree, the placement of the trees, relative to major roadways or other diesel emission sources, and how densely they are planted, are important considerations in using trees as a strategy to reduce air pollution exposure. Trees should be planted between land uses and the source of emissions, and as densely as feasible, while still maintaining the health of the trees. Additionally, some trees emit volatile organic compounds (VOCs) which can lead to the formation of ozone. Care should be taken that trees planted with the intent to reduce fine PM do not also emit high levels of VOCs.

Research is continuing to determine and quantify the effectiveness of planting of trees near a source of particulate matter in reducing exposure.

The Urban Forest Ecosystems Institute at California Polytechnic University, San Luis Obispo (which partners with CalFire, the U.S. Forest Service and PG&E) maintains SelecTree, a tree selection tool 40 planning healthy places | 2016 | Bay area air Quality Management District designed to help users select appropriate trees based on a number of considerations, including leaf and flower characteristics, site conditions and constraints (such as soil conditions, soil pH, seaside exposure, etc.), pest and disease information, health and safety concerns (non-native CA species, fire resistance, biogenic emissions, root damage potential, etc.) and special values (attracts wildlife).

The Air District encourages the ues of this tool to assist in making comprehensive decisions on tree selection while also taking into consideration a tree’s biogenic emissions.

In addition, the Air District may undertake a guidance document on trees, which will include recommendations on the types of tress that are preferred for air quality (biogenic emissions), exposure reduction, and climate protection/carbon sequestration, while also considering other factors including water quality, pest management, pollen reduction, aesthetics and more. limit grounD fl oor uses
Placing residential development on the second floor of a building or higher can be an effective strategy for reducing exposure to local pollutants from a nearby at-grade highway or busy roadway.

This strategy is often applied to mixed use buildings on infill sites, where the ground floor is reserved for commercial space and the second and subsequent floors are used for residential. Limiting ground floor residential development is generally most effective when the adjacent roadway is not elevated. alternative truck routes
Truck routes can be planned or re-routed through non-residential neighborhoods, and to avoid other sensitive land uses such as daycare centers, schools, and elderly facilities. For example, the City of Oakland recently worked with community groups to re-route trucks away from residential streets around the Oakland Coliseum to address local concern about air pollution levels. 41 planning healthy places | 2016 | Bay area air Quality Management District aPPenDix c
tecHnic
al no
tes
The mapping tool created by the Air District include blue and purple areas (located: www.baaqmd. gov/planninghealthyplaces).

The blue areas represent “large and/or complex” sources where further study is recommended. The Air District relied on ARB’s document entitled, “2005 Air Quality Land Use Handbook: A Community Health Perspective” (ARB Land Use Handbook) to define “large and/or complex” sources, and their associated further study areas. The further study areas are defined below:
•

0.5 miles around all major air
ports, including OAK, SFO, SJC;
• 0.5 miles around all oil refineries;
• 0.5 miles around the P
ort of Oakland; 1,000 feet around all other seaports;
• 1,000 f
eet around railyards (except Caltrain yards in San Jose & San Francisco - these are included in AQ modeling in purple areas) • 150 f
eet around medium gas stations (based on Air District emissions data); and
• 300 fee
t around large gas stations (based on Air District emissions data).

The purple areas on the maps are based on a screening level, cumulative analysis of all mobile and stationary sources of air pollution in the region. To create the purple areas, the Air District identified areas that exceed 100 in a million for cancer risk, and/or exceed fine PM concentrations of 0.8 micrograms per cubic meter, and/or are within 500 feet of a freeway, 175 feet of a major roadway (>30k AADT), or 500 feet of a ferry terminal. Implementation of best practices to reduce emissions and exposure will reduce the health risks; however, the emissions and exposures will not be completely eliminated.

The Air District will be releasing a document that will provide greater detail on the methodology used to model the estimated levels of air pollutants and health risks on a cumulative basis throughout the region. This document will be available at: www.baaqmd.gov/planninghealthyplaces upon completion (est. late spring / early summer 2016).
42 planning healthy places | 2016 | Bay area air Quality Management District From:

Subject: Dec 5 AGENDA COMMENTS - FW: Environmental Impact Report for the Fresno South Central Specific Plan (SCH # 2019079022)
Date: Wednesday, December 4, 2024 11:36:42 AM
Attachments: Outlook-cbd.circle

12.04 fresno south central specific plan comments .pdf
Additional Comments to send out for the Dec 5 agenda.

If you can’t figure out which item it belongs to, please let me know.

Todd Stermer, CMC
City Clerk
559-621-7650

From:

Jennifer Clark
Sent: Wednesday, December 4, 2024 8:10 AM
To: Todd Stermer
Cc: Kristi Costa ; Sophia Pagoulatos
Ashley Atkinson Michelle Zumwalt
Subject: FW: Environmental Impact Report for the Fresno South Central Specific Plan (SCH # 2019079022)

Todd,

See additional comments for the record.

Thanks.

From: Frances Tinney
Sent: Wednesday, December 4, 2024 8:07 AM
To:

Jennifer Clark >; Sophia Pagoulatos
; Michelle Zumwalt >
Cc: John Buse >; Sofia Prado-Irwin
>
Subject:

Environmental Impact Report for the Fresno South Central Specific Plan (SCH # 2019079022)

External Email: Use caution with links and attachments

Good morning,

Please find attached the Center for Biological Diversity's comments on the EIR for the Fresno South Central Specific Plan.

The references are available at this link:

Fresno SCSP References

Please confirm receipt of the letters and references.

Thank you,

Frances

Frances Tinney
Attorney
Urban Wildlands Program
CENTER for

BIOLOGICAL DIVERSITY

December 4, 2024
Sent via email
Jennifer Clark, Director
Planning and Development Department
c/o Sophia Pagoulatos, Planning Manager
Planning and Development Department
2600 Fresno Street, Room 3065
Fresno, CA 93721

Re: Environmental Impact Report for the Fresno South Central Specific Plan (SCH # 2019079022)
Dear Ms. Clark:

These comments are submitted on behalf of the Center for Biological Diversity (the “Center”) regarding the Fresno South Central Specific Plan (the “Project”). The Center has reviewed the Environmental Impact Report (“EIR”) closely and is concerned about the harm the Project will do to an Environmental Justice Community, the Project’s inadequate analysis of water supply and water quality impacts, its serious and unmitigated impacts to biological resources, and the unacceptable impacts to air quality for residents who already breathe some of the worst air in the country. The Center urges the City of Fresno to revise the EIR to disclose and analyze these impacts, and mitigate accordingly.

The Center is a non-profit, public interest environmental organization dedicated to the protection of native species and their habitats through science, policy, and environmental law.

The Center has over 1.7 million members and online activists throughout California and the United States. The Center has worked for many years to protect imperiled plants and wildlife, open space, air and water quality, and overall quality of life for people in Fresno County.

As an initial matter, in August of this year the Fifth Appellate Court of Appeal issued an order invalidating the General Plan Amendment and accompanying Programmatic Environmental Impact Report that the City of Fresno enacted in 2021. The court instructed the City to correct CEQA violations in those documents. The City has not yet complied. The City now attempts to enact a specific plan. But specific plans implement the general plan. Because specific plans must be consistent with General Plans and cannot be adopted until a General Plan is adopted, the City should not proceed with a specific plan until it has a correctly amended General Plan in place, along with a EIR that complies with CEQA. (Gov. Code § 65450.)

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I. THE DEIR FAILS TO ADEQUATELY CONSIDER THE PROJECT’S
SIGNIFICANT ENVIRONMENTAL JUSTICE IMPACTS.

The EIR fails to analyze or mitigate the ways in which the Project’s significant and unavoidable impacts will contribute to the area’s status as an environmentally burdened community. The Project will bring more industrial pollution to a community with more poverty, higher unemployment, and less formal education than 90% of California. (CalEnviroScreen 2022.) The neighborhood has some of the dirtiest air in the state, and in fact, the country. (Ibid., ALA 2024.) The residents suffer rates of asthma in the 97rd percentile. (CalEnviroScreen 2022.) Needless to say, the Project will be within an Environmental Justice Community boundary. (DEIR at 4.3-28.) Seventy-four percent of the residents are Latine, 20% are Black, and only

2.5% are white. (CalEnviroScreen 2022) Studies in California consistently show that decisionmakers are significantly more likely to site warehouses in neighborhoods where a higher percentage of the residents are not white. This pattern forces people of color to bear a disproportionate share of the health and safety burdens that come with toxic land uses, in a textbook example of environmental racism. (Martinez, 2021; Commission for Racial Justice, 1987 [landmark study in the development of the environmental justice framework])

The State of California has responded to the moral urgency of alleviating unfair environmental burdens by codifying environmental justice principles in statute. California law now calls on officials to “eliminat[e] pollution burdens for populations and communities experiencing the adverse effects of that pollution, so that the effects of the pollution are not disproportionately borne by those populations and communities.” (Pub. Res. Code § 30107.3(b)(2); § 71111.) The Attorney General has pointed out that this responsibility extends to local governments through the CEQA process, which “require[s] that local lead agencies consider how the environmental and public health burdens of a project might specially affect certain communities.” (AGO, 2012. See CEQA Guidelines, §§ 15064(e), 15131(a), 15131(b), 15382.) Yet this EIR ignores a community-led alternative to instead add more pollution to one of the most burdened areas in the state. It does this without even admitting the unequal effects of the environmental injustice it will perpetrate.

The mandate to consider environmental justice requires the City to adopt the Community Plan Alternative. That alternative reduces industrial uses in the plan area to increase quality of life for residents, and it was developed through a community outreach and engagement process. (DEIR at 6-7.) Inexplicably, the City fails to even fully consider this alternative. (DEIR at 6-6-7.) The City claims that because the Community Plan Alternative was developed through community participation instead of “developed for the purposes of CEQA, that is, to reduce the potential for significant environmental effects” it is not a CEQA alternative and therefore not fully analyzed. (DEIR at 6-7.) The explanation has no basis in law or fact. There is no requirement that CEQA alternatives be developed in any particular way. In fact, public participation is a primary goal of CEQA and the Attorney General encourages lead agencies to work with affected communities to develop CEQA alternatives. (Cal. Code Regs., tit. 14, § 15201; AGO 2012.) Asking the community to participate in designing an alternative just to dismiss that alternative precisely because it was designed by the community is surprising, to say the least. Further, the City’s claim that developing an alternative to reduce environmental December 4, 2024

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impacts is somehow at odds with developing an alternative in response to community concerns is simply wrong. The community’s concerns include air quality, traffic, health risks, and general plan consistency to protect sensitive receptors. These are environmental impacts.

And so, although the Community Plan alternative might not have been developed for the purpose of reducing environmental impacts, it would in fact reduce them. High-intensity industrial land uses drive this Project’s significant air quality, traffic, land use and greenhouse gas impacts. Replacing those land uses with office and retail uses will reduce the Project’s significant impacts. By failing to even fully analyze the Community Plan alternative, the EIR fails to meet CEQA’s requirement to study alternatives and the City fails to meet its responsibility to advance environmental justice. (Pub. Res. Code § 21002; CEQA Guidelines §§ 15002(a)(3), 15021(a)(2), 15126(d).)

II. THE EIR LACKS AN ADEQUATE ANALYSIS OF AND MITIGATION FOR THE PROJECT’S IMPACTS TO BIOLOGICAL RESOURCES.

The EIR fails to adequately describe and analyze the Project’s impacts to special-status species in and near the Plan area. The EIR also fails to mitigate the Project’s significant impacts to local and regional wildlife connectivity.

A. The EIR Improperly Omits Analysis of Special-Status Crustaceans Information provided in the EIR is inadequate to establish a reliable baseline of existing environmental conditions present at the Project site. Under CEQA, an environmental review document must evaluate the potential environmental impacts of the project as compared to the existing environmental conditions (the “baseline”), so that the Project’s impacts can be meaningfully analyzed and compared to alternatives. (CEQA Guidelines § 15125(a); see County of Amador v. El Dorado County Water Agency (1999) 76 Cal.App.4th 931, 952; Neighbors for Smart Rail v. LA County Metropolitan Transit Authority (2013) 57 Cal.4th 310, 315.) The evidence provided in the EIR’s analysis of impacts to biological resources does not meet this standard.

The EIR claims that two special-status crustaceans, vernal pool fairy shrimp and vernal pool tadpole shrimp, are not expected to occur because the Plan Area “does not support vernal pool or wetland habitat suitable for this species.” However, the EIR also states that “Some agricultural parcels in the Plan Area contain mapped freshwater emergent wetlands and freshwater ponds, all of which are documented in the National Wetlands Inventory as excavated or human-created (USFWS 2019)…Based on spatial and field reconnaissance data, the areas mapped as freshwater emergent wetland and ponds in the Plan Area are artificial or altered seasonally filled wet depressions, which may serve as industrial or agricultural effluent ponds or flood control.” (DEIR at 4.4-5, 4.4-6). A noted in the Recovery Plan for Vernal Pool Ecosystems of California and Southern Oregon, “Vernal pool tadpole shrimp occur in a wide variety of ephemeral wetland habitats (Helm 1998). The species has been collected in vernal pools ranging from 2 to 356,253 square meters (6.5 square feet to 88 acres) in surface area (Helm 1998)… Vernal pool tadpole shrimp have been found in pools with water temperatures ranging from 10 degrees Celsius (50 degrees Fahrenheit) to 29 degrees Celsius (84 degrees Fahrenheit) and pH ranging from 6.2 to 8.5 (Syrdahl 1993, King 1996)” (U.S. Fish and Wildlife Service, 2005). The vernal pool fairy shrimp can also be found in a wide variety of ephemeral freshwater habitats, December 4, 2024

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“including extremely small or marginal ones that fill with water for just long enough to allow the individuals to complete their lifecycle” (US Fish and Wildlife Service, 2024). Importantly, even though these wetlands may be isolated from other intact habitat, dispersal to isolated water bodies can occur via waterfowl, livestock, and humans (US Fish and Wildlife Service, 2024).

Based on the information provided in the EIR, the wetlands present in the project area may in fact be suitable for vernal pool shrimp. The EIR must conduct protocol surveys as described in the Survey Guidelines for the Listed Large Branchiopods (US Fish and Wildlife Service, 2017) to determine whether these two species are indeed present in the Plan Area. If evidence of vernal pool fairy shrimp or vernal pool tadpole shrimp is found, the Applicant must fully mitigate impacts to the species.

B. The EIR Fails to Adequately Analyze and Mitigate the Project’s Impacts to Birds that Occupy Agricultural Lands
The intensification of agriculture throughout the United States has led to severe declines in farmland and grassland birds (Stanton et al., 2018). Despite this, some species—including numerous sensitive and special-status birds—are able to use agricultural lands as nesting and/or foraging habitat (Iglay et al., 2017). For example, white-faced ibis, long-billed curlew, and Swainson’s hawk all depend to various extends on alfalfa fields for wintering, breeding, and foraging habitat respectively (Hartman & Kyle, 2010). Extremely large colonies of tricolored blackbird colonies exploit triticale crops in the San Joaquin Valley Swainson’s hawk, and alfalfa, rice paddies, and irrigated pasture provide foraging habitat (Graves et al., 2013; Wilsey et al., 2019). While species richness is lower in agricultural landscapes than intact native habitats, diverse bird communities can still exist in orchards and vineyards. For example, a study of bird community composition across different types of orchards and vineyards in Australia observed 56 species in almond orchards and 48 in vineyards (Luck et al., 2015). As noted in the DEIR, orchards and vineyards make up the majority of the agricultural landscape of the Plan Area (DEIR at 4.4-5), and destruction of these landscapes may have significant impacts on native birds.

As stated in the DEIR, “approximately 1,874 acres [of the Plan Area] is open space, farmland, and vacant. Approximately 700 acres of the Plan Area is conservatively assumed to be developed with non-residential uses, primarily industrial, by the year 2040. While it is not possible to identify where future development would occur, it is reasonable to assume that—in addition to some redevelopment—open space, farmland and vacant areas would be developed.” (DEIR at 3-15). The EIR also acknowledges that Swainson’s hawk and tricolored blackbird may occur in the Plan Area. The EIR fails to acknowledge that yellow-headed blackbirds, a California species of special concern which also regularly forage in agricultural areas, may also be present in the Plan Area.1 If these species (or any other native birds) are present, it is likely that they are using agricultural landscapes for foraging, wintering, and potentially nesting habitat. Conversion of agricultural land to other land uses is therefore extremely likely to impact these species, yet the EIR failed to acknowledge this and failed to provide any analysis on the impacts of agricultural land loss to these species whatsoever. The EIR therefore fails to sufficiently analyze and mitigate its impacts in violation of CEQA.

1 A search of the California Natural Diversity Database (CNDDB) QuickView “Species in 9 Quads” tool centered on the Fresno South quad conducted on 15 Nov 2024 indicates yellow-headed blackbird are present in the region and could be present in the Plan Area.

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C. The EIR Fails to Adequately Analyze and Mitigate the Project’s Impacts to Wildlife Movement and Habitat Connectivity.

Habitat connectivity is vital for wildlife movement and biodiversity conservation.

Limiting movement and dispersal with barriers (e.g., development, roads, or fenced-off croplands) can affect animals’ behavior, movement patterns, reproductive success, and physiological state, which can lead to significant impacts on individual wildlife, populations, communities, and landscapes (Ceia-Hasse et al., 2018; Cushman, 2006; Haddad et al., 2015; Trombulak & Frissell, 2000; Van Der Ree et al., 2011). Individuals can die off, populations can become isolated, sensitive species can become locally extinct, and important ecological processes like plant pollination and nutrient cycling can be lost. In addition, connectivity between high quality habitat areas in heterogeneous landscapes is important to allow for range shifts and species migrations as climate changes (Cushman et al., 2013; Heller & Zavaleta, 2009). Lack of wildlife connectivity results in decreased biodiversity and degraded ecosystems.

The EIR does not adequately analyze the Project’s impacts to wildlife movement and connectivity. We acknowledge that the DEIR is correct in stating “According to the California Essential Habitat Connectivity Project, the Plan Area is not located within a Natural Landscape Block or Essential Habitat Connectivity area (Spencer et al. 2010). (DEIR at 4.4-27). However, the DEIR goes on to claim that “Urban and agricultural land use areas generally do not provide quality habitat for wildlife migration, as they lack vegetation which wildlife may use for refuge and foraging… Future development under the proposed plan in urban and agricultural land areas would not interfere substantially with the movement of any native resident or migratory wildlife species because the Plan Area does not currently provide an important connection between any areas of natural habitat that would otherwise be isolated.” This claim is incorrect. While the agricultural lands in the Plan Area may not be prime habitat for many species, that does not mean that species do not regularly occupy and/or pass through them. In fact, numerous wildlife species—especially birds, as noted above—use agricultural areas as permanent and migratory habitat. Second, increased traffic, edge effects, and new construction resulting from the proposed Project would impact any species that are present in these agricultural areas.

Edge effects include increased noise, light, and human presence that can disrupt wildlife outside of the Project footprint. Edge effects of development can impact key wide-ranging predators, such as mountain lions, bobcats, and American badgers (Crooks, 2002; Delaney et al., 2010; Lee et al., 2012; Riley et al., 2006; Smith et al., 2015, 2017; Vickers et al., 2015; Wang et al., 2017), as well as smaller species with smaller home ranges, such as song birds, bats and other small mammals, and herpetofauna (Benítez-López et al., 2010; Bunkley & Barber, 2015; Cushman, 2006; Delaney et al., 2010; Gray, 2017; Kociolek et al., 2011; McClure et al., 2013; Slabbekoorn & Ripmeester, 2008; Ware et al., 2015). Limiting movement and dispersal can affect species’ ability to find food, shelter, mates, and refugia after disturbances like fires or floods. Individuals can die off, populations can become isolated, sensitive species can become locally extinct, and important ecological processes like plant pollination and nutrient cycling can be lost. The proposed Project would result in habitat loss and edge effects due to increased human presence and activities that will degrade agricultural areas. Additionally, the increased traffic, noise, and activity on roads serving the Project that would result from Project operations would hinder wildlife movement, impairing local connectivity. The EIR fails to adequately disclose, assess, and mitigate the Project’s impacts to wildlife connectivity and therefore fails to comply with CEQA.

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Development of new buildings in open spaces (including agricultural areas) can also significantly impair movement of many wildlife species. Light pollution affects many nocturnal species, including bats, birds, insects, and terrestrial mammals, among others. Impacts of light pollution are varied, and different species respond to artificial light at night in different ways.

Individual impacts of light pollution include numerous physiological (e.g. hormone levels) and behavioral changes (e.g. shifts in activity patterns) that can impact an animal’s ability to survive and thrive in their environment (Aulsebrook et al., 2020; Dominoni, 2015; Gaston et al., 2014; Kempenaers et al., 2010). These individual impacts cascade into ecological changes like phenological mismatches and shifts in predation patterns that can disrupt population and ecosystem-level dynamics, including mortality, fecundity, and community productivity, among others (Ditmer et al., 2021; Gaston et al., 2013; Sanders et al., 2020). Light pollution was only considered in the context of aesthetics in the DEIR (at 4.1-12), and its impacts to wildlife were not analyzed or mitigated. This must be remedied through a full analysis.

Collisions with buildings are one of the primary sources of mortality for birds (Basilio et al., 2020; Klem, 2009). Recent studies have shown that birds are experiencing significant declines, with estimates indicating losses of up to three billion birds in the past half-century (Rosenberg et al., 2019). This potential impact must be analyzed and mitigation for the proposed Project. Mitigation must include the implementation of bird collision reductions measures to preventing further decline of our avian biodiversity.

The EIR erroneously concludes that Project impacts to wildlife movement would be less than significant and fails to adequately assess and mitigate impacts to wildlife connectivity.

I. The DEIR fails to accurately analyze and mitigate the GHG impact of destroying habitat and converting agricultural land.

CEQA requires a thorough disclosure and analysis of a project’s impact on climate change. (See Communities for a Better Env’t v. City of Richmond (2010) 184 Cal.App.4th 70, 89- 91). In order to meet CEQA’s requirement for adequate analysis, an EIR must disclose all potential sources of a GHG emission resulting from the project. Lead agencies are required to quantify and disclose GHG emissions that would occur during construction, and the South Coast Air Quality Management District requires construction GHG emissions to be amortized over the life of the project, defined as 30 years. Yet the DEIR fails to comply with these requirements.

When describing the emissions sources during construction activities, the DEIR only lists emissions from “the use of heavy-duty off-road equipment, materials transport, and worker commute trips.” (DEIR at 4.8-12). The DEIR fails to account for carbon emissions from intact habitats or agricultural landscapes that would be dug up and paved over during construction as well as the annual loss of carbon that existing open space would have continued to sequester for 30 years if the Project were not constructed.

As detailed in a 2023 Center Report, “Hidden in Plain Sight: California’s Native Habitats are Valuable Carbon Sinks” (Yap et al., 2023), non-forest arid and semi-arid habitats, like the annual grasslands in the Project area, can store significant amounts of carbon by keeping it from being released and sequester it by removing it from the atmosphere. Agricultural lands also sequester carbon (Kroodsma & Field, 2006). Carbon cycling in agricultural landscapes can vary greatly depending on crop species and agricultural practices, and some agricultural lands can act as carbon sources themselves (Ceschia et al., 2010). However, numerous crop types—including orchards and vineyards, which make up the majority of the agricultural landscape of the Plan December 4, 2024

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Area (DEIR at 4.4-5)—are able to sequester carbon, and act as carbon sinks (Kroodsma & Field, 2006; Scandellari et al., 2016).

Given the increased urgency in combatting climate change and new knowledge that California is currently not on track to meet its GHG reduction targets, it is important to accurately quantify and mitigate for loss of carbon storage and sequestration potential. However, the EIR fails entirely to consider the impact of conversion of native habitats, open space and agricultural landscapes and the loss of carbon sequestration capacity of these landscapes on GHG emissions. As stated in the DEIR, “approximately 1,874 acres [of the Plan Area] is open space, farmland, and vacant. Approximately 700 acres of the Plan Area is conservatively assumed to be developed with non-residential uses, primarily industrial, by the year 2040. While it is not possible to identify where future development would occur, it is reasonable to assume that—in addition to some redevelopment—open space, farmland and vacant areas would be developed.” (DEIR at 3-15). This is an egregious oversight, as the conversion of 700+ acres of open space to industrial, commercial, residential, or uses necessitates the removal of 700+ acres of vegetation that is potentially—and depending on the habitat (e.g. native grassland), certainly—sequestering carbon. This exclusion is particularly inexcusable because CalEEMod Version 2022.1, upon which the EIR relies, has the modelling capabilities to calculate the loss of carbon from changes in land use and the associated loss of vegetation (CAPCOA, 2022). The modelling also provides detailed inputs for calculating GHG emissions from land use changes and vegetation loss. While the EIR uses CalEEMod 2022.1 to model the Project’s GHG emissions from construction and operations (DEIR at 4.8-10), the EIR fails to perform any of the available analyses of land use change and sequestration (DEIR, App. B., 24/31).

The failure to account for emissions resulting from land conversion and loss of sequestration ability of existing vegetation leads to an inaccurate estimation of total Project emissions. The omission of any sequestration analysis is particularly concerning, as the loss of sequestration ability of existing landscapes will lead to a continued increased net emissions into the future. The DEIR states that “the analysis presented herein quantifies GHG emissions resulting from anticipated development under the plan through the planning horizon of 2040, and describes, calculates, and estimates those emissions.” However, because the EIR performs no analysis of carbon sequestration whatsoever, the document fails to account for the annual loss of carbon that existing habitats would have continued to sequester for 30 years if the Project were not constructed. The EIR therefore fails to meet this requirement.

Because the EIR does not account for either carbon emissions resulting from land use conversion, which could be substantial in the proposed Plan, nor the loss of carbon sequestration capacity that will result from such land use conversion, the estimate of total emissions is inaccurate, and the true levels of GHG emissions resulting from the proposed Plan are likely to be higher than stated in the EIR. A supplemental EIR must disclose the carbon emissions associated with the loss of currently stored carbon as well as the loss of future carbon that would have been sequestered by these habitats over the Project lifetime.

CEQA requires the adequate assessment and mitigation of a project’s impacts.

Destroying native habitats, agricultural landscapes, and open space could release significant amounts of carbon that must be accounted for. An EIR must disclose and “give the public and decision makers the most accurate and understandable picture practically possible of the project’s likely near-term and long-term impacts.” (Guidelines § 15125(a).) By failing to disclose the carbon emissions associated with the loss of currently stored carbon as well as the loss of December 4, 2024

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future carbon that would have been sequestered by these habitats over the Project lifetime the FEIR fails as an informational document.

II. THE EIR’S ANALYSIS AND MITIGATION OF WATER SUPPLY
IMPACTS IS INADEQUATE.

The regional aquifer that would supply the Project is in a state of critical overdraft, and the surface water supplies are unable to meet the Project Area’s current needs in dry years. (DEIR at 4.16-9, 10.) This EIR violates CEQA as it fails to adequately analyze the Project’s impacts on water supply, which precludes its ability to serve as an informational document. (Kings County Farm Bureau v. City of Hanford (1990) 221 Cal.App.3d 692, 711-712.) a. The DEIR Must Consider Climate Change in Its Analysis of Water Supply Impacts.

The EIR fails to discuss the reality of climate change and its effects on water supply. As climate change progresses, California will likely experience more frequent drought conditions similar to those in 2012-2016. (Bedsworth 2018.) The only time the EIR considers drought conditions and how water supply will be affected is in its “Emergency Water Supply Conditions” discussion. (DEIR at 4.16-12.) Without integrating climate change’s impacts throughout the discussion of water supply, the “Emergency Water Supply Conditions” section is realistically the only part of the water supply discussion that reflects the reality of water resources in the face of climate change.

The failure to properly consider climate change throughout the EIR’s discussion of water supply has led to unrealistic conclusions regarding the sufficiency of water supply for the Project. The City of Fresno must reevaluate its determinations of water supply sufficiency and impacts within the context of climate change, and if necessary, implement mitigation measures. (Los Angeles Unified Sch. Dist. v. City of Los Angeles (1997) 58 Cal.App.4th 1019, 1029.)

According to the EIR, switching from the General Plan to the Specific Plan would amount to a 6% decrease in water demand. (EIR at 4.16-3.) Even if the EIR is correct in its analysis of water demand, that does not mean that the Project Area will have sufficient water supply as climate change progreses, as it is estimated that some areas of the Central Valley will face a reduction of up to 40% in surface and subsurface flows. (Hanson 2012.)

i. The EIR’s Conclusions Regarding the Sufficiency of Future Surface Water and Groundwater Supplies Ignore the Impacts of Climate Change

1. The EIR must engage with modern climate science

Fresno must consider the best available science on environmental impacts. (Wild Fish Conservancy v. Irving (2016) 221 F.Supp.3d 1224, 1234.) Agencies are only able to discount information on climate change when it is either inconclusive or the impacts are too speculative. (County of Butte v. Department of Water Resources (2023) 90 Cal.App.5th 147, 169.) That is not December 4, 2024

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the case here. California is facing unprecedented challenges in its effort to allocate and conserve limited water resources, especially as water supply dwindles in the face of climate change and population growth. Studies predict that in the next 35 to 60 years, if emissions of carbon dioxide and other greenhouse gases continue unchecked, the American West’s snowpack will continuously shrink, disappearing for a decade or more at a time. (Siirila-Woodburn 2021.) Groundwater reserves in the Central Valley have been declining over the past decades, and without proper mitigation, that decline will continue at an accelerated rate due to climate change. (Alam 2019.) Consequently, a recent Executive Order from Governor Newsom declared that California must “redouble near-, medium-, and long-term efforts to adapt its water management to a changing climate, shifting precipitation patterns, and water scarcity.” (Executive Order N-7- 22 (2022).)

Fresno must engage in a meaningful analysis of climate change’s effects on water resources that is in step with evolving scientific knowledge and state regulatory schemes, and include that analysis in its discussion of water supply for the Project. (Natural Resources Defense Council v. Kempthorne (2007) 506 F.Supp.2d 322, 369; County of Butte, 90 Cal.App.5th at 162, internal citations omitted; Cleveland National Forest Foundation v. San Diego Association of Governments (2017) 3 Cal.5th 497, 504.) Considering that both groundwater and surface water supplies will be negatively impacted by climate change, the DEIR must discuss what measures will be implemented to ensure that future water needs will be met.

2. Surface water supplies and impacts

Climate change will cause surface water flows in California to diminish due to decreased rainfall and reduced snowpack levels. (Bedsworth 2018.) Fresno has consistently relied on surface water flows to meet its water needs in conjunction with groundwater supplies. (DEIR at

4.16-9.) The EIR indicates that surface water will continue to be a core source of water for the City. (DEIR at 4.16-10.)

A large portion of the water supplied to Fresno is from the Central Valley Project (CVP), which allots 60,000 afy of water from the San Joaquin River. (DEIR at 4.16-9.) The Central Valley’s water system is substantially vulnerable to long-term changes in the climate resulting from lengthening periods of drought and a decreased frequency of precipitation events. (Ray 2020.) Even the EIR concedes that in critical, dry, and normal dry years, Fresno’s allotment via the CVP has not been fully met; in those years, the City has received between 0 and 75 percent of its annual allocation. (DEIR at 4.16-9.) Fresno’s annual use of surface water supplies is projected to increase until 2030, where it will hit and maintain a maximum level of 191,600 afy through 2045. (DEIR at Table 4.16-1.) Considering that the trend of multiple dry years and drought conditions is likely to increase, the City is going to face reductions of up to 31% of the annual surface water supplies through 2045. It seems unlikely that Fresno will be able to meet future water demands without either cutting back on water uses, or increasing withdrawals from an already overdrafted aquifer.

3. Groundwater supplies and impacts

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The aquifer that supplies the City of Fresno with groundwater, the Kings River Subbasin (the “Subbasin”), is currently in a state of critical overdraft. (DEIR at 4.10-10.) Between 2011 and 2015, Fresno pumped approximately 111,522 acre-feet per year (afy) of water from the aquifer, which exceeds the its sustainable yield of 72,500 afy. (DEIR at 4.10-12.) The Subbasin is primarily recharged via subsurface flows originating from the Sierras, which are likely to be negatively impacted by climate change. (DEIR at 4.10-10; Hanson 2012.) This will diminish the quantity of water that naturally recharges the Subbasin, which Fresno will continue to rely on for the foreseeable future. (DEIR at 4.16-10, 11.) Meanwhile, Fresno’s groundwater extraction is projected to “increase substantially” due to regional growth – increasing from 138,090 afy in 2025 to 154,490 afy in 2040. (DEIR at 4.10-21.)

In calculating the rates of aquifer recharge and concluding that future groundwater supplies will be sufficient for the Project, the EIR relies on data from 1964-2004, a time when the impacts of climate change were less severe. (DEIR at 4.10-12; UWMP at 6-11, 12.) There is a time lag between GHG emissions and the measurable impacts associated with them. (Ricke 2014.) Global GHG emissions have been increasing rapidly, and some studies suggest that emissions rates continue to accelerate. (Fyson 2023.) Considering that the EIR states that the Project area will continue to rely on groundwater supplies for the forseeable future, it is critical for the EIR to support its analysis of future groundwater supplies with more modern data.

Further, the Project itself will likely reduce groundwater recharge. It will pave a significant acreage of current agricultural land, and so replace pervious surfaces with impervious ones. The EIR must account for reduced future groundwater recharge.

The EIR’s lack of climate change analysis in its water supply discussions also implicates environmental justice concerns. Communities in unincorporated areas often rely on shallow wells to supply individual households with water, and, as groundwater levels are depleted, these wells are often the first to go dry. (Bostic 2023.) The EIR must determine if the Project’s groundwater usage will impact the shallow wells in the surrounding unincorporated areas. If those impacts are determined to be significant, the EIR must implement mitigation measures that protect these communities from losing their access to water.

b. The EIR’s Conclusions Regarding Water Supply Demands And Impacts Are Unsupported And Inconsistent

i. The Water Demands Associated with Different Land Uses are Unsupported by the Record.

According to the EIR, the adoption of the Specific Plan would result in a lower water demand than what is projected under the General Plan. (DEIR at 4.16-3.) The EIR states that, under the Specific Plan, the annual demand for water would be approximately 17,300 afy, which is 1,100 afy less than the demand projected under the General Plan, a 6% reduction. (DEIR at Table 4.16-3.) The conclusion that the Specific Plan’s cumulative land uses will demand less water on average than the General Plan’s cumulative land uses is largely based on the fact that there will be less heavy industrial land use, and more light industrial land use. (DEIR at Table

4.16-3.) The DEIR does not make it clear why these changes would result in the reduction in December 4, 2024

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Fresno City CouncilDecember 5, 2024

Fresno City Council
December 5, 2024