Carmel-by-the-Sea City Council
November 4, 2025

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 43 Table 8. Mean percentage above overall 85th percentile thresholds ([concentration – 85th percentile]/85th percentile) for constituents in receiving water samples at each ASBS site.

Site Arsenic Cadmium Chromium Copper Lead Mercury Nickel Selenium Silver Zinc A 33% 76% 45% 103% 126% -17% 196% 3% -62% 112%
B 4% -33% -63% 725% 204% -43% -71% -58% -62% 51%
C 32% -3% -63% 80% 265% -20% -68% -62% -49% 156%
D -6% -20% -55% 543% 809% 106% -38% -65% -59% 700%
E -11% -62% -89% -44% -83% -93% -85% -73% -56% -87%
F -9% -16% -59% 307% 617% 46% -48% -38% -56% 468%
G -17% -52% -74% 359% -28% 35% -59% -66% -87% 9%
H 6% -20% -9% -8% 53% -25% -3% -59% -80% -9%
Overall 3% -18% -49% 267% 252% -1% -27% -88% -63% 180%
Site
TSS Fecal
Coliform Enterococcus E. coli Nitrate Orthophosphate Urea PAHs Organophosphate
Pesticides Pyrethroids
A 240% 156% 158% 303% -43% -81% -100% NC1 0% 0%
B -26% 276% 336% 292% -64% -25% -10% NC 0% 0%
C -38% 656% 119% 414% -58% -67% 45% NC 0% NC
D -42% 2895% 2794% 2665% -28% -44% 68% NC NC 0%
E -67% -64% -53% -71% -60% -92% -100% NC NC NC
F -21% 6350% 10598% 5172% -53% -69% 298% NC 0% NC
G -39% 4807% 139% 73% -43% 23% 32% 0% 0% 0%
H 56% 120% 46% -75% -65% -67% -35% 0% 0% 0%
Overall 8% 1899% 1767% 1097% -52% -53% 26% NC NC NC
1 = PAHs detected, but percentage above the threshold is not calculable because 85th percentile threshold is 0.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 44 In order to understand the effect that periodically high constituent concentrations can have on the relationship between the percentage of samples above the overall 85th percentile threshold and the average magnitude of concentrations above the threshold, examination of data for individual storms is necessary. In the case of arsenic, most values above the threshold were very small in magnitude, with 1 larger excursion above the threshold at each of sites A and C (Figure 9a). Similarly, there were very sporadic receiving water concentrations greater than overall 85th percentile thresholds for cadmium, chromium, and mercury (Figure 9b, c, and d) and for nickel, selenium, and silver (Figure 10a, b, and c), with most values below the threshold.

Site A had the most samples and highest concentrations above the overall 85th percentile threshold for all 7 of these trace metals, except for mercury, which had periodical concentrations much above the threshold at sites D, F, and G (Figure 9d). Site A exhibited high concentrations of arsenic, cadmium, chromium, selenium, and zinc in storm 6 (Figure 9a, b, c, and Figure 10a and b), but no other site had peak concentrations of arsenic, cadmium, chromium, lead, nickel, mercury, selenium or silver in any particular storm.

In contrast to the infrequent occurrence of receiving water concentrations above the overall 85th percentile threshold for these 7 trace metals, copper, lead and zinc had frequent and widespread occurrences of concentrations above the threshold (Figure 11a, b, and c). These 3 trace metals have been globally associated with urban storm runoff (Brown and Peake 2006; Göbel et al, 2007; O’Sullivan et al, 2011). The spatial and temporal patterns exhibited by these 3 trace metals were similar to that seen for mercury, with the highest concentrations above the threshold occurring at sites B, D, F, and G for copper and at B, D, and F for lead and zinc. Storm 1 at sites B, D, and F and storm 3 at Site D exhibited peaks for all 3 of these trace metals. None of these peak concentrations appear to be obviously related to the amount of rainfall prior to sampling (Table 9), except perhaps for some trace metals at Site A (Figures 9 and 10).

FIBs also exhibited sporadic receiving water concentrations much above the overall 85th percentile threshold at sites D, F, and G (Figure 12). There were also many receiving water samples across all sites that had FIB concentrations near or below the threshold that were essentially invisible compared to the large magnitudes of concentrations above the threshold.

The storms with high FIB concentrations at sites D and F corresponded to the same storms that exhibited peaks for copper, lead and zinc.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 45 Figure 9. Magnitude of receiving water concentrations for arsenic, cadmium, chromium and mercury above and below the overall 85th percentile in receiving water samples during each storm at each site. Letters A–H along the x-axis refer to sites and numbers 1–6 refer to storms. “Exceedance” refers to the concentration above or below the overall 85th percentile threshold.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 46 Figure 10. Magnitude of receiving water concentrations for nickel, selenium, and silver above and below the overall 85th percentile in receiving water samples during each storm at each site. Letters A–H refer to sites and numbers 1–6 refer to storms. “Exceedance” refers to the concentration above or below the overall 85th percentile threshold.

Nutrients were also below the overall 85th percentile threshold in most receiving water samples (Figure 13). Moreover, there was no apparent coherence among nitrate, orthophosphate, ammonia and urea in their respective temporal patterns of high and low concentrations.

Nevertheless, the temporal patterns in TSS at Site A (Figure 13e) closely corresponded to those for arsenic, cadmium, chromium, nickel, and selenium (Figure 9a, b, c, and Figure 10a and b) at that site.

These results reveal no consistent patterns across sites in receiving water concentrations above the overall 85th percentile threshold. Moreover, the temporal patterns are inconsistent among constituents and sites, with no obvious relationship to rainfall.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 47 Figure 11. Magnitude of receiving water concentrations for copper, lead and zinc above and below the overall 85th percentile in receiving water samples during each storm at each site.

Letters A–H along the x-axis refer to sites and numbers 1–6 refer to storms. “Exceedance” refers to the concentration above or below the overall 85th percentile threshold.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 48 Table 9. Rainfall for each storm prior to sample collection at each site (inches).

Site
Storm A B C D E F G H
1 0.34 0.3 0.8 0.27 0.37 0.66 0.1 0.29
2 0.2 2.6 0.07 .013 0.1 0.09 0.35 .013
3 0.1 0.42 3.85 3.69 2.25 3.67 0.36 0.17
4 0.85 0.67 0.33 0.69 0.36 0.33 0.36 .063
5 0.64 2.28 0.74 0.93 0.48 0.68 0.69 0.33
6 1.83 0.37 0.94 2.41 2.28 0.89 2.28 1.08
Figure 12. Magnitude of receiving water concentrations for a) E. Coli, b) Enterococcus and c) fecal coliform, above and below the overall 85th percentile in receiving water samples during each storm at each site. Letters A–H along the x-axis refer to sites and numbers 1–6 refer to storms. “Exceedance” refers to the concentration above or below the overall 85th percentile threshold.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 49 Figure 13. Magnitude of receiving water concentrations for a) nitrate, b) ammonia, c) urea, d) orthophosphate, and e) TSS above and below the overall 85th percentile in receiving water samples during each storm at each site. Letters A–H along the x-axis refer to sites and ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 50 numbers 1–6 refer to storms. “Exceedance” refers to the concentration above or below the overall 85th percentile threshold.

ANOVA was used to test for differences among groups of sample types. ANOVA compares the variation within sample types to the variation between sample types. If there is high variation within sample types compared to variation between sample types, ANOVA returns a nonsignificant result. Consequently, another test was performed to determine whether there were small, but systematic differences between pre-storm and receiving water samples collected in the same storm at each site. The matched-pair test is often performed on test subjects in drug trials in which the ability of a drug to affect a measurable attribute, such as blood pressure or heart rate, is being tested. It compares a before-drug measurement with an after-drug measurement on each subject. In our case, we compared the before-storm with the after-storm constituent concentrations, without considering whether the receiving water concentrations were above or below the 85th percentile threshold.

The matched-pair test revealed that there were significant increases in all constituents, except selenium, silver, Enterococcus, nitrate, orthophosphate, the sum of pyrethroid pesticides, and the sum of organophosphate pesticides during storms when all sites were tested together (Table 10). Thus, the concentrations of arsenic, cadmium, chromium, copper, lead, nickel, mercury, zinc, TSS, fecal coliform, E. coli, ammonia, urea, and PAHs increased in receiving water samples overall during the storms that were sampled.

Another matched-pair test was performed within each site to determine whether sites differed in the constituents that increased in receiving water samples (Table 11). Site B had the fewest constituents with statistically higher concentrations in receiving water samples than in corresponding pre-storm samples; only nickel and E. coli exhibited increases at Site B. Sites A and H had significantly higher receiving water concentrations of 3 constituents, cadmium, mercury, and Enterococcus and chromium, zinc, and TSS, respectively. Sites E and F each had 4 constituents with higher receiving water concentrations, lead, mercury, fecal coliform, Enterococcus and copper, nickel, mercury, and zinc, respectively. Sites C, D, and G each had 8 constituents with significantly higher concentrations in receiving water samples as follows: C) arsenic, chromium, copper, lead, nickel, mercury, zinc, and Enterococcus; D) cadmium, chromium, copper, lead, nickel, mercury, zinc, and Enterococcus; G) chromium, copper, lead, nickel, zinc, Enterococcus, E. coli, and orthophosphate.

These results indicate that ASBS sites with high percentages of receiving water constituents above the overall 85th percentile threshold do not necessarily exhibit significant increases in receiving water samples from pre-storm samples. For example, a comparison of Table 7 with Table 11 reveals that there is no significant relationship between pre-storm and receiving water concentrations of arsenic at Site A, where 80% of the receiving water samples were above the threshold. At this site there were also no significant increases for cadmium, copper, lead, nickel, or zinc, all of which were above their respective thresholds in 60–80% of samples. Conversely, Site B exhibited significantly increased concentrations of nickel in receiving water samples, but none of its samples were above the overall 85th percentile threshold for nickel. The matchedpair results tell us that increased concentrations of some constituents at some sites occur during storms. They do not tell us whether the discharges or some other feature of storms (e.g., ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 51 resuspension of fine sediment particles and organic material due to wave action) are causing the increases.

Table 10. Results from matched-pair (pre-storm vs. receiving water) statistical tests to determine whether receiving water concentrations were higher than pre-storm concentrations for each constituent across all ASBS sites.

Analyte Mean Difference (in units of
measure)
p1
Arsenic, µg/L 0.213 0.007
Cadmium, µg/L 0.016 0.001
Chromium, µg/L 0.541 <0.001
Copper, µg/L 3.717 <0.001
Lead, µg/L 0.672 <0.001
Nickel, µg/L 0.688 0.002
Mercury, µg/L 3.549 <0.001
Selenium, µg/L 0.007 0.175
Silver, µg/L 0.023 0.169
Zinc, µg/L 6.72 <0.001
TSS, mg/L 15.92 0.001
Fecal Coliform, MPN/100ml 2813 0.012
Enterococcus, MPN/100ml 4245 0.062
E. coli, MPN/100ml 1452 0.010
Nitrate, mg/L 0.060 0.055
Orthophosphate, mg/L 0.010 0.145
Ammonia, mg/L 0.015 0.013
Urea, µg/L 7.447 0.016
Sum PAHs, µg/L 0.029 0.006
Sum Pyrethroids, µg/L 0.002 0.085
Sum Org. Pesticides, µg/L 0.004 0.093
1 = Probabilities <0.05 are statistically significant. If p >0.05, there is no statistical difference.

Constituent loads were estimated for each storm sampled, as another element in the analyses designed to determine whether storm discharges alter receiving water constituent concentrations. Among trace metals, zinc and copper had the highest average loads, nearly 5 and 3 grams per site per storm (Table 12). Silver and cadmium had the lowest average loads, <0.001 g per storm. TSS loads averaged >2 Kg per storm and oil and grease averaged >1 Kg per storm, although it was detected in discharges from only sites B, E, and F.

Estimation of constituent loads from each site also revealed differences among sites that did not always match results from other analyses. For example, average loads of every trace metal from Site B ranked among the top 3 across all sites (Table 12), whereas nickel was the only trace metal that exhibited significant increases in receiving water at that site (Table 11) and the ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 52 percentages of samples that were above the overall 85th percentile threshold for trace metals at site B were generally low, except for copper, lead, and zinc (Table 8). Conversely, average loads from
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 53 Table 11. Results from matched-pair (pre-storm vs. storm) statistical tests for each constituent across individual sites. (con’t on next page)
Site
Analyte

A B C D
Mean Diff. p1 Mean Diff. p Mean Diff. p Mean Diff. p
Arsenic 0.4754 0.1176 0.173 0.1724 0.9327 0.0337 0.0543 0.4014 Cadmium 0.0341 0.1637 0.0091 0.2592 0.0306 0.0881 0.0226 0.0413 Chromium 1.6055 0.0246 0.4244 0.0593 0.4249 0.0086 0.525 0.0405 Copper 1.2942 0.0805 9.0977 0.0898 1.631 0.0297 6.8838 0.0277 Lead 0.3309 0.1679 0.6628 0.0869 0.6930 0.014 1.7449 0.0387 Nickel 2.9979 0.0895 0.2474 0.0498 0.3391 0.0347 0.7695 0.0228 Mercury 2.0512 0.0028 2.344 0.0516 2.2298 0.0231 8.1222 0.0179 Selenium 0.0424 0.1631 0.0238 0.0736 -0.0005 0.5104 0.006 0.2407 Silver -0.122 0.8444 0.03 0.0701 0.1 0.1174 0.065 0.2205 Zinc 3.7743 0.1085 3.8816 0.0511 0.0070 0.007 20.52 0.0329 TSS 52.83 0.0808 14.833 0.1317 8.8333 0.0671 1.6667 0.4093 Fecal Coliform 363 0.1059 527 0.0778 1010 0.117 4114 0.0507 Enterococcus 578 0.0439 988 0.1043 487 0.0425 6535 0.0395 E. coli 504 0.1626 482 0.0462 584 0.082 3293 0.0621
Nitrate 0.1667 0.1917 0.02 0.3995 0.0167 0.1816 0.1333 0.0606 Orthophosphate -0.0083 0.7691 0.034 0.2551 0.0017 0.4655 0.0133 0.2441 Ammonia 0.0183 0.0884 0.014 0.187 0.0117 0.1816 0.0383 0.1816 Urea -1.6667 0.8184 6.8 0.177 1.6667 0.4195 14.33 0.0712 Sum PAHs 0.0401 0.0668 0.0625 0.1252 0.0060 0.1398 0.0382 0.0833 Sum Pyrethroids - - - - 0.0027 0.0894 0.0095 0.1816
Sum Org. Pesticides - - - - - - 0.0097 0.1816
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 54 Table 11 (continued). Results from matched-pair (pre-storm vs. storm) statistical tests for each constituent across individual sites.

Site
Analyte

E F G H
Mean Diff. p Mean Diff. p Mean Diff. p Mean Diff. p
Arsenic -0.0213 0.7113 -0.027 0.8286 0.05567 0.163 0.0838 0.211 Cadmium -0.005 0.9242 0.0241 0.0503 0.0037 0.1552 0.01408 0.1414 Chromium 0.0013 0.475 0.4788 0.0898 0.2021 0.012 0.9003 0.0301 Copper 0.4892 0.1362 3.9895 0.0392 4.8488 0.0362 0.5774 0.0645 Lead 0.0279 0.0093 1.4571 0.1021 0.1087 0.0106 0.2161 0.0676 Nickel 0.016 0.2732 0.5285 0.0372 0.4118 0.0317 0.5560 0.1145 Mercury 0.2568 0.0112 5.7857 0.0448 5.4523 0.0957 2.1507 0.0682 Selenium -0.02 0.8582 0.0232 0.1389 0.0037 0.1996 -0.0246 0.766 Silver 0.0517 0.2654 0.0617 0.2154 -0.0533 0.905 0.028 0.2271 Zinc -0.0104 0.5155 14.19 0.0352 2.2047 0.0485 1.4559 0.0486 TSS 5.1667 0.1155 14.17 0.129 7.5 0.0752 22.33 0.0309
Fecal Coliform 35.8333 0.0403 9144 0.1078 7007 0.1753 302 0.1434 Enterococcus 96.5 0.0274 24418 0.1498 536 0.044 323 0.1463 E. coli 17.83 0.0996 6508 0.0825 207 0.0318 17.5 0.1958 Nitrate 0.15 0.21 -0.05 0.7188 0.05 0.3325 -0.0167 0.8184 Orthophosphate -0.025 0.965 -0.0217 0.9249 0.0817 0.0471 0.005 0.2075 Ammonia - - 0.03 0.1816 0.0083 0.1816 - -
Urea -1.6667 0.8184 31.17 0.0944 11.17 0.0608 -2.3333 0.7659 Sum PAHs 0.0009 0.1816 0.0681 0.1651 - - 0.0137 0.0765
Sum Pyrethroids - - - - 0.0012 0.0935 - -
Sum Org. Pesticides - - 0.02033 0.1816 - - - - 1 = Probabilities <0.05 are statistically significant. If p >0.05, there is no statistical difference.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 55 Table 12. Mean loads per storm of constituents from each ASBS sites with discharge >36 inches.

Site
Arsenic
µg
Cadmium
µg
Chromium
µg
Copper
µg
Lead
µg
Mercury
ng
Nickel
µg
Selenium
µg
Silver
µg
Zinc
µg
TSS
mg
A 530,183 34,809 378,328 2,420,360 214,473 4,917,557 4,374,025 444,851 1,286 6,215,007 5,314,818 B 16,412,528 945,768 13,005,416 387,530,118 36,422,944 196,600,212 25,461,760 1,944,429 741,669 450,419,834 161,197,354 C 4,343,968 238,213 2,922,933 36,022,503 7,939,545 25,913,887 4,454,268 437,172 74,030 192,625,406 54,850,926 D 55,348,120 1,602,423 25,428,000 280,022,877 62,559,322 449,944,057 47,374,373 4,365,811 861,686 1,085,251,315 363,610,574 E 383,307 9,673 194,899 6,106,999 279,155 2,556,803 488,712 41,316 9,643 4,376,360 2,490,809 F 10,894,319 1,296,434 16,398,804 164,405,793 40,489,232 284,800,398 30,544,077 1,557,180 729,049 727,532,401 252,323,348 G 4,748,518 399,273 8,558,681 122,733,913 4,561,021 111,232,291 21,553,366 934,042 0 93,064,842 41,686,164 H 796,273 42,096 2,518,377 14,125,995 2,209,773 18,316,941 3,359,593 190,124 4,021 35,212,884 36,094,925 Average 10,813,976 530,947 8,094,703 118,325,979 17,924,249 128,573,161 16,057,436 1,171,281 284,736 301,305,320 109,965,666 Load/acre 185,455 9,746 163,431 2,705,170 280,412 2,370,324 396,781 30,277 5,369 4,960,121 2,287,507 Site
O&G
mg
Fecal
coliform
MPN/100 mL
x 103
Enterococcus
MPN/100 mL
x 103
E. coli
MPN/100 mL
x 103
Nitrate
mg
OPhos.
mg
Ammonia
mg
Urea
µg
PAHs
µg
OP pest
µg
Pyreth.
µg
A 0 1,530,485 3,967,756 2,060,600 2,683,734 29,242 0 2,684,735 54 0 0 B 1,175,156 30,804,715 27,345,109 37,629,078 4,116,669 2,848,803 204,358 213,370,294 662,639 0 58,216 C 0 14,628,925 19,962,169 11,320,004 1,779,511 870,644 308,013 161,208,681 175,038 60,190 140,868 D 0 222,402,280 166,479,730 196,948,185 22,669,174 8,164,688 943,636 826,594,940 1,139,509 0 523,753 E 182,786 838,849 2,589,575 522,832 73,582 23,598 2,314 7,824,020 9,739 12,862 3,142 F 8,485,661 357,997,681 302,975,585 236,139,201 21,808,338 3,336,451 1,294,889 1,660,298,390 936,701 410,455 33,367 G 0 5,166,210 3,479,606 888,881 5,928,235 1,679,245 184,658 204,244,622 19,572 0 10,544 H 0 497,651 1,972,353 663,902 641,946 146,786 27,369 32,193,220 87,798 0 2,480 Average 1,166,502 74,357,695 61,593,883 57,242,279 7,089,197 1,996,010 356,153 369,286,198 355,897 55,789 89,737 Load/acre 33,384 1,091,906 1,120,313 880,071 143,840 30,677 6,032 5,688,906 6,477 1,832 1,668 <36”
Average 41,112 1,652,277 553,606 1,507,598 - - - - - - - <36”
Load/acre 5,773 139,454 109,361 145,923 - - - - - - -
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 56 Site D of every trace metal, except copper, were the highest of any site, which corresponded to the numerous trace metals at that site with significant increases in receiving water samples.

Average loads of trace metals from Site F also ranked among the top 3 across all sites and, similar to Site B, it only had 4 trace metals that increased significantly in receiving water samples and the percentage of samples above the threshold was generally low, except for copper, lead and zinc (Table 7).

Loads of FIBs, nutrients and PAHs were more consistent with other indicators than were loads of trace metals. Sites D and F had the highest average loads of FIBs, nutrients, and PAHs. Site F also had the highest loads of oil and grease and organophosphate pesticides (Table 12).

Receiving water samples from both Site D and Site F were nearly always above the overall 85th percentile threshold for FIBs, although only Enterococcus at Site D exhibited significant increases in receiving water samples. Receiving water samples from these 2 sites also exceeded the threshold for ammonia and urea in 50–67% of samples.

A final test was performed to examine the relationship between discharges and the change in concentrations (delta) of constituents from pre-storm to receiving water samples. The estimated load of each constituent was compared to the delta. A significant regression inferred a link between estimated loads of a constituent and changes in its receiving water concentrations (i.e., an effect of load on the change in concentration). Previous comparisons of this type for trace metals, performed on single constituents across all storms, had not revealed consistent relationships between loads and delta. These negative results could be attributed 1) the variation among storms, 2) differences in the point in the storm at which sampling was conducted, or 3) differences among field crew in the location in the receiving where samples were collected, and differences in wave action (i.e., resuspension). Consequently, a different approach was taken for trace metals, in which regressions were performed using the estimated loads and deltas within each storm for all constituents. This method provides more points for each regression than the 6 that would be available if each constituent was plotted for all storms. It also was not limited by differences among storms in loads, storm conditions, or sampling location in the receiving water.

Trace metals exhibited numerous significant regressions for load versus delta, with apparent differences among storms and sites (Table 13 and Figure 14). Slight slope differences in the significant regressions reveal differences among storms in the relationships between loads and deltas, as predicted from previous results. Sites E and A had the fewest significant regressions, with 2 and 3, respectively (i.e., 33% and 50% of storms). These 2 sites, along with Site H, also consistently had the lowest average loads for every trace metal (Table 12). Site B, which consistently had among the top 3 average loads for each trace metal, exhibited only 4 significant regressions, whereas sites C and G, which had much lower average loads of trace metals, had 5 and 6 significant regressions, respectively. Sites D and F, which also had consistently high average trace metal loads, had 6 and 5 significant regressions, respectively.

The differences among sites in the correspondence between loads and the number of significant regressions probably reflected differences in site conditions that affected dilution of storm runoff as it entered the ocean, such as beach slope and wave exposure.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 57 Table 13. Results from regressions of trace metal load vs delta. (continued on next page) Site & storm Date R2 p1 Y-int
A1 2/6/14 0.2255 0.1655 1.24E-01
A2 2/26/14 0.0596 0.4965 5.55E-01
A3 2/7/15 0.5548 0.0135 5.67E-01
A4 4/7/15 0.6942 0.0028 -1.11E-01
A5 1/5/16 0.5469 0.0145 1.20E+00
B1 2/6/14 0.9543 <0.0001 1.49E-02
B2 2/26/14 0.3222 0.087 -3.12E-02
B3 12/12/14 0.9630 <0.0001 1.26E-01
B4 1/5/16 0.9956 <0.0001 5.36E-02
B5 2/18/16 0.7808 0.0007 2.56E-01
B6 3/6/16 0.3701 0.062 6.06E-02
C1 2/6/14 0.9694 <0.0001 4.68E-01
C2 2/26/14 0.9752 <0.0001 2.42E-01
C3 12/12/14 0.9297 <0.0001 7.29E-01
C4 2/7/15 0.6682 0.0039 -1.59E-02
C5 1/5/16 0.2821 0.1142 6.77E-01
C6 2/18/16 0.7120 0.0021 2.03E-01
D1 2/6/14 0.9920 <0.0001 1.99E-01
D2 2/26/14 0.9207 <0.0001 1.88E-01
D3 12/12/14 0.9988 <0.0001 -2.88E-02
D4 1/5/16 0.6759 0.0035 6.97E-02
D5 2/18/16 0.9607 <0.0001 5.99E-01
D6 3/6/16 0.9728 <0.0001 1.09E-01
E1 2/6/14 0.0051 0.8442 -0.0121
E2 2/26/14 0.5985 0.0087 0.0764
E3 12/12/14 0.0728 0.4508 -3.14E-02
E4 2/7/15 0.0793 0.4306 4.10E-03
E6 1/5/16 0.1771 0.2259 3.49E-02
E6 3/6/16 0.4841 0.0255 1.76E-02
F1 2/6/14 0.9799 <0.0001 6.92E-01
F2 2/26/14 0.9743 <0.0001 3.58E-01
F3 12/12/14 0.8747 <0.0001 1.84E-01
F4 2/7/15 0.9979 <0.0001 7.15E-02
F5 1/5/16 0.9273 <0.0001 1.18E-01
F6 2/18/16 0.4071 0.0471 -4.69E-02
G1 2/26/14 0.8336 0.0002 6.22E-02
G2 2/7/15 0.7907 0.0006 -8.63E-03
G3 4/7/15 0.9783 <0.0001 2.40E-01
G4 1/5/16 0.9106 <0.0001 -2.24E-01
G5 2/18/16 0.7996 0.0005 -9.97E-02
G6 3/6/16 0.7038 0.0024 -1.43E+00
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 58 Table 13. Results from regressions of trace metal load vs delta. (continued) Site & storm Date R2 p1 Y-int
H1 2/26/14 0.7383 0.0014 1.56E-01
H2 2/6/14 0.6628 0.0041 7.23E-01
H3 4/7/15 0.4895 0.0243 -2.54E-03
H4 12/3/15 0.6454 0.0051 9.96E-03
H5 1/5/16 0.3741 0.0602 1.40E-01
1 = Probabilities <0.05 are statistically significant.

Estimated loads of FIBs for the 3 sites with the greatest magnitude of concentrations above the overall 85th percentile threshold across all 3 FIBs (C, D, and F) also were regressed against the delta. The regressions were significant, with estimated load explaining 54%, 84% and 72% of differences between pre-storm and receiving water concentrations of E. coli, Enterococcus, and fecal coliforms, respectively (Figure 15), suggesting that discharge of bacteria were driving the storm-related increases in receiving water concentrations at these 3 sites.

Examining the correspondence of nutrient load with difference between pre-storm and receiving water concentrations demonstrated that there was no significant relationship for any of the nutrients (Figure 10), suggesting that the increase in nutrient concentrations during storms could be related to factors other than discharge, such as sediment re-suspension.

5.3.1 Conclusion: Do storm discharges alter receiving water quality? Concentrations of constituents in receiving water samples were usually above overall 85th percentile thresholds. Nevertheless, for some constituents, the concentrations measured at reference sites were greater than the highest concentrations measured in receiving waters and there were reference samples above overall 85th percentile thresholds. Moreover, while ANOVA revealed that concentrations in discharge samples were usually significantly greater than in other sample types, receiving water samples were not statistically different from concentrations in pre-storm or reference samples. When examined with a matched-pair test on all sites combined, an effect of storms was observed in which most constituents had higher concentrations in receiving waters than in pre-storm samples. When examined site-by-site, with attendant lower sample sizes, these differences between pre-storm and receiving water values were not universally observed. Comparisons of estimated loads to changes in receiving water concentrations also indicated that many discharges did alter receiving water quality, although none of them necessarily correlate with site differences in the percentage of receiving water samples above the overall 85th percentile threshold, their magnitude above the threshold or the estimated loads of these constituents.

Selenium and silver were illustrative of these apparent contradictions. Selenium and silver in receiving water were above their respective 85th percentile thresholds in 11% and 15% of all samples, respectively. Moreover, 40% and 20% of receiving water samples were above the selenium and silver thresholds, respectively, at Site A (Table 7). Nevertheless, their average concentrations in receiving water samples were 88% and 62% below their respective 85th percentile thresholds (Table 8) and neither one exhibited significant differences between prestorm and receiving water samples in matched-pair tests, either across all sites or within any site (Table 11).

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 59 Figure 14. Regressions of changes in receiving water concentrations of all trace metals during storms (delta) against each metal’s estimated respective load within each storm. Lines indicate statistically significant regressions. Note log scale on each axis. All units are consistent with those in Table 12.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 60 Figure 15. Relationship between A) E. coli (circles) delta versus load, delta (MPN/100 mL)=1.55x10-8 (load, MPN)+1163, r2=0.54, p=4.9x10-
4 ; B) Enterococcus (triangles) delta versus load, delta (MPN/100 mL)=9.1x10-8(Load, MPN)-4322, r2=0.84, p=1.1x10-7; C) Fecal coliform (squares) delta versus load; delta (MPN/100 mL)=1.89x10-8(Load, MPN)+1012, r2=0.72, p=6.9x10-6; at sites C (red symbols), D (green symbols), and F (blue symbols). Note log scale on each axis.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 61 Regressions of estimated constituent loads versus changes in concentrations from pre-storm to receiving water (delta) were mostly significant and explained substantial variation in delta, although this results did not necessarily mean receiving water concentrations were less than overall 85th percentile thresholds.

5.4 Question 4: Are alterations of receiving water quality due to anthropogenic waste?
Determining whether trace metals in discharges are from anthropogenic or natural sources is challenging. Trace metals are ubiquitous in the natural environment and are contained in sediments and geologic formations in pristine environments. In an effort to distinguish between natural and anthropogenic sources of trace metals, the relationships between concentrations of trace metals and TSS were compared among sample types (Figure 16). For most trace metals, the concentrations per TSS in discharge samples were elevated above other sample types.

Exceptions to this pattern were evident for arsenic and silver, in which the relationship between TSS and arsenic was very similar among all sample types and silver concentrations relative to TSS were often lower in discharge samples than in other sample types. The consistent relationships between trace metals and TSS in reference samples shown in Table 3 are also evident in Figure 16, in which concentrations of all trace metals, except silver, increase with increasing TSS concentrations.

Systematic differences among sample types in the concentrations of constituents per unit of TSS could help differentiate between natural and anthropogenic sources, at least for trace metals. The concentrations of constituents per unit of TSS were calculated and tested with ANOVA, the Wilcoxon nonparametric test and Student’s t test between each pair of samples. All trace metals, except arsenic, chromium, selenium, and silver were significantly enriched relative to TSS in discharge samples (Table 14), suggesting a geologic source. Examination of sitespecific data suggested that patterns of elevated of trace metals relative to TSS were not uniform.

To test whether there were significant differences among sites in relationships between TSS and trace metals, a 2-way ANOVA was performed to test for the effects of site, TSS, and interactions between site and TSS on trace metal concentrations in discharge samples. If a significant interaction occurred between site and TSS, it meant that the effect of TSS differed among sites, which was equivalent to testing for differences among sites in the slope of TSS versus trace metal. Only copper, lead, mercury, and zinc exhibited significant interactions (Table 15).

To determine whether there were systematic differences among sites that could account for the significant interactions for copper, lead, mercury, and zinc, plots of these 4 trace metals versus TSS were made for each site and arranged together (Figures 17 and 18). For all four metals, and especially copper and zinc, there were 2 basic patterns. One pattern, illustrated by sites A, E, and H, exhibited trace metal concentrations in the discharge samples that were elevated above all other samples, but fell along a rough line whose slope approximated that for all the other samples types combined. The other 5 sites tended to have much more elevated discharge trace metal concentrations that were accompanied by substantially elevated concentrations in receiving water. In the discharge samples at these 5 sites, there also was no apparent relationship between trace metal concentrations and TSS. While these patterns were less consistent from site to site for lead and mercury, sites A and H always exhibited positive ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 62 Figure 16. Relationships between trace metal concentration (µg/L except mercury=ng/L) and TSS concentrations (mg/L) across all stations by sample type.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 63 slopes for TSS versus trace metal and no elevation of receiving water concentrations above the other sample types. The relationship between TSS and lead was more indicative of a positive slope at all sites, except G.

Table 14. Results from comparisons among sample types of constituent concentrations per TSS.

Constituent ANOVA Wilcoxon
R2 p1 p
A posteriori
Result2
Arsenic 0.0512 0.019 <0.001 P D Rw Rf
Cadmium 0.0816 0.001 0.003 D P Rw Rf
Chromium 0.0253 0.183 <0.001 D P Rw Rf 3
Copper 0.1536 <0.001 <0.001 D Rw P Rf
Lead 0.3136 <0.001 <0.001 D Rw P Rf
Nickel 0.0966 <0.001 <0.001 D P Rw Rf
Mercury 0.1842 <0.001 <0.001 D Rw Rf P Pr
Selenium 0.0218 0.2436 <0.001 D P Rw Rf 3
Silver 0.1101 >0.001 <0.001 P Rf Rw D
Zinc 0.2840 <0.001 <0.001 D Rw P Rf
Oil & Grease 0.0585 0.012 <0.001 D P Pr Rw Rf
Fecal Coliform 0.0493 0.030 <0.001 D Rw Rf B P Pr
Enterococcus 0.0730 0.003 <0.001 D Rw P Rf Pr
E. coli 0.0136 0.5705 <0.001 Rf D Rw P Pr3
Nitrate 0.0136 0.574 <0.001 Rf D P Rw Pr3
Orthophosphate 0.1013 <0.001 <0.001 D P Rw Rf Pr
Ammonia 0.1173 <0.001 <0.001 D Rw Rf P Pr
Urea 0.1808 <0.001 <0.001 D P Rw Pr Rf
PAHs 0.3827 <0.001 <0.001 D Rw P Rf Pr
Orthophosphate Pesticides 0.0218 0.323 0.183 D Rw P Rf Pr Pyrethroid Pesticides 0.1502 <0.001 <0.001 D Rw Pr P Rf 1 = P ≤ 0.05 is statistically significant. If p >0.05, there is no statistical difference. 2 = D is Discharge, P is Pre-storm, Pr is Reference Dry Weather, and Rf is Reference, and Rw is storm Receiving Water. Sample type on left has the highest mean. 3 = One test gave a non-significant result
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 64 Table 15. Results from 2-way ANOVA to test for the effects of site, TSS and interactions of site and TSS on trace metal concentrations in discharge samples.

Constituent Model Site TSS Interaction
R2 p1 p p p
Arsenic 0.363 0.528 0.920 0.312 0.433
Cadmium 0.558 0.029 0.146 0.692 0.089
Chromium 0.437 0.244 0.745 0.409 0.223
Copper 0.738 <0.001 <0.001 0.171 0.005
Lead 0.927 <0.001 <0.001 <0.001 <0.001
Nickel 0.611 0.007 0.120 0.956 0.734
Mercury 0.689 <0.001 0.193 0.791 <0.001
Selenium 0.521 0.064 0.991 0.808 0.541
Silver 0.470 0.155 0.184 0.029 0.195
Zinc 0.627 0.005 0.112 0.047 0.037 1 = P ≤ 0.05 is statistically significant. If p >0.05, there is no statistical difference.

Figure 17. Plots of a) copper concentration (µg/L) versus TSS concentration (mg/L) and b) lead concentration (µg/L) versus TSS concentration (mg/L) by site (A – H). *
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 65 Figure 18. Plots of a) mercury concentration (ng/L) versus TSS concentration (mg/L) and b) zinc concentration (µg/L) versus TSS concentration (mg/L) by site (A – H).

There was evidence that trace metals discharged at some sites were mostly in the dissolved fraction, rather than associated with particulate material. During 2 storms in 2016, discharges from the sites that were sampled were analyzed for total and dissolved concentrations of all trace metals, except mercury (Table 16). There were substantial differences among sites and trace metals in the percentages measured in the dissolved fraction. For those trace metals exhibiting a strong affinity for TSS in reference samples across the entire study area (i.e., copper, lead, mercury and zinc), discharges of high percentages in the dissolved fraction indicate different processes controlling their release into the environment from the processes that controlled reference trace metal concentrations, and suggests anthropogenic sources.

Moreover, several studies have found high loads of dissolved copper, zinc, and lead in runoff from building roofs made of copper, zinc-coated metal and slate (Bannerman et al, 1993; Barron 2006; Gromaire et al, 2001; Lye 2009; O’Sullivan et al, 2011; Pitt et al, 2004; Wicke et al, 2014).
*
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 66 While trace metals have been amenable to inferences regarding anthropogenic versus natural sources, FIBs and nutrients are also ubiquitous in the environment, but the available data do ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 67 Table 16. Percent of metals in the dissolved fraction from discharge samples collected during 2 storms in 2016. Values over 100% indicate higher concentrations measured in aliquots for dissolved metals than in aliquots for total metals. Silver was not detected in any of these samples
February 18, 2016
Site Arsenic Cadmium Chromium Copper Lead Nickel Selenium Zinc All metals B 72.8% 44.7% 17.7% 66.3% 19.2% 75.2% 47.6% 62.6% 42.3% C 122.2% 33.3% 28.2% 77.4% 13.6% 76.1% 59.0% 64.3% 48.9% D 118.2% 55.1% 24.3% 76.7% 22.3% 75.7% 85.5% 78.0% 55.7% F 146.7% 49.1% 79.2% 80.1% 30.7% 81.5% 70.8% 59.4% 62.9% G 8.0% 7.6% 3.2% 57.5% 0.6% 18.4% 49.2% 5.7% 15.1%
All sites 99.7% 39.4% 32.3% 72.4% 17.5% 67.1% 65.8% 56.1% March 6, 2016
B 85.0% 64.6% 65.2% 76.9% 28.2% 91.9% 85.5% 53.6% 61.5% D 95.2% 50.1% 61.1% 87.0% 33.9% 93.4% 73.9% 68.4% 60.9% E 101.0% 100.4% 66.9% 37.1% 7.9% 96.1% 144.9% 7.9% 57.1% G 83.4% 65.7% 40.3% 87.8% 18.8% 66.1% 75.9% 54.1% 52.2% All sites 91.1% 70.2% 58.4% 72.2% 22.2% 86.9% 95.0% 46.0% Mean for 2 events
B 78.9% 54.7% 41.4% 71.6% 23.7% 83.6% 66.5% 58.1% 51.9% D 106.7% 52.6% 42.7% 81.8% 28.1% 84.5% 79.7% 73.2% 58.3% G 45.7% 36.6% 21.7% 72.6% 9.7% 42.2% 62.5% 29.9% 33.7%
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 68 not allow determinations of sources. Nevertheless, FIBs stand out because their maximum concentrations in receiving water samples were greater than those at reference sites. If not strictly anthropogenic, their concentrations were highest in receiving water samples, although not significantly, and they exhibited significant associations between estimated loads and changes in receiving water concentrations. Thus, it appears that storm receiving waters at the base of watersheds affected by human development tend to have high concentrations of FIBs, although it is unknown whether these were from anthropogenic sources.

Nutrients cannot be chemically directly linked to anthropogenic sources. They are present in soil and natural organic matter. Moreover, there were no significant increases in receiving water concentrations over pre-storm concentrations at any site and no relationship between loads and changes in receiving water concentrations.

PAHs are naturally present in the marine environment. Along the California coastline from near San Simeon southward into the Santa Barbara Channel, there are natural seeps that result in tar balls on beaches and rocky intertidal areas. Nevertheless, we are not aware of any natural seeps onshore within the study area. Consequently, PAHs detected in the discharges and receiving water samples were presumed to be from anthropogenic sources, such as motorized vehicles and other types of combustion processes.

Organophosphate and pyrethroid pesticides are exclusively man-made compounds that have no natural sources. Although they were infrequently detected, their presence in any sample, including receiving water, was presumed to derive from an anthropogenic source.

5.4.1 Conclusion: Are alterations of receiving water quality due to anthropogenic waste?
Determining whether trace metals in discharges are from anthropogenic or natural sources is challenging. Trace metals are ubiquitous in the natural environment and are contained in sediments and geologic formations even in pristine environments. An analysis was performed using the relationships between trace metals and TSS in an effort to distinguish between natural and anthropogenic sources of trace metals. For most trace metals, the concentrations per TSS in discharge samples were elevated above other sample types. Exceptions to this pattern were evident for arsenic and silver, in which the relationship between TSS and arsenic was very similar among all sample types and silver concentrations relative to TSS were often lower in discharge samples than in other sample types. The relationships between trace metals and TSS in reference samples were consistent with concentrations of all trace metal concentrations increasing with increasing TSS concentrations, except for silver.

Analysis of variance (ANOVA) revealed that the amounts of various constituents per unit of TSS were similar among all sample types, except for discharges. Discharge samples often had significantly increased constituent concentrations per unit of TSS. Moreover, another ANOVA showed that the relationship of TSS to copper, lead, mercury, and zinc differed among sites.

Some sites exhibited relationships between these trace metals and TSS in their discharges that paralleled the positive slopes of these relationships in other sample types, but with elevated trace metals per unit of TSS. Other sites exhibited elevated trace metals in their discharges that ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 69 had no relationship to TSS concentrations, and which were often accompanied by elevations of the trace metals per unit of TSS in receiving water samples. Discharges of trace metals with high percentages in the dissolved fraction indicate different processes controlling their release into the environment from the processes that controlled reference trace metal concentrations, and suggests anthropogenic sources. Moreover, several other studies have found high loads of dissolved copper, zinc, and lead in runoff from building roofs made of copper, zinc-coated metal and slate.

FIBs and nutrients are also ubiquitous in the environment, but the available data do not allow determinations of sources. Nevertheless, FIBs stand out because their maximum concentrations in receiving water samples were greater than those at reference sites. If not strictly anthropogenic, their concentrations were highest in receiving water samples, although not significantly, and they also exhibited significant associations between estimated loads and changes in receiving water concentrations. Thus, it appears that storm receiving waters at the base of watersheds affected by human development tend to have high concentrations of FIBs, although it is unknown whether these have anthropogenic sources.

Nutrients cannot be chemically directly linked to anthropogenic sources. They are present in soil and natural organic matter. Moreover, there were no significant increases in receiving water concentrations over pre-storm concentrations at any site and no relationship between loads and changes in receiving water concentrations.

PAHs are naturally present in the marine environment. Along the California coastline from near San Simeon southward into the Santa Barbara Channel, there are natural seeps that result in tar balls on beaches and rocky intertidal areas. Nevertheless, we are not aware of any natural seeps onshore within the study area. Consequently, PAHs detected in the discharges and receiving water samples are presumed to be from anthropogenic sources, such as motorized vehicles and other types of combustion processes.

Organophosphate and pyrethroid pesticides are exclusively man-made compounds that have no natural sources. Although they were infrequently detected, their presence in any sample, including receiving water, is presumed to derive from an anthropogenic source.

5.5 Question 5: Are marine biological resources being affected by ASBS storm discharges?
As the purpose of the ASBS beneficial use is to ensure protection of water quality needed to support important biological resources, the answer to this question requires consideration of ASBS biological resources or their surrogates. There are 5 components of the Central California Regional Monitoring Program that are particularly pertinent to this consideration.

1. Rocky Intertidal Monitoring. The purpose of this component was to determine whether stormwater discharges in ASBS could be affecting biological communities.

2. Mussel Bioaccumulation Monitoring. The purpose of this component was to determine whether harmful contaminants are accumulating in resident organisms in ASBS.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 70 3. Toxicity Testing. This purpose of this component was to determine whether receiving waters in ASBS are toxic by integrating all constituents, even those that were not measured and regardless of the concentrations of measured constituents.

4. Measurement of Nutrients. This component was included due to the known relationship between harmful algal blooms and excess nutrients.

5.5.1 Rocky Intertidal Monitoring
Rocky intertidal communities were sampled by Dr. Pete Raimondi using methods that have been applied in ASBS rocky intertidal monitoring programs in other parts of California (Appendix C). Sampling was conducted in the fall of 2014 at the following sites:

1. Alder Creek discharge (Duxbury Reef ASBS)

2. Fitzgerald Marine Reserve discharge

3. Año Nuevo discharge

4. Hopkins discharge (Pacific Grove ASBS)

5. Stillwater discharge (Carmel Bay ASBS)

6. Point Lobos discharge (Point Lobos State Marine Reserve)

7. Bolinas Point reference

8. Pigeon Point reference
Data from each site were compared with current and historic data from numerous sites along the central California coast. Sites were selected using broad criteria to ensure similar substrate among sites and safe access. Sessile and mobile organisms were quantified using random quadrats and point contact methods, respectively. The following null hypotheses were tested:

1. Species richness will not vary as a function of site type (Discharge, Reference)

2. Community composition of sessile species will not vary as a function of site type

3. Community composition of mobile species will not vary as a function of site type

4. An assessment of both mobile and sessile species will not identify particular sites as being substantially different from the expectation based on all sites. This is a way to look at specific sites rather than site types.

There were no differences found between sample types for species richness, community composition of sessile species, or community composition of mobile species and null hypotheses 1, 2, and 3 were not rejected. Null hypothesis 4 was rejected, as the composition of sessile organisms at Fitzgerald Marine Reserve differed substantially from what was expected for other rocky intertidal communities in the region, likely due to natural factors.

5.5.1.1 Rocky Intertidal Conclusions
Based on the results of these analyses, there is no support for the idea that discharges along the central California coast generate impacts to diversity or community composition in the nearby rocky intertidal habitats. This does not ensure that there are no impacts to organisms in the communities. Other attributes such as individual growth and reproduction could be affected with no subsequent impact to diversity or composition.

Some sites stood out as differing substantially from what was expected for biological ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 71 communities in the region. In particular, Fitzgerald Marine Reserve was an outlier with respect to sessile species composition. It is likely that this difference in community structure is the result of the geomorphology at the site. The intertidal zone at Fitzgerald Marine Reserve is a very wide and flat bench surrounded by sand and subject to considerable scour. In addition, the reef tends to hold water because it is flat and the key mid intertidal species, Chthamalus spp., Mytilus californianus and Mastocarpus spp., which are species that dominate on hard rock surfaces with extended periods of emersion are all uncommon at this site.

5.5.2 Mussel Bioaccumulation Monitoring
The bioaccumulation monitoring originally included analysis of resident mussels from Lovers Point in Pacific Grove. This expectation assumed collaboration with California State Mussel Watch, which had been funded the Central Coast regional Water Board to sample Lovers Point and other sites for contaminants in mussel tissues. Unfortunately, the collaboration could not be arranged and that site was not sampled for the Central California regional ASBS program.

Instead, the bioaccumulation studies relied on collaboration with the Central Coast Long-term Environmental Assessment Network (CCLEAN).

CCLEAN has measured several persistent organic pollutants (POPs) polychlorinated biphenyls (PCBs), the flame retardants polybrominated diphenyl ethers (PBDEs), and chlorinated pesticides in mussels at 5 sites around Monterey Bay (Figure 19) for over 13 years and the long database permits analysis for spatial and temporal patterns. This collaboration provided the ASBS program participants with access to a long-term dataset on the condition of an important biological resource in rocky intertidal communities around Monterey Bay, including ASBS.

In the collaboration between this project and CCLEAN a site was added at Point Reyes National Seashore and the list of analytes normally measured by CCLEAN was expanded to include organophosphate pesticides, pyrethroid pesticides and acid-positive pharmaceuticals. None of the organophosphate or pyrethroid pesticides were detected in any of the samples. Acidpositive refers to the type of extraction to recover the compounds from the sample matrix.

The acid-positive pharmaceuticals include a broad range of drugs, such as antibiotics, antihistamines, mood stabilizers, replacement hormones, calcium channel blockers, cardiac glycosides and caffeine. Only 8 of these acid-positive compounds were detected in mussels from any site (Table 17) out of the 45 that were analyzed. The detected pharmaceuticals were mostly those with both human and animal (veterinary or livestock) uses. Seven out of the 8 compounds detected have veterinary or animal husbandry uses and 6 have human applications. Two of the antibiotics, Enrofloxacin and Virginiamycin M1, have exclusively animal uses. The fluoroquinolone antibiotic Lomefloxacin was the most widely detected pharmaceutical, having been measured at 5 out of 6 sites and in 10 out of 12 samples. Moreover, Lomefloxacin concentrations were substantially higher in 2015 than in 2014 at all but 1 of the sites where it was detected in either year. Caffeine was also detected at 5 out of 6 sites, having been measured in 7 out of 12 samples. Caffeine comes exclusively from humans along the Central Coast. The only non-human source of caffeine in the continental USA is from a holly plant native only to the southeastern USA. The strongest human signature occurred over the 2 years of pharmaceutical analyses at The Hook in Capitola where 6 drugs with veterinary and/or human ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 72 applications were detected. While there are no human health alert levels for pharmaceuticals in shellfish, the ubiquitous occurrence of them is an indicator of the extent to which they have been released into the environment. The site in Carmel Bay ASBS (Carmel River Beach) had among the lowest rates of pharmaceutical occurrence of any site.

Concentrations of several POPs in mussels along the Monterey Bay area coastline have exceeded or nearly exceeded various alert levels for the protection of human health. EPA and the California Office of Environmental Health Hazard Assessment (OEHHA) have published several applicable human health alert levels. USEPA has published risk-based alert levels that are based on the relative amount of tissue that is predicted to be eaten, which are called the subsistence and recreational fisher alert levels (EPA 2000). OEHHA has published a variety of alert levels (Klasing and Brodberg 2008, 2011). These include the Fish Contaminant Goal (FCG), which represents the concentration below which there are no significant health risks from eating a single 8-ounce serving of fish per week, for life. OEHHA also published several levels of Advisory Tissue Level (ATL), which differ in the amount of fish that can be eaten at minimal health risk, from three servings per week down to no consumption recommended. These alert levels are indicated in the following figures, as appropriate.

Concentrations of several POPs in mussels have declined over time at some sites. Consistent with providing data on the effects of POPs on beneficial uses, data have been presented on a wet-weight basis, as human health alert levels are based on wet-weight concentrations because they are developed using assumptions about human consumption. Because most POPs are lipid-soluble, tracking trends of contaminant concentrations in organisms is often based on concentrations normalized to lipid content (i.e., ng POP per g of lipid), which gives a better estimate of the actual burden of POPs being carried by the organisms. Consequently, lipid normalizations were performed for concentrations of Dieldrin, Chlordanes, PCBs, and DDTs in mussels to parallel those POPs that are most problematic on Monterey Bay waters and are presented side-by-side with the wet-weight data (Figures 20, 21, 22, and 23). PBDEs are also considered here (Figure 24) because they are chemically very similar to PCBs, they have serious consequences for human health (Windham et al, 2015), and a 2006 ban of certain forms in California has resulted in declines in some areas (Sutton et al, 2014). In addition, exceptionally high concentrations were measured in women’s breasts in the San Francisco Bay area (Windham et al, 2010), which is not far from Monterey Bay.

Overall trends of Dieldrin, Chlordanes, PCBs, DDTs, and PBDEs have been mostly downward at all sites. The legacy pesticide Dieldrin has been detected above the USEPA recreational fisher screening value at two sites between 2002 and 2008 (Figure 20a). While the legacy pesticide Dieldrin has exceeded the USEPA recreational fisher screening level at 2 sites in 2002 and 2008 and it continues to be broadly detected at concentrations near or slightly above the EPA subsistence fisher screening level (Figure 20a), there have been downward trends at some sites, with similar trends in lipid-weight concentrations (Figure 20b). Wet-weight concentrations of Chlordanes, another legacy pesticide, have not exceeded any human health alert level in any mussel samples (Figure 21a). Nevertheless, concentrations of Chlordane at The Hook were just below the OEHHA Fish Contamination Goal in 2002 and have been consistently higher there than at the other sites (Figure 21a). Concentrations at Laguna Creek were intermediate ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 73 Figure 19. Locations of mussel bioaccumulation monitoring sites.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 74 Table 17. Acid-positive pharmaceuticals detected in mussels in 2014 and 2015. All compounds reported in ng/g dry weight.

Site PRNS1 Scott Laguna Hook Fanshell Carmel R
Compound Uses 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 Caffeine Constituent in
coffee ND2 61.8 61.2 47.9 50.4 271 117 ND ND 122 ND ND
Diltiazem
Calcium channel
blocker for heart
failure in pets and
humans
ND ND ND ND ND ND 0.719 ND ND ND ND ND
Diphenhydramine
Antihistamine for
pets, horses and
humans

1.44 ND ND ND ND ND ND ND ND ND ND ND
Enrofloxacin
Broad-spectrum
veterinary
antibiotic
ND ND ND ND ND ND 7.16 ND ND 59.3 ND ND
Erythromycin-H2O
Broad-spectrum
human and
veterinary
antibiotic
ND ND ND ND ND 5.73 ND 6.51 ND ND ND ND
Lomefloxacin
Broad-spectrum
human and
veterinary
antibiotic
ND ND 42.6 103 58 44.2 58.6 228 89.5 233 46.3 130
Penicillin V
Narrow-spectrum
human and
veterinary
antibiotic
ND ND ND 8.97 ND ND ND ND ND ND ND ND
Virginiamycin M1
Growthenhancing
livestock
antibiotic

11.4 ND ND ND 17 ND 32.7 ND 18.6 ND 24.8 ND
1 = Point Reyes National Seashore
2 = Not Detected
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 75 between The Hook and other sites in 2002, but declined to concentrations very similar to the other sites by 2009. DDTs have been detected at wet-weight concentrations near or above the USEPA subsistence fishers screen value at Laguna Creek and The Hook between 2002 and 2005 (Figure 22a). Wet-weight concentrations of DDTs were also similar at Laguna Creek and The Hook, with Laguna Creek often exceeding The Hook during this period. Wet-weight concentrations of PCBs in mussels have remained substantially below any human health alert level (Figure 23). As with Chlordanes, wet-weight concentrations of PCBs at The Hook have remained substantially above those at the other sites, except for Point Reyes, which exhibited a greater concentration than any of the other sites (Figure 23a). In all cases, the POP concentrations in mussels at Carmel River Beach have been among the lowest of any site, and near or below those at Point Reyes National Seashore.

Figure 20. Wet-weight (A) and lipid-weight (B) concentrations of Dieldrin measured in mussels during the wet season from 5 CCLEAN sites in the Monterey Bay area and 1 site at Point Reyes National Seashore.

A
USEPA Rec. Fisher Screening Value
OEHHA Fish Contaminant Goal
USEPA Subsistence Fisher Screening Value
B
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 76 Figure 21. Wet-weight (A) and lipid-weight (B) concentrations of Chlordanes measured in mussels during the wet season from 5 CCLEAN sites in the Monterey Bay area and 1 site at Point Reyes National Seashore.

Figure 22. Wet-weight (A) and lipid-weight (B) concentrations of DDTs measured in mussels during the wet season from 5 CCLEAN sites in the Monterey Bay area and 1 site at Point Reyes National Seashore.

A
B
USEPA Subsistence Fishers Screening Value
OEHHA Fish Contaminant Goal
B
OEHHA Fish Contaminant Goal A
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 77 Figure 23. Wet-weight (A) and lipid-weight (B) concentrations of PCBs measured in mussels during the wet season from five CCLEAN sites in the Monterey Bay area and 1 site at Point Reyes National Seashore.

Figure 24. Wet-weight (A) and lipid-weight (B) concentrations of PBDEs measured in mussels during the wet season from five CCLEAN sites in the Monterey Bay area and 1 site at Point Reyes National Seashore.

B
OEHHA Fish Contaminant Goal A
A
B
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 78 5.5.2.1 Mussel Bioaccumulation Conclusions
Concentrations of POPs in mussels have been declining over recent years around Monterey Bay and have been consistently low at Carmel River Beach. Dieldrin in mussels along the northern shore of Monterey Bay have remained below the USEPA recreational fisher screening value at all sites for the sixth year in a row, although Dieldrin still exceeds the subsistence fisher screening level at several sites. DDTs also remain below human health alert levels. PBDEs also have declined significantly over time at all sites, except for The Hook. Acid-positive pharmaceuticals detected in the mussels consisted entirely of compounds with both human and veterinary or animal husbandry uses, suggesting runoff from pets and livestock operations.

More pharmaceuticals were detected at The Hook than at other sites. The broad-spectrum antibiotic Lomefloxacin was detected in more samples than any other pharmaceutical.

Concentrations of contaminants measured in mussels from Point Reyes National Seashore were generally lower than at the other CCLEAN sites around Monterey Bay, except for PCBs, which were higher at Point Reyes. There is no evidence contaminants from stormwater are accumulating or causing adverse effects on mussels in the Carmel Bay ASBS.

5.5.3 Toxicity Testing
Toxicity tests are widely accepted laboratory procedures that are intended to indicate whether a particular sample of water could cause harm to aquatic organisms. They often rely on measurements of physiological responses, such as egg fertilization, embryonic development, gametophyte germination, and growth that are likely to be more sensitive than survival as a test endpoint. Being conducted in the laboratory, the test conditions, such as temperature, oxygen concentration, and water quality, can be monitored in order to minimize the effects of uncontrolled variables on test outcomes. Bringing samples into the laboratory for testing allows a specific set of organisms to be observed throughout the course of the test, which is very difficult under ambient conditions. Conversely, while toxicity tests do not mimic natural conditions and no direct conclusions can be drawn about biological effects in ambient waters, they are effective screening tools (Chapman, 2000). In the case of the ASBS monitoring program, toxicity tests can be indicators of potential effects on biological resources.

Toxicity was not presumed to be a normal feature of natural water quality (Schiff et al, 2016).

Consequently, the occurrence of toxicity at reference sites, even in <2% of reference samples (Table 18) is surprising. The 3 toxicity tests that exhibited failures at references sites were the kelp growth, mussel embryonic development, and mussel embryo survival tests. These 3 tests also failed in 8%, 2%, and 3% of receiving water samples. Kelp growth failures occurred at sites B, C, E, and H. Single failures of mussel development and survival occurred at Site A. The urchin fertilization test exhibited a higher rate of failure in receiving water samples, with 1/3 of samples at Site C and single samples at sites B and F failing. Discharge samples tested with the urchin fertilization test failed in 24% of samples overall. Two-thirds of the samples at Site D and half the samples at sites C, F, and G also failed. Discharges smaller than 36 inches failed the urchin fertilization test in 17% of samples. The high rates of failure in the discharge samples from sites C, D, F, and G were not consistently associated with larges numbers of failures in receiving water samples. In fact, sites D and G, which had failure rates of 67% and 50%, respectively, in discharge samples, had no failures in receiving water samples.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 79 Table 18. Percentage of failed toxicity tests at each ASBS and all Reference Sites.

Discharge Receiving Water
Site
Urchin
Fertilization
Urchin
Fertilization
Kelp
Germination
Kelp
Growth
Mussel
Development
Mussel
Survival
Receiving
Water
Overall
A 0% 0% 0% 0% 17% 17% 8%
B 17% 17% 0% 17% 0% 0% 8%
C 50% 33% 0% 17% 0% 0% 13%
D 67% 0% 0% 0% 0% 0% 0%
E 17% 0% 17% 17% 0% 0% 8%
F 50% 17% 0% 0% 0% 0% 4%
G 50% 0% 0% 0% 0% 0% 0%
H 17% 0% 0% 17% 0% 0% 4%
Overall 24% 8% 2% 8% 2% 3% 5%
Mean <36
inches 17% - - - - - -
Overall Ref - 0% 0% 0.4% 0.4% 0.4% 1.2%
North Ref - 0% 0% 0% 0% 0% 0%
South Ref - 0% 0% 0.6% 0.6% 0.6% 1.8%
A statistical procedure was used to examine potential causes of toxicity. A step-wise linear regression was performed in an effort to determine whether toxicity numerical endpoints were significantly associated with certain constituents. The toxicity test endpoints were the dependent variables and the chemical constituents were the independent variables. All independent variables were put into the model and it was run backward until only variable with statistically significant (p<0.05) associations with toxicity remained. All sites, including discharge, receiving water, and reference, were combined for this test.

Thirty-nine percent of the variation in the numerical endpoints of urchin fertilization toxicity tests was accounted for by a combination of trace metals and pesticides (Table 19). The results suggest that the negative effects of zinc and pyrethroid pesticides accounted for most of the variation, while the positive effects of nickel and PAHs, and the negative effects of arsenic and silver were less important.

Table 19. Results of step-wise linear regression of urchin fertilization numerical endpoints versus constituent concentrations in all samples.

R2 p Model

0.3856 <0.0001 Tox endpoint = -2.03(arsenic)6 + 0.57(nickel)5 – 14.5(silver)4 – 0.24(zinc)1 + 27.5(sum of PAHs)3 - 151(sum of pyrethroids)2 + 99.3
1–6 = Order of variable importance as determined by partial correlations.

While 61% of the variation in urchin fertilization results is unaccounted for in this model, the results are surprising. Previous studies of storm runoff in the area have indicated trace metals as a major contributor to toxicity (Phillips et al, 2004). When the summed concentrations of the trace metals that were found to have negative associations with urchin fertilization (i.e., ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 80 arsenic, silver and zinc; Table 19) are plotted versus the percent of successful fertilization, there is no apparent relationship between the summed concentrations of these trace metals and the samples with failed toxicity tests (Figure 16). This suggests that constituents in the water, other than the trace metals, are ameliorating the toxic effects. Numerous authors have reported reductions in toxicity of trace metals in aquatic samples related to concentrations of dissolved organic carbon and compounds that tend to bind the metals (Cao et al 1995, Meador 1991, Tatara et al 1997). Consequently, the samples that failed the toxicity tests indicated in Figure 25 could have been “cleaner” with less dissolved organic material in them than the samples with higher concentrations of trace metals that passed the toxicity test.

Effects of pyrethroids (permethrin) on urchin fertilization have recently been reported (Erkmen, 2015). Inhibitory concentrations for IC25 and IC50 were 0.58 µg/L and 0.94 µg/L, respectively.

This author also reported toxicity to urchin embryos as concentration-dependent inhibition of normal development, with IC25 and IC50 of 0.195 µg/L and 0.346 µg/L, respectively. Maximum concentrations of pyrethroids detected in the discharges from ASBS sites did not reach the fertilization IC values (Table 20), but summed permethrin (i.e., cis + trans) concentrations approached the IC25 in some samples and could have affected fertilization success.

Figure 25. The relationships between summed concentrations of the trace metals that exhibited significant negative associations with numerical endpoints for urchin fertilization success (see Table 18) for tests that passed and failed the toxicity test. 0 25
50 75
100
0 50 100 150 200
Summed Concentrations of Arsenic, Silver, and Zinc (µg/L) Percent Successful Fertilization
Toxicity
Failed
Passed
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 81 Table 20. Maximum concentrations (µg/L) of pyrethroid pesticides detected in discharge samples.

Site
BifenthrinCyfluthrintotalCyhalothrinTotal lambdaCypermethrintotalDeltamethrin/TralomethrinEsfenvalerate/FenvaleratetotalFenpropathrin Sum of Permethrin A 0 0 0 0 0 0 0 0
B 0.0087 0.007 0 0 0.0105 0 0 0.438
C 0.0066 0.069 0.0109 0 0.012 0.0023 0 0
D 0.005 0.018 0 0 0 0 0 0.135
E 0.0117 0.011 0 0 0 0 0 0.106
F 0.03 0.0066 0 0 0 0 0 0
G 0 0.154 0 0 0.0061 0 0 0
H 0.0115 0 0 0 0 0.006 0 0
The results of the stepwise linear regression modeling reveal statistical associations and do not establish causal links between the significant constituents and toxicity. Nevertheless, they provide guidance for any future toxicity identification evaluations.

Trace metals in the dissolved fraction are more likely to be taken up and cause toxicity to aquatic organisms than those in the particulate fraction (Lorenzo et al, 2002; Rainbow 1995, Bellas et al 2001). It is primarily these dissolved trace metals, with apparent non-natural sources, that account for high concentrations in some discharge samples and are potentially contributing to toxicity. The high percentages of trace metals in the dissolved fraction in discharges at some sites (Table 16) suggest it could be challenging to control toxicity caused by trace metals. Moreover, the influence of other constituents in the water on the bioavailability of trace metals provides an additional complication for reducing trace metal toxicity.

5.5.3.1 Toxicity Testing Conclusions
Toxicity measured in receiving water samples suggests that marine biological resources could be affected by ASBS discharges. Toxicity in discharge and receiving water samples has been statistically associated with some trace metals and pyrethroid pesticides. Toxicity associated with dissolved trace metals will be challenging to control (Barron 2006; Dierkes et al, 2005).

5.5.4 Nutrient Effects on Algal Blooms
It is assumed that the measurement of nutrients in this program was required because of their potential effects on algal blooms. Harmful algal blooms have become a global problem (Hallegraeff 1993, Anderson et al. 2002, Paerl and Huisman 2008). Last year saw an extraordinary bloom along the entire west coast of the United States of Pseudonitzschia, which is a diatom that produces the neurotoxin domoic acid. Kudela et al (2008) linked seasonal red ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 82 tides along the northern bight of Monterey Bay to discharges of agricultural nutrients from the Pajaro River.

Concentrations of nutrients near-shore in Monterey Bay are mainly affected by riverine inputs and wastewater discharges, whereas concentrations offshore are principally affected by upwelling (CCLEAN 2016). Dr. Raphael Kudela of UCSC studied the effects of nutrients from rivers and streams, and municipal wastewater discharges for CCLEAN. Loads from rivers and streams averaged 1,100,000 kg N/year, whereas wastewater averaged 1,300,000 kg N/year over 7 years. Annual loads from each wastewater discharge ranged from 26,000 kg to 614,000 kg. Using satellite imagery to measure sea-surface chlorophyll, Dr. Kudela noted significant (p<0.05) positive correlations between discharges of nitrogen from wastewater and increased sea-surface chlorophyll at all wastewater sites. Compared with these constant river and wastewater sources, the transient loads of nitrogen from nitrate, ammonia, and urea into ASBS receiving waters during storms (averaging approximately 14.5 kg total N/storm/site; see Table 12) are insignificant (i.e., 0.00043% of combined annual loads from rivers and wastewater) and do not affect nutrient concentrations in Monterey Bay, which despite the large sources of nutrients mentioned above, are low year-round. For example, concentrations of ammonia are typically below 0.5 µmoles N/L (0.007 µg N/L) and concentrations of nitrate vary between 1-5 µmoles N/L (0.014-0.07 µg N/L) (Kudela and Dugdale 2000). Moreover, we are not aware of any nuisance growths or algal blooms being reported in nearshore areas of ASBS during or following storms. The relatively small loads compared to riverine inputs and the nearshore turbulence both prevent buildup of nutrients to the point of causing algal blooms.

5.5.4.1 Nutrient Effects on Algal Blooms Conclusion
Annual loads of nitrogen in nitrate, ammonia, and urea from rivers and wastewater discharges average 3,400,000 kg. Average loads of these nutrients from the ASBS discharges monitored in this program were 14.5 kg/site/storm, which amounts to 0.00043% of the annual load from rivers and wastewater. Nutrients discharged into ASBS have had no noticeable effect on algal blooms or nuisance growths.

5.5.5 Conclusion: Are marine biological resources being affected by ASBS storm discharges?
Effects of stormwater discharges on resident biological communities in ASBS monitored by this program have not been observed. Rocky intertidal communities and mussels sampled for bioaccumulation have shown no demonstrable effects of stormwater discharges. Rocky intertidal communities have been similar to communities at reference sites, although sessile species at Fitzgerald Marine Reserve are different from what would be expected at other similar sites. This difference is more likely due to the effects of sand that surrounds and scours the site, as well as the tendency of the rocky bench to retain water. The concentrations of various contaminants in resident mussels have been declining over the past 13 years throughout the Monterey Bay area, with lower concentrations at the site in Carmel Bay ASBS than at other sites. There is no evidence contaminants from stormwater are accumulating or causing adverse effects on mussels in the Carmel Bay ASBS. Loads of nutrients from stormwater discharges into ASBS are multiple orders of magnitude lower than those from rivers and ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 83 municipal wastewater and there is also no evidence that they are causing nuisance growth or blooms of algae in ASBS.

Toxicity tests exhibited a small number of failures with greater numbers of discharge samples failing than receiving water samples. The urchin fertilization test was most sensitive. Toxicity could be affecting sensitive life stages of some organisms and sources should be determined and reduced, wherever possible.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 84 6.0 Regulatory Considerations
This monitoring program has documented significant geographic differences in reference area natural water quality. This fact means that it may be more appropriate to compare receiving water samples from a particular ASBS to an 85th percentile threshold calculated from relevant reference site(s). The primary water quality threshold for judging whether natural water quality is being achieved in ASBS is the 85th percentile of values from reference sites in the ocean at the mouths of streams with <5% of their watersheds under human development. This threshold essentially requires ASBS participants to achieve better water quality than is present in the ocean at the mouths of these clean reference streams. It is far from certain that this level of water quality is achievable. It seems a very daunting task to ensure that water quality along a city shoreline be better than the best water quality available along undeveloped shoreline of the state. If we can assume that the water quality at reference sites fully supports sensitive marine life, then requiring the same, and not necessarily better, water quality should provide a robust level of protection for marine life in ASBS.

If this approach were utilized, a more reasonable threshold would be the 95th percentile. This would ensure that any value falling outside the bounds of most reference values would become the focus of corrective measures. Such a threshold would have a profound effect on the number of constituents potentially being flagged for mitigation measures, while still leaving plenty of room for improving water quality (Table 21).

Notwithstanding consideration of a revised threshold for natural water quality, the water quality objectives in the Ocean Plan were specifically developed to protect marine resources and human health. The toxicities of constituents were considered and appropriate safety margins were added to ensure that constituent concentrations falling under Ocean Plan objectives would not cause toxicity. In the case of organic compounds, objectives were set to protect human health based upon assumptions about bioaccumulation of compounds into seafood consumed by people and added health risks. Consequently, these compounds do not have instantaneous maxima for the protection of marine life. FIB Ocean Plan objectives also were developed to protect human health from diseases transmitted via contact with water.

They were established from epidemiological studies that determined the increased risks of becoming infected after swimming in water with increased FIB concentrations and also include built-in safety factors.

A comparison of receiving water data with Ocean Plan objectives reveals sporadic occurrences of concentrations above objectives (Table 22). There was a single receiving water sample at Site B with a concentration of copper above the Ocean Plan objective. Chromium and nickel Ocean Plan objectives were each exceeded in a single reference sample. Fecal coliform and Enterococcus concentrations were above Ocean Plan objectives at most sites, ranging from 17% to 100% of samples. These 2 FIBs were above Ocean Plan objectives in 6% and 22% of reference samples, respectively PAHs were above the Ocean Plan 30-day average objective for the protection of human health at most ASBS sites, ranging from 17% to 50% of samples per site.

Four percent of reference samples were also above the 30-day average objective for PAHs. If an instantaneous maximum is estimated for PAHs using the 10:1 ratio used for instantaneous maximum to 6-month median concentrations for other constituents, an estimated instantaneous maximum for PAHs would approximate 0.088 µg/L and the percentage of ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 85 samples above the objective would decrease substantially across all ASBS and reference sites.

Moreover, if receiving water is not meeting natural water quality according to the Compliance Flowchart of the Special Protections (Figure 1), actions can be required that either bring offending constituents below Ocean Plan objectives or reduce their loads by 90%.

Comparisons of ASBS receiving water data with pertinent thresholds (e.g., 95th percentile and Ocean Plan), while reducing the number of samples above the Special Protections 85th percentile threshold, have emphasized that identifying sources and reducing anthropogenic loads of FIBs and PAHs into ocean waters should be a high priority, along with determining sources of and reductions in toxicity.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 86 Table 21. Percentage of samples above a reference 95th percentile threshold for constituents in receiving water samples at each ASBS site.

Site Arsenic Cadmium Chromium Copper Lead Mercury Nickel Selenium Silver Zinc TSS A 0% 0% 0% 0% 0% 0% 0% 40% 0% 0% 0%
B 0% 0% 0% 33% 0% 0% 0% 17% 0% 0% 0%
C 0% 0% 0% 0% 0% 0% 0% 0% 17% 0% 0%
D 0% 0% 0% 17% 33% 0% 0% 0% 17% 33% 0%
E 0% 0% 0% 0% 0% 0% 0% 0% 17% 0% 0%
F 0% 0% 0% 0% 17% 0% 0% 33% 17% 17% 0%
G 0% 0% 0% 17% 0% 0% 0% 0% 0% 0% 0%
H 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Overall 0% 0% 0% 8% 6% 0% 0% 11% 8% 6% 0%
Site
Fecal
Coliform Enterococcus E. coli Nitrate Orthophosphate Ammonia Urea PAHs Organophosphate
Pesticides Pyrethroids Overall
A 17% 33% 17% 0% 0% 33% 0% 50% 0% 0% 4%
B 17% 17% 33% 0% 0% 20% 20% 33% 0% 0% 4%
C 33% 17% 33% 0% 0% 17% 67% 17% 0% 33% 6%
D 50% 67% 67% 0% 0% 17% 50% 50% 17% 17% 10%
E 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1%
F 83% 83% 83% 0% 0% 17% 67% 33% 17% 0% 10%
G 17% 33% 0% 0% 0% 17% 33% 0% 0% 33% 3%
H 17% 17% 0% 0% 0% 0% 17% 50% 0% 0% 3%
Overall 29% 33% 29% 0% 0% 15% 32% 26% 4% 10% 5%
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 87 Table 22. Number of samples above Ocean Plan water quality objective for instantaneous maximum. PAHs have no instantaneous maximum objective and one was estimated at 10x the 30-day average (10 x 0.0088µg/L = 0.088µg/L).

Site Chromium Copper Nickel
Fecal
Coliform Enterococcus PAHs
30-day
average
Estimated Instantaneous
Maximum
A 0% 0% 0% 17% 33% 17%/50%* 0%/33%*
B 0% 17% 0% 33% 67% 33% 17%
C 0% 0% 0% 33% 67% 17% 0%
D 0% 0% 0% 67% 100% 33% 17%
E 0% 0% 0% 0% 33% 0% 0%
F 0% 0% 0% 83% 100% 33% 17%
G 0% 0% 0% 33% 67% 0% 0%
H 0% 0% 0% 17% 33% 50% 0%
Reference 2% 0% 2% 6% 22% 4% 0%
* = Percentage of discharge samples with PAHs detected / percentage of receiving water samples above the objective.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 88 6.0 References Cited
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 92 Appendix A. Scope of Work
Central California Areas of Special Biological Significance Storm Water Monitoring to Satisfy Special Protections
Scope of Work
I. Introduction
The Central Coast ASBS Regional Monitoring Program will be implemented during the 2012– 2013 and 2013-2014 storm seasons and includes all ASBS responsible parties2 on the Central Coast, covering an area from Big Sur, in Monterey County, to Pt. Reyes, in Marin County. This Scope of Work for the Central Coast ASBS Regional Monitoring Program has been developed through discussions with staff from State and Regional Water Boards, as well as the responsible parties discharging storm water into Areas of Special Biological Significance (ASBS).

II. Technical Program
In all specifications for storm water and receiving water monitoring that follow, the minimum requirement for a storm shall satisfy the criteria specified in the Special Protections (i.e., >0.10 inches of rainfall resulting in runoff, >72 hours from the previous storm). Moreover, every attempt shall be made to satisfy the criteria for storm runoff monitoring conducted by the Monterey Bay National Marine Sanctuary (i.e., sheeting water on roadways, heavy flow through the storm drain system and conductivity levels less than 1000 micro Siemens (µS) and declining) and ensure sufficient time after the initiation of rainfall to allow for time of concentration to include flow runoff from all parts of the catchment or watershed.

This Scope of Work covers monitoring requirements specified in the Special Protections for 12 participants1 designated as Responsible Parties, as follows:
• National Park Service, Point Reyes National Seashore
• Marin County
• San Mateo County
• Monterey Bay Aquarium
• Hopkins Marine Station
• City of Monterey
• City of Pacific Grove
• Carmel by the Sea
• Pebble Beach Company
• Monterey County
• California Department of Parks and Recreation
• Caltrans
2 It should be noted that three participants, Caltrans, National Park Service and California Department of Parks and Recreation, have not yet committed to full participation in the Central Coast regional program. These State and Federal Agencies may contract separately to implement their monitoring requirements, but with a commitment that they use the same monitoring design, laboratories for sample analysis and provide their data for analysis with the other participants.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 93 While the City of Monterey is a Responsible Party, it does not operate any storm runoff outfalls of its own that drain into an ASBS. It does, however, contribute runoff to an ASBS outfall operated by the City of Pacific Grove. Storm water, sediment, receiving water and reference site monitoring will be performed under this Scope of Work for Monterey Bay Aquarium and Hopkins Marine Station in compliance with the individual Draft Mitigated Negative Declaration documents issued to each. These two participants have other monitoring requirements for seawater discharges that are being performed outside this Scope of Work.

A. Core Monitoring

1. Runoff Flow Measurements
Total annual storm runoff from each participant shall be estimated (modeled) by using measured rainfall and the amount of impervious area (to be provided by each participant) in each catchment. Targeted ground-truth measurements will be made to calibrate the model. This runoff modeling will permit estimates of total annual and event-specific loads for each participant.

2. Discharge Monitoring
All outfalls ≥18 inches shall be sampled, as follows:
a. 1 storm in each of 2 years, except for discharges at receiving water sites, which shall be sampled in the same 3 storms sampled for receiving water; b. Each sample shall be analyzed for oil and grease, total suspended solids and fecal indicator bacteria;
c. Annual samples (1 storm in each year) shall be analyzed for critical life stage chronic toxicity with a sea urchin using salted-up water.

All samples from outfalls ≥36 inches shall be sampled, as follows:
a. 1 storm in each of 2 years, except for discharges at receiving water sites, which shall be sampled in the same 3 storms each year that are sampled for receiving water; b. Each sample shall be analyzed for oil and grease, total suspended solids and fecal indicator bacteria, California Ocean Plan trace metals, polynuclear aromatic hydrocarbons, organophosphorous pesticides, pyrethroid pesticides and nutrients (ammonia, nitrate, urea and phosphate);
c. Annual samples (1 storm in each year) shall be analyzed for critical life stage chronic toxicity with a sea urchin test using salted-up discharge water.

B. Receiving Water and Reference Monitoring

1. Receiving Water Monitoring
Receiving water (receiving water = in the surf zone at the point of contact between runoff and the ocean) at 11 large storm water outfalls selected to represent worst-case conditions shall be sampled as follows:
a. Samples shall be collected before and during 3 storms in each of 2 years; b. Each sample shall be analyzed for oil and grease, total suspended solids, fecal indicator bacteria, California Ocean Plan trace metals, polynuclear aromatic ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 94 hydrocarbons, organophosphorous pesticides, pyrethroid pesticides and nutrients (i.e., nitrate, ammonia, urea, orthophosphate);
c. Samples collected during storms shall be analyzed for critical life stage chronic toxicity with 3 marine species (sea urchin, mussel and giant kelp).

2. Reference Site Monitoring
Ocean water at 11 selected reference sites (reference site = in the surf zone at the mouth of a watershed with >90% open space and no listed water quality impairments) shall be sampled as follows:
a. Samples shall be collected during 3 storms in each of 2 years; b. Each sample shall be analyzed for oil and grease, total suspended solids, fecal indicator bacteria, California Ocean Plan trace metals, polynuclear aromatic hydrocarbons, organophosphorous pesticides, pyrethroid pesticides and nutrients; c. Each sample shall be analyzed for of critical life stage chronic toxicity with 3 marine species (sea urchin, mussel and giant kelp).

The proposed locations for reference sites span the study region. One reference site described below is not part of this Scope of Work, but is included because State Water Board staff requested that the Central Coast regional program determine the location of that reference site. Locations of sites south of Point Lobos were selected based upon a reconnaissance survey made on November 19, 2012. Several of these southern sites involve either substantial hikes, permission from property owners or special permission for vehicle access. Consequently, adjustments to site locations may be necessary.

Moreover, access to at least one site will require crossing a creek to reach the beach at the creek mouth and extreme precautions will be necessary during storm events. The proposed reference locations for water quality monitoring are as follows:

Region Specific Site
North of Point Reyes Salmon Creek (USAF responsibility, not covered by this Scope of Work))
San Mateo County
Tunitas Creek
Gazos Creek
South of Año Nuevo Scott Creek
Non-urban shoreline in Monterey Bay
La Selva Beach
Marina State Beach
South of Point Lobos
Malpaso Creeka
Doud Creek
Soberanes Creek
Big Sur coasta
Big Sur River
Sycamore Creek
Big Creek
Total covered by the Scope of Work 11
a = Beach access to ocean requires crossing the creek.

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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 95 Specific locations of outfalls to be monitored are as follows:
>18” >36” Responsible Party Location Longitude Latitude Nearest SWRCB Site ID Longitude Latitude
Xa Marin County Trailhead at Agate Beach -122.71059 37.89749 DUX009 -122.71058 37.89757 X San Mateo County Maritime Walk -122.517537 37.531153 FIT012 -122.51756 37.53115 X San Mateo County Juliana -122.516679 37.529092 FIT015 -122.51667 37.52915 X San Mateo County Distillery -122.513269 37.517706 FIT028 -122.51355 37.51789 X San Mateo County Madrone -122.511592 37.514237 FIT029 -122.51067 37.51246 Xa San Mateo County Weinke Way -122.516958 37.528645 FIT016 -122.5173 37.5282 X California State Parks Año Nuevo -122.32181 37.11666 ANO012 -122.32181 37.11666 X California State Parks Point Lobos -121.93812 36.5187 PTL004 -121.93812 36.5187 Xa California State Parks Año Nuevo -122.33662 37.13245 ANO027 -122.33662 37.13245 X California State Parks Point Lobos -121.94775 36.51524 PTL034 -121.94775 36.51524 X California State Parks Julia Pfeiffer Burns -121.68885 36.17192 PFE008 -121.68885 36.17192 X California State Parks Julia Pfeiffer Burns -121.68629 36.17072 PFE011 -121.68629 36.17072 X California State Parks Julia Pfeiffer Burns -121.68281 36.16924 PFE012 -121.68281 36.16924 X California State Parks Julia Pfeiffer Burns -121.6773 36.16634 PFE015 -121.6773 36.16634 X California State Parks Julia Pfeiffer Burns -121.6764 36.16569 PFE016 -121.6764 36.16569 X California State Parks Julia Pfeiffer Burns -121.66883 36.1553 PFE026 -121.66883 36.1553 X California State Parks Julia Pfeiffer Burns -121.66781 36.15469 PFE027 -121.66781 36.15469 Xa Pacific Grove Lover’s at Ocean View -121.91614 36.6246 PCG120 -121.91613 36.6246 X Pacific Grove Ocean View between Fountain Avenue
and 15th Street -121.914835 36.62381 PCG215 -121.91484 36.62378 X a b Pacific Grove Ocean View between 12th Street and
13th Street -121.913831 36.622873 PCG219 -121.91381 36.62281 X Pacific Grove Ocean View at 15th Street -121.91472 36.62339 PCG217 -121.91472 36.62339 X Pacific Grove Ocean View between Clyte Street and
Naiad Street -121.919561 36.627369 PCG069 -121.91955 36.62735 X Pacific Grove Northwest corner of Lover’s Point Park
at Ocean View Boulevard -121.916596 36.626648 PCG098 -121.91657 36.6266 X Pacific Grove Grand Avenue at Ocean View -121.914835 36.62381 PCG215 -121.91484 36.62378 X Pacific Grove 8th Street at Ocean View -121.910348 36.621624 PCG229 -121.91036 36.62162 X X a c Pacific Grove Ocean View at the Hopkins Marine
Laboratory Stanford University -121.90305 36.61897 PCG257PCG258 -121.90305 36.61897 X Pacific Grove At Ocean View between 7th Street and -121.909634 36.621125 PCG230 -121.90995 36.62115 ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 96 >18” >36” Responsible Party Location Longitude Latitude Nearest SWRCB Site ID Longitude Latitude
5th Street
Xa County of Monterey TBD (12”) -121.93286 36.54439 CAR029 -121.93286 36.54439 Xa Carmel 4th Avenue -121.93075 36.55610 CAR062 -121.93075 36.55605 X Carmel Ocean Avenue -121.93030 36.55502 CAR061 -121.93033 36.55501 X Carmel 8th Avenue -121.92940 36.55250 CAR059 -121.92933 36.55275 X Carmel 10h Avenue -121.92898 36.55007 CAR050 -121.92904 36.55003 X Carmel 11th Avenue -121.92877 36.54883 CAR046 -121.92877 36.54881 X Carmel 13th Avenue -121.92903 36.54641 CAR037 -121.9291 36.5464 X Carmel parking lot at Del Mar near Ocean
Avenue -121.93003 36.55442 CAR060 -121.93006 36.55439
X Carmel 9th Avenue -121.92890 36.55117 CAR055 -121.92891 36.55117 X Carmel Scenic Road & Santa Lucia Avenue -121.92962 36.54552 CAR093 -121.92968 36.54547 X Carmel 12th Avenue -121.92857 36.54765 CAR044 -121.92854 36.54767 X Pebble Beach Company Stillwater Pier -121.942739 36.566625 CAR279 -121.94274 36.56655 X Pebble Beach Company 18th Fairway PBGL -121.948014 36.567247 CAR299 -121.94803 36.5672 X Pebble Beach Company 18th Green PBGL / Lodge -121.950131 36.567372 CAR221 -121.9501 36.56738 Xa Pebble Beach Company 18th Green PBGL / Lodge -121.950097 36.567383 CAR220 -121.95001 36.56741 X Pebble Beach Company 9th Green PBGL -121.933397 36.560394 CAR076 -121.93337 36.5603 Xa Caltrans Fitzgerald -122.51771 37.53154 FIT011 -122.51771 37.53154 X Caltrans Año Nuevo -122.29297 37.10714 ANO035 -122.29297 37.10714 X Caltrans Año Nuevo -122.297 37.11084 ANO034 -122.297 37.11084 X Caltrans Año Nuevo -122.29764 37.1113 ANO032 -122.29764 37.1113 Xa Caltrans Año Nuevo -122.29881 37.11202 ANO033 -122.29881 37.11202 X Caltrans Año Nuevo -122.30121 37.11334 ANO030 -122.30121 37.11334 X Caltrans Carmel Bay -121.9247 36.52453 CAR007 -121.9247 36.52453 X Caltrans Carmel Bay -121.92457 36.52469 CAR026 -121.92457 36.52469 a = Sites selected for discharge receiving water monitoring b = Monitoring of this site will be shared between the cities of Pacific Grove and Monterey c = Monitoring of this site will be shared among Pacific Grove, Monterey Bay Aquarium and Hopkins Marine Station ATTACHMENT 1

Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 97 3. Biological Monitoring
Recent studies have examined whether rocky intertidal communities vary in response to storm water discharges. Initial results from southern California suggest that 2 out 11 discharge sites exhibited community composition and abundances that could be consistent with storm water discharges (Raimondi et al, 2012). Consequently, monitoring of rocky intertidal communities shall be part of this program. The community structure in rocky intertidal habitats shall be measured once at 6 sites near ASBS storm water discharges and at 2 reference sites. Sampling shall involve point-contact estimates of substrate coverage by species along transects from the high intertidal zone to the low intertidal zone.

Biological monitoring sites have been selected in consultation among permittees and regulatory agencies with consideration for the locations of sites with existing data.

Rocky intertidal communities will be sampled at the following sites:

ASBS Sampling Site Name
Año Nuevo Point and Islands ASBS Año Nuevo
Carmel Bay ASBS Stillwater
Duxbury Reef ASBS Bolinas Point
James V. Fitzgerald Marine Reserve ASBS Fitzgerald Marine Reserve Pacific Grove ASBS Hopkins
Point Lobos Ecological Reserve ASBS Point Lobos
Reference Santa Maria Creek
Reference Pigeon Point

4. Bioaccumulation Monitoring
California mussels are known to accumulate concentrations of pollutants in their tissues to concentrations much higher that found in the surrounding water.

Consequently, they have been widely applied in studies of water quality status and trends (e.g., CCLEAN, 2012; Davis et al, 1999). Consequently, concentrations of contaminants shall be measured in resident mussels from sites near ASBS storm water discharges and from reference sites distant from urbanized ASBS areas utilizing existing programs, wherever possible, as follows:
a. Population composites of mussels of roughly uniform shell length shall be collected from each of 7 sites.
b. Each composite shall be thoroughly homogenized and analyzed for polynuclear aromatic hydrocarbons, polychlorinated biphenyls, polybrominated diphenyl ethers, chlorinated pesticides, pyrethroid pesticides and Lomefloxacin.

These analytes are slightly different from those measured in sections A and B and, except for pyrethroids and Lomefloxacin, are consistent with those measured by CCLEAN.

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Central Coast Regional ASBS Monitoring Program Interim Report 2013–2016 98 The following sites will be sampled for bioaccumulation:

Sites
Point Reyes
Scott Creek
Laguna Creek
41st Avenue, Capitola
Lovers Point
Fanshell Overlook, 17-Mile Drive
Carmel River Beach
Total = 7
C. Mooring Field Operations (Pebble Beach Company only)

1. Receiving Water
Ocean receiving water at the mooring facility shall be sampled as follows:
a. Samples shall be collected monthly from May through October on a high use weekend in each month.
b. Samples shall be analyzed for Ocean Plan indicator bacteria, residual chlorine, copper, zinc, grease and oil, methylene blue active substances (MBAS), and ammonia nitrogen.

2. Sediments
Subtidal sediment shall be sampled, as follows:
a. Samples shall be collected annually from within the mooring field and below the pier.
b. Samples shall be analyzed for Ocean Plan Table 1 metals (for marine aquatic life beneficial use), acute toxicity (using Eohaustorius estuaries), PAHs, and tributyltin.

D. General Requirements

1. Ensure Data Quality
a. All sampling and analysis shall conform to a Sampling and Analysis Plan (SAP) and to a Quality Assurance Program Plan (QAPP) that are consistent with requirements of the State of California Surface Water Ambient Monitoring Program (SWAMP). At a minimum, sampling shall be conducted so as to ensure that samples are representative of the site and matrix being sampled and to minimize the introduction of extraneous contamination into samples.

Ultra-clean techniques shall be used for collection samples to be analyzed for organic contaminants and trace metals.
b. Samples of the same type shall all be performed by the same laboratory and shall include appropriate lab blanks, certified reference materials, matrix spikes and matrix spike duplicates and reporting limits shall equal or be lower than those required by SWAMP.

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Central Coast Regional ASBS Monitoring Program Interim Report 2013–2016 99 c. An audit will be prepared describing laboratory performance relative to data quality objectives prescribed in the QAPP.

2. Ensure data availability
All chemical data will be uploaded to the California Environmental Data Exchange Network annually.

3. Reporting
Annual reports shall be delivered within 6 months of the completion of laboratory analyses. At a minimum, annual reports shall include a complete description of sampling methods, sites and analytical methods and analysis of data, including comparison of data from discharges and their respective receiving water sites with those from reference sites and the California Ocean Plan and shall be comparable to Schiff et al (2011). The annual report for the second year will be cumulative, including analysis of all data from both years to provide a characterization of storm water discharges and their effects on receiving water quality in Areas of Special Biological Significance.

4. Areas of Special Biological Significance Included
Storm runoff from program participants flows into the following ASBS:
• National Park Service, Point Reyes National Seashore
o Point Reyes Headlands ASBS
o Double Point ASBS
o Duxbury Reef ASBS
• County of Marin
o Duxbury Reef ASBS
• County of San Mateo
o James V. Fitzgerald ASBS
• California State Department of Parks and Recreation
o Año Nuevo ASBS
o Point Lobos ASBS
o Julia Pfeiffer Burns ASBS
• Monterey Bay Aquarium
o Pacific Grove ASBS
• Hopkins Marine Station
o Pacific Grove ASBS
• City of Monterey
o Pacific Grove ASBS
• City of Pacific Grove
o Pacific Grove ASBS
• City of Carmel-by-the-Sea
o Carmel Bay ASBS
• Pebble Beach Company
o Carmel Bay ASBS
• County of Monterey
o Carmel Bay ASBS
• Caltrans
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Central Coast Regional ASBS Monitoring Program Interim Report 2013–2016 100
o James V. Fitzgerald ASBS
o Año Nuevo ASBS
o Carmel Bay ASBS
Literature Cited
CCLEAN. 2012. Central Coast Long-term Environmental Assessment Network Annual Report, 2010–2011.

Davis, JA, Stephenson M, Hardin, D, Gunther AJ, Sericano J, Bell D, Scelfo GH, Gold J, Crick J. 1999. Long term bioaccumulation monitoring with transplanted bivavles in San Francisco Bay. Marine Pollution Bulletin. 38:170–181.

Raimondi, P., K. Schiff and D. Gregorio. 2012. Characterization of the rocky intertidal ecological communities associated with southern California Areas of Special Biological Significance. Southern California Coastal Water Research Project Technical Report 703 – May 2012. Costa Mesa, CA.

Schiff, K.C., B. Luk, D. Gregorio and S. Gruber. 2011. Southern California Bight 2008 Regional Monitoring Program: II. Areas of Special Biological Significance. Southern California Coastal Water Research Project. Costa Mesa, CA.

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Central Coast Regional ASBS Monitoring Program Interim Report 2013–2016 101
Appendix B. Laboratory QA Summaries
The Central Coast Areas of Special Biological Significance Regional Monitoring Program and Reference Site
Monitoring: Water Year 2014
June 15, 2015
Submitted to:

Monterey Regional Water Pollution Control Agency
Submitted by:
4749 Bennett Drive, Suite L
Livermore, CA 94551
925-373-7142
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Central Coast Regional ASBS Monitoring Program Final Report 2013–2016 102
List of Terms
AMS
CRM
FB
FIB
FD

LCS/LCSD
LPM
MBAS
MDL
MPSL
MQO
MS/MSD
ND
O&G
QA
QAO
QAPP
QC
RL
RPD
SWAMP
TM
TSS
WY
Applied Marine Sciences, Inc.

Certified Reference Material
Field Blank
Fecal Indicator Bacteria
Field Duplicate
Lab Control Sample / Lab Control Sample Duplicate
Laboratory Project Manager
Monterey Bay Analytical Services
Method Detection Limit
Marine Pollution Studies Laboratory
Measurement Quality Objective
Matrix Spike / Matrix Spike Duplicate
Non Detect
Oil & Grease
Quality Assurance
Quality Assurance Officer
Quality Assurance Project Plan
Quality Control
Reporting Limit
Relative Percent Difference
Surface Water Ambient Monitoring Program
Trace Metal
Total Suspended Solids
Water Year
List of QA Codes
BRK No concentration sample container broken
ERV Exceeds reference control limits
EUM/VEUM LCS is outside of control limits
GB/VGB Matrix spike recovery not within control limits
GN Surrogate recovery is outside of control limits
IL/VIL RPD exceeds laboratory control limit
IP/VIP Analyte detected in field or lab generated blank PG Calibration verification outside control limits
VFDP Field duplicate RPD above QC limit; flagged by QAO VREL Target RLs not achieved due to change in lab capabilities VQCP QA/QC protocols were not met for precision, flagged by QAO VRVQ Based on professional judgment QA/QC protocols were not met, flagged by QAO
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Central Coast Regional ASBS Monitoring Program Interim Report 2013–2016 103
Introduction
Below are narrative summaries of reviews of QA/QC samples analyzed with reported field samples for WY2014. QA/QC samples were evaluated using the procedures and measurement quality objectives (MQOs) described in the project QAPP (AMS 2013). WY2014 monitoring activities included within this review incorporated the following components: (1) water quality monitoring, (2) sediment quality monitoring; and (3) bioaccumulation monitoring. Each of these components is described in detail in the following sections.

Water Quality Monitoring
WY2014 monitoring was conducted between October 14, 2013 and March 26, 2014. Monitoring included both pre-storm sampling and post-storm sampling associated with each precipitation event.

With one notable exception (discussed in more detail below in Section 0), QA/QC results generally met project MQOs, with some minor deviations.

Results of individual analytes, or groups of analytes, are described in more detail below, along with the laboratory responsible for its analysis. The groupings of individual analytes follow the convention of SWAMP3 where applicable.

Hg
Hg was analyzed by the Marine Pollution Studies Laboratory at Moss Landing (MPSL). MPSL analyzed both freshwater samples (i.e., discharge samples) and marine samples (i.e., receiving water and reference samples), using the same method but diluting the marine samples before processing.

Consistent with Project MQOs (AMS 2013), QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs) or Certified Reference Materials (CRMs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and laboratory duplicates.

Sensitivity
MPSL achieved target RL for Hg for all WY2014 analyses. Approximately 13% of samples (including analysis of field samples and lab duplicates) were reported as Non Detect (ND).

Lab Blanks
Reported results for all analyses of TMs are blank corrected. Lab blank contamination was identified in only one batch of samples in WY2014, lab batch MPSL-DFG_Hg13-044w_W_Hg associated with the 10/15/13 sampling event, For this batch, one of the three method blanks reported slightly exceeded the RL and was flagged by the lab with an “IP” qualifier. Data associated with this lab batch (with detectable concentrations) were flagged with a “VIP” qualifier but were not censored because, consistent with MPSL protocols, the average result of the blanks is below MDL. All other method blanks processed in WY2014 were reported as ND.
3 Available at http://checker.swamp.mpsl.mlml.calstate.edu/QAPRP/QAPrPTableReferenceToC.php.

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104
Recovery
MPSL analyzed and reported CRM results with each lab batch; the CRM was a freshwater sample, which raises uncertainty for its usage with marine samples. Recovery results were good, with all recoveries within the 92% to 99% range, well within QAPP MQO of 75 to 125%. MPSL also analyzed MS samples with each batch. Recoveries for these samples ranged from 85 to 107%, well within the QAPP MQO of 75 to 125%.

Precision
Precision was calculated only for field sample / lab duplicate and MS/MSD sample pairs where an analyte was detected in both samples. Reported Relative Percent Difference (RPD) for field samples ranged from 0 to 17%; RPDs for MS samples ranged from 0 to 22%, both within the Project MQO of <25%.

Field QA Samples
Six Field Blank (FB) results were collected and analyzed blind, each of which reported by the laboratory as ND. Six Field Duplicate samples were collected and analyzed blind. Five of the six were within control limits (<25% RPD). The RPD for the remaining sample pair was reported as 73%, and affected samples from this batch were flagged with a VFDP qualifier.

Trace Metal Suite
Initially, the trace Metals (TMs) As, Cd, Cr, Cu, Ni, Pb, Se, Ag, and Zn were analyzed by MPSL. Due to reliability and quality assurance concerns that arose over the course of the season, AMS made the decision with consent of MPSL to transfer marine samples to Physis for ongoing analyses. Consistent with Project QAPP, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs) or Certified Reference Materials (CRMs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and laboratory duplicates.

Sensitivity
MPSL and Physis achieved QAPP target RLs for all TMs for all analyses conducted.

Lab Blanks
For MPSL analyses, lab blank contamination was identified in one lab batch associated with analyses of Cu (batch MLML-TM_HiResICP112213_W_TM for 11/22/13 samples), one lab batch associated with analyses of Zn (batch MLML-TM_HiResICP112213_W_TM for 11/22/13 samples), and three lab batches associated with analyses of Ag (MPSL-DFG_WTM031314_W_TM for 3/13/14 sampling, MPSLDFG_WTM040814_W_TM for 4/8/14 sampling, and MPSL-DFG_WTM041414AMS_W_TM for 4/14/14 sampling). Affected data are flagged with either IP, if qualified by the lab, or VIP, if qualified by the Quality Assurance Officer (QAO).

For Physis analyses, all lab blanks were reported as ND.

Recovery
MPSL analyzed and reported CRM results with each lab batch. Recovery results were good, with all recoveries within QAPP MQO of 75 to 125%. MPSL also analyzed MS samples with each batch.

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105
Recoveries for these samples were generally good (i.e., within QAPP MQO of 75 to 125%), with the exception that some MS recovery results for Ag (66% to 73% recovery in batch MLMLTM_HiResICP112213_W_TM) were outside of the control limits and flagged by the laboratory with a “GB” qualifier. This is not unexpected, as native concentrations of silver within the spiked samples were below detection limits. Affected samples within the lab batch were flagged with “VGB” qualifier.

Physis analyzed and reported LCS results with each lab batch. Recovery results were good for each trace metal analyzed, with all recoveries within QAPP MQO of 75 to 125%. Physis did not analyze and report MS/MSD pairs, instead reporting LCS/LCSD pairs for analysis of precision consistent with their laboratory quality control protocols, so no additional measurement of recovery can be made.

Precision
For MPSL results, precision was calculated only for field sample / lab duplicate and MS/MSD sample pairs where an analyte was detected in both samples. In general, reported RPDs for all results fell within Project MQO of <25% for most pairs. Reported RPD for one MS/MSD pair for analysis of Zn within lab batch MPSL-DFG_WTM040814_W_TM fell just outside of control limits (25.5% RPD); this result was associated with a non-project sample (flagged with an “IL” qualifier by the lab) that was identified by the laboratory as containing sand in the aqueous sample, which contributed to sample heterogeneity. Data that were potentially affected by the MS/MSD results were in this case not qualified because of the noted issue with non-project sample heterogeneity and the other measures of precision associated with this batch that exhibit laboratory control of the process, namely that one lab duplicate and two field duplicates each exhibited very low RPDs (0 to 4%).

For Physis results, precision was calculated only for field sample / lab duplicate and LCS/LCSD sample pairs where an analyte was detected in both samples. In general, reported RPDs for field sample / lab duplicate pairs fell within Project MQO of <25%. The following lab duplicates fell outside the QAPP control limits: (1) Cd and Se within lab batch Physis_E-7146_W_TM; (2) Ag and Se within lab batch Physis_E-7122_W_TM; and (3) Cu and Se within lab batch Physis_E-7120_W_TM. Five of the six results were flagged by the lab with an “IL” qualifier, and the sixth was flagged by the QAO with “VIL” qualifier as it was reported as exactly 25%, which falls within the lab control limits, but does not meet the QAPP MQO. Affected data were flagged with “VIL” qualifier. For LCS/LCSD pairs, all reported RPDs fell within QAPP control limits (75 to 125%).

Field QA Samples
Two FB samples were collected for each analyte and analyzed blind by MPSL. There was minor blank contamination present in at least one result reported for As, Cr, Cu, Pb, and Se within batch MPSLDFG_WTM040814_W_TM; affected samples were flagged with a “VIP” qualifier.

Two FD samples were collected and analyzed blind by MPSL. Field RPDs ranged from 0% to 14%, within QAPP MQOs.

Three FB samples were collected for each analyte and analyzed blind by Physis. There was minor blank contamination present in one result reported for Zn within batch Physis_E-7122_W_TM and all three results associated with analysis of Ag (batches Physis_E-7120_W_TM, Physis_E-7121_W_TM, and Physis_E-7122_W_TM); affected samples were flagged with a “VIP” qualifier.

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General
During WY2014, MPSL had ongoing difficulty with the reliability of the laboratory equipment used to analyze TMs in marine samples. This often caused delays in analyzing and reporting, and eventually contributed to the decision to transfer analyses of TMs to a different lab for WY2015, Physis. In general, the data quality of the reported analytes is considered good and results are acceptable for Project purposes.

Nutrients
Nutrients, including Ammonia as N, Nitrate as N, and Orthophosphate as P, were analyzed by Monterey Bay Analytical Services (MBAS). Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and laboratory duplicates.

Sensitivity
For ammonia, MBAS achieved target MRLs for all analyses of nutrients with the exception of ammonia.

For two field samples collected on 11/18/13, MBAS employed an alternative distillation method for analysis (SM4500 NH3 B,C), which resulted in two samples exceeding target MRL. As there was no appropriate qualifier to match this situation, AMS applied the recently-added “VREL” qualifier, which is more related to lab capabilities, and added a LabResultComment to indicate that the elevated RL is associated with the use of alternate method.

Blanks
All laboratory blanks were reported as ND.

Recovery
MBAS analyzed and reported LCS results with each lab batch. Recovery results were good, with all recoveries within QAPP MQO of 80 to 120% (ranging from 90 to 106% for Ammonia, 95 to 104% for Nitrate, and 86 to 110% for Ortho-P).

MBAS also analyzed MS samples with each batch. Recoveries for these samples in several cases (four analyses of Ortho-P associated with batches MBAS_20131015_W_PO4P and MBAS_20140228_W_PO4P, and two analyses of Ammonia associated with batches MBAS_20140311_W_NH3 and MBAS_20140321_W_NH3) fell outside of QAPP MQO of 80 to 120% recovery. Each of the results outside of control limits was associated with analysis of non-project media. Affected samples within the various lab batches were flagged with “VGB” qualifier.

Precision
MBAS reported precision based upon results of MS/MSD samples. Reported RPDs ranged from 0 to 11% for the three nutrients analyzed, all within the QAPP MQO of <25%.

Field QA Samples
Seven FB samples were collected for each analyte and analyzed blind. All results were reported as ND.

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Six FD samples were collected and analyzed blind. Field RPDs ranged from 0% to 22%, all within QAPP MQOs. For the majority of samples, no RPD could be calculated as one or both samples were reported below RLs.

Fecal Indicator Bacteria
Fecal Indicator Bacteria (FIB) analyses included measurement of Fecal Coliform, E. coli, and Enterococcus. QC checks required by the Project QAPP in place during WY2014 include reporting on laboratory controls, laboratory duplicates (when a sufficient number of duplicates have been analyzed), and laboratory blanks (if samples are diluted).

The majority of FIB samples were analyzed by MBAS. A small number of samples, collected from sampling locations within Marin County south to Tunitas Creek, were sent to Alpha Labs (Alpha) in order to achieve QAPP hold time requirements.

Sensitivity
Both MBAS and Alpha achieved target MRLs for all analyses.

Controls
Both laboratories indicated that results of all required positive and negative controls were acceptable.

Blanks
For MBAS samples, all associated lab blanks were reported below detection and reporting limits.

For Alpha samples, no lab blank information was reported. All results were flagged with “VRVQ” qualifier, indicating that overall QA protocols were not met. The data was not rejected outright, as discussions with the Laboratory Project Manager (LPM) indicated that the analyses were in control throughout Project implementation. Data should, however, be reviewed in context with what was reported.

Precision
For MBAS samples, precision was calculated only for two field sample / lab duplicate pairs where the analyte was detected in both samples. Both RPDs for field sample and duplicate pairs for analysis of Fecal Coliform within batch MBAS_20140206_W_FC exceeded Project MQO of <25% (66 and 70%, respectively). Affected samples within this lab batch were flagged with “VIL” qualifier. All other field samples were flagged with a “VQCP” qualifier, indicating that QAPP protocols for precision were not achieved / reported by lab.

For Alpha samples, precision was not reported. Per above, all data flagged with “VVQ” qualifier.

Field QA Samples
Field Blanks are not required by Project QAPP.

For MBAS samples, six to seven FD samples were collected for each FIB analyte and analyzed blind.

MBAS reported RPDs for each FIB analyte generally fell within the Project QAPP control limits of 75 to 125%. One of six Fecal Coliform samples (17%) and one of seven Enterococcus samples (14%) exceeded ATTACHMENT 1

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control limits, and were flagged with the “VFDP” qualifier. No additional qualification was required as, per above, all FIB field samples were flagged with the “VQCP” qualifier.

Field duplicate samples were not submitted to Alpha during WY2014.

General
Based upon the inadequacy of QA data reporting associated with WY2014 analyses reported by Alpha, AMS reviewed the QA issues with the Alpha LPM. Alpha informed AMS that the required QA data had been conducted, but it was not worth the effort to compile and report for such a small number of analyses. Based upon this discussion, AMS decided to identify and use an alternate lab for the required FIB analyses for WY2015 monitoring.

After reviewing options, AMS determined that there were no labs in close enough proximity to the monitoring locations to meet hold time requirements. Therefore, AMS made the decision to continue to work with Alpha, but with requirements in place, and additional funding, to support the additional effort required to fully document performance against MQOs.

Per the above discussion, WY2014 analyses of FIB did not report performance relative to all QAPP MQOs. The data for these analytes are qualified, but not censored for use, based upon two factors: (1) current ELAP certifications held by the two labs; and (2) discussions with Laboratory Project Managers that indicated that analyses were conducted consistent with laboratory protocols and were in control.

Regarding future usage of the FIB results, all WY2014 FIB data should be viewed within the context of the above discussion. AMS is pursuing corrective actions with each laboratory in hopes of obtaining future analytical results more in line with QAPP requirements.

Conventional Parameters.

Any analytes not listed within specific SWAMP groupings of analytes were analyzed per the MQOs associated with conventional parameters, including Urea, which is often grouped with Nutrients.

Conventional ASBS parameters include the following:
• Oil & Grease (O&G)
• Total Suspended Solids (TSS)
• Urea
All conventional parameters were analyzed by MBAS. Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, lab dups, LCSs (not applicable for TSS), and Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs (not applicable for TSS).

Sensitivity
For all three conventional parameters, MBAS achieved target MRLs. For O&G, approximately 5% of samples were of detectable concentration. For TSS, 98% of samples were detectable. And for Urea, 43% of samples were detectable.

Blanks
All laboratory blanks were reported as ND associated with each of the three conventional parameters.

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Recovery
For analysis of Urea, MBAS reported recoveries associated with analysis of CRM and LCS samples alternately. Recoveries ranged between 79 and 121%, with the majority of results reported within Project MQO of 80 to 120%. CRM / LCS results outside of the control limits were flagged by the lab with an “ERV” qualifier; affected samples within the lab batch were flagged with “VEUM” qualifier. MBAS also reported recoveries associated with analyses of MS/MSD samples. Three of fifteen recoveries reported fell outside of Project MQO of 80 to 120%, each associated with lab batch MBAS_20140218_W_Urea.

Affected samples were flagged with a “VGB” qualifier.

For analysis of O&G, MBAS reported recoveries associated with analysis of LCS. Recoveries ranged between 73 and 99%, with the two analyses outside the Project MQO control limits flagged by the laboratory with the “ERV” qualifier. Affected data within the same lab batch were flagged with the “VEUM” qualifier. MBAS reported MS/MSD information on a subset of lab batches; for each, recoveries fell within MQO control limits.

Precision
For analysis of Urea, MBAS reported precision based upon results of MS/MSD samples. Reported RPDs ranged from 0 to 3%, all within the QAPP MQO of <25%.

For analysis of O&G, MBAS reported precision based upon results of MS/MSD samples. Reported RPDs ranged from 5 to 9%, all within the QAPP MQO of <25%.

For analysis of TSS, MBAS reported precision based upon results of lab duplicate analyses. Reported RPDs ranged from 0 to 29%, with a single result falling outside of the QAPP MQO of <25%. Affected data associated with the 29% RPD (lab batch MBAS_20140210_W_TSS) were flagged with a “VGB” qualifier.

Field QA Samples
Seven and six field blank samples were submitted blind to the laboratory for analysis of O&G and Urea, respectively. All results were reported as ND, with the exception of one O&G sample that was broken in the lab, and flagged with a “BRK” qualifier.

Six Urea FD samples were collected and analyzed blind. For three of the samples, no RPD could be calculated as one or both samples were reported below RLs. Field RPDs for remaining field sample / field duplicate pairs ranged from 4% to 27%, with a single result falling outside of QAPP MQO (<25%). The field duplicate result of 27% was flagged with a “VFDP” qualifier, but remaining samples within the batch were not qualified as three of the four FD samples analyzed associated with this batch met QAPP MQOs.

Seven TSS FD samples were collected and analyzed blind. For one of the samples, no RPD could be calculated as one or both samples were reported below RLs. Field RPDs for remaining field sample / field duplicate pairs ranged from 0% to 31%, with a single result falling outside of QAPP MQO (<25%).

Affected data associated with the 31% field RPD were flagged with a “VFDP” qualifier.

General
Data quality assurance is generally good, and all data are considered acceptable for reporting purposes.

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Organophosphorus Pesticides.

Organophosphorus (OP) Pesticides were analyzed by California Department of Fish and Wildlife’s Water Pollution Control Laboratory (WPCL). Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, laboratory duplicates, and surrogate recoveries.

Sensitivity
The majority of OP pesticide analyses achieved QAPP target RLs. For those analyses that exceeded RLs and resulted in ND, data were flagged with the “VREL” qualifier, indicating that lab capabilities had changed over time from what was originally quoted for the project.

It should be noted that, in general, there were very few field samples that generated results at detectable concentrations (<3% for all analytes). Therefore, estimation techniques used to quantify the ND results may greatly influence interpretation.

Blanks
All laboratory blanks were reported as ND.

Recovery
WPCL analyzed and reported LCS results with each lab batch. Recovery results were generally good, with only one recovery reported outside of the QAPP MQO of 50 to 150% for LCS samples, an analysis of chlorpyrifos, methyl for which a second reported LCS fell within the MQO control limits. The affected datapoint was flagged with the “EUM” qualifier by the laboratory.

WPCL also analyzed MS samples on a subset of lab batches. In all cases, recoveries fell within QAPP MQO of 50 to 150% recovery.

WPCL also reported surrogate recoveries associated with each lab batch. With one exception, recoveries fell within the QAPP MQO standard range of 50 to 150% - for lab batch WPCL_L-088-14_B1_W_OP, one surrogate result fell slightly outside of control limits (155%), and was flagged with “GN” qualifier.

Precision
WPCL reported precision based upon results of LCS/LCSD or MS/MSD analyses. Reported RPDs ranged from 0 to 15% for all OP pesticides analyzed, all within the QAPP MQO of <25%.

Field QA Samples
Six FB samples were collected for each analyte and analyzed blind. All results were reported as ND.

Five FD samples were collected and analyzed blind. In each case, no RPD could be calculated as one or both samples within the field sample / field duplicate pair was reported below RLs.

General
WPCL reported several instances of Continuing Calibration Verification (CCV) exceeding acceptance criteria. These exceedances are flagged with a “PG” qualifier. In each case, the results for associated ATTACHMENT 1

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field samples all were reported as ND; according to the lab, high CCV results do not adversely affect ND results, and data quality is therefore not considered to be affected.

Pyrethroid Pesticides
Pyrethroids were analyzed by WPCL. Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, laboratory duplicates, and surrogate recoveries.

Sensitivity
The majority of Pyrethroid analyses achieved QAPP target RLs. A total of four analyses of TransPermethrin slightly exceeded the target RL (0.11 µg/L actual vs. 0.1 µg/L target). For these four analyses, each of which was reported as ND, data were flagged with the “VREL” qualifier.

It should be noted that, in general, there were relatively few field samples that generated results at detectable concentrations, ranging from a low of 2% for Cypermethrin to a high of 21% for Cyfluthrin.

Therefore, estimation techniques used to quantify the ND results may greatly influence interpretation.

Blanks
All laboratory blanks were reported as either ND or DNQ (for results between DL and RL).

Recovery
WPCL analyzed and reported LCS results with each lab batch. Recovery results were generally good, with a total of four LCS recoveries reported outside of the QAPP MQO of 50 to 150%; each of these qualified data points is associated with analysis of Deltamethrin/Tralomethrin (within lab batches WPCL_L-573- 13_W_PYD, WPCL_L-057-14_B2_W_PYD, and WPCL_L-057-14_B3_W_PYD). Affected data was flagged with either the “EUM” or “VEUM” qualifier, depending on whether the flag was added by the laboratory or QAO. The low recoveries, which were not considered unusual for WPCL analyses of Deltamethrin/Tralomethrin, may indicate a potential low bias in results.

WPCL also analyzed MS samples with each lab batch. Recoveries generally fell within QAPP MQO of 50 to 150% recovery. The major exception to this was associated with analysis of lab batch WPCL_L_633_13_PYD, in which MS/MSD results from a re-extraction and re-analyses were reported with several Pyrethroid compounds above 150% recovery (152 to 181% recoveries). All affected data were flagged with either “GB” or “VGB” qualifier, depending on whether the flag was applied by the lab or QAO.

WPCL also reported surrogate recoveries associated with each lab batch. With one major exception, surrogate recoveries were generally good - recoveries reported by WPCL associated with lab report WPCL_L-057-14 (lab batches WPCL_L-057-14_B2_W_PYD and WPCL_L-057-14_B3_W_PYD) were low and outside of QAPP control limits (50 – 150%). The laboratory investigated and determined that the surrogate solution had degraded over time, and, combined with other QA measures reviewed, are not indicative of low quality data. The affected surrogate recoveries were flagged with a “GN” qualifier by WPCL; a LabResultComment was added to affected data to clarify the cause of the low recoveries.

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Precision
WPCL reported precision based upon results of LCS/LCSD and/or MS/MSD analyses. Reported RPDs for LCSDs ranged from 0 to 89% for all Pyrethroid pesticides analyzed. Two results fell outside of the QAPP MQO of <25% (analyses of Deltamethrin/ Tralomethrin in lab batches WPCL_L-057-14_B2_W_PYD and WPCL_L-573-13_ W_PYD; affected samples were flagged by the laboratory with an “IL” qualifier. All reported RPDs for MSDs fell within QAPP MQO of <25%.

Field QA Samples
Six FB samples were collected for Pyrethroids analysis and analyzed blind. All results were reported as ND.

Five FD samples were collected and analyzed blind. In the vast majority of cases, no RPD could be calculated as one or both samples within the field sample / field duplicate pair was reported below RLs.

For the four pairs for which RPDs could be calculated, RPDs ranged from 1 to 15%, within the Project QAPP MQO of <25%.

General
WPCL reported several instances of Continuing Calibration Verification (CCV) exceeding acceptance criteria. These exceedances are flagged with a “PG” qualifier. In the vast majority of cases, the results for associated field samples were reported as ND; according to the lab, high CCV results do not adversely affect ND results, and data quality is therefore not considered to be affected. Where field sample results are quantified within affected lab batches, there may be a slight high bias in reported results.

PAHs
PAHs were analyzed by WPCL. Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, laboratory duplicates, and surrogate recoveries.

Sensitivity
All analyses of PAHs achieved QAPP target RLs.

Blanks
All laboratory blanks were reported as ND.

Recovery
WPCL analyzed and reported LCS results with each lab batch. Recovery results were generally good, with all LCS recoveries falling within the QAPP MQO of 50 to 150%.

WPCL also analyzed MS samples with each lab batch. Recoveries generally fell within QAPP MQO of 50 to 150% recovery. The exception to this was associated with analysis of lab batch WPCL_L-088- 14_B1_W_PAH, in which MS/MSD recovery results for several PAH compounds were reported outside of control limits on both the high and low end. All affected data were flagged with either “GB” or “VGB” qualifier, depending on whether the flag was applied by the lab or QAO.

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WPCL also reported surrogate recoveries associated with each lab batch. Surrogate recoveries were generally good; six total surrogate results fell outside of QAPP control limits (50 to 150%) – affected data were flagged with “GN” qualifier.

Precision
WPCL reported precision based upon results of both LCS/LCSD and MS/MSD analyses. Reported RPDs for LCSDs ranged from 0 to 10% for all PAH compounds analyzed, all within the Project MQO of <25%.

RPDs for MS/MSD sample pairs ranged from 0 to 62%. Each of the eleven PAH compound RPDs reported outside of Project QAPP control limits (0 to 25%) were associated with lab batch WPCL_L-088- 14_B1_W_PAH. Affected samples were flagged with either an “IL” or “VIL” qualifier depending on whether it was applied by the lab or QAO.

Field QA Samples
Six FB samples were collected for analysis of PAH compounds and analyzed blind. Only Naphthalene (detected in four of six field blanks) and Pyrene (detected in two of six field blanks) were reported at detectable concentrations. Affected samples were flagged with “VIP” qualifier. These finding suggest that it may be difficult to obtain a “true” concentration of Naphthalene using existing techniques.

Five FD samples were collected and analyzed blind. In the vast majority of cases, no RPD could be calculated as one or both samples within the field sample / field duplicate pair were reported below RLs.

For the remaining pairs for which an RPD could be calculated, the reported RPDs associated with sample 203PAC010-DIS-36 collected on 2/26/14 exhibited high RPDs (ranging from 24 to 67%), which may indicate a difference between the two samples in water sampled, sampling technique, laboratory process, or other. Affected samples were flagged with a “VFDP” qualifier.

General
WPCL reported a major lab error in analysis of PAHs in lab batch L-088-14 and L-089-14. A surrogate solution added to all samples contained the following target compounds:
• Methylfluorene, 1-,
• Fluorenes, C1,
• Methyldibenzothiophene, 4-,
• Dibenzothiophene, C1,
• Dimethylphenanthrene, 3,6-,
• Phenanthrenes/Anthracenes, C2,
• Methylfluoranthene, 2-,
• Fluoranthene/Pyrenes, C1
Concentrations of the above PAH compounds were quantified by the laboratory using an alternative extract (from Pyrethroids analysis), but was reported only as a screening type value due to potential loss that might have occurred through that process. Data were flagged with a “VRVQ” qualifier and “Rej” compliance code by the QAO to indicate their rejection based upon QA concerns. The affected PAH compounds are not among those regulated by the California Ocean Plan and did not affect reported ATTACHMENT 1

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total PAHs or interim exceedances of the preliminary 85th percentile threshold. All other PAH results are considered acceptable for Project usage.

Toxicity
All toxicity testing was conducted by UC Davis Marine Pollution Studies Laboratory at Granite Canyon (Granite Canyon). The toxicity of discharge samples was assessed with the purple sea urchin (Strongylocentrotus purpuratus) fertilization test. Receiving water samples were tested with the sea urchin fertilization test, as well as the mussel larval development test with Mytilus galloprovincialis, and the giant kelp germination and growth test with Macrocystis pyrifera. QC checks required by the Project QAPP in place during WY2014 include laboratory controls (positive, negative, and brine, where applicable) and checks on water quality parameters and test conditions.

Controls
Results of all laboratory controls were acceptable.

Water Quality Parameters
For MBAS samples, all associated lab blanks were reported below detection and reporting limits.

Field QA Samples
Required water quality measurements (i.e., Ammonia, Dissolved Oxygen, pH, Salinity, Temperature) were reported associated with each lab batch. The vast majority of reported water quality parameters fell within recommended ranges listed within QAPP, where applicable. Those falling outside of the recommended ranges are not thought to affect test results or interpretation.

General
Toxicity summary results for two lab batches processed by Granite Canyon for the mussel development test are flagged with an ”H” qualifier, indicating the analyses were conducted beyond recommended hold time. In both cases, control organisms used did not meet initial test acceptability criteria. Batches and GC_ASBSMG4_W_TOX and GC_ASBSMG4_W_TOX were analyzed five and six days after collection, respectively, consistent with laboratory protocols. All toxicity results are considered acceptable for Project usage.

References
AMS, 2013. Quality Assurance Project Plan: The Central Coast Areas of Special Biological Significance Regional Monitoring Program and Reference Site Monitoring, Version 1.0. Prepared by Applied Marine Sciences, Inc. April 15, 2013.

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Laboratory QA Summary
The Central Coast Areas of Special Biological
Significance Regional Monitoring Program and
Reference Site Monitoring: Water Years 2015 and 2016
June 23, 2016
Submitted to:

Monterey Regional Water Pollution Control Agency
Submitted by:
4749 Bennett Drive, Suite L
Livermore, CA 94551
925-373-7142
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List of Terms
AMS
CRM
FB
FIB
FD
GC

LCS/LCSD
LPM
MBAS
MDL
MPSL
MQO
MS/MSD
ND
O&G
PR
QA
QAO
QAPP
QC
RL
RPD
SWAMP
TM
TSS
WPCL
WY
Applied Marine Sciences, Inc.

Certified Reference Material
Field Blank
Fecal Indicator Bacteria
Field Duplicate
Granite Canyon
Lab Control Sample / Lab Control Sample Duplicate
Laboratory Project Manager
Monterey Bay Analytical Services
Method Detection Limit
Marine Pollution Studies Laboratory
Measurement Quality Objective
Matrix Spike / Matrix Spike Duplicate
Non Detect
Oil & Grease
Percent Recovery
Quality Assurance
Quality Assurance Officer
Quality Assurance Project Plan
Quality Control
Reporting Limit
Relative Percent Difference
Surface Water Ambient Monitoring Program
Trace Metal
Total Suspended Solids
Water Pollution Control Lab
Water Year
List of QA Codes
BRK No concentration sample container broken
BY/VBY Sample received at improper temperature, flagged by QAO CVH Continuing calibration verification high, no bias
CS/VCS QC criteria not met due to analyte concentration near RL DF Reporting limits elevated due to matrix interferences ERV Exceeds reference control limits
EUM/VEUM LCS is outside of control limits
GB/VGB Matrix spike recovery not within control limits
GN/VGN Surrogate recovery is outside of control limits
IL/VIL RPD exceeds laboratory control limit
IP/VIP Analyte detected in field or lab generated blank PG/VPG Calibration verification outside control limits
RE/VRE Elevated reporting limits due to limited sample volume TW/VTW Water quality parameters outside recommended test method ATTACHMENT 1

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ranges
VFDP Field duplicate RPD above QC limit; flagged by QAO VREL Target RLs not achieved due to change in lab capabilities VQCP QA/QC protocols were not met for precision, flagged by QAO VRVQ Based on professional judgment QA/QC protocols were not met, flagged by QAO
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1. Introduction
Below are narrative summaries of the QA/QC review of WY2015 and WY2016 ASBS monitoring results. These assessments were made using the procedures and measurement quality objectives (MQOs) described in the project QAPP (AMS 2014). Monitoring activities included within this review incorporated the following components: (1) water quality monitoring and (2) sediment quality monitoring. These components are described in detail in the following sections.

2. Water Quality Monitoring
WY2015 water quality monitoring was conducted between December 9, 2014 and April 8, 2015.

WY2016 water quality monitoring was conducted between December 2, 2015 and March 9, 2016.

QA/QC results generally met project MQOs, with some minor deviations.

Results of individual analytes, or groups of analytes, are described in more detail below, along with the laboratory responsible for its analysis. The groupings of individual analytes follow the convention of SWAMP4 where applicable.

2.1. Hg
Hg was analyzed by the Marine Pollution Studies Laboratory at Moss Landing (MPSL). MPSL analyzed both freshwater samples (i.e., discharge samples) and marine samples (i.e., receiving water and reference samples), using the same method but diluting the marine samples before processing.

Consistent with Project MQOs (AMS 2013), QC checks reported by the lab included laboratory blanks, Certified Reference Materials (CRMs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and Laboratory Duplicates.

2.1.1. Sensitivity
MPSL achieved target RL for Hg for all WY2016 analyses. Approximately 5% of samples (9 of 170 samples)5 were reported as Non Detect (ND).

2.1.2. Lab Blanks
All method blanks processed in WY2015 and WY2016 were reported as ND.

2.1.3. Recovery
MPSL assessed recovery based upon analysis of CRM samples. Recovery results were good, with all recoveries meeting the QAPP MQO of 75 to 125%. MPSL also analyzed MS samples with each batch.

Recoveries for these samples also fell within the QAPP MQO of 75 to 125% for each batch.

2.1.4. Precision
Precision was calculated for field sample / lab duplicate and MS/MSD sample pairs where an analyte was detected in both samples. For one sample within batch MPSL-DFG_20160204_W_Hg, the laboratory failed to report an RPD in the EDD; it was therefore calculated manually by QA Officer (QAO) and 4 Available at http://checker.swamp.mpsl.mlml.calstate.edu/QAPRP/QAPrPTableReferenceToC.php. 5 Including field samples and field duplicate samples.

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entered into the data deliverable as part of the review process. In all cases, reported Relative Percent Difference (RPD) for both field and MS sample pairs achieved the Project MQO of <25%.

2.1.5. Field QA Samples
One Field Blank (FB) sample was collected and analyzed blind. The result was reported as ND.

There were no field duplicates analyzed during this period.

2.2. Trace Metal Suite
For the various monitoring events, all or a subset of Trace Metals (TMs) Al, As, Cd, Cr, Cu, Fe, Ni, Pb, Se, Ag, and Zn were analyzed by Physis. Consistent with Project QAPP, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs) or Certified Reference Materials (CRMs), Lab Control Sample / Lab Control Sample Duplicate (LCS/LCSD) pairs, and Laboratory Duplicates.

2.2.1. Sensitivity
Physis achieved QAPP target RLs for all TMs for all analyses conducted. With the exception of Silver (29% NDs), analyses of trace metals generally resulted in detectable concentrations. The relative proportion of field samples and field duplicate samples that were reported at detectable concentrations is summarized in Error! Reference source not found..

Table 1. Proportion of WY2015 and WY2016 Trace Metal Samples Reported as Non Detects Analyte Total
NDs
Total
Analyses % ND
Aluminum 0 23 0%
Arsenic 0 176 0%
Cadmium 0 176 0%
Chromium 2 176 1%
Copper 0 176 0%
Iron 0 23 0%
Lead 7 176 4%
Nickel 0 176 0%
Selenium 1 176 1%
Silver 51 176 29%
Zinc 9 176 5%

2.2.2. Lab Blanks
All lab blanks were reported as ND.

2.2.3. Recovery
Physis analyzed and reported LCS results with each lab batch. Recovery results generally were good for each trace metal analyzed, with the great majority of all reporting recoveries falling within QAPP MQO of 75 to 125%.

There were two exceptions to the above. For lab batch Physis_E-10059_W_TM, the PR reported for analysis of Ag fell outside of QAPP control limits and for lab batch Physis_E-10138_W_TM, the PR ATTACHMENT 1

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reported for Fe did as well. In both cases, the laboratory flagged the data with the qualifier “Q” to indicate that, while results fell outside of QAPP control limits they did meet laboratory acceptance criteria. A qualifier of “VEUM” was added to the LCS results and affected field samples to indicate the disagreement with the QAPP control limits.

2.2.4. Precision
Physis calculated precision for field sample / lab duplicate and LCS/LCSD sample pairs where an analyte was detected in both samples. In general, reported RPDs for field sample / lab duplicate pairs fell within Project MQO of <25%, with the following exceptions:
• Analysis of Ag in lab batch Physis_E-10059_W_TM;
• Analysis of Se in lab batch Physis_E-10107_W_TM;
• Analysis of Zn in lab batch Physis_E-10116_W_TM;
• Analysis of Pb, Se, and Zn in lab batch Physis_E-10138_W_TM.

In each case, the QA samples and affected field samples were flagged with the “VIL” qualifier indicating results fell outside QAPP MQO control limits.

In the case of LCS/LCSD pairs analyzed, reported RPDs all achieved the Project MQO of <25%.

2.2.5. Field QA Samples
Four FD samples were collected and analyzed blind by MPSL in WY2015 and WY2016. Field RPDs ranged from 0% to 105%. Of the 36 sample pairs analyzed, eight (22%) did not achieve the QAPP MQO of an RPD <25%. Those pairs not meeting QAPP MQOs were flagged with a “VFDP” qualifier.

Three FB samples were collected for each analyte and analyzed blind by Physis in WY2015 and WY2016.

There was blank contamination present in results reported for Ni and Zn within batch Physis_E10138_W_TM and Cu and Zn within batch Physis_E-10116_W_TM. Affected field blanks and field samples (i.e., analytes with results <5x the concentration reported for the field blank) were flagged with a “VIP” qualifier.

2.3. Nutrients
Nutrients, including Ammonia as N, Nitrate as N, Orthophosphate as P, and Urea, were analyzed by Monterey Bay Analytical Services (MBAS). Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and Laboratory Duplicates.

2.3.1. Sensitivity
MBAS achieved target MRLs for all analyses of nutrients with the exception of ammonia. Intermittently in WY2015, as in prior years, the MRL reported for ammonia analyses exceeded the target MRL (within lab batches MBAS_20150824_W_NH3 and MBAS_20150720_W_NH3). As in prior years, this change in lab capabilities was not attributable to any issue with the media or analysis, and AMS applied the “VREL” qualifier to those data that had elevated RLs and non-detectable data.

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Consistent with prior years, in WY2015 and WY2016 for all nutrients analyzed, a sizable proportion of field samples analyzed resulted in NDs. The relative proportion of field samples and field duplicate samples that were reported at detectable concentrations is summarized in Table 2.

Table 2. Proportion of WY2015 and WY2016 Nutrient Samples Reported as Non Detects Analyte Total NDs Total Analyses
% ND
Ammonia 108 132 82%
Nitrate as N 19 125 15%
OilandGrease; HEM 152 160 95%
Orthophosphate as P 15 125 12%
Total Suspended Solids 5 166 3%
Urea 67 125 54%

2.3.2. Blanks
For analysis of Ortho-P in lab batch MBAS_20160219_W_PO4P, one of the two lab blanks analyzed showed minor contamination resulting in the lab blank result being reported at the MRL. The blank in question, along with all field samples analyzed that were reported at less than 5 times the blank result, were flagged with the “VIP” qualifier indicating blank contamination. All remaining laboratory blanks, for Ortho-P and all other nutrients analyzed, were reported as ND.

2.3.3. Recovery
MBAS analyzed and reported LCS / CRM results with each lab batch. Recovery results were generally good, with the great majority of all recoveries for all analytes falling within QAPP MQO of 80 to 120%.

The exceptions falling outside of QAPP control limits are as follows:
• Analysis of two Nitrate LCS samples in lab batch MBAS_20160106_W_NO3N fell outside of control limits (135% and 140% recovery, respectively). The affected LCS samples were flagged with the “VEUM” qualifier, but associated field samples were not qualified as the laboratory also ran MS/MSD analyses, which remained in control.
• Analysis of six Nitrate LCS samples associated with lab batches MBAS_20160217_W_NO3, MBAS_20160218_W_NO3, MBAS_20160219_W_NO3, MBAS_20160307_W_NO3, MBAS_20160307_W_NO3, and MBAS_20160309_W_NO3 also fell outside of control limits (ranging from 135 to 140% recoveries for each). The affected LCS samples were flagged with the “VEUM” qualifier, but associated field samples were not qualified as the laboratory also ran MS/MSD analyses, which again remained in control.

MBAS also analyzed MS samples with each batch. Recoveries for each of these analyses fell within QAPP control limits of 80 to 120%.

2.3.4. Precision
MBAS reported precision based upon results of lab duplicate, LCS/LCSD, and/or MS/MSD samples. For all nutrients analyzed, all reported RPDs met the QAPP MQO of <25%.

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2.3.5. Field QA Samples
For the two-year period, a total of five FB samples were collected for each analyte and analyzed blind.

For the analysis of Nitrate in batch MBAS_20160104_W_NO3N, Nitrate in the FB was reported at a concentration above MRL; the FB and all associated field samples that were reported at a concentration less than 5X the FB concentration were flagged with the “VIP” qualifier. All remaining FB results were reported as ND.

Six FD samples were collected and analyzed blind over this period. Field RPDs ranged from 0% to 67%, with approximately 13% of total sample / duplicate pairs failing to meet QAPP MQO of less than 25% RPD; these pairs were flagged with the “VFDP” qualifier. For just under half of the sample pairs collected (45%), no RPD could be calculated as one or both samples were reported below RLs.

2.4. Fecal Indicator Bacteria
Fecal Indicator Bacteria (FIB), including measurement of Fecal Coliform, E. coli, and Enterococcus, were analyzed by MBAS. QC checks required by the Project QAPP include reporting on Laboratory Controls, Laboratory Duplicates (when a sufficient number of duplicates have been analyzed to calculate precision), and laboratory blanks (if samples are diluted).

2.4.1. Sensitivity
MBAS achieved target MRLs for all analyses.

2.4.2. Controls
MBAS indicated that results of all required positive and negative controls were acceptable.

2.4.3. Blanks
The QAPP requires analysis of blank water associated with FIB analyses only when sample dilution is required as part of the analysis. With one exception, the results of all lab blanks run on dilution water were reported below detection and reporting limits, and no additional qualification was required. For one batch that required sample dilution (i.e., analysis of Enterococcus associated with lab batch MBAS_20151204_W_Entero), no lab blank data was reported. Therefore, Enterococcus field sample results within this batch were flagged with the qualifier “VQCA” indicating the results are lacking QA data needed to assess accuracy of results.

2.4.4. Precision
Consistent with QAPP requirements, MBAS assessed precision through calculation of Rlog from a running mean of the prior 15 positive samples generated by the laboratory6 . In each case reported,
precision results achieved the QAPP MQO (Rlog of the sample pair ≤ 3.27 * Rlog of the running mean).

2.4.5. Field QA Samples
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2.5. Solids
Solids-type ASBS parameters, analyzed by MBAS, include the following:
• Total Suspended Solids (TSS)
Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks and lab dups.

2.5.1. Sensitivity
MBAS achieved target MRLs. For TSS, 98% of samples were of a detectable concentration.

2.5.2. Blanks
All laboratory blanks were reported as ND.

2.5.3. Recovery
Not required by QAPP.

2.5.4. Precision
For analysis of TSS, MBAS assessed precision based upon results of lab duplicate analyses. Reported RPDs all met the QAPP MQO of <25%.

2.5.5. Field QA Samples
Collection of field Blanks is not a QAPP requirement.

Over the two-year period, nine FD samples were collected and analyzed blind for TSS. All results were reported above RLs, with five of the nine samples achieving the QAPP MQO of <25% RPD. The field duplicates exceeding 25% RPD were flagged with a “VFDP” qualifier.

2.6. Conventional Parameters
Conventional ASBS parameters, analyzed by MBAS, include the following:
• Oil & Grease (O&G)
Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, lab dups, Laboratory Control Samples (LCSs), and Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs.

2.6.1. Sensitivity
MBAS achieved target MRLs. For O&G, approximately 5% of samples were of a detectable concentration.

2.6.2. Blanks
All laboratory blanks were reported as ND.

2.6.3. Recovery
For analysis of O&G, MBAS reported recoveries associated with analysis of LCS. Recovery results were good, with all recoveries within QAPP MQO of 80 to 120%.

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MBAS also reported MS/MSD information on a subset of lab batches; for each, recoveries fell within MQO control limits of 80 to 120%. For a subset of batches (MBAS_20150910_W_O&G and MBAS_20150929_W_O&G), no MS/MSD data was reported and affected data were flagged with the “VQCA” qualifier, indicating a lack of QA accuracy data as required by the QAPP.

2.6.4. Precision
For analysis of O&G, MBAS reported precision based upon results of field sample / field dup or MS/MSD pairs. Reported RPDs all met the QAPP MQO of <25%.

2.6.5. Field QA Samples
Five field blank samples were submitted blind to the laboratory for analysis of O&G. All results were reported as ND.

Over the two-year period, six FD samples were collected and analyzed blind for O&G. RPDs could not be calculated for five of the six pairs due to one or both results falling below the RL. For the remaining sample pair, the RPD met the QAPP MQO of <25%. No qualification of data was therefore required.

2.7. Organophosphorus Pesticides
Organophosphorus (OP) Pesticides were analyzed by California Department of Fish and Wildlife’s Water Pollution Control Laboratory (WPCL). Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, Laboratory Duplicates, and surrogate recoveries.

2.7.1. Sensitivity
The majority of OP pesticide analyses achieved QAPP target RLs. The exception to this occurred in a subset of WY2016 batches (WPCL_L-096-16_W_OP) for which RLs were elevated due to sample containers received that contained slightly less than the required 1L volume. For those analyses that exceeded RLs and resulted in ND, data were flagged with the “VRE” qualifier, indicating that the higher RLs were associated with delivery of a limited sample volume rather than any change in lab capabilities.

It should be noted that, in general, there were very few field samples that generated results at detectable concentrations. Therefore, estimation techniques used to quantify the results falling below detection limits may greatly influence interpretation.

2.7.2. Blanks
All laboratory blanks were reported as ND.

2.7.3. Recovery
WPCL analyzed and reported LCS results with each lab batch. LCS recovery results associated within a subset of OP analytes measured within batch WPCL_L-166-15_BS052_W_OP fell below control limits, ranging from 0 to 49% recovery compared with control limits of 50 to 150%. The laboratory attributed this to lower molecular weight compounds being lost in the extraction process for the LCS analysis.

Based upon results of the LCSD and field sample surrogate analyses, the laboratory considers the results acceptable for reporting; affected LCS and field samples were flagged with the “EUM” / “VEUM” qualifier depending on whether the laboratory or QAO qualified a specific result.

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WPCL also experienced intermittent trouble achieving QAPP MQOs for recovery of the Chlorpyrifos, methyl LCS. In WY2015, five total batches failed to meet the MQO (WPCL_L-066-15_BS022_W_OP, WPCL_L-066-15_BS023_W_OP, WPCL_L-066-15_BS024_W_OP, WPCL_L-662-14_BS227_W_OP, and WPCL_ L-662-14_BS228_W_OP). Associated data were flagged with the “EUM” / “VEUM” qualifier as described previously.

Recovery results for remaining LCS samples fell within the QAPP MQO of 50 to 150% for percent recovery.

Where sufficient sample volume allowed, WPCL also analyzed recovery of MS samples. Similar to the case for LCS samples, WPCL again experienced issues with recovery of Chlorpyrifos, methyl. In batches WPCL_L-066-15_BS022_W_OP, WPCL_L-066-15_BS023_W_OP, WPCL_L-066-15_BS024_W_OP, and WPCL_L-662-14_BS227_W_OP, MS recoveries fell below QAPP MQOs. Affected MS samples and field samples were flagged either “GB” or “VGB”, respectively, depending on whether the qualification was performed by the lab or QAO.

WPCL also reported surrogate recoveries associated with each lab batch. In general recoveries met QAPP MQO of 50 to 150% recovery with the following exceptions:
• For lab batch WPCL_L-066-15_BS022_W_OP, surrogate recovery for Triphenyl Phosphate (Surrogate) was reported as 157%.
• For lab batch WPCL_L-066-15_BS024_W_OP, surrogate recovery for Triphenyl Phosphate (Surrogate) was reported as 255%.

In each case, affected data were flagged with “GN” qualifier by the laboratory.

2.7.4. Precision
WPCL reported precision based upon results of MS/MSD analyses, where sufficient sample volume allowed for its analysis, and LC/LCSD analyses. Reported RPDs all met the QAPP MQO of <25%.

2.7.5. Field QA Samples
Ten FB samples were collected and analyzed blind. All results were reported as ND.

Seven FD samples were collected and analyzed blind. No field RPD could be calculated for OP samples as one or both results within the field sample / field duplicate pair was reported below RLs in each case.

2.7.6. General
WPCL reported several instances of Continuing Calibration Verification (CCV) exceeding acceptance criteria. These exceedances are flagged with either a “PG” or “CVH” qualifier.7 In most cases, the results for associated field samples all were reported as ND. Per the lab narrative, high CCV results do not adversely affect ND results (i.e., a potential high bias is not reflected in data), and data quality is therefore not considered to be affected. In the cases for 2016 and beyond where this situation occurs, the new CEDEN QA qualifier “CVH” is applied by the laboratory to indicate no bias in the data. Where 7 The CVH qualifier was added to the CEDEN data dictionary between WY2015 and WY2016 with the intent of more easily distinguishing between CCV results that can potentially bias results and those that don’t.

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potential bias does exist (i.e., detectable concentrations), data beginning with 2016 results are qualified with the “PG” or “VPG” qualifier. Data reported WY2015 and prior will need to be reviewed on a caseby-case basis to determine if a potential bias is present.

2.8. Pyrethroid Pesticides
Pyrethroids were analyzed by WPCL. Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, Laboratory Duplicates, and surrogate recoveries.

2.8.1. Sensitivity
The majority of OP pesticide analyses achieved QAPP target RLs. The exception to this occurred in one batch (WPCL_L-096-16_W_PYD) for which RLs were elevated due to sample containers received that contained slightly less than the required 1L volume. For those analyses that exceeded RLs and resulted in ND, data were flagged with the “VRE” qualifier, indicating that the higher RLs were associated with delivery of a limited sample volume rather than any change in lab capabilities.

It should be noted that, in general, there were very few field samples that generated results at detectable concentrations. Therefore, estimation techniques used to quantify the results falling below detection limits may greatly influence interpretation.

2.8.2. Blanks
All laboratory blanks were reported as ND.

2.8.3. Recovery
WPCL analyzed and reported LCS results with each lab batch. Recovery results were generally good, with all results achieving the QAPP MQO of 50 to 150%.

WPCL also analyzed MS samples with each lab batch where sufficient sample volume allowed. Recovery results were generally good, with all results achieving the QAPP MQO of 50 to 150%.

WPCL also reported surrogate recoveries associated with each lab batch. Surrogate recoveries were generally good – over the two-year period a total of five surrogate analyses fell below QAPP control limits (50% to 150% recovery), ranging from 39% to just below 50%. The affected surrogate recoveries were flagged with a “GN” qualifier by WPCL.

2.8.4. Precision
WPCL reported precision based upon results of MS/MSD analyses, where sufficient sample volume allowed for its analysis, and LC/LCSD analyses. All reported RPDs for analysis of MSDs and LCSDs achieved QAPP MQO of <25%.

2.8.5. Field QA Samples
Ten FB samples were collected and analyzed blind. All results were reported as ND.

Seven FD samples were collected and analyzed blind. In the vast majority of cases, no RPD could be calculated as one or both samples within the field sample / field duplicate pair were reported below RLs.

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individual Pyrethroid compounds that would allow calculation of RPD. For this sample, one Pyrethroid compound was quantified in both the field sample and field duplicate, with resultant RPD of 14%, achieving the QAPP MQO of <25% RPD. Therefore, no qualification was required.

2.8.6. General
As discussed previously under the analysis of OP pesticides, WPCL reported several instances of Continuing Calibration Verification (CCV) exceeding acceptance criteria. These exceedances are flagged with either a “PG” or “CVH” qualifier. In most cases, the results for associated field samples all were reported as ND. Per the lab narrative, high CCV results do not adversely affect ND results (i.e., a potential high bias is not reflected in data), and data quality is therefore not considered to be affected.

In the cases for 2016 and beyond where this situation occurs, the new CEDEN QA qualifier “CVH” is applied to indicate no bias in the data. Where potential bias does exist (i.e., detectable concentrations), data beginning with 2016 results are qualified with the “PG” or “VPG” qualifier (e.g., WY2016 analyses of Permethrin, Trans and Cyfluthrin, Total in lab batch WPCL_L-096-16_W_PYD), depending on whether the qualification is done by the lab or QAO. Data reported WY2015 and prior will need to be reviewed on a case-by-case basis to determine if a potential bias is present.

2.9. PAHs
PAHs were analyzed by WPCL. Consistent with Project MQOs, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, Laboratory Duplicates, and surrogate recoveries.

2.9.1. Sensitivity
The majority of PAH analyses achieved QAPP target RLs. The exception to this occurred in a subset of WY2016 batches (WPCL_L-096-16_W_PAH) for which RLs were elevated due to sample containers received that contained slightly less than the required 1L volume. For those analyses that exceeded RLs and resulted in ND, data were flagged with the “VRE” qualifier, indicating that the higher RLs were associated with delivery of a limited sample volume rather than any change in lab capabilities.

It should be noted that, in general, there were very few field samples that generated results at detectable concentrations. Therefore, estimation techniques used to quantify the results falling below detection limits may greatly influence interpretation.

2.9.2. Blanks
All laboratory blanks were reported as ND.

2.9.3. Recovery
WPCL analyzed and reported LCS results with each lab batch. LCS recovery results associated with a subset of PAH analytes measured within WY2015 batch WPCL_L-166-15_BS052_W_PAH fell well below control limits, ranging from 0 to 24% recovery compared with control limits of 50 to 150%. The laboratory attributed this to lower molecular weight compounds being lost in the extraction process for the LCS analysis. Based upon results of the LCSD and field sample surrogate analyses, the laboratory considers the results acceptable for reporting; affected LCS and field samples were flagged with the “EUM” / “VEUM” qualifier depending on whether the laboratory or QAO qualified a specific result.

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Recovery results for remaining batches were generally good, with all LCS recoveries falling within the QAPP MQO of 50 to 150%.

WPCL also analyzed MS samples with each lab batch where sufficient sample volume allowed.

Recoveries generally fell within QAPP MQO of 50 to 150% recovery. The exception to this was associated with analysis of WY2015 lab batch WPCL_L-662-14_BS227_W_PAH, in which MS/MSD recovery results for several PAH compounds were reported outside of control limits on the low end (35% to 41% recoveries). All affected data were flagged with either “GB” or “VGB” qualifier, depending on whether the flag was applied by the lab or QAO.

WPCL also reported surrogate recoveries associated with each lab batch. Surrogate recoveries were generally good; with results of intermittent individual PAH compounds falling outside of QAPP control limits (50 to 150%) in both WY2015 and WY2016. Affected data were flagged with “GN” qualifier by the laboratory. These exceptions were typically identified by WPCL as falling outside of control limits due to matrix effects.

2.9.4. Precision
WPCL reported precision based upon results of MS/MSD analyses, where sufficient sample volume allowed for its analysis, and LC/LCSD analyses. In general, the reported RPDs for LCSDs met the the Project MQO of <25%. The main exception to this is associated with the loss of lower molecular weight compounds within the analysis of the LCS for lab batch WPCL_L-088-14_B1_W_PAH discussed previously. Due to the losses experienced while extracting the LCS, the resultant RPDs all exceeded QAPP MQO of <25%. Affected data were flagged “IL” by the laboratory.

WPCL reported precision based upon results of MS/MSD analyses, where sufficient sample volume allowed for its analysis, and LC/LCSD analyses. All reported RPDs for analysis of MSDs and LCSDs achieved QAPP MQO of <25%.

2.9.5. Field QA Samples
Five FB samples were collected and analyzed blind over the two-year period. Only Naphthalene collected and analyzed in lab batch WPCL_L-659-14_BS226_W_PAH exceeded the RL, and was flagged with VIP. As all field samples associated with this FB were reported as ND, no additional qualification of field data was required.

Seven FD samples were collected and analyzed blind. In the vast majority of cases, no RPD could be calculated as one or both samples within the field sample / field duplicate pair were reported below RLs.

Only sample 205SAN030-DIS-36 collected on February 6, 2016 resulted in measurable concentrations of individual Pyrethroid compounds that would allow calculation of RPDs. For this sample, 13 PAH compounds were quantified in both the field sample and field duplicate, with resultant RPDs ranging from 0 to 21%, achieving the QAPP MQO of <25% RPD. Therefore, no qualification was required.

2.10. Toxicity
All toxicity testing was conducted by UC Davis Marine Pollution Studies Laboratory at Granite Canyon (Granite Canyon). The toxicity of discharge samples was assessed with the purple sea urchin (Strongylocentrotus purpuratus) fertilization test. Receiving water samples were tested with the sea urchin fertilization test, as well as the mussel larval development test with Mytilus galloprovincialis, and ATTACHMENT 1

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the giant kelp germination and growth test with Macrocystis pyrifera. QC checks required by the Project QAPP in place during WY2015 and WY2016 include Laboratory Controls (positive, negative, and brine, where applicable) and checks on water quality parameters and test conditions.

2.10.1. Controls
Tox summary results for three of the four WY2016 lab batches processed by Granite Canyon for the mussel development test did not meet initial test acceptability criteria for control organisms and required re-testing consistent with laboratory protocols.8 For two of these sampling events, associated with lab batches GC_ASBSMG22_W_TOX and GC_ASBSMG20_W_TOX, appropriate data are flagged with a ”VH” qualifier as the re-tests were initiated outside of QAPP hold times (seven to eight days and six days, respectively). For the remaining lab batch that failed initial test acceptability criteria (GC_ASBSMG25_W_TOX), the tests were re-initiated within the 48-hr hold time, and therefore require no additional qualification. In each case that a re-test was required, follow-on test results met acceptability requirements and all toxicity results are considered acceptable for Project usage.

2.10.2. Water Quality Parameters
Required water quality measurements (i.e., Ammonia, Dissolved Oxygen, pH, Salinity, Temperature) were reported associated with each lab batch. All measurements fell within QAPP MQO recommended ranges.

3. Sediment
WY2016 sediment sampling was conducted on August 5, 2015. Monitoring included collection of subtidal soft bottom sediments from the mooring field and below the pier in Stillwater Cove at Pebble Beach.

Results of individual analytes, or groups of analytes, are described in more detail below, along with the laboratory responsible for its analysis. The groupings of individual analytes follow the convention of SWAMP where applicable.

3.1. Organotins
Organotins (Butyltin, Dibutyltin, Tributyltin, and Tetrabutyltin) in sediment were analyzed by ALS Global (ALS). Consistent with Project QAPP, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and surrogate recoveries.

3.1.1. Sensitivity
ALS achieved QAPP target RLs for all analyses.

3.1.2. Lab Blanks
All lab blanks were reported as ND.
8 Problems with mussel gamete quality is an ongoing issue that was first identified by Granite Canyon beginning with the WY2015 storm season, and is unrelated to the ASBS monitoring conducted.

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3.1.3. Recovery
ALS analyzed and reported LCS results with each lab batch. Reported recovery results achieved the QAPP MQO of 50 to 150%.

ALS also reported recoveries associated with analysis of MS samples. Reported recovery results achieved the QAPP MQO of 50 to 150%.

ALS also reported surrogate recoveries associated with organotin analyses. Surrogate recoveries were generally good; with all reported results achieving QAPP MQO control limits (50 to 150%).

3.1.4. Precision
ALS calculated precision for MS/MSD sample pairs where an analyte was detected in both samples. In all cases, reported RPDs for MS/MSD samples met the Project MQO of <25%.

3.1.5. Field QA Samples
No field duplicate samples were collected in either WY2015 or WY2016.

3.2. Mercury
Hg in sediment was analyzed by MPSL. Consistent with Project QAPP, QC checks reported by the lab included laboratory blanks, Certified Reference Materials (CRMs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and Laboratory Duplicates.

3.2.1. Sensitivity
MPSL achieved QAPP target RLs for all analyses.

3.2.2. Lab Blanks
All lab blanks were reported as ND.

3.2.3. Recovery
MPSL analyzed and reported CRM results with each lab batch. Reported recovery results achieved the QAPP MQO of 75 to 125%.

MPSL also reported recoveries associated with analysis of MS samples. Reported recovery results achieved the QAPP MQO of 75 to 125%.

3.2.4. Precision
MPSL calculated precision for field sample / lab duplicate and MS/MSD sample pairs where an analyte was detected in both samples. In both cases, reported RPDs for field sample / lab duplicate pairs and MS/MSD samples fell within Project MQO of <25%.

3.2.5. Field QA Samples
No field duplicate samples were collected in either WY2015 or WY2016.

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3.3. Trace Metal Suite
Trace Metals (TMs) As, Cd, Cr, Cu, Ni, Pb, Se, Ag, and Zn in sediment were analyzed by MPSL. Consistent with Project QAPP, QC checks reported by the lab included laboratory blanks, Laboratory Control Samples (LCSs) or Certified Reference Materials (CRMs), Matrix Spike / Matrix Spike Duplicate (MS/MSD) pairs, and Laboratory Duplicates.

3.3.1. Sensitivity
MPSL achieved QAPP target RLs for all TMs for all analyses conducted.

3.3.2. Lab Blanks
All lab blanks were reported as ND.

3.3.3. Recovery
MPSL analyzed and reported LCS (for Se only) or CRM (for all remaining analytes) results with each lab batch. In each case, reported recovery results achieved the QAPP MQO of 75 to 125%.

MPSL also reported recoveries associated with analysis of MS samples. Analyses of percent recoveries for either the MS or MSD sample for Ag (127%), Cd (133%), and Se (151%) all fell outside of QAPP MQO control limits of 75 to 125%. These samples were flagged with the “GB” qualifier by the lab and affected field samples were flagged with the “VGB” qualifier by the QAO.

3.3.4. Precision
MPSL calculated precision for field sample / lab duplicate and MS/MSD sample pairs where an analyte was detected in both samples. Reported RPDs for field sample / lab duplicate pairs fell within Project MQO of <25%.

The sole MS/MSD sample not achieving the QAPP MQO of <25% RPD was that of Se. Both the QA samples and affected field samples associated with analysis of Se were flagged with the “VIL” qualifier indicating results fell outside QAPP MQO control limits.

3.3.5. Field QA Samples
No field duplicate samples were collected in either WY2015 or WY2016.

3.4. Toxicity
All toxicity testing was conducted by UC Davis Marine Pollution Studies Laboratory at Granite Canyon (Granite Canyon). Sediment samples were tested with the amphipod Eohaustorius estuarius, QC checks required by the Project QAPP in place during WY2015 and WY2016 include Laboratory Controls (positive, negative, and brine, where applicable) and checks on water quality parameters and test conditions.

3.4.1. Controls
Results of all Laboratory Controls were acceptable.

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3.4.2. Water Quality Parameters
Required water quality measurements (i.e., Ammonia, Dissolved Oxygen, pH, Salinity, Temperature) were reported associated with each lab batch. Most reported water quality parameters fell within recommended ranges listed within QAPP, where applicable. The one exception to this is that for lab batch GC_ASBSEE19_S_TOX, the salinity of overlying water was measured up to 3ppt above the recommended criteria (ranging from 23 to 26 ppt), but still well within the tolerance range of the organisms. These datapoints were flagged with the “VTW” qualifier, but did not affect toxicity test result data quality.

4. References
AMS, 2014. Quality Assurance Project Plan: The Central Coast Areas of Special Biological Significance Regional Monitoring Program and Reference Site Monitoring, Version 1.4. Prepared by Applied Marine Sciences, Inc. May 19, 2014.

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Appendix C. Characterization of the rocky intertidal
ecological communities associated with Central California Areas of Special Biological Significance
Pete Raimondi, Maya George, Melissa Redfield, Laura Anderson, Nate Fletcher, Karah Ammann, Rachel Williams, Sara Worden, Rani Gaddam August 29, 2016
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Introduction
The regulatory environment
The California Ocean Plan defines water quality objectives for State waters and is the basis of regulation of discharges to the marine environment. In 1972, there was recognition that certain areas had biological communities with ecological value or that were fragile. These areas were deemed to deserve enhanced protection to preserve and maintain natural (not affected by anthropogenic influences) water quality. These areas were designated Areas of Special Biological Significance (ASBS). As a result, regulations were enacted to prohibit discharges into ASBS as well as to any nearby waters that could affect the natural water quality in ASBS. In 1974, the State Water Board (SWB) designated 33 ASBS. An additional area was designated in 1975; there have been no subsequent designations.

ASBS have been designated to protect marine species or biological communities from an undesirable alteration in natural water quality. Furthermore, ASBS provide intrinsic value or recognized value to man for scientific study, commercial use, recreational use, or esthetic reasons. Consistent with previous versions of the Ocean Plan, the 2009 Ocean Plan states:
“Waste shall not be discharged to areas designated as being of special biological significance.

Discharges shall be located a sufficient distance from such designated areas to assure maintenance of natural water quality conditions in these areas.” This absolute waste discharge prohibition in the Ocean Plan stands unless an “exception” is granted. The requirements for an exception are included in the Ocean Plan. When granting exceptions, the State Water Board must determine that the public interest is served, and that protections of beneficial uses are not compromised. Despite the prohibition against waste discharges to ASBS, in 2003, there were approximately 1,658 outfalls to these marine water quality protected areas (SCCWRP 2003). As a result, the State Water Board has initiated regulatory actions, establishing special protections through the Ocean Plan’s exception process.

The key attribute that underlies the ASBS water quality regulations is the standard of “natural water quality”. The logic of the standard is that natural water quality is attainable using limited spatial regulations (prohibition of discharges in some areas) and essential for certain biological communities. Unfortunately for California ASBS, coastal waters may no longer be pristine, even in areas distant from discharges, making “natural water quality” more difficult to define.

Since a definition of natural water quality did not exist, a committee of scientists, termed the ASBS Natural Water Quality Committee, was formed to provide such a definition for the State Water Board. In 2010 the ASBS Natural Water Quality Committee provided the State Water Board with its findings (Dickson 2010), including an operational definition of natural water quality with the following criteria. These criteria address the two tenets of ASBS protections. 1) It should be possible to define a reference area or areas for each ASBS that currently approximate natural water quality and that are expected to exhibit the likely natural variability that would be found in that ASBS.
2) Any detectable human influence on the water quality must not hinder the ability of marine life to respond to natural cycles and processes. Such criteria will ensure that the beneficial uses identified by the Ocean Plan are protected for future generations.

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This operational definition of natural water quality allows for the assessment of biological impacts related to water quality in ASBS and it provides the basic design elements for the assessment. In particular, the use of reference areas for each ASBS allows for control of natural and temporal variability in biological communities.

The ecological environment
Because most discharges are into intertidal areas (defined as that area between low and high tides), there has been concern that impacts would be primarily manifested in ecological communities in sandy beach and rocky intertidal systems. Ecological communities in sandy beach habitats are extraordinarily dynamic (McLachlan 1993, Defeo 2008) and attribution of change to anthropogenic causes is quite difficult, mainly due to low statistical power. Species associated with rocky intertidal areas are also dynamic, but much less so than those in or on sandy beaches. As a result, attribution of the cause of change is easier for species or communities associated with rocky intertidal habitats (Littler and Murray 1975, Minchinton and Raimondi 2005, Conway-Cranos and Raimondi 2007, Pinedo et al. 2007, Arevalo et al. 2007).

Within rocky intertidal communities, species have a variety of life histories that affect the assessment of potential causes of change. Shorter lived species like Chthamalus, Ulva, and Porphyra often are associated with disturbance, while longer lived species like Balanus, fucoid algae, and mussels tend to be associated with more stable environments. Hence, communities with higher cover of the more ephemeral species are often considered to be indicative of recent or ongoing perturbation. Clearly, perturbations can be due to both natural and anthropogenic causes and hence the design of the sampling program is critical for separating these two general mechanisms of change.

Here we report on a project designed to: (1) characterize the ecological community living on rocky intertidal habitats near discharges inside central California ASBS and at reference areas far from discharges and, (2) use the comparison between ASBS discharge and reference areas as a means to assess the likelihood that differences in ecological community structure could be due to water quality degradation within ASBS. The methods used are consistent with those used in phase I and II assessments for ASBS in southern and northern California (Raimondi et al. 2012, Raimondi 2014, Raimondi et al 2015).

Methods
Comprehensive sampling of ecological communities on rocky intertidal habitats was done using protocols developed by the coastal biodiversity surveys (www.pacificrockyintertidal.org). The general approach is described below.

Site selection: Discharge and Reference – Based on the operational definition of natural water quality described above, along with the regulations prohibiting discharge in ASBS, we selected sites as follows. Sites were selected within ASBS that (1) had sufficient rocky intertidal habitat to be suited for sampling (as described below) and, (2) were located near an active discharge.

Reference areas were selected such that they met criterion 1 (above) and were not near an active discharge. For this analysis we also included sites sampled from our Marine Protected ATTACHMENT 1

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Area (MPA) and regular biodiversity sampling from the central coast to provide a large set of reference sites. This approach provided a large number of reference sites and also allowed us to account for biogeographic effects on the community.

The sampling procedure used was identical to that used by the coastal biodiversity survey (CBS) program housed at UCSC and administered by Peter Raimondi. In order to be cost-efficient, data from sites previously sampled by the CBS program were used in the analyses. New sampling was done to supplement existing data.

Selecting an appropriate location within a site - Within a site, the ideal location to do a CBS is on a bench that 1) is at least 30m wide, 2) gently slopes from the high to low zone and most importantly, 3) contains a representative sample of the intertidal community of the entire site.

If it is not possible to find a contiguous 30m stretch of coastline, the survey can be done on a 20 m bench or split between two adjacent benches. When this is done, the survey should be divided as evenly as possible between the two benches.

Set-Up - Once an appropriate area of shoreline was selected, it was sampled using a series of parallel transect lines extending from the high zone to the low zone. To facilitate the setup of these lines, two permanent 30m horizontal baselines (parallel to the ocean) were first established. The upper baseline was placed in the high zone above the upper limit of the organisms, while the lower baseline was placed in the mid-intertidal zone, parallel to the upper baseline. Depending on the amount of beach traffic or site regulations, the ends of these lines were permanently marked with either hex or carriage bolts.

Once these two baselines were established, parallel transect lines were run down the shore every three meters along the upper base line (or every 2m if a 20m bench was selected). To ensure that transect lines were parallel, samplers made sure to intersect upper and lower base lines at the same meter mark (e.g. transect began at the upper baseline at meter mark 6 and crossed the lower baseline at meter mark 6). In general, the transect lines were allowed to follow the contours of the bench. When necessary, rocks were placed along the lines to prevent them from being shifted by heavy winds. To ensure repeatability of the layout of each parallel transect, the meter mark where each transect crossed the lower baseline was noted.

To facilitate resurveys of the site, a map was drawn showing the location of the bolts relative to notable landmarks or other pre-existing permanent plot markers (from other survey types).

Photographs were also taken that included prominent visual reef characteristics for orientation (e.g. a large crack or tidepool). The distance and bearing between the baseline endbolts were measured. When possible, measurements were also taken between the endbolts and any preexisting permanent plot markers. Other pertinent information, such as the compass heading of the vertical transects, the sampling interval along the transects, weather conditions, site complications, and challenges with taxonomic identification, was also recorded. All such information was used to make the mapping of the site more spatially explicit and repeatability more straightforward.

In addition to the spatial information described above, we recorded descriptive information about the site including bench rock type, relief, slope, extent of habitat, and characteristics of the surrounding coast. This information was used to provide spatial context for the site.

Point-Contact Surveys - Each vertical transect was sampled using the point intercept method.

An average of 100 points were sampled on each transect line. For example, the interval between points would be 20cm for a 20m long transect, and 10cm for a 10m long transect. The ATTACHMENT 1

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basis of this design was to ensure that there was a similar density of sampled points per vertical unit of tidal elevation for all sites. For each point, two types of data were collected: data that were used to determine relative abundance (% cover), and data that were used to describe spatial distributions. The relative abundance data were collected by identifying all taxa that fell directly under each point, including rock, sand, and tar. If there was layering of species, the taxa occupying the different layers were identified and assigned a letter; A for the top layer, B for the second layer, and C for the third. (Note: each layer must be a different taxon). If the point fell on an epibiont living on a host species, the epibiont was noted. Also recorded was whether the species under the point was in a pool, on cobble, or on boulders. A total of up to three taxa were identified per point.

If fewer than three taxa were recorded under a point, then the next one or two species closest to that point were also noted. These ‘nearby’ species had to differ from those found under the point, and had to occur within a circle centered over the point with a radius half the length of the sampling interval.

Mobile Invertebrate Surveys - Although point-contact surveys are good at determining the abundance of spatially common species, particularly sessile species, they do not sample rare or spatially uncommon species very well. Because most mobile species are not spatially common, their abundances were sampled in 50 x 50 cm quadrats placed at three locations along each transect. Each transect was first divided into three zones; the low zone (below the mussel and rockweed zone), the mid-zone (typically dominated by mussels and rockweeds, and the high zone (above mussels/rockweeds; usually dominated by barnacles and littorines). Within each zone a quadrat was randomly placed adjacent to the transect and all mobile species found within the quadrat were identified and counted. Sub-sampling was used when there were more than one hundred individuals of one species in a quadrat. If a quadrat landed in a deep pool or in an area dominated by sand, a new location within the defined zone was randomly selected.

Vouchers - We collected field vouchers for all species that could not be identified in the field.

Voucher samples were labeled with the date, site, name of sampler, and transect line on which it was found.

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Specific hypotheses tested - The general goal of this project was to compare the ecological communities in discharge and reference locations. To do this we developed the following specific (null) hypotheses:
1) Species richness will not vary as a function of site type (Discharge, Reference) 2) Community composition of sessile species will not vary as a function of site type 3) Community composition of mobile species will not vary as a function of site type 4) An assessment of both mobile and sessile species will not identify particular sites as being substantially different from the expectation based on all sites. This is a way to look at specific sites rather than site types.

For hypotheses 1-3 our model looked at the relationship between type of site (near to a discharge in an ASBS vs a reference site) and the response variable, species richness or community composition. Point contact (mainly sessile or sedentary organisms) and quadrat data (mobile organisms) were evaluated using a PERMANOVA approach to compare communities between discharge and reference sites. Species richness was assessed using ANOVA. For hypotheses 1-3 we set the critical p-value at 0.05 (null hypothesis not rejected unless p<0.05). Prior to either type of assessment, we clustered sites so as to identify biogeographical regions. All comparisons (discharge vs reference) were done separately for each region having discharge sites.

The results for hypotheses 1-3 could indicate no systemic difference between discharge and reference sites for community composition or species richness. We were concerned that particular discharge sites might be compromised and that these single site impacts would be masked in a general analysis. Hence, we also determined whether particular sites were outliers relative to other sites, which could indicate the effects of compromised water quality (hypothesis 4). To do this, we generated site similarity matrices (using Bray Curtis values) then calculated quasi Mahalanobis distances using values from the two matrices. These distances are the distance from a multivariate centroid (for each biogeographic region) accounting for the covariance structure among variables. Small values indicate that a given site is similar to a hypothetical typical site, while large distances indicate sites very different from the hypothetical typical site. Prediction limits were used to assess the likelihood of inclusion of samples. For example, an 80% prediction limit would contain 80% of samples drawn from a pool of samples coming from the same population. This differs from confidence limits, which are used to assess the inclusion likelihood of means of samples from a population.

Results
Sites sampled and site attributes - Sampling locations are shown in Figure 1 (a-c). Description of site metadata and site characteristics are in tables 1 and 2 respectively. Also see Appendix 1 for detailed site descriptions.

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Figure 1a: Map of sampling locations. Color of symbol represents site type: Red = Discharge site in ASBS, Blue = reference site. Note that on this map the symbol for Alder Creek is red but hidden beneath Bolinas Pont. Yellow boxes are inset in figures 1b and 1c.

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Figure 1b: Map (inset in figure 1a) of sampling locations. Color of symbol represents site type: Red = Discharge site in ASBS, Blue = reference site.

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Figure 1c: Map (inset in figure 1a) of sampling locations. Color of symbol represents site type: Red = Discharge site in ASBS, Blue = reference site.

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Table 1: Metadata for site attributes.
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Table 2: Site characteristics. See table 1 for attribute descriptions. P indicates presence. Sites shown in table 2 represent the sampling funded by this study. Many other sites were also included in the evaluation of the potential discharge impacts (figure 1). Attributes for these sites can be found at www.pacificrockyintertidal.org
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Biogeographic patterns – sessile species from the point contact surveys Results of the cluster analysis are shown in figure 2.

Figure 2: Biogeography of the sessile community from the point contact surveys. Seven clusters were defined. All discharge sites are indicated with red arrows and occur in two clusters: f and g.
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Biogeographic patterns – mobile species from the quadrat surveys Results of the cluster analysis are shown in figure 3.

Figure 3: Biogeography of the mobile community from the quadrat surveys. Three clusters were defined. All Discharge sites are indicated with red arrows and occur in two clusters: b and c.
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Species Richness Analysis
For sessile species, there was no effect on species richness that was associated with Site Type for either biogeographic region containing discharge sites (figure 2, clusters f and g), indicating no difference between discharge and reference sites (Table 3 and Figure 4).

Table 3: ANOVA results for species richness: sessile species.

For mobile species, there was no effect on species richness that was associated with Site Type for either biogeographic region containing discharge sites (figure 3, clusters b and c), indicating no difference between discharge and reference sites (Table 4 and Figure 5).

Table 4: ANOVA results for species richness: mobile species !"#$%F'()
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Figure 4: Species richness as a function of Site Type for sessile species. Note the two clusters (f and g).

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Figure 5: Species richness as a function of Site Type for mobile species. Note the two clusters (b and c).

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Community composition of sessile species
There was no significant effect of Site Type indicating no difference between ASBS discharge and reference sites (Table 5). The results are shown below in the PERMANOVA tables for both clusters containing discharge sites.

Table 5: PERMANOVA table for effect of site type on the community composition of sessile species.

Community composition of mobile species
There was no significant effect of Site Type indicating no difference between ASBS discharge and reference sites (Table 6). The results are shown below in the PERMANOVA tables for both clusters containing discharge sites.

Table 6: PERMANOVA table for effect of site type on the community composition of mobile species.
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Outlier assessment of both mobile and sessile species
The results above indicate that there was no systemic difference between discharge and reference sites for community composition or species richness for either sessile or mobile species. The results of the outlier analyses indicate that the general pattern of no discharge effects is also true for specific sites (figure 6 for sessile species and figure 7 for mobile species).

Two prediction limits are shown: 80 and 95%. Values beyond these limits indicate communities at specific sites that differ from expected (at the 80 or 95% level).

Figure 6: Outlier distances (Z distance from multidimensional centroid) for sessile species for all sample sites. 80 and 95% prediction limits are also shown. Note both clusters are shown (f and g).

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Figure 7: Outlier distances (Z distance from multidimensional centroid) for mobile species for all sample sites. 80 and 95% prediction limits are also shown. Note both clusters are shown (b and c).

The results for sessile species suggest that Fitzgerald Marine Reserve (a discharge site) and Pigeon Point (a reference site) had significantly different communities than expected based on their biogeographic clusters. Figure 8 shows the pattern of difference in species cover between Fitzgerald and the average value for each species. This pattern is consistent with low lying, flat reefs that are exposed to sand.

The results for the mobile species suggest that Alcatraz and Point Pinos (both reference sites) had significantly different communities than expected based on their biogeographic clusters.

Raimondi et al., Rocky Intertidal ASBS Assessment May 12, 2016 Page | 19
Figure 7: Outlier distances (Z distance from multidimensional centroid) for mobile species for all sample sites. 80 and 95% prediction limits are also shown. Note both clusters are shown (b and c).

The results for sessile species suggest that Fitzgerald Marine Reserve (a discharge site) and Pigeon Point (a reference site) had significantly different communities than expected based on their biogeographic clusters. Figure 8 shows the pattern of difference in species cover between Fitzgerald and the average value for each species. This pattern is consistent with low lying, flat reefs that are exposed to sand.

The results for the mobile species suggest that Alcatraz and Point Pinos (both reference sites) had significantly different communities than expected based on their biogeographic clusters.

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Figure 8: Deviation from expected cover at Fitzgerald Marine Reserve (FMR). Values shown are the differences in cover between FMR and the average value for the species. The arrow shows the transition change from species being more common at FMR to being less common. !"# !$ # $ "#
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Discussion
There are many natural local (site scale) drivers of community structure including rock type, bedding orientation, sand influence, orientation of the rock surface to the prevailing swell direction, local swell height and period, and upwelling. There are also many local humaninduced drivers of community structure that are not related to discharges. These include collecting, trampling, and non-point source pollution. The integration of these factors is the background driver of community structure against which the effect of discharge is measured.

In this study, we used a sampling program designed to minimize this integrated driver. We found that there was no general difference in species richness or biological communities at discharge versus reference sites.

In this study we examined whether actual species composition differed from the expected species composition, and if such deviation was associated with whether the site was near or far from a discharge. The general questions are whether the biological community is affected by discharge of water and associated components, and if so, in what way? If a difference is found, then specific expectations need to be evaluated. Here, the specific expectations consistent with an impact due to compromised water quality are (Arevelo et al. 2007, Pineda et al. 2007):
1) Generally decreased abundance of species at discharge sites compared to reference sites. This was not the case for any sampled discharge site. 2) Communities at discharge sites are characterized by disturbance-associated species.

There is not strong support for this general prediction. a. There is support for the idea that specific sites are outlier sites. With respect to discharge sites, one site, Fitzgerald Marine Reserve (FMR) was quite different from expected (figures 6 and 8). There are three general hypotheses for differences seen at FMR: (1) Geomorphology at FMR produces a wide and flat reef that differs from many reefs in the region, (2) Human visitation which likely leads to trampling, (3) Stormwater discharge. All three could independently or jointly yield the type of community seen at FMR, which has lower than expected cover of longer-lived sessile species. Resolving this may require a follow-up study based on a spatial gradient design (as we proposed for sites in southern CA ASBS with substantial community deviations). In such a design transects are arranged in a gradient away from putative perturbations. Geomorphology does not vary enough spatially at FMR to create a meaningful gradient. However, distance from a parking lot and distance from discharge are meaning spatial gradients that could be taken advantage of. In particular distance from a parking lot is a proxy for trampling and unlikely to be directional, while distance from the discharge is a proxy for discharge impacts and likely to be directional due to prevailing nearshore currents.

In summary, this project provides the first comprehensive condition report for the rocky intertidal zone in central California Areas of Special Biological Significance and serves as a good basis and trigger for focused additional work. The use of standardized sampling consistent with the primary intertidal monitoring program along the West Coast (PISCO/MARINe see www.pacificrockyintertidal.org) allows for the results of earlier sampling to be incorporated ATTACHMENT 1

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into the study (because the monitoring uses identical methodologies) and gives context for the ASBS sampling. This approach was used previously in the southern California ASBS assessments, which was a two phase approach (Raimondi et al. 2012, Raimondi 2014) and in the more recent northern California assessment (Raimondi et al. 2015). Phase 1 was identical to the current central California assessment. In southern California, there was a second phase where all sites were evaluated a second time. The goal of the second assessment was to determine how temporal variation in community dynamics might affect the results of the assessment of discharge impacts. In that study, we found that results from repeated surveys were generally in agreement, and as a result, there was a regulatory conclusion that the evaluated discharges did not hinder the ability of marine life to respond to natural cycles and processes and were therefore not inconsistent with ASBS goals.

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Conclusions and recommendations
1) Conclusions
a. Based on the results of these analyses, there is no support for the idea that discharges in the North Coast generate region-wide impacts to diversity or community composition in the nearby rocky intertidal sites. This does not mean that there are no impacts to species in the communities. Other attributes such as individual growth and reproduction could be affected with no subsequent impact to diversity or composition.
b. Some sites stood out as differing substantially from what was expected for biological communities in the region. In particular, Fitzgerald Marine Reserve was an outlier with respect to sessile species composition. It is likely that this difference in community structure is the result of the geomorphology at the site. The intertidal zone at Fitzgerald Marine Reserve is a very wide and flat bench surrounded by sand and subject to considerable scour. In addition, the reef tends to hold water because it is flat and the key mid intertidal species, Chthamalus spp, Mytilus californianus and Mastocarpus spp, which are species that dominate on hard rock surfaces with extended period of emersion are all uncommon at this site (see figure 8).
2) Recommendations
a. The protocols used in this ASBS assessment are identical to those used in Southern California. Because the analytical approach used for these assessments can incorporate geographic effects on community composition (e.g. biogeography), it would be possible to conduct a comprehensive statewide meta-assessment that could be much more powerful (able to detect impacts) than regionally-based assessments.
b. With respect to the Central Coast Assessment, one discharge site was an outlier:

Fitzgerald Marine Reserve. A second year of sampling was used in southern California to confirm impacted sites and increase confidence in statements about deviations in natural water quality for other sites. It is likely that the results presented here reflect the geomorphology and or the effects of trampling by visitors at the site, however there should be considered for a phase two assessment modeled after the southern California ASBS approach.

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References
Arevalo R., S. Pinedo, E. Ballesteros. 2007. Changes in the composition and structure of Mediterranean rocky-shore communities following a gradient of nutrient enrichment:

Descriptive study and test of proposed methods to assess water quality regarding macroalgae. Marine Pollution Bulletin, Volume 55, Issues 1-6. Pages 104-113 Conway-Cranos and Raimondi. 2007. Spatial and temporal variation in recruitment to rocky shores: Relationship to recovery rates of intertidal communities. Coastal Research Center, Marine Science Institute, University of California, Santa Barbara, California. MMS Cooperative Agreement Number 14-35-0001-30758
Defeo O., A. McLachlan, D. Schoeman, T. Schlacher, J. Dugan, A. Jones, M. Lastra, F. Scapin. 2009. Threats to sandy beach ecosystems: A review. Estuarine, Coastal and Shelf Science, Volume 81, Issue 1, Pages 1-12
Dickson A., R. Gossett, D. Gregorio, B. Jones, S. Murray, B. Posthumus and K. Schiff. 2010 Summation of Findings Natural Water Quality Committee 2006-2009. Technical Report 625.

Southern California Coastal Water Research Project, Costa Mesa, CA Littler, M. and S. Murray. 1975. Impact of sewage on the distribution, abundance and community structure of rocky intertidal macro-organisms. Marine Biology, Volume 30, Number 4, 277-291.

McLachlan, A. 1993. Sandy beach macrofauna communities and their control by the physical environment: a geographical comparison. Journal of Coastal Research 15: 27-38 Minchinton T. and P. Raimondi. 2007. Effect of Temporal and Spatial Separation of Samples on Estimation of Impacts. MMS OCS Study 2005-002. Coastal Research Center, Marine Science Institute, University of California, Santa Barbara, California. MMS Cooperative Agreement number 14-35-0001-30758. 85 pages
Raimondi P., K. Schiff, D. Gregorio. 2012. Characterization of the rocky intertidal ecological communities associated with southern California Areas of Special Biological Significance.

SCCWRP Technical Report. 80 pages
Raimondi, P. T. 2014. Characterization of the Rocky Intertidal Ecological Communities Associated with Southern California Areas of Special Biological Significance: Phase II Raimondi, P. T., M. George, M. Redfield, S. Worden, R. Williams, N. Fletcher, L. Anderson, D.

Lohse, R. Gaddam. 2015. Characterization of the Rocky Intertidal Ecological Communities Associated with Northern Southern California Areas of Special Biological Significance. 33 pages
Pinedo S., M. Garcia, M. Paola Satta, M. de Torres, E. Ballesteros. 2007. Rocky-shore communities as indicators of water quality: A case study in the Northwestern Mediterranean. Marine Pollution Bulletin, Volume 55, Issues 1-6. Pages 126-135 Southern California Coastal Water Research Project . 2005. California Ocean Plan. Sacramento, CA
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Appendix 1: Site locations,
descriptions, pictures and site
specific cover and density of
species
Sites shown in Appendix 1 represent the sampling funded in this survey. Many other sites were also included in the evaluation of the potential discharge impacts (figure 1). Attributes for these sites can be found at www.pacificrockyintertidal.org.

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Bolinas Point
Bolinas Point is located in the North Central Coast region of California, within the Gulf of the Farallones National Marine Sanctuary and Point Reyes National Seashore. This site is located in an Area of Special Biological Significance (Duxbury Reef Reserve and Extension ASBS), within the Duxbury Reef State Marine Conservation Area established by the State of California, and is

1.4 mi northwest of the Duxbury Reef Point Mussel Watch site.

The site is northwest of the town of Bolinas. This gently sloping site consists of relatively flat terrain. The survey plots are located on the outermost intertidal bench and consist primarily of sedimentary rock outcrops with folded layers oriented in a NW-SE direction. This site receives low visitation by tidepoolers.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 150 meters (seaward).

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Alder Creek; Duxbury
Alder Creek; Duxbury is located in the North Central Coast region of California, within the Gulf of the Farallones National Marine Sanctuary.

This site is located within the Duxbury Reef State Marine Conservation Area and Agate Beach County Park. This site is also located in an Area of Special Biological Significance (Duxbury Reef Point ASBS). This gently sloping site consists of relatively flat terrain. Alder Creek;Duxbury is dominated by sand and the area surrounding the site is comprised of sandy beach. The primary coastal orientation of this site is southwest.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 80 meters (seaward).

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Fitzgerald Marine Reserve
Fitzgerald Marine Reserve is located in the North Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within the Montara State Marine Reserve and San Mateo County Park. This site is also located in an Area of Special Biological Significance (James V. Fitzgerald Marine Reserve ASBS). This gently sloping site consists of relatively flat terrain. This is a site that receives the greatest number of tide-poolers in all of central California.

Fitzgerald Marine Reserve is dominated by a mixture of consolidated bedrock and sandy beach and the area surrounding the site is sandy beach. The primary coastal orientation of this site is west/southwest.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 40 meters (seaward).

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Pigeon Point
Pigeon Point is located in the Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within Pigeon Point Lightstation State Historic Park. This site receives high visitation by tidepoolers and is often visited by school groups. This gently sloping site consists of extremely uneven terrain, containing many deep cracks and folds.

Pigeon Point is dominated by a mixture of consolidated conglomerate rock and boulder fields, and the area surrounding the site is comprised of a mixture of consolidated conglomerate rock, boulder fields, and sandy beach. The primary coastal orientation of this site is south/southwest.

The Biodiversity Survey grid encompasses one section that is approximately 33 meters (along shore) x 33 meters (seaward).

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Año Nuevo
Año Nuevo is located in the Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within the Año Nuevo State Marine Reserve and Año Nuevo State Park. This site is also located in an Area of Special Biological Significance (Año Nuevo Point and Islands ASBS), and is near the Año Nuevo Island Mussel Watch site. This gently sloping site consists of moderately uneven terrain, containing few cracks and folds and is essentially not visited by tide-poolers.

Año Nuevo is dominated by a mixture of consolidated sedimentary rock and sandy beach, and the area surrounding the site is comprised of a mixture of consolidated bedrock and sandy beach. The primary coastal orientation of this site is west.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 40 meters (seaward).

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Hopkins
Hopkins is located in the Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within the Lovers Point State Marine Reserve. This site is also located in an Area of Special Biological Significance (Pacific Grove Marine Gardens Fish Refuge and Hopkins Marine Life Refuge ASBS), and is 0.6 miles southeast of the Pacific Grove/Lovers Point Mussel Watch site. This site is part of Hopkins Marine Reserve. There is an abundance of historical and ongoing research at this site, and this site has moderate visitation by researchers. This moderately sloping site consists of extremely uneven terrain, containing many deep cracks and folds.

Hopkins is dominated by a mixture of consolidated granite and boulder fields, and the area surrounding the site is comprised of a mixture of consolidated granite, boulder fields, and cobble beach. The primary coastal orientation of this site is north/northwest.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 20 meters (seaward).

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Stillwater
Stillwater is located in the Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within the Carmel Bay State Marine Conservation Area.

This site is also located in an Area of Special Biological Significance (Carmel Bay ASBS), and is near the Carmel Bay/Arrowhead Point Mussel Watch site. This site is accessed through the 17 Mile Drive toll road. This site is directly below Pebble Beach Golf Course and receives low visitation by tidepoolers. This gently sloping site consists of moderately uneven terrain, containing few cracks and folds.

Stillwater is dominated by consolidated bedrock, and the area surrounding the site is comprised of a mixture of consolidated bedrock, boulder fields, and sandy beach. The primary coastal orientation of this site is south/southwest.

The Biodiversity Survey grid encompasses one section that is approximately 30 meters (along shore) x 10 meters (seaward).

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Point Lobos
Point Lobos is located in the Central Coast region of California, within the Monterey Bay National Marine Sanctuary. This site is located within the Point Lobos State Marine Reserve and Point Lobos State Park. This site is also located in an Area of Special Biological Significance (Point Lobos Ecological Reserve ASBS), and is 0.3 mi northwest of the Point Lobos/Weston Beach Mussel Watch site. This site receives high visitation by tidepoolers and is often visited by school groups. This gently sloping site consists of extremely uneven terrain, containing many deep cracks and folds.

Point Lobos is dominated by consolidated conglomerate rock and sandstone, and the area surrounding the site is comprised of a mixture of consolidated conglomerate rock and sandstone, boulder fields, and cobble beach. The primary coastal orientation of this site is southwest.

The Biodiversity Survey grid encompasses two sections that are approximately 8.4 meters (along shore) x 20 meters (seaward), and 12 meters (along shore) x 20 meters (seaward).

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2024 MRSWMP
Dry Run & First Flush
Monitoring Report
September 18, 2025
Prepared by:

Katie Siegler, Urban Water Quality Program Coordinator
California Marine Sanctuary Foundation l Monterey Bay National Marine Sanctuary Funded by: Monterey Stormwater Education Alliance
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2 Table of Contents
Methods 1
Results 11
Nutrients 11
Metals 16
Clarity 19
Fecal Indicator Bacteria 22
Liquid Waste IdenƟfiers 24
Results by JurisdicƟon 27
Conclusion 43
Appendix 1: MRSWMP Monitoring Sites 45
Appendix 2: Results by Analyte (listed alphabeƟcally) 47 Appendix 3: Results by Jurisdiction (listed alphabetically) 62 ATTACHMENT 2

3 Introduction
The Monterey Stormwater EducaƟon Alliance (Monterey SEA) water quality monitoring program is modeled aŌer Monterey Bay NaƟonal Marine Sanctuary’s (MBNMS) Dry Run-First Flush monitoring program. MBNMS’ Dry Run and First Flush volunteers collect water samples from storm drain ouƞalls prior to the first major rainstorm of the year (Dry Run) and during the first major rainstorm of the winter season (First Flush). Samples are analyzed for common urban pollutants such as surfactants, bacteria, metals, sediment, and nutrients.

Samples collected during dry weather prior to the first major rainstorm provide informaƟon about dry weather flows such as pollutant concentraƟons, amount of groundwater base flow, or contribuƟons from urban sources such as car washing, pressure washing, irrigaƟon, or illicit discharges. Dry weather flows are an important component of water quality monitoring since contaminants are more concentrated which causes them to be more easily detected and traced to their source. While water samples collected during the first major rainstorm of the winter season provide informaƟon on the concentraƟon of contaminants in stormwater aŌer months of dry weather accumulaƟon of pollutants on land in urban areas. All runoff from the Monterey region eventually flows into MBNMS except in areas where diversions have been installed that capture some dry and wet weather storm drain system flows and divert it to the sanitary sewer.

The ciƟes of Pacific Grove, Salinas, and Carmel-by-the-Sea have constructed diversions which facilitate reuse of runoff while minimizing contaminants flowing to MBNMS. It is hoped that this data collected for Monterey Regional Stormwater Management Program’s (MRSWMP) water quality monitoring program provides local ciƟes with informaƟon on where to implement best management pracƟces focused on improving water quality.

The MRSWMP water quality monitoring program promotes volunteer parƟcipaƟon, stewardship, and environmental educaƟon while providing important data regarding the quality of water flowing into MBNMS. This monitoring program is designed to meet E.8.ii requirements under the Phase II Stormwater Permit, saƟsfying public involvement and parƟcipaƟon elements of the permit. This monitoring program does not fulfill the E.13 requirements pertaining to ASBS Monitoring, TMDL Monitoring, 303(d) Monitoring, or Receiving Water Monitoring which are completed by permiƩees individually or through regional programs.

Sites selected for MRSWMP monitoring include storm drains where the highest discharges of runoff from urbanized areas and city streets are anƟcipated during storm events (Figure 1, 2A, 2B, 2C). Some sites flow directly into MBNMS while others flow through diversions into sanitary sewer systems. Two inland sites are also included to capture those sources.

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4 Figure 1. Sites monitored during the MRSWMP 2024 First Flush.

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5 2a. Pacific Grove, Monterey and Seaside
2b. Salinas
2c. Carmel
Figures 2a, 2b, 2c. Sites monitored by region during the MRSWMP 2024 First Flush ATTACHMENT 2

6 The First Flush event has typically occurred between September and December, with the majority of years falling in October and November.

Figure 3. First Flush sampling dates from 2006-2024
Methods
The same protocols and laboratory analyses are used for both Dry Run and First Flush.

Volunteers aƩend an online or in-person training that covers monitoring goals and protocols.

Dry Run is an opportunity to parƟcipate in hands-on training, and volunteers spend a half-day demonstraƟng their skills, visiƟng ouƞall sites, and collecƟng water samples where possible. In 2024, Dry Run was conducted on 9/28 and 10/2.

During Dry Run, field measurements (water temperature, pH, electrical conducƟvity, and transparency) are taken in-situ and water samples are collected for lab analysis of nutrients (nitrate, orthophosphate, ammonia, and urea), bacteria (Eschericia coli and enterococcus), metals (copper, lead, and zinc), total suspended solids, color, Methylene Blue AcƟve Substances (MBAS) surfactants, hardness (as CaCO3), potassium, and turbidity.

Once Dry Run has been completed, the wait for the first major rainstorm begins. WaiƟng for a storm big enough to conduct First Flush sampling can take weeks or someƟmes months.

Volunteers are mobilized for the First Flush when at least 0.10 inches of rain has fallen, there is sheeƟng water on roadways, and storm water conducƟvity is at or below 1000 µS. During First 10-Sep
20-Sep
30-Sep
10-Oct
20-Oct
30-Oct
9-Nov
19-Nov
29-Nov
9-Dec
19-Dec
29-Dec
2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 First Flush Sample Dates (2006-2024)
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7 Flush, volunteers take the same field measurements as during Dry Run (water temperature, pH, electrical conducƟvity, and transparency) and collect the same water samples for lab analysis of nutrients (nitrate, orthophosphate, ammonia, and urea), bacteria (Eschericia coli and Enterococcus), metals (copper, lead, and zinc), total suspended solids, color, Methylene Blue AcƟve Substances (MBAS) surfactants, hardness (as CaCO3), potassium, and turbidity. Samples and field equipment are delivered immediately to the monitoring coordinator once sample collecƟon is complete. Once all the samples are received, the monitoring coordinator delivers the samples to a cerƟfied lab.

Dry Run monitoring entails collecƟng a single grab sample from each site with flowing water.

During First Flush, two sets of grab samples are collected 30 minutes apart for two Ɵme series samples. In this report, First Flush results reported by analyte are averaged between the two Ɵme series, and First Flush results reported by jurisdicƟon have individual Ɵme series results listed.

The CiƟes of Carmel-by-the-Sea and Pacific Grove operate storm drain diversions for dry and some wet weather flows to the storm drain system. Two sites in Carmel-by-the-Sea, 4th Avenue and Ocean Avenue, are within the city’s diversion system boundaries, where dry weather storm drain flows are retained and diverted into infiltraƟon trenches. The City of Carmel’s diversion is never on during First Flush. The 4th Avenue (Carmel) site is monitored above the connecƟon point with the diversion system for Dry Run and First Flush due to access issues at the ouƞall. Ocean Avenue is sampled at the ouƞall for both Dry Run and First Flush events as ouƞall access is available year-round.

The City of Pacific Grove’s diversion collects dry and wet weather storm drain flows and pumps them into the sanitary sewer. Sites monitored in 2024 within the City of Pacific Grove’s diversion system boundaries were: 8th Street, Greenwood Park, and Lover’s Point. During dry weather, 8th Street and Lover’s Point are sampled at the ouƞall, which may or may not have flow as any flow is diverted unless the system is blocked by debris and some water bypasses the diversion.

Greenwood Park is always monitored above the diversion system in both dry and wet weather due to safety and access issues at the ouƞall. If the City of Pacific Grove’s diversion remains on during First Flush, as it was this year, sites are sƟll monitored as follows: Greenwood Park and Lover’s Point are monitored above the diversion system; 8th Street is monitored at the ouƞall if flow is present, as it was this year. Water quality data from sites monitored above any dry or wet weather diversions are sƟll valuable as contaminants idenƟfied in the runoff may not flow to the ocean.

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8 Most results (lab and field) in this study are compared to receiving water standards established for beneficial uses in a stream, lake, or the ocean (Table 1). These receiving water quality standards are not meant for end-of-pipe monitoring, such as for this MRSWMP water quality monitoring program, except for the analytes that are required by the MS4 General Permit.

However, lacking standards for most end-of-pipe monitoring, receiving water standards are used for comparison. MBAS surfactants and metal results are compared to the Water Quality Control Plan for the Central Coast Basin (Basin Plan) Water Quality ObjecƟves (WQO) set by the Regional Water Quality Control Board (RWQCB) for the protecƟon of marine or aquaƟc life.

Since there are no numerical water quality objecƟves in the Basin Plan for E. coli, Enterococcus, nitrate, orthophosphate, and total suspended solids (TSS), those results are compared with the U.S. Environmental ProtecƟon Agency (U.S. EPA) WQOs or Central Coast Ambient Monitoring Program’s (CCAMP) AcƟon Levels. The U.S. EPA objecƟves are for the protecƟon of human health while CCAMP’s AcƟon Levels are benchmarks that are set for receiving water concentraƟons at which pollutants may impact cold-water fish. AcƟon Levels typically represent exisƟng regulatory standards; levels derived from the literature or other agency references; or from data that shows levels are elevated relaƟve to the data distribuƟon for that parameter on the Central Coast.

Both RWQCB Basin Plan Water Quality ObjecƟves and CCAMP AcƟon Levels are established for receiving waters and not for end-of-pipe discharges, such as is collected for the MRSWMP monitoring. There are no end-of-pipe objecƟves for most of the monitored analytes of the MRSWMP monitoring program, however, the State Water Resources Control Board’s (SWRCB) NaƟonal PolluƟon Discharge and EliminaƟon System (NPDES) MS4 General Permit does provide end-of-pipe water quality AcƟon Levels for: ammonia, color, hardness, potassium, and turbidity.

For turbidity, the SWRCB NPDES MS4 AcƟon Levels have been supplanted by CCAMP AcƟon Levels that are more protecƟve of water quality.

Grab sample results are reported as concentraƟon, consistent with how the water quality objecƟves are defined. However, this does not give an indicaƟon of the load of pollutants being discharged. To facilitate calculaƟon of instantaneous load, instantaneous flow was measured by filling a container of known volume (a bucket), Ɵming how fast the container filled, and esƟmaƟng how much of the flow was captured while filling the container. We provide this informaƟon to give an indicaƟon of the amount of flow at the Ɵme of sampling.

ATTACHMENT 2

9 Table 1: Receiving Water Quality ObjecƟves
Parameter (reporƟng units) Water Quality ObjecƟves Source of ObjecƟve Ammonia (mg/L) Not to exceed 50 SWRCB NPDES MS4 General Permit Color (color units) Not to exceed 500 SWRCB NPDES MS4 General Permit Copper (µg/L) Not to exceed 301 Water Quality Control Plan for the Central Coast- RWQCB E. coli (MPN/100mL) Not to exceed 2352 NaƟonal Recommended Water Quality Criteria Enterococcus (MPN/100mL) Not to exceed 104 NaƟonal Recommended Water Quality Criteria Hardness as CaCO3 (mg/L) Not ≤ 10 or ≥ 2,000 SWRCB NPDES MS4 General Permit Lead (µg/L) Not to exceed 301 Water Quality Control Plan for the Central Coast- RWQCB MBAS Surfactants (mg/L) Not to exceed 0.2 Water Quality Control Plan for the Central Coast- RWQCB Nitrate as N (mg/L) Not to exceed 2.253 Central Coast Ambient Monitoring Program (CCAMP) Orthophosphate as P (mg/L) Not to exceed 0.124 Central Coast Ambient Monitoring Program (CCAMP) pH (pH units) Not < 6.5 or > 8.5 Water Quality Control Plan for the Central Coast- RWQCB Potassium (mg/L) Not to exceed 20 SWRCB NPDES MS4 General Permit Total Suspended Solids (TSS)
(mg/L) Not to exceed 5005
Central Coast Ambient Monitoring Program (CCAMP)
Transparency (cm) Not less than 25 6 Central Coast Ambient Monitoring Program (CCAMP) Turbidity (NTU) Not to exceed 25 Central Coast Ambient Monitoring Program (CCAMP) Zinc (µg/L) Not to exceed 200 1 Water Quality Control Plan for the Central Coast- RWQCB Note: Urea is not listed because it does not have a Water Quality ObjecƟve or AcƟon Level. 1 Water Quality Control Plan for Central Coast Cold Water ObjecƟve for Hard Water. 2 Environmental ProtecƟon Agency, Updated WQO.
3 Central Coast Ambient Monitoring Program, Pajaro River Watershed CharacterizaƟon Report 1998, rev 2003. 4 Williamson, The Establishment of Nutrient ObjecƟves, Sources, Impacts and Best Management PracƟces for the Pajaro River and Llagas Creek, 1994.
5 Central Coast Ambient Monitoring Program, Salinas River Watershed CharacterizaƟon Report 1999, rev. 2000. 6 Based upon equivalent guideline value used for 303(d) LisƟng Guideline Value (Sigler et al., 1985).

ATTACHMENT 2

10 During the 2024/25 MRSWMP permit year:
● Dry Run sampling was conducted on 9/28 and 10/2. Six of the 14 sites had enough flowing water to be sampled for the Dry Run in 2024. Employees from CMSF and ten volunteers assisted with sample collecƟon.
● One First Flush event was conducted on 11/11/24. Eleven sites were visited with the help of 22 volunteers who donated over 100 hours total to collect and deliver storm drain samples. Volunteers were mobilized in the aŌernoon. ● The final First Flush event occurred for one site in Monterey (Twins) during a separate rain event on 2/4/25 in the aŌernoon.

Flow was measured by volunteers at the Ɵme of sampling except at sites where low flow during Dry Run prohibited accurate flow measurements or during First Flush when an ISCO pump was used to pull sample water from a collecƟon area. ISCOs were used during First Flush at Las Palmas (Monterey County), Bay Street (Seaside-Sand City), and Lover’s Point (Pacific Grove). All other instantaneous flow esƟmates are listed in Table 2.

Table 2: Instantaneous flow esƟmates in gallons per minute (gpm) per site. NA= data not available due to flow collected by ISCO pump; NF= No flow Jurisdiction Site Description Site ID Dry Run
(gpm)
First Flush
(gpm)
Carmel 4
th Avenue 307-CASD-01 22.4 256
Ocean Avenue 307-CASD-02 NF 720
Monterey
Hopkins/Mon 309-PGSD-09 0.0125 Not Visited
Twins 309-MSD-03 4.6 130
San Carlos Beach 309-MSD-04 NF 120
Steinbeck 309-MSD-05 NF 77
Monterey County Boronda 309-SASD-01 NF 30
Las Palmas 309-SASD-02 NF NA
Pacific Grove
8 th Street 309-PGSD-01 NF 1
Greenwood Park 309-CENTR-31 2.51 1,550
Hopkins/PG 309-PGSD-08 NF Not Visited
Lover’s Point 309-PGSD-03 0.33 NA
Pico 309-PGSD-04 20 130
Seaside/ Sand City Bay Street 309-SSD-02 NF 240
ATTACHMENT 2

11 Results
Each analyte is described below and includes a reporƟng of the Minimum DetecƟon Limit (MDL), the lowest concentraƟon that lab equipment can reliably detect. MDL is a staƟsƟcal analysis of the confidence of results. Results below the MDL are reported as non-detect since lab equipment cannot reliably determine where the results lay between zero and the MDL. An average of historic data for each site is included in the figures when the data is available. For some sites, data is available as far back as 2000, but other sites, such as the two Hopkins sites, have more sporadic data available.

Nutrients
Excess concentraƟons of nitrate, orthophosphate, ammonia, and urea can assist in idenƟfying a discharge of sewage as well as industrial or commercial liquid wastes. Primary sources of nitrate in runoff include overwatering of ferƟlized lawns, agricultural and pasture lands, construcƟon sites, and sepƟc or sewer system leachate.

ATTACHMENT 2

12 Nitrate in runoff can lead to excessive levels of nitrate in groundwater or increased growth of algal blooms that degrade water quality as algae die off and consume oxygen in their decomposiƟon.

The CCAMP AcƟon Level for nitrate as N (NO3-N) is 2.25 mg-N/L. The MDL was 0.04 mg-N/L for the Dry Run and 0.16 mg-N/L for the First Flush for all sites. All ouƞall results are listed in Appendix 2.

Figure 4. Nitrate concentraƟons (mg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: In 2024, one of the six flowing ouƞalls (17%) exceeded the AcƟon Level at 4th Avenue in Carmel.
● First Flush: None of the ouƞall sites exceeded the AcƟon Level in 2024. 0 1
2 3
4 5
6 Nitrate as N (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 2.25 mg/L ATTACHMENT 2

13 Phosphorus is also an essenƟal element for plant growth. Orthophosphate is a form of phosphorus commonly found bound to soil parƟcles, in sewage, ferƟlizers, and in surfactants that contain phosphates. In aquaƟc systems, orthophosphate is rapidly taken up by algae and aquaƟc plants. When excessive amounts are present, large algal blooms can occur which can lead to degraded water quality condiƟons toxic to marine or aquaƟc life.

The CCAMP AcƟon Level for orthophosphate (PO4-P) is 0.12 mg/L. The MDL was 0.04 mg/L for the Dry Run and 0.16 mg/L for First Flush for all sites.

Figure 5. Orthophosphate concentraƟons (mg/L) for First Flush and Dry Run sites in 2024 and historical averages.
● Dry Run: In 2024, none of the ouƞalls exceeded the AcƟon Level. Five sites were reported as non-detect at 4th St, Twins, Greenwood Park, Lovers Point, and Pico. ● First Flush: All results exceeded the AcƟon Level in 2024. The highest average result of

1.3 mg/L was Steinbeck in Monterey.
0 0.5
1 1.5
2 2.5
Orthophosphate as P (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 0.12 mg/L ATTACHMENT 2

14 Ammonia, in conjuncƟon with other analytes, can assist in idenƟfying a discharge of sewage as well as industrial or commercial liquid wastes. The SWRCB NPDES MS4 General Permit AcƟon Level for ammonia as N is 50 mg/L; the MDL was 0.05 mg/L for the Dry Run and First Flush. The WQO is not depicted on the graph, because all results since 2013 are far below the WQO of 50 mg/L.

Figure 6. Ammonia concentraƟons (mg/L) for First Flush and Dry Run sites in 2024 and historical averages.
● Dry Run and First Flush: None of the ouƞall sites exceeded the AcƟon Level in 2024.

Four sites were reported as non-detect during the Dry Run at 4th Avenue, Hopkins/Mon, Lovers Point, and Pico.
0 0.5
1 1.5
2 2.5
3 3.5
Ammonia-N (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run ATTACHMENT 2

15 Urea is an organic compound that is oŌen used in agricultural and urban ferƟlizers. While there is not an established AcƟon Level or WQO, urea concentraƟons are compared between sites.

The MDL for urea was 8 µg/L for Dry Run and First Flush. There is no WQO for urea.

Figure 7. Urea concentraƟons (µg/L) for First Flush and Dry Run sites in 2024 and historical averages.  Dry Run: In 2024, the highest urea result of 65 µg/L was from Twins in Monterey.

Two sites were reported as non-detect at Lovers Point and Pico in Pacific Grove.  First Flush: In 2024, the highest result of 765 µg/L was from Bay Street in Seaside. 0 200
400
600
800
1000
1200
1400
1600
Urea-N (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run ATTACHMENT 2

16 Metals
Metals in higher concentraƟons can be toxic to marine organisms and can cause reduced reproducƟon, developmental deformiƟes, reduced photosynthesis, and mortality. Heavy metal toxicity can be miƟgated by the presence of sediment or other binding compounds that may reduce the metal’s bioavailability. For this monitoring, the focus is on copper, lead, and zinc.

Copper is present in some brake pads, pesƟcides, wood preservaƟves, roofing materials, and architectural structures such as guƩers and downspouts.

The Basin Plan WQO established for total copper is 30 µg/L; the MDL for copper was 7 µg/L for Dry Run and First Flush.

Figure 8. Copper concentraƟons (µg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: For 2024, no sites exceeded the WQO. Five sites were reported as non-detect at 4th Avenue in Carmel, Lovers Point and Greenwood Park in Pacific Grove, and Hopkins/Mon and Twins in Monterey.
● First Flush: Results from eight of the twelve ouƞall sites (67%) exceeded the WQO in 2024. The highest average result of 126 µg/L was from Ocean Avenue in Carmel.

0.0 20.0

40.0 60.0

80.0 100.0
120.0
140.0
Copper (μg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 30 μg/L ATTACHMENT 2

17 Lead is present in some types of paint, water distribuƟon systems, auto emissions, and can also be passed through the food web via uptake by plants that are grown in lead contaminated soils.

The Basin Plan WQO established for total lead is 30 μg/L; the MDL for lead was 0.1 µg/L for both the Dry Run and First Flush.

Figure 9. Lead concentraƟons (µg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: None of the ouƞall sites exceeded the WQO in 2024. Two sites were reported as non-detect at Ocean Avenue in Carmel and Lovers Point in Pacific Grove. ● First Flush: None of the sampled ouƞall sites exceeded the WQO in 2024. The highest average result of 26.7 µg/L was from Boronda in Salinas. 0 5
10 15
20 25
30 35
Lead (µg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 30 µg/L ATTACHMENT 2

18 Zinc is present in Ɵres, paint, and outdoor zinc surfaces such as galvanized surfaces.

The Basin Plan WQO for total zinc is 200 µg/L. The zinc MDL was 10 µg/L for the Dry Run and First Flush.

Figure 10. Zinc concentraƟons (µg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: One of the ouƞall sites, Hopkins in Monterey, exceeded the WQO in 2024. Four non-detects were reported at 4th Avenue in Carmel, Pico in Monterey, and Greenwood Park and Lovers Point in Pacific Grove.
● First Flush: For 2024, Four of the 12 sites sampled (33%) exceeded the WQO at Ocean Avenue in Carmel, San Carlos in Monterey, Bay Street in Seaside, and Boronda in Salinas.

The highest concentraƟon was 512 μg/L at Boronda in Salinas. 0 100
200
300
400
500
600
Zinc (µg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 200 μg/L ATTACHMENT 2

19 Clarity
Color, in conjuncƟon with other analytes, can assist in idenƟfying a discharge of sewage, wash water, as well as industrial or commercial liquid wastes. The SWRCB NPDES MS4 General Permit AcƟon Level for color is 500 color units; the MDL for color was 3 color units for the Dry Run and 30 color units for First Flush.

Figure 11. Color (color units) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: None of the ouƞall sites exceeded the AcƟon Level in 2024. ● First Flush: One of the ouƞall sites exceeded the AcƟon Level in 2024 at 875 color units at Boronda in Salinas.
0 100
200
300
400
500
600
700
800
900
1000
Color (color units)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 500 cu ATTACHMENT 2

20 Total suspended solids (TSS) are measured because high amounts of sediment can destroy habitat, suffocate eggs in freshwater systems, limit the food supply, clog gills, or impair an organism’s vision when feeding. While TSS measures the weight of the solids in the water that contribute to less water clarity, turbidity measures the transparency of water. Both are useful measurements for water clarity but have different methodologies for analysis.

The CCAMP AcƟon Level for TSS is 500 mg/L; the MDL was 2 mg/L for both the Dry Run and First Flush.

Figure 12. Total Suspended Solids (mg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: No sites exceeded the AcƟon Level in 2024. One non-detect was reported at Lovers Point in Pacific Grove.
● First Flush: One site exceeded the AcƟon Level in 2024 at 545 mg/L at Boronda in Salinas.
0 100
200
300
400
500
600
Total Suspended Solids (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 500 mg/L ATTACHMENT 2

21 Turbidity measures the transparency of water while TSS measures the weight of the solids in the water that contribute to less transparency. Both are useful measurements for water clarity but have different methodologies for analysis.

The AcƟon Level for turbidity provided by the State Board in the General Permit is not greater than 1000 NTU. As a comparison, CCAMP lists turbidity to be not greater than 25 NTU; the CCAMP AcƟon Level will be used for this set of data as it is more protecƟve of water quality. The MDL was 0.1 NTU for the Dry Run and between 0.1 and 0.5 NTU for the First Flush.

Figure 13. Turbidity (NTUs) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: One site exceeded the AcƟon Level in 2024 at 27 NTU at Pico in Pacific Grove. ● First Flush: Ten of the twelve sampled sites (83%) exceeded the AcƟon Level in 2024.

The highest average result of 525 NTU was from Boronda in Salinas. 0 100
200
300
400
500
600
Turbidity (NTU)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 25 NTU ATTACHMENT 2

22 Fecal Indicator Bacteria
Escherichia coli (E. coli) and Enterococcus are both a type of indicator bacteria found in warmblooded animals. While concentraƟons of generic E. coli and Enterococcus are not closely correlated with disease in humans, they are pollutants of concern because detecƟon above the objecƟve indicates the potenƟal presence of pathogens that may cause disease in humans and wildlife.

The naƟonal recommended water quality criteria for E. coli is 235 MPN/100mL. The MDL for E. coli was 20 MPN/100mL for the Dry Run and between 100 MPN/100mL for the First Flush.

Figure 14. E. coli (MPN/100mL) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: Four of the six (67%) flowing ouƞall sites exceeded the WQO for E. coli in 2024. The highest E. coli result of 2,518 MPN/100mL was from Greenwood Park in Pacific Grove.
● First Flush: All of the flowing ouƞall sites exceeded the WQO in 2024. The highest average result of 106,205 MPN/100mL was from Las Palmas in Monterey County.
0 20000
40000
60000
80000
100000
120000 E. coli (MPN/100mL)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 235 MPN/100 ATTACHMENT 2

23 The naƟonally recommended water quality criteria for Enterococcus is 104 MPN/100mL. The MDL for Enterococcus was between 1-100 MPN/100mL for the Dry Run and 100 MPN/100mL for the First Flush.

Figure 15. Enterococcus (MPN/100mL) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: Three of the six (50%) flowing ouƞall sites monitored exceeded the WQO for Enterococcus in 2024. The highest enterococcus result of 4,764 MPN/100mL was from Greenwood Park in Pacific Grove.
● First Flush: All ouƞall sites monitored exceeded the WQO in 2024. The highest average result of 126,675 MPN/100mL was from Bay Street in Seaside. 0 20000
40000
60000
80000
100000
120000
140000
Enterococcus (MPN/100mL)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 104 MPN/100mL ATTACHMENT 2

24 Liquid Waste Identifiers
Hardness (as CaCO3) in conjuncƟon with other analytes, can assist in idenƟfying a discharge of sewage, wash water, tap water, and industrial or commercial liquid wastes. AddiƟonally, when hardness increases, the amount of dissolved metals biologically available to aquaƟc and marine life decreases resulƟng in a decrease in the toxicity of the metal.

The SWRCB NPDES MS4 General Permit AcƟon Level for hardness is not less than or equal to 10 mg/L or greater than or equal to 2,000 mg/L; the MDL for hardness (as CaCO3) was 1 mg/L for the Dry Run and First Flush. The WQO level is not shown in this figure because the levels were substanƟally lower than 2000 mg/L.

Figure 16. Hardness (CaC03) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run and First Flush: None of the ouƞall results exceeded the acceptable range in 2024.
0 100
200
300
400
500
600
700
800
Hardness (CaC03)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run ATTACHMENT 2

25 Potassium, in conjuncƟon with other analytes, can assist in idenƟfying a discharge of sewage, industrial, or commercial liquid wastes. The SWRCB NPDES MS4 General Permit AcƟon Level for potassium is 20 mg/L; the MDL was 0.3 mg/L for both the Dry Run and First Flush.

Figure 17. Potassium (mg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run and First Flush: None of the ouƞall results exceeded the acceptable range in 2024.
0 5
10 15
20 25
30 35
40 45
50 Potassium (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 20 mg/L ATTACHMENT 2

26 MBAS surfactants in sample water can indicate a discharge from sewage or wash water, and in conjuncƟon with other analytes, can assist in idenƟfying a discharge of industrial or commercial liquid wastes.

The Basin Plan’s WQO established for MBAS surfactants is 0.2 mg/L; the MDL for MBAS surfactants was 0.02 mg/L for the Dry Run and First Flush.

Figure 18. MBAS Surfactants (mg/L) for First Flush and Dry Run sites in 2024 and historical averages. ● Dry Run: Two of the six (33%) flowing ouƞall sites were above the WQO for MBAS concentraƟons in 2024. The highest result was at Hopkins in Monterey at 0.22 mg/L. ● First Flush: Seven of the 12 sites monitored (58%) were above the WQO. The highest average result of 0.435 mg/L was from Bay Street in Seaside. 0 0.1

0.2 0.3

0.4 0.5

0.6 0.7

0.8 Surfactants (mg/L)
Avg First Flush 2024 First Flush Avg Dry Run 2024 Dry Run WQO 0.2 mg/L ATTACHMENT 2

27 Results by Jurisdiction
The following secƟon is broken out by city or county for this permit year. All 2024 ouƞall results can be found in Appendix 2 and by jurisdicƟon in Appendix 3. Sites shaded in red exceeded the WQO. Sites shaded in green were below the WQO. Sites labeled NF shaded in grey had no flow.

Carmel-by-the-Sea
For the 2024-2025 permit year, two sites were monitored in Carmel: 4th Avenue and Ocean Avenue.

Table 3: Data by analyte for Carmel-by-the-Sea.

Analyte Site Name Jurisdiction Run Date Result Units WQO Nitrate 4th Avenue
Carmel-by-theSea
DR 9/28/2024 3 mg/L 2.25
FF 11/11/2024 0.6 mg/L 2.25
FF 11/11/2024 0.4 mg/L 2.25
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 2.25
FF 11/11/2024 0.5 mg/L 2.25
FF 11/11/2024 0.3 mg/L 2.25
Orthophosphate 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0 mg/L 0.12
FF 11/11/2024 0.61 mg/L 0.12
FF 11/11/2024 0.57 mg/L 0.12
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 0.66 mg/L 0.12
FF 11/11/2024 0.54 mg/L 0.12
Ammonia 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0 mg/L 0.12
FF 11/11/2024 0.61 mg/L 0.12
FF 11/11/2024 0.57 mg/L 0.12
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 0.66 mg/L 0.12
FF 11/11/2024 0.54 mg/L 0.12
Urea 4th Avenue
Carmel-by-theSea
DR 9/28/2024 21 μg/L n/a
FF 11/11/2024 207 μg/L n/a
FF 11/11/2024 201 μg/L n/a
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF μg/L n/a
FF 11/11/2024 330 μg/L n/a
FF 11/11/2024 217 μg/L n/a
Copper 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0 μg/L 30
FF 11/11/2024 140 μg/L 30
FF 11/11/2024 112 μg/L 30
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 115 μg/L 30
ATTACHMENT 2

28 FF 11/11/2024 112 μg/L 30
Lead 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0 μg/L 30
FF 11/11/2024 21.4 μg/L 30
FF 11/11/2024 17.9 μg/L 30
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 10.4 μg/L 30
FF 11/11/2024 10.3 μg/L 30
Zinc 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0 μg/L 200
FF 11/11/2024 183 μg/L 200
FF 11/11/2024 153 μg/L 200
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF μg/L 200
FF 11/11/2024 286 μg/L 200
FF 11/11/2024 234 μg/L 200
Color 4th Avenue
Carmel-by-theSea
DR 9/28/2024 50 color units 500
FF 11/11/2024 250 color units 500
FF 11/11/2024 200 color units 500
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF color units 500
FF 11/11/2024 100 color units 500
FF 11/11/2024 100 color units 500
TSS 4th Avenue
Carmel-by-theSea
DR 9/28/2024 2 mg/L 500
FF 11/11/2024 237 mg/L 500
FF 11/11/2024 146 mg/L 500
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 500
FF 11/11/2024 62 mg/L 500
FF 11/11/2024 42 mg/L 500
Turbidity 4th Avenue
Carmel-by-theSea
DR 9/28/2024 1.8 NTU 25
FF 11/11/2024 88 NTU 25
FF 11/11/2024 55 NTU 25
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF NTU 25
FF 11/11/2024 26 NTU 25
FF 11/11/2024 21 NTU 25
E.coli 4th Avenue
Carmel-by-theSea
DR 9/28/2024 558 MPN/100mL 235
FF 11/11/2024 39805 MPN/100mL 235
FF 11/11/2024 17850 MPN/100mL 235
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF MPN/100mL 235
FF 11/11/2024 16160 MPN/100mL 235
FF 11/11/2024 14140 MPN/100mL 235
Enterococcus 4th Avenue
Carmel-by-theSea
DR 9/28/2024 22 MPN/100mL 104
FF 11/11/2024 33140 MPN/100mL 104
FF 11/11/2024 19890 MPN/100mL 104
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF MPN/100mL 104
FF 11/11/2024 26030 MPN/100mL 104
FF 11/11/2024 24890 MPN/100mL 104
Hardness
4th Avenue
Carmel-by-theSea
DR 9/28/2024 390 mg/L 2000
FF 11/11/2024 85 mg/L 2000
FF 11/11/2024 71 mg/L 2000
ATTACHMENT 2

29 Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 2000
FF 11/11/2024 57 mg/L 2000
FF 11/11/2024 42 mg/L 2000
Potassium 4th Avenue
Carmel-by-theSea
DR 9/28/2024 9.3 mg/L 20
FF 11/11/2024 15.7 mg/L 20
FF 11/11/2024 12.6 mg/L 20
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 20
FF 11/11/2024 10.5 mg/L 20
FF 11/11/2024 7.7 mg/L 20
Surfactants 4th Avenue
Carmel-by-theSea
DR 9/28/2024 0.21 mg/L 0.2
FF 11/11/2024 0.2 mg/L 0.2
FF 11/11/2024 0.24 mg/L 0.2
Ocean Avenue
Carmel-by-theSea
DR 9/28/2024 NF mg/L 0.2
FF 11/11/2024 0.25 mg/L 0.2
FF 11/11/2024 0.24 mg/L 0.2
For the Dry Run:
● 4th Avenue had exceedances for nitrate, E.coli, and surfactants. ● There was no flow at Ocean Avenue.

For the First Flush:
 4th Avenue had exceedances for orthophosphate, ammonia, copper, lead, turbidity, E.coli, Enterococcus and surfactants.
 Ocean Ave had exceedances for orthophosphate, ammonia, copper, lead, zinc, turbidity, E.coli, Enterococcus and surfactants.

Monterey
For the 2024-2025 permit year, three sites were monitored: Twins, San Carlos, and Steinbeck.

Twins had flowing water during the Dry Run. Twins ouƞall is owned by the U.S. Army Presidio of Monterey, and is uƟlized by both the City and Presidio of Monterey. Hopkins in Monterey was not sampled during the First Flush.

Table 4: Data by analyte for the City of Monterey. Blank cells represent sites not sampled.

Analyte Site Name Jurisdiction Run Date Result Units WQO Nitrate HopkinsMon Monterey
DR 10/2/2024 0.9 mg/L 2.25
FF 11/11/2024 mg/L 2.25
FF 11/11/2024 mg/L 2.25
San Carlos Monterey
DR 9/28/2024 NF mg/L 2.25
FF 11/11/2024 0.4 mg/L 2.25
ATTACHMENT 2

30 FF 11/11/2024 0.3 mg/L 2.25
Steinbeck Monterey
DR 9/28/2024 NF mg/L 2.25
FF 11/11/2024 0.8 mg/L 2.25
FF 11/11/2024 0.4 mg/L 2.25
Twin 51 Monterey
DR 9/28/2024 0.8 mg/L 2.25
FF 2/4/2025 0.4 mg/L 2.25
FF 2/4/2025 0.3 mg/L 2.25
Orthophosphate
HopkinsMon Monterey
DR 10/2/2024 0.12 mg/L 0.12
FF 11/11/2024 mg/L 0.12
FF 11/11/2024 mg/L 0.12
San Carlos Monterey
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 0.37 mg/L 0.12
FF 11/11/2024 0.3 mg/L 0.12
Steinbeck Monterey
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 1.2 mg/L 0.12
FF 11/11/2024 1.4 mg/L 0.12
Twin 51 Monterey
DR 9/28/2024 0 mg/L 0.12
FF 2/4/2025 0.26 mg/L 0.12
FF 2/4/2025 0.08 mg/L 0.12
Ammonia
HopkinsMon Monterey
DR 10/2/2024 0 mg/L 50
FF 11/11/2024 mg/L 50
FF 11/11/2024 mg/L 50
San Carlos Monterey
DR 9/28/2024 NF mg/L 50
FF 11/11/2024 0.78 mg/L 50
FF 11/11/2024 0.63 mg/L 50
Steinbeck Monterey
DR 9/28/2024 NF mg/L 50
FF 11/11/2024 1.92 mg/L 50
FF 11/11/2024 2.14 mg/L 50
Twin 51 Monterey
DR 9/28/2024 0.22 mg/L 50
FF 2/4/2025 0.39 mg/L 50
FF 2/4/2025 0.21 mg/L 50
Urea
HopkinsMon Monterey
DR 10/2/2024 19 μg/L n/a
FF 11/11/2024 μg/L n/a
FF 11/11/2024 μg/L n/a
San Carlos Monterey
DR 9/28/2024 NF μg/L n/a
FF 11/11/2024 580 μg/L n/a
FF 11/11/2024 153 μg/L n/a
Steinbeck Monterey
DR 9/28/2024 NF μg/L n/a
FF 11/11/2024 409 μg/L n/a
FF 11/11/2024 496 μg/L n/a
Twin 51 Monterey
DR 9/28/2024 65 μg/L n/a
FF 2/4/2025 589 μg/L n/a
FF 2/4/2025 558 μg/L n/a
Copper
HopkinsMon Monterey
DR 10/2/2024 13 μg/L 30
FF 11/11/2024 μg/L 30
FF 11/11/2024 μg/L 30
ATTACHMENT 2

31 San Carlos Monterey
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 54 μg/L 30
FF 11/11/2024 52 μg/L 30
Steinbeck Monterey
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 64 μg/L 30
FF 11/11/2024 76 μg/L 30
Twin 51 Monterey
DR 9/28/2024 0 μg/L 30
FF 2/4/2025 26 μg/L 30
FF 2/4/2025 18 μg/L 30
Lead
HopkinsMon Monterey
DR 10/2/2024 1.5 μg/L 30
FF 11/11/2024 μg/L 30
FF 11/11/2024 μg/L 30
San Carlos Monterey
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 8.3 μg/L 30
FF 11/11/2024 9.5 μg/L 30
Steinbeck Monterey
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 4.3 μg/L 30
FF 11/11/2024 7.7 μg/L 30
Twin 51 Monterey
DR 9/28/2024 0.2 μg/L 30
FF 2/4/2025 10.8 μg/L 30
FF 2/4/2025 12 μg/L 30
Zinc
HopkinsMon Monterey
DR 10/2/2024 346 μg/L 200
FF 11/11/2024 μg/L 200
FF 11/11/2024 μg/L 200
San Carlos Monterey
DR 9/28/2024 NF μg/L 200
FF 11/11/2024 205 μg/L 200
FF 11/11/2024 199 μg/L 200
Steinbeck Monterey
DR 9/28/2024 NF μg/L 200
FF 11/11/2024 297 μg/L 200
FF 11/11/2024 276 μg/L 200
Twin 51 Monterey
DR 9/28/2024 16 μg/L 200
FF 2/4/2025 194 μg/L 200
FF 2/4/2025 132 μg/L 200
Color
HopkinsMon Monterey
DR 10/2/2024 63 color units 500
FF 11/11/2024 color units 500
FF 11/11/2024 color units 500
San Carlos Monterey
DR 9/28/2024 NF color units 500
FF 11/11/2024 100 color units 500
FF 11/11/2024 150 color units 500
Steinbeck Monterey
DR 9/28/2024 NF color units 500
FF 11/11/2024 125 color units 500
FF 11/11/2024 150 color units 500
Twin 51 Monterey
DR 9/28/2024 30 color units 500
FF 2/4/2025 200 color units 500
FF 2/4/2025 100 color units 500
TSS HopkinsMon Monterey DR 10/2/2024 11 mg/L 500
ATTACHMENT 2

32 FF 11/11/2024 mg/L 500
FF 11/11/2024 mg/L 500
San Carlos Monterey
DR 9/28/2024 NF mg/L 500
FF 11/11/2024 40 mg/L 500
FF 11/11/2024 44 mg/L 500
Steinbeck Monterey
DR 9/28/2024 NF mg/L 500
FF 11/11/2024 25 mg/L 500
FF 11/11/2024 56 mg/L 500
Twin 51 Monterey
DR 9/28/2024 2 mg/L 500
FF 2/4/2025 110 mg/L 500
FF 2/4/2025 60 mg/L 500
Turbidity
HopkinsMon Monterey
DR 10/2/2024 2.7 NTU 25
FF 11/11/2024 NTU 25
FF 11/11/2024 NTU 25
San Carlos Monterey
DR 9/28/2024 NF NTU 25
FF 11/11/2024 33 NTU 25
FF 11/11/2024 33 NTU 25
Steinbeck Monterey
DR 9/28/2024 NF NTU 25
FF 11/11/2024 23 NTU 25
FF 11/11/2024 40 NTU 25
Twin 51 Monterey
DR 9/28/2024 1.9 NTU 25
FF 2/4/2025 37 NTU 25
FF 2/4/2025 36 NTU 25
E.coli
HopkinsMon Monterey
DR 10/2/2024 83 MPN/100mL 235
FF 11/11/2024 MPN/100mL 235
FF 11/11/2024 MPN/100mL 235
San Carlos Monterey
DR 9/28/2024 NF MPN/100mL 235
FF 11/11/2024 2950 MPN/100mL 235
FF 11/11/2024 4280 MPN/100mL 235
Steinbeck Monterey
DR 9/28/2024 NF MPN/100mL 235
FF 11/11/2024 9590 MPN/100mL 235
FF 11/11/2024 19890 MPN/100mL 235
Twin 51 Monterey
DR 9/28/2024 524 MPN/100mL 235
FF 2/4/2025 6700 MPN/100mL 235
FF 2/4/2025 5460 MPN/100mL 235
Enterococcus
HopkinsMon Monterey
DR 10/2/2024 106 MPN/100mL 104
FF 11/11/2024 MPN/100mL 104
FF 11/11/2024 MPN/100mL 104
San Carlos Monterey
DR 9/28/2024 NF MPN/100mL 104
FF 11/11/2024 6440 MPN/100mL 104
FF 11/11/2024 6270 MPN/100mL 104
Steinbeck Monterey
DR 9/28/2024 NF MPN/100mL 104
FF 11/11/2024 11450 MPN/100mL 104
FF 11/11/2024 13960 MPN/100mL 104
Twin 51 Monterey
DR 9/28/2024 172 MPN/100mL 104
FF 2/4/2025 15290 MPN/100mL 104
ATTACHMENT 2

33 FF 2/4/2025 13140 MPN/100mL 104
Hardness
HopkinsMon Monterey
DR 10/2/2024 550 mg/L 2000
FF 11/11/2024 mg/L 2000
FF 11/11/2024 mg/L 2000
San Carlos Monterey
DR 9/28/2024 NF mg/L 2000
FF 11/11/2024 48 mg/L 2000
FF 11/11/2024 41 mg/L 2000
Steinbeck Monterey
DR 9/28/2024 NF mg/L 2000
FF 11/11/2024 48 mg/L 2000
FF 11/11/2024 39 mg/L 2000
Twin 51 Monterey
DR 9/28/2024 380 mg/L 2000
FF 2/4/2025 127 mg/L 2000
FF 2/4/2025 223 mg/L 2000
Surfactants
HopkinsMon Monterey
DR 10/2/2024 9.5 mg/L 20
FF 11/11/2024 mg/L 20
FF 11/11/2024 mg/L 20
San Carlos Monterey
DR 9/28/2024 NF mg/L 20
FF 11/11/2024 3.7 mg/L 20
FF 11/11/2024 3.2 mg/L 20
Steinbeck Monterey
DR 9/28/2024 NF mg/L 20
FF 11/11/2024 5.6 mg/L 20
FF 11/11/2024 5 mg/L 20
Twin 51 Monterey
DR 9/28/2024 7.5 mg/L 20
FF 2/4/2025 4.3 mg/L 20
FF 2/4/2025 4.8 mg/L 20
Surfactants
HopkinsMon Monterey
DR 10/2/2024 0.22 mg/L 0.2
FF 11/11/2024 mg/L 0.2
FF 11/11/2024 mg/L 0.2
San Carlos Monterey
DR 9/28/2024 NF mg/L 0.2
FF 11/11/2024 0.2 mg/L 0.2
FF 11/11/2024 0.29 mg/L 0.2
Steinbeck Monterey
DR 9/28/2024 NF mg/L 0.2
FF 11/11/2024 0.26 mg/L 0.2
FF 11/11/2024 0.29 mg/L 0.2
Twin 51 Monterey
DR 9/28/2024 0.03 mg/L 0.2
FF 2/4/2025 0.42 mg/L 0.2
FF 2/4/2025 0.29 mg/L 0.2
For the Dry Run:
● Hopkins in Monterey had exceedances for zinc and surfactants. ● Twins had exceedances for E.coli and Enterococcus.

During the First Flush:

ATTACHMENT 2

34 ● San Carlos had exceedances for orthophosphate, copper, lead, zinc, turbidity, E.coli, Enterococcus and surfactants.
● Steinbeck had exceedances for orthophosphate, copper, lead, zinc, turbidity, E.coli, Enterococcus and surfactants.
● Twins had exceedances for orthophosphate, lead, turbidity, E.coli, Enterococcus and surfactants.

Monterey County
For the 2024-2025 permit year, two sites were monitored: Boronda and Las Palmas. There was no flow at either site during the Dry Run.

Table 4: Data by analyte for the County of Monterey.

Nitrate Boronda Salinas
DR 9/28/2024 NF mg/L 2.25
FF 11/11/2024 0.6 mg/L 2.25
FF 11/11/2024 0.6 mg/L 2.25
Las Palmas Salinas
DR 9/28/2024 NF mg/L 2.25
FF 11/11/2024 0.4 mg/L 2.25
FF 11/11/2024 0.4 mg/L 2.25
Orthophosphate Boronda Salinas
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 0.51 mg/L 0.12
FF 11/11/2024 0.54 mg/L 0.12
Las Palmas Salinas
DR 9/28/2024 NF mg/L 0.12
FF 11/11/2024 0.5 mg/L 0.12
FF 11/11/2024 0.71 mg/L 0.12
Ammonia Boronda Salinas
DR 9/28/2024 NF mg/L 50
FF 11/11/2024 1.26 mg/L 50
FF 11/11/2024 1.26 mg/L 50
Las Palmas Salinas
DR 9/28/2024 NF mg/L 50
FF 11/11/2024 0.33 mg/L 50
FF 11/11/2024 0.36 mg/L 50
Urea Boronda Salinas
DR 9/28/2024 NF μg/L n/a
FF 11/11/2024 418 μg/L n/a
FF 11/11/2024 321 μg/L n/a
Las Palmas Salinas
DR 9/28/2024 NF μg/L n/a
FF 11/11/2024 214 μg/L n/a
FF 11/11/2024 253 μg/L n/a
Copper Boronda Salinas
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 48 μg/L 30
FF 11/11/2024 46 μg/L 30
Las Palmas Salinas
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 0 μg/L 30
FF 11/11/2024 0 μg/L 30
Lead Boronda Salinas DR 9/28/2024 NF μg/L 30
ATTACHMENT 2

35 FF 11/11/2024 26 μg/L 30
FF 11/11/2024 27.3 μg/L 30
Las Palmas Salinas
DR 9/28/2024 NF μg/L 30
FF 11/11/2024 2.6 μg/L 30
FF 11/11/2024 2.6 μg/L 30
Zinc Boronda Salinas
DR 9/28/2024 NF μg/L 200
FF 11/11/2024 502 μg/L 200
FF 11/11/2024 522 μg/L 200
Las Palmas Salinas
DR 9/28/2024 NF μg/L 200
FF 11/11/2024 111 μg/L 200
FF 11/11/2024 70 μg/L 200
Color Boronda Salinas
DR 9/28/2024 NF color units 500
FF 11/11/2024 750 color units 500
FF 11/11/2024 1000 color units 500
Las Palmas Salinas
DR 9/28/2024 NF color units 500
FF 11/11/2024 150 color units 500
FF 11/11/2024 100 color units 500
TSS Boronda Salinas
DR 9/28/2024 NF mg/L 500
FF 11/11/2024 560 mg/L 500
FF 11/11/2024 530 mg/L 500
Las Palmas Salinas
DR 9/28/2024 NF mg/L 500
FF 11/11/2024 166 mg/L 500
FF 11/11/2024 114 mg/L 500
Turbidity Boronda Salinas
DR 9/28/2024 NF NTU 25
FF 11/11/2024 500 NTU 25
FF 11/11/2024 550 NTU 25
Las Palmas Salinas
DR 9/28/2024 NF NTU 25
FF 11/11/2024 80 NTU 25
FF 11/11/2024 70 NTU 25
E.coli Boronda Salinas
DR 9/28/2024 NF MPN/100mL 235
FF 11/11/2024 17890 MPN/100mL 235
FF 11/11/2024 26030 MPN/100mL 235
Las Palmas Salinas
DR 9/28/2024 NF MPN/100mL 235
FF 11/11/2024 92080 MPN/100mL 235
FF 11/11/2024 120330 MPN/100mL 235
Enterococcus Boronda Salinas
DR 9/28/2024 NF MPN/100mL 104
FF 11/11/2024 111990 MPN/100mL 104
FF 11/11/2024 111990 MPN/100mL 104
Las Palmas Salinas
DR 9/28/2024 NF MPN/100mL 104
FF 11/11/2024 72700 MPN/100mL 104
FF 11/11/2024 81640 MPN/100mL 104
Hardness Boronda Salinas
DR 9/28/2024 NF mg/L 2000
FF 11/11/2024 144 mg/L 2000
FF 11/11/2024 148 mg/L 2000
Las Palmas Salinas
DR 9/28/2024 NF mg/L 2000
FF 11/11/2024 81 mg/L 2000
ATTACHMENT 2