MONITORING FOR INDICATORS OF ANTIFOULING PAINT POLLUTION IN 

CALIFORNIA MARINAS 

Nan Singhasemanon, Ellen Pyatt, and Juanita Bacey 

June 2009 

CALIFORNIA ENVIRONMENTAL PROTECTION AGENCY 

Environmental Monitoring Branch 

California Department of Pesticide Regulation

1001 I St., Sacramento, California 95812

EH08-05
ABSTRACT 

In the summer and fall of 2006, the Department of Pesticide Regulation (DPR) sampled water and sediment 

from 23 marinas to assess the geographical scope and severity of pollution stemming from the use of 

antifouling paint (AFP) pesticides in California. Copper, zinc, Irgarol, M1 (the primary breakdown product 

of Irgarol), and aquatic toxicity were selected as potential indicators of AFP pollution. 

The highest dissolved copper concentrations were observed in larger salt water marinas along California’s 

Central and South Coast, the lowest were seen in freshwater lake marinas. Copper and zinc concentrations 

were almost always higher in the marinas than in the adjacent local reference sites, indicating that 

significant sources of metals existed in the marinas. Concentrations of zinc never exceeded California’s 

water quality standards. In contrast, concentrations of dissolved copper in salt and brackish water marinas 

were frequently above California water quality standards established for the protection of aquatic life. 

Developmental toxicity tests on the copper-sensitive embryo of the mussel Mytilus galloprovincialis were 

conducted on 47 marina water samples. Eight of these samples showed a statistically significant toxic 

response and copper was the likely cause of the toxicity. Several copper toxicity models that account for 

copper bioavailability to aquatic organisms were used to predict toxicity for the 517 samples for which sitespecific water quality data were available. These models were the freshwater Biotic Ligand Model (BLM), 

the saltwater BLM, and the Dissolved Organic Carbon Model. Model output indicated that copper 

sensitive aquatic organisms, particularly M. galloprovincialis in saltwater marinas, were occasionally 

exposed to toxic levels of copper. 

Forty-five marina water samples were collected and analyzed for Irgarol and M1. Irgarol and M1 were 

detected in all of these samples. Irgarol concentrations were often higher than sub-lethal toxicity thresholds 

for marine algae. In some saltwater marinas, Irgarol concentrations exceeded benchmark values used to 

determine if risk to aquatic plants exists. 

Source evaluation suggests that during dry periods, boat AFPs are a major source of copper, Irgarol, and 

M1 to salt and brackish water marinas. The contribution of zinc AFPs to the total marina zinc load is likely 

to be less than the contribution of copper AFPs to the total marina copper load due to the input of zinc from 

the corrosion of sacrificial zinc anodes in salt water marinas and due to the much lower use of zinc AFPs 

compared to copper AFPs. 

i
ACKNOWLEDGEMENTS 

This study was conducted with DPR resources and supplemental funding from the U.S. Environmental 

Protection Agency (U.S. EPA) and from California’s State Water Resources Control Board (SWRCB). 

The authors would like to thank the following groups and individuals for their contribution to the 

successful completion of this study: 

• Surface Water Program scientific student assistants: Catherine Cruger-Hansen, Steven Doo, 

and Michele Gibson for their tireless efforts in the field. 

• DPR scientist–Albert Wang for his work on the Quality Assurance Project Plan. 

• DPR scientists–Terri Barry, Mark Pepple, and Frank Spurlock for their invaluable input and 

review. 

• Copper Antifouling Paint Sub-Workgroup of the Marina and Recreational Boating 

Workgroup of the Non-Point Source Interagency Coordinating Committee for their input in 

the study design. 

• Ray Arnold, (Copper Development Association), Linda Candelaria (Santa Ana Regional 

Water Quality Control Board), Richard Looker (San Francisco Bay Regional Water Quality 

Control Board), Kelly Moran (TDC Environmental), and Paul Salop (Applied Marine 

Science) for their input on the study design. 

• Southern California Coastal Water Research Project staff: Ken Schiff and Darrin Greenstein 

for their work on toxicity and toxicity identification evaluation. 

• University of California, Department of Agricultural and Natural Resources Laboratory 

chemists: Dirk Holstege and Tracy Francis, and their analytical chemists for their hard work 

on sample analysis. 

• National Oceanic and Atmospheric Administration, Hollings Marine Laboratory staff: Ed 

Worth and Yelena Sapozhnikova for their work on Irgarol and M1 analysis. 

• Robert Santore and Adam Ryan of Hydroqual, Inc for their assistance with the salt water 

Biotic Ligand Model. 

• Melenee Emanuel and Diane Edwards of SWRCB Non-Point Source Program for their role as 

contract managers. 

• The Central Valley Regional Water Quality Control Board for the use and operation of their 

sampling vessel for sediment collection. 

• City of Long Beach Lifeguards for the use and operation of their sampling vessels for the 

sampling of the two Long Beach marinas. 

• Participating marinas associated with the California Association of Harbor Masters and Port 

Captains (CAHMPC) and the Marina Recreation Association (MRA) and their marina 

managers for their knowledge, cooperation, and assistance. 

• Bill Krause (APEX Group), Eric Endersby (CAHMPC), and Mariann Timms (MRA) for 

facilitating communications with CAHMPC and MRA members. 

FEDERAL DISCLOSURE STATEMENT 

Funding for this project has been provided, in part, by U.S. EPA pursuant to Assistance Agreement No. C9­

96906801 and any amendments thereto which have been awarded to SWRCB for the implementation of 

California’s Nonpoint Source (NPS) Pollution Control Program. The contents of this document do not 

necessarily reflect the views and policies of the U.S. EPA or SWRCB, nor does mention of trade names or 

commercial products constitute endorsement or recommendation for use. 

ii
TABLE OF CONTENTS 

ABSTRACT ................................................................................................................................................... i

ACKNOWLEDGEMENTS ......................................................................................................................... ii

FEDERAL DISCLOSURE STATEMENT ................................................................................................ ii

TABLE OF CONTENTS............................................................................................................................ iii

LIST OF TABLES........................................................................................................................................ v

LIST OF FIGURES..................................................................................................................................... vi

LIST OF FREQUENTLY-USED ABBREVIATIONS AND ACRONYMS.......................................... vii

I. Introduction............................................................................................................................................... 1

II. Materials and Methods ........................................................................................................................... 2

Sampling Approach ................................................................................................................................ 2

Assumptions & Limitations.................................................................................................................... 3

Site Selection Criteria............................................................................................................................. 3

Sampling Vessels.................................................................................................................................... 8

Sampling Method and Frequency........................................................................................................... 8

Marina Sites - Copper, Zinc, TSS, and BLM-Associated Parameters .............................................. 10

Marina Sites - Irgarol/M1................................................................................................................. 11

Marina Sites - Toxicity and TIEs...................................................................................................... 11

Local Reference Sites - Copper, Zinc, TSS, and BLM-Associated Parameters ............................... 11

Local Reference Sites - Irgarol, Toxicity, and TIEs ......................................................................... 11

Sample Collection - Water ................................................................................................................... 11

Sample Collection - Sediment .............................................................................................................. 12

Field Quality Assurance/Quality Control ............................................................................................. 12

Sample Handling and Custody ............................................................................................................. 12

Sample Analysis ................................................................................................................................... 13

Water Samples - Copper, Zinc, TSS, and BLM-Associated Parameters .......................................... 13

Water Samples - Irgarol/M1 ............................................................................................................. 13

Water Samples - Toxicity and TIEs.................................................................................................. 13

Sediment Samples - Copper and Zinc .............................................................................................. 13

Sediment Samples - Irgarol/M1........................................................................................................ 14

Laboratory Quality Assurance/Quality Control.................................................................................... 14

Statistical Analysis ............................................................................................................................... 15

Predictive Toxicity Models .................................................................................................................. 16

Biotic Ligand Model - Freshwater.................................................................................................... 16

DOC Model - Salt and Brackish Water ............................................................................................ 17

Biotic Ligand Model - Salt and Brackish Water............................................................................... 17

III. Results................................................................................................................................................... 18

Dissolved Copper ................................................................................................................................. 18

Total Copper......................................................................................................................................... 24

Dissolved Zinc...................................................................................................................................... 28

Total Suspended Solids ........................................................................................................................ 31

Correlations of Total Suspended Solids and Dissolved Organic Carbon with Metals.......................... 34

Irgarol and M1...................................................................................................................................... 37

Total Copper - Sediment....................................................................................................................... 39

Total Zinc - Sediment........................................................................................................................... 40

Toxicant Identification Evaluation (TIE) ............................................................................................. 42

Comparisons to Water Quality Standards............................................................................................. 42

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CTR Freshwater CMC and CCC Water Quality Standards - Copper............................................... 43

CTR Freshwater CMC and CCC Water Quality Standards - Zinc ................................................... 43

CTR Saltwater CMC and CCC Water Quality Standards - Copper.................................................. 43

CTR Saltwater CMC and CCC Water Quality Standards - Zinc...................................................... 44

Comparisons to Toxicity Benchmark Values ....................................................................................... 44

Comparison of Sediment Concentrations to NOAA’s Biological-Effects Guidelines - Copper....... 44

Comparison of Sediment Concentrations to NOAA’s Biological Effects Guidelines - Zinc ........... 45

Comparison of Irgarol Concentrations to Toxicological Endpoints and Aquatic Toxicity

Benchmarks...................................................................................................................................... 45

Comparison of M1 Concentrations to Toxicological Endpoints ...................................................... 46

BLM-Associated Water Quality Data .................................................................................................. 46

Laboratory-Analyzed Constituents................................................................................................... 46

In-Situ Measurements....................................................................................................................... 46

Predictive Toxicity Models .................................................................................................................. 49

Biotic Ligand Model - Freshwater.................................................................................................... 49

DOC Model - Salt and Brackish Water ............................................................................................ 50

Biotic Ligand Model - Salt and Brackish Water............................................................................... 52

IV. Discussion.............................................................................................................................................. 55

Occurrence and Magnitude................................................................................................................... 55

Water Column – Copper................................................................................................................... 55

Water Column – Zinc ....................................................................................................................... 56

Water Column – Irgarol/M1 ............................................................................................................. 57

Sediment – Copper and Zinc ............................................................................................................ 57

Comparison to Water Quality Standards or Biological Effects Thresholds.......................................... 57

Copper .............................................................................................................................................. 57

Zinc................................................................................................................................................... 58

Irgarol/M1 ........................................................................................................................................ 58

Marina versus Local Reference Site ..................................................................................................... 59

Water Column .................................................................................................................................. 59

Sediment........................................................................................................................................... 60

Marina Sources of Copper.................................................................................................................... 60

Wood Preservatives.......................................................................................................................... 60

Background Load ............................................................................................................................. 61

Marina Sediment .............................................................................................................................. 61

Antifouling Paints............................................................................................................................. 61

Marina Sources of Zinc ........................................................................................................................ 63

Antifouling Paints............................................................................................................................. 63

Wood Preservatives.......................................................................................................................... 63

Sacrificial Anodes ............................................................................................................................ 63

Toxicity/Toxicant Identification Evaluation......................................................................................... 64

Predictive Copper Toxicity Models...................................................................................................... 65

V. Conclusions ............................................................................................................................................ 66

VI. References............................................................................................................................................. 67

Appendix A: Marina Description and History........................................................................................ 71

Appendix B: Analytical Methods (ANR Analytical Laboratory).......................................................... 81

Appendix C: SCCWRP’s Toxicity and Phase I TIE methods ............................................................ 111

Appendix D: Method Validation Results (ANR Laboratory) and Summary of Laboratory QA/QC 

Results (All Laboratories)........................................................................................................................ 122

Appendix E: Field Quality Assurance/Quality Control Results and Discussion................................ 128

iv
Appendix F: Metals, TSS, & BLM-Related Water Column Data (ANR Laboratory and DPR 

Laboratory)............................................................................................................................................... 134 

Appendix G: Water Quality Data (ANR Laboratory and DPR), Irgarol/M1 (NOAA HML), & 

Toxicity Results (SCCWRP).................................................................................................................... 153 

Appendix H: Sediment Metals Data (ANR Analytical Laboratory) ................................................... 172

Appendix I: SCCWRP Toxicity/TIE Data Report ............................................................................... 174

Appendix J: Comparison of Freshwater Dissolved Copper Results to CTR Hardness-Based 

Freshwater Acute (CMC) and Chronic (CCC) Standards ................................................................... 180 

Appendix K: Comparison of Freshwater Dissolved Zinc Results to California Toxics Rule HardnessBased Freshwater Acute (CMC) and Chronic (CCC) Standards ........................................................ 185 

Appendix L: Freshwater Biotic Ligand Model Output........................................................................ 190

Appendix M: Dissolved Organic Carbon Model Output ..................................................................... 193

Appendix N: Saltwater Biotic Ligand Model Output........................................................................... 205

LIST OF TABLES 

Table 1. List of Marina Study Areas 

Table 2. Sampling Scheme/Frequency 

Table 3. Summary of Analytical Methods and Detection Limits 

Table 4. Freshwater BLM Operating Parameters 

Table 5. Saltwater BLM Operating Parameters 

Table 6. Range of Metal (in μg/L) and Total Suspended Solids (in mg/L) Concentrations in Water by 

Marinas 

Table 7. Median Metal (in μg/L) and Total Suspended Solids (in mg/L) Concentrations in Water by 

Marinas (arranged in ascending order of marina median dissolved copper concentrations) 

Table 8. Summary of Median Metal Concentrations (in μg/L) in Water 

Table 9. Summary of Median Irgarol and M1 Concentrations (in ng/L) in Water Samples 

Table 10. Summary of Median Metal Concentrations (in mg/kg dry weight) in Sediment Samples 

Table 11. Water Samples with Statistically Significant Toxic Endpoints 

Table 12. Toxicant Identification Evaluation Treatment Results 

Table 13. Marina Median Concentrations of Biotic Ligand Model-Related Constituents (in mg/L) 

Table 14. Ranges of BLM-Related Constituent Concentrations (in mg/L) for Each Marina Area 

Table 15. Freshwater BLM Output for Samples with Predicted Toxicity 

Table 16. DOC Model FCC and FAC Output Ranges 

Table 17. Saltwater BLM Output for Samples with Predicted Toxicity 

Table A-1. Marina GPS Coordinates 

Table D-1. Method Validation Results (Low Level Spikes) 

Table D-2. Method Validation Results (High Level Spikes) 

Table D-3. Analyte-Specific Accuracy, Precision, and Recovery Limits 

Table D-4. Sample Holding Time Guidelines and Calculated Average Holding Times 

Table E-1. Distribution of DCu Concentrations in Field QC Samples 

Table E-2. Field and Rinse Blank Results 

Table F-1. Metals, TSS, & BLM-Related Data 

Table G-1. Water Quality Data (ANR Laboratory and DPR), Irgarol/M1 (NOAA HML), & Toxicity 

(SCCWRP) Results 

Table H-1. Sediment Data – TCu and TZn Concentrations 

Table J-1. Comparison of Freshwater DCu Results to CTR Hardness-Based Freshwater Acute (CMC) and 

Chronic (CCC) Standards 

v
Table K-1. Comparison of Freshwater DZn Results to CTR Hardness-Based Freshwater Acute (CMC) and 

Chronic (CCC) Standards 

Table L-1. Freshwater BLM Output - Predicted Toxicity Results 

Table M-1. DOC Model Output - Predicted Toxicity Results 

Table N-1. Saltwater BLM Output - Predicted Toxicity to M. galloprovincialis 

LIST OF FIGURES 

Figure 1. Statewide Map of Marina Sampling Areas 

Figure 2. Close-Up Map of San Francisco Bay Area Marina Sampling Areas 

Figure 3. Diagram of Sampling Locations Within Marinas 

Figure 4. Distribution of Dissolved Copper Concentrations in Freshwater Marina Areas 

Figure 5. Distribution of Dissolved Copper Concentrations in Brackish Water Marina Areas 

Figure 6. Distribution of Dissolved Copper Concentrations in Saltwater Marina Areas (San Francisco Bay 

Area) 

Figure 7. Distribution of Dissolved Copper Concentrations in Saltwater Marina Areas (Central and South 

Coast) 

Figure 8. Distribution of Marina Median Dissolved Copper Concentrations by Water Type 

Figure 9. Distribution of Total Copper Concentrations in Freshwater Marina Areas 

Figure 10. Distribution of Total Copper Concentrations in Brackish Water Marina Areas 

Figure 11. Distribution of Total Copper Concentrations in Saltwater Marina Areas (San Francisco Bay 

Area) 

Figure 12. Distribution of Total Copper Concentrations in Saltwater Marina Areas (Central and South 

Coast) 

Figure 13. Distribution of Marina Median Total Copper Concentrations by Water Type 

Figure 14. Correlation Plot of Log Dissolved versus Log Total Copper Concentrations 

Figure 15. Distribution of Dissolved Zinc Concentrations in Freshwater Marina Areas 

Figure 16. Distribution of Marina Median Dissolved Zinc Concentrations in Brackish Water Marina Areas 

Figure 17. Distribution of Marina Median Dissolved Zinc Concentrations in Saltwater Marina Areas (San 

Francisco Bay Area) 

Figure 18. Distribution of Marina Median Dissolved Zinc Concentrations in Saltwater Marina Areas 

(Central and South Coast) 

Figure 19. Distribution of Marina Median Dissolved Zinc Concentrations by Water Type 

Figure 20. Distribution of Marina Median Total Suspended Solids Concentrations (in mg/L) by Water Type 

Figure 21. Distribution of Total Suspended Solid Concentrations in Freshwater Marina Areas 

Figure 22. Distribution of Total Suspended Solid Concentrations in Brackish Water Marina Areas 

Figure 23. Distribution of Total Suspended Solid Concentrations in Saltwater Marina Areas (San Francisco 

Bay Area) 

Figure 24. Distribution of Total Suspended Solid Concentrations in Saltwater Marina Areas (Central and 

South Coast) 

Figure 25. Correlation of Total Suspended Solids and Total Copper Concentrations in Brackish Waters 

Figure 26. Correlation of Total Suspended Solids vs. Dissolved Copper Concentrations at Benicia and 

Vallejo Marina Areas 

Figure 27. Correlation of Total Suspended Solids vs. Dissolved Zinc Concentrations at Benicia and Vallejo 

Marina Areas 

Figure 28. Marina Median Irgarol and M1 Water Concentrations by Rounds 

Figure 29. Correlation of Irgarol vs. M1 Concentrations in Marina Waters 

Figure 30. Distribution of Total Copper Concentrations in Marina Area Sediments 

Figure 31. Distribution of Total Zinc Concentrations in Marina Area Sediments 

Figure 32. BLM-Predicted Toxic Units (Observed DCu/BLM-Predicted M. galloprovincialis EC50) vs. 

Observed Toxicity 

Figure E-1. Field Blank Results Above Detection Limit 

Figure E-2. Rinse Blank Results Above Detection Limit 

vi
LIST OF FREQUENTLY-USED ABBREVIATIONS AND ACRONYMS 

AFP – antifouling paint 

ANOVA – analysis of variance 

ANR – Agriculture and Natural Resources 

BLM – Biotic Ligand Model 

CAHMPC – California Association of Harbor Masters and Port Captains 

CCC – Criterion Continuous Concentration 

CDEC – California Data Exchange Center 

CI – confidence interval 

CMC – Criterion Maximum Concentration 

CTR – California Toxics Rule 

CuPt2 – copper pyrithione 

CWA – Clean Water Act 

DCu – dissolved copper 

DO – dissolved oxygen 

DOC – dissolved organic carbon 

DPR – Department of Pesticide Regulation 

DZn – dissolved zinc 

EC – electrical conductivity 

EC50 – effective concentration (50% observed effect) 

ERA – ecological risk assessment 

ERL – Environmental Risk Limit (Irgarol toxicity threshold) 

ERL – Effects Range Low (sediment quality guideline) 

ERM – Effects Range Medium 

FAC – Final Acute Concentration 

FAV – Final Acute Value 

FCC – Final Chronic Concentration 

GPS – global positioning system 

HML – Hollings Marine Laboratory 

LC50 – lethal concentration (50% mortality) 

LNB – Lower Newport Bay 

LOD – limit of detection 

LOEC – lowest observed effect concentration 

LRS – local reference site 

M1 – primary degrade of Irgarol 

MBYH – Marina Bay Yacht Harbor (Richmond Marina) 

MDL – method detection limit 

MdR – Marina del Rey 

MRA – Marina Recreation Association 

NOAA – National Oceanic and Atmospheric Administration 

NPS – non point source 

ppb – parts per billion (equivalent to μg/L) 

ppm – parts per million (equivalent to mg/L or mg/kg) 

ppt – parts per trillion (equivalent to ng/L) 

QA/QC – quality assurance/quality control 

QAPP – Quality Assurance Project Plan 

RWQCB – Regional Water Quality Control Board 

SCCWRP – Southern California Coastal Water Research Project 

SWRCB – State Water Resources Control Board 

TCu – total copper concentrations 

TDS – total dissolved solids 

TIE – Toxicity Identification Evaluation 

TMDL – Total Maximum Daily Load 

TSS – total suspended solids 

TZn – total zinc 

U.S. EPA – United States Environmental Protection Agency 

ZnPt2 – zinc pyrithione 

vii
I. Introduction 

In California, the Department of Pesticide Regulation (DPR) registers antifouling paint (AFP) products 

since they are pesticides. Like other pesticides, AFP products are formulated by combining pesticide active 

ingredients with other compounds (e.g., solvents, adjuvants, inert ingredients) to produce useable and 

effective products. In AFP products, active ingredients are often referred to as biocides. Copper oxide, 

copper hydroxide, copper thiocyanate, zinc pyrithione (ZnPt2), and Irgarol are the biocides that are most 

frequently formulated into AFP products currently registered in California (DPR, 2008a). A single AFP 

product may actually contain multiple biocides. Copper oxide is the most popular of these biocides, 

appearing in more than 90 percent of California AFP products. 

The leaching of copper from AFPs used on recreational boats has been determined to be the major source 

of copper pollution in a large boat basin known as the Shelter Island Yacht Basin (SIYB) in San Diego Bay 

(SDRWQCB, 2005). Water column levels of dissolved copper (DCu) at SIYB are high enough to impair 

beneficial uses and exceed California Toxics Rule (CTR) standards for copper (chronic value of 3.1 μg/L 

and acute value of 4.8 μg/L). More recently, AFPs are suspected as being the significant source of copper 

in two other large boat basins in Southern California: Marina del Rey and Lower Newport Bay 

(LARWQCB, 2005; U.S. EPA, 2002). 

In the past, monitoring data used in the evaluation of AFP pollution in California have been largely 

generated from the San Diego Bay region (Singhasemanon, 2005). Recently, an increase in the 

development of copper-related Total Maximum Daily Loads (TMDLs) and the placement of additional 

copper-impaired water bodies on the Clean Water Act section 303(d) list challenges the presumption that 

this issue is only limited to San Diego Bay. 

Irgarol has been detected with increasing frequency at ecologically sensitive levels in coastal water 

worldwide. In the U.S., Irgarol (and its major metabolite M1) has been detected in the waters of 

Chesapeake Bay and Florida (Hall and Gardinali, 2004). The United States Environmental Protection 

Agency (U.S. EPA) has expressed concern over the potential phytotoxic effects of Irgarol on aquatic plants 

and algae (U.S. EPA, 2003a). In 2005, the National Oceanic and Atmospheric Administration (NOAA) 

conducted an investigation of the occurrence and magnitude of Irgarol and M1 in a number of marinas in 

San Diego Bay (Sapozhnikova et al., 2007). However, this was the only investigation that has been 

completed on these constituents in California thus far. 

Zinc from the use of AFP products is another potential contaminant in marinas. With the use of ZnPt2 

AFPs growing in recent years and the possibility that more zinc-based products could enter the AFP 

market, zinc levels in California marinas need to be evaluated. There is currently little documentation of 

zinc concentrations in marinas. 

For this study, DPR collected water and sediment samples from a number of California marinas and 

analyzed them for indicators of AFP pollution. The biocides of particular interest are copper oxide, copper 

hydroxide, copper thiocyanate, ZnPt2, and Irgarol. This monitoring study focused specifically on marinas 

because these areas harbor densely packed recreational and commercial vessels, most of which have hulls 

that are painted with AFPs. Moreover, marinas are purposefully constructed to shelter boats from currents 

and waves thus resulting in poor flushing in the marina. Thus, AFP pollution at these locations should 

represent some of the worst water quality conditions resulting from the use of AFPs on boats. 

Findings from this monitoring study will help DPR, State Water Resources Control Board (SWRCB), 

Regional Water Quality Control Boards (RWQCBs), and other interested agencies to more fully evaluate 

the potential adverse effects of current AFP use on aquatic organisms. These findings will also help DPR 

determine if mitigation actions (regulatory and/or voluntary) are needed on a local or a statewide scale. 

The primary objective of this study is to: 

• Determine the occurrences and concentrations of copper, zinc, and Irgarol and M1 in the water and 

sediment of selected California marinas and determine whether these levels exceed water quality 

standards (e.g., CTR copper and zinc standards, site-specific objectives), criteria (e.g., U.S. EPA’s 

1
updated aquatic life criteria for copper), guidelines (e.g., NOAA’s sediment quality guidelines), and 

other ecologically relevant values (e.g., LC50, EC50). 

Secondary objectives are to: 

• Determine if copper and zinc concentrations in marinas are significantly higher than concentrations in 

the adjacent local reference sites (LRS). 

• Determine whether AFP analyte concentrations differ between salt water, brackish water, and fresh 

water marinas. 

• Apply U.S. EPA’s Biotic Ligand Model (BLM) or other scientifically-relevant models to estimate 

bioavailability and toxicity of copper. 

• Measure the toxicity of marina waters using metal-sensitive test species and endpoints, compare 

measured toxicity with copper and zinc concentrations and BLM-predicted toxicity, and identify the 

likely cause of observed toxicity using Toxicity Identification Evaluation (TIE) procedures. 

II. Materials and Methods 

Sampling Approach 

Copper, zinc, Irgarol, and M1 are the principal AFP-related constituents of interest. Water and sediment 

samples were taken from inside and outside of marinas and evaluated for AFP analytes as well as a number 

of other water quality parameters. If marina levels of these analytes are consistently and statistically higher 

than ambient levels outside the marina, this would provide strong evidence that marinas are a significant 

source of AFP pollutants. Moreover, if AFP sources of AFP analytes can be shown to be large compared to 

other in-marina non-AFP sources, particularly during dry periods, then it is likely that AFPs are the primary 

cause of elevated concentrations of these analytes in marinas. This approach does not apply to Irgarol since 

AFPs are the most significant use of this biocide in surface waters. Hence, samples collected for Irgarol 

and M1 analysis were all taken inside the marinas. 

The sampling period was confined to California’s summer months (July through October) to avoid 

confounding hydrologic factors that could be introduced by storm events. Processes such as flushing, 

dilution, mixing, and sediment resuspension affect the water and sediment concentrations of AFP indicators 

and other analytes. Moreover, during storm periods, other non-marina inputs of AFP analytes into the 

marina could exceed AFP sources within the marina. Because DPR is interested in evaluating pollution 

from AFP use, sampling for this study was conducted in the dry summer months. 

Due to the popularity of copper-based AFPs and exceedances of copper water quality standards, this study 

focuses on copper as an AFP analyte. Particular attention is given to the dissolved fraction of copper (i.e., 

DCu) as the aforementioned standards are defined in this manner. DCu concentrations at any given time 

are heavily influenced by the level of suspended solids in the water column. Copper that becomes bound to 

solids will be accounted for in the total copper (TCu) measurement. To improve the understanding and 

interpretation of DCu results, concentrations of TCu and total suspended solids (TSS) were also assessed. 

To provide input into U.S. EPA’s copper BLM, a number of water quality parameters and constituents were 

measured for each water sample taken. These include alkalinity, calcium, salinity (as chloride), dissolved 

organic carbon, magnesium, pH (in situ), potassium, sodium, sulfate, and temperature (in situ). Although 

only the fresh water BLM was available at the onset of this study, DPR measured its input parameters for 

all of the study’s ambient water samples in anticipation of the availability of the salt water BLM. 

Developers of the salt water BLM disclosed that similar parameters would be also be required for the salt 

water version of the model (R. Arnold., personal communication, 2006). 

A subset of salt and brackish marina water samples were assessed for toxicity using U.S. EPA’s method for 

short-term chronic toxicity test on mussel (Mytilus galloprovincialis) embryo development. Since this test 

is sensitive to copper, the results will help establish whether levels of DCu correspond to sample toxicity. 

The determination of actual toxicity will also be useful toward the validation of the salt water BLM. 

2
TIEs were used to better link chemistry results with adverse biological effects. TIE procedures were 

conducted on a subset of toxic samples to confirm the cause of the observed toxicity. TIE treatments are 

designed to selectively remove or neutralize classes of compounds and their associated toxicity to identify 

the most likely cause of the toxicity. Results from toxicity/TIE samples were compared to DCu and 

predicted toxicity. 

This study was conducted in accordance with the Quality Assurance Project Plan (QAPP) document, which 

was prepared and approved prior to the onset of sampling activities (DPR, 2006). 

Assumptions & Limitations 

Certain assumptions were made in the development of the study design: 

• AFP-painted boats are sources of copper, zinc, Irgarol, and M1 to marina waters and sediments. 

• The pathways that introduce AFP pollutants to marina waters and sediments are passive leaching 

and underwater hull-cleaning of AFP-painted boats. 

• AFP-painted boats in the marina represent the most significant source of Irgarol and M1 detected 

in the marina. 

Certain limitations were recognized in the development of the study design: 

• There are also non-AFP sources of copper and zinc inside of marinas 

• DCu and dissolved zinc (DZn) concentrations at any given site could vary over time due to a 

number of factors including tidal influence, site-specific hydrology, and fluctuations in total 

suspended solid levels. The design of this study attempts to address these limitations; however, 

these factors may still have some effects on study results. 

Site Selection Criteria 

Hundreds of California marinas were evaluated using information produced by the Marina Mapping SubWorkgroup of the Non-Point Source Interagency Coordinating Committee’s Marina and Recreational 

Boating Workgroup. The pool of candidate marinas were then reduced from the large initial list with the 

use of maps and application of the following considerations (in descending order of importance): 

1) Marina contains a relatively high number of slips for its water body type. 

2) Marina is located in an area that receives poor flushing (the likelihood of finding elevated levels of 

marina-borne pollutants over an extended period is high.) 

3) Marinas are distributed over various regions of the state. 

Forty candidate marinas remained after this initial reduction. Aerial photos and more-detailed maps were 

used to further evaluate individual marina layouts and relevant local and regional anthropogenic, geologic, 

and hydrologic features. DPR staff then visited many of the candidate marinas to gauge collaborative 

interest from individual marina managers and to discuss logistical and site-specific criteria with them. 

These criteria include: 

1) The marina owner/operator is cooperative. 

2) The marina has a boat on-site that can be used for sampling. 

3) The marina contains slip areas that are sufficiently isolated from adjacent or surrounding sources 

(e.g., boatyards, industrial discharges, mining discharges). 

4) Historical and current activities (e.g., dredging, construction) in the marina area will not 

significantly interfere with the interpretation of results. 

Twenty-three marina areas were ultimately chosen (Table 1 and Figure 1). Many of the selected marinas 

are located in the San Francisco Bay Area (Figure 2). 

3
Table 1. List of Marina Study Areas1 

Water Body County City 

Total 

Slips 

Percent 

Occupancy 

Estimate2 

FRESHWATER 

1) Folsom Lake Marina Folsom Lake El Dorado Folsom 675 not available 

2) Tahoe Keys Marina Lake Tahoe El Dorado Lake Tahoe 250 100 

3) Sacramento City Marina Sacramento River Sacramento Sacramento 547 97 

4) Village West Marina Sacramento-San Joaquin San Joaquin Stockton 700 85 

BRACKISH WATER 

1) Antioch Marina San Joaquin River Contra Costa Antioch 310 82 

2) Pittsburg Marina Sacramento River Contra Costa Pittsburg 486 75 

3) Benicia Marina Carquinez Strait Solano Benicia 320 95 

4) Vallejo Municipal Marina Mare Island Strait Solano Vallejo 800 60 

SALTWATER 

1) Clipper Yacht Harbor Richardson Bay Marin Sausalito 735 90 

2) San Francisco Marina San Francisco Bay San Francisco San Francisco 700 95 

3) South Beach Harbor San Francisco Bay West San Francisco San Francisco 700 95 

4) Berkeley Marina San Francisco Bay East Alameda Berkeley 1,052 91 

5) Marina Bay Yacht Harbor San Francisco Bay East Contra Costa Richmond 850 85 

6) Loch Lomond Marina San Francisco Bay North Marin San Rafael 517 82 

7) Ballena Isle Marina San Francisco Bay East Alameda Alameda 504 75 

8) Coyote Point Marina San Francisco Bay West San Mateo San Mateo 565 85 

9) Santa Cruz Harbor Santa Cruz Harbor Santa Cruz Santa Cruz 1,000 100 

10) Monterey Harbor Monterey Bay Monterey Monterey 413 85 - 90 

11) Santa Barbara Harbor Santa Barbara Channel Santa Barbara Santa Barbara 1,133 95 

12) Marina del Rey Basins D, E, F Marina del Rey Harbor Los Angeles Marina del Rey ~ 3,000 not available 

13) Marina del Rey Basins A, B, C, G, H Marina del Rey Harbor Los Angeles Marina del Rey ~5,000 not available 

14) Alamitos Bay Marina Alamitos Bay Los Angeles Long Beach 1,191 95 

15) Downtown Shoreline Marina L. A. - Long Beach H. Los Angeles Long Beach 1,800 90 1

 Source: Marina and Recreational Boating Workgroup – Mapping Sub-Workgroup 2

 Percent occupancy estimates were provided by individual marina managers via email surveys in 2007.

4
Figure 1. Statewide Map of Marina Sampling Areas 

5
Figure 2. Close-Up Map of San Francisco Bay Area Marina Sampling Areas 

6
Since copper levels in marinas have been documented to some extent in San Diego Bay, Mission Bay, 

Oceanside Harbor, and Dana Point Harbor, these regions were excluded (Singhasemanon, 2005; Schiff, 

2007). Moreover, the Santa Ana RWQCB initiated a monitoring study of metals in Newport Bay Area 

marinas in the summer of 2006 (L. Candelaria, personal communication, 2006). Therefore, this study did 

not include sites in the Newport Bay Area; however, findings from these studies may be referred to later in 

this report. 

DPR does not require use reporting for most AFPs. Thus, it is difficult to get a clear assessment of local 

and regional use of these pesticides. Anecdotal evidence suggest that copper-based AFPs are not as 

commonly applied to boats in fresh water areas because hard fouling organisms (e.g., barnacles, mollusks, 

and tubeworms), which predominantly occurs in salt water, are not a major pest concern. AFPs (e.g., noncopper and thin-film Teflon® paints) that are effective against soft fouling organisms (e.g., algae and 

aquatic weeds) are often adequate for use in fresh water. Furthermore, many of the smaller-sized boats that 

are used in fresh water areas spend a considerable amount of time out of the water in the off-season and 

may not have AFPs on their hulls at all. Field observations in Northern California and conversations with 

boating industry professionals suggest that copper-based AFPs are still used to a limited extent in fresh 

water areas. 

AFP use for boats maintained in salt and brackish water areas tends to be higher than in fresh water areas. 

In California, fouling pressure is generally greatest on hulls of boats that moor or spend a considerable 

amount of time in more saline water. Since the largest marinas and the highest amount of AFP use occurs 

in saltwater areas, there was an emphasis on saltwater marinas. For the study, 15 saltwater marinas were 

chosen compared to four brackish water marinas and four freshwater marinas. 

Measured electrical conductivity (EC) data were used to determine the water types in which each marina 

was located. USGS and DWR gauging station data were generally used to verify field-crew measured EC 

values. These measurements were compared to frequently-used ranges for EC of the three water types. 

The EC ranges were 0–1,500, 1,500–15,000, and > 15,000 micro Siemens/centimeter (μS/cm) for 

freshwater, brackish water, and saline water, respectively. Sea water has an EC reading of about 50,000 

μS/cm. 

The four marinas situated in the far north area of the San Francisco Bay Estuary (Vallejo, Benicia, 

Pittsburg, and Antioch marinas) were difficult to classify into water types. Measured EC data at Vallejo 

and Benicia during the study were almost always above the 15,000 μS/cm EC threshold and could arguably 

be categorized as saltwater marinas; although, salinity values at these two marinas are much lower than 

those observed in San Francisco Bay Area and coastal marinas. Pittsburg Marina exhibited salinity that is 

mostly in the brackish range of 1,500–15,000 μS/cm; however, EC readings there occasionally dipped 

below the freshwater threshold of 1,500 μS/cm. Even Antioch Marina, which exhibited salinity that is 

considered to be in the high end of the freshwater range during the entire sampling period, is difficult to be 

neatly categorized. The annual salinity profile from a gauging station data (CDEC, 2008) near the Antioch 

marina shows that its salinity levels are in the brackish range about six months of the year. 

For the purpose of analysis and discussion, we have classified brackish water marinas as those marinas that 

are exposed to salinity that is in the brackish EC range during some part of the year. Thus, Vallejo, 

Benicia, Pittsburg, and Antioch marinas are considered brackish in nature. 

Local Reference Sites (LRSs) for each marina were identified to help determine whether concentrations of 

copper and zinc inside of the marina were attributable to local background concentrations, which were 

measured just outside of each marina. Marina owners, operators, managers, and dock masters and harbor 

masters (these individuals will be collectively referred to from here forth as marina managers) were 

consulted to determine the best locations for these LRS sites at their respective marina areas. LRSs were 

initially identified with the marina managers’ help and then selected by DPR staff using the following 

criteria (in descending order of importance): 

1) The site was located outside the influence of marina activities and potential sources of AFPs, but 

adjacent to the marina area and within the same body of water. 

2) The site was sufficiently isolated from potentially confounding inputs (e.g., boatyards, industrial 

7
discharges, and various historical contamination). 

3) Recent and current activities (e.g., dredging, construction) in the immediate area would not 

significantly interfere with the interpretation of results. 

4) The site contained underlying sediment that can be collected and analyzed. 

5) There was suitable and safe access to the site. 

The exact locations of the LRSs were determined on the first day of sampling at each marina when the field 

crew were able to evaluate site-specific conditions. Once LRSs were determined in the first sampling 

round, they were resampled in the second and third round. 

More detailed information on the study marinas, including the description, history, and Global Positioning 

System (GPS) locations can be found in Appendix A. Marina managers provided this marina-specific 

information via an email survey conducted in 2007. Survey responses greatly aided DPR in the 

interpretation of study data. 

Sampling Vessels 

Larger marinas tend to have at least one vessel for use by marina staff. During the site selection process, 

DPR staff established verbal agreements with each marina manager to verify his or her interest in 

participating in the study. DPR staff also confirmed the availability of a sampling vessel and an operator 

with the marina manager. In the case of the two Long Beach marinas, the City of Long Beach lifeguards 

provided the necessary vessels and operators to make sampling at these locations possible. 

DPR staff established a sampling schedule to coordinate marina visits with each marina manager and his or 

her staff. DPR’s sampling crew, who are based in Sacramento, CA, transported all necessary supplies and 

sampling equipment to each sampling site. 

Sampling Method and Frequency 

Table 2 provides an overview of the sampling scheme and sampling frequencies. When deviations from 

the original monitoring plan occurred, the planned number of samples are shown in parentheses. These 

deviations are noted and discussed throughout this Materials and Methods Section. 

8
2

345

1Table 2. Sampling Scheme and Number of Samples Taken 

Round 1 Round 2 Round 

Water Water Water 

Sampling Location Water Type1 

Abbreviated 

Site ID 

Metals/ 

BLM2 Irgarol/M1 

Metals/ 

BLM2 

Toxicity/ 

TIE 

Metals/ 

BLM2 Irgarol/M1 

Metals

Folsom Lake Marina F FL1 to FL8 8 8 8 

Tahoe Keys Marina F TK1 to TK8 8 8 0(8) 

Sacramento City Marina F SA1 to SA8 8 8 8 

Village West Marina F VW1 to VW8 7 (8) 8 8 

Antioch Marina B AM1 to AM8 8 8 8 

Pittsburg Marina B PM1 to PM8 8 2 8 4 8 2 

Benicia Marina B BM1 to BM8 8 2 8 4 8 2 

Vallejo Municipal Marina B VM1 to VM8 8 2 8 4 8 2 

Clipper Yacht Harbor S CY1 to CY8 8 8 8 

San Francisco Marina S SF1 to SF8 8 2 8 4 8 2 

South Beach Harbor S SH1 to SH8 8 2 8 4 8 2 

Berkeley Marina S CB1 to CB8 8 2 8 4 8 2 

Marina Bay Yacht Harbor S MB1 to MB8 6(8) 2 6(8) 4 6(8) 2 

Loch Lomond Marina S LL1 to LL8 8 2 8 4 8 2 

Ballena Isle Marina S BI1 to BI8 8 2 8 4 8 1(2) 

Coyote Point Marina S CP1 to CP8 8 8 0(8) 

Santa Cruz Harbor S SC1 to SC8 8 8 8 

Monterey Harbor S MH1 to MH8 8 8 8 

Santa Barbara Harbor S SB1 to SB8 8 8 8 

MdR Basins D, E, F (Back Basins) S BB1 to BB4 4 4(2) 4 4 4 2(2) 

MdR Basins A, B, C, G, H (Front 

Basins) S FB1 to FB8 8 8 8 

Alamitos Bay Marina S AB1 to AB8 8 2 8 4 8 2 

Downtown Shoreline Marina S DS1 to DS8 8 0(2) 8 4 8 0(2) 

Total 177(180) 24(24) 178(180) 48 162(180) 21(24) 

 Sampling sites located in F: Freshwater; B: Brackish water; S: Saltwater 

Metals and BLM parameters include: DCu, TCu, DZn, TSS, Dissolved Organic Carbon (DOC), Salinity (Cl), Sulfate, Alkalinity, Mg, Ca, Na, and K. 

 TOC = Total Organic Carbon  For sediment samples, metals parameters include TCu and TZn Numbers in parentheses indicate the number of samples planned in the original sampling plan. 
Marina Sites - Copper, Zinc, TSS, and BLM-Associated Parameters 

Water and sediment samples were taken by boat from four points within each of the 23 marinas for DCu, DZn, TCu, TSS, 

and BLM-associated parameters. Water samples were collected about once a month at each site over a three-month period. 

Sediment samples were only collected in the third sampling round. Sediment samples were collected after water samples 

at each sampling point to help minimize contamination of the water samples. Note that sediment samples were only 

collected from three freshwater marina sites (Folsom Lake Marina, Sacramento Marina, and Village West Marina). 

Consideration of safety and logistical concerns resulted in the decision to not collect sediment samples for the remaining 20 

marinas as was planned in the original monitoring plan and QAPP. 

Past monitoring studies of copper in marinas suggested that copper levels in both water and sediment tend to be highest 

near the area of moored vessels and lowest toward the entrance of the marina (SDRWQCB, 2005; Pap, 2004). Marina 

sampling sites were therefore located in areas where vessels were moored. To maintain site-to-site consistency and to 

ensure that samples taken were not too close to boat hulls, DPR staff chose sampling sites that were located near the center 

of the fairway (common term for the channels between the docks) and adjacent to the midway point of the dock/pier 

structure (see Figure 1, below). 

Figure 3. Diagram of Sampling Locations in a Marina 

L a nd 

D o c k 

s lip s lip 

s lip 

D o c k D o c k D o c k D o c k 

X X X X 

M a rin a E n tr a n c e 

O p en W a te r 

X = S a m p lin g 

p o in t is m id d le o f 

fa ir w a y an d 

a d ja c e n t to m id -

p o in t of d o c k 

Although each marina has a unique layout of docks and slips, DPR staff identified the candidate fairways at each marina 

using marina layout diagrams and assigned numbers to the fairways. Four fairways were randomly chosen in each marina 

as sampling sites. Fairways that were adjacent to docks with less than 50% of their slips filled during the first sampling 

round were not selected and another fairway was chosen using the same selection criteria. 

10 
To precisely relocate sampling sites during subsequent rounds, each sampling location was initially identified using a GPS 

unit to mark the exact latitudinal and longitudinal coordinates. Field crew also recorded the identification numbers of 

adjacent docks. If a marina or sampling site was inaccessible during a revisit, the sampling event was rescheduled within 

the shortest possible time of the original scheduled date. 

Marina Sites - Irgarol/M1 

Water samples were collected for Irgarol analysis from 12 of the 23 study marinas. Holding time constraints and shipping 

schedules limited which marinas the DPR field crew could sample for Irgarol. The 12 chosen marinas are identified in 

Table 2. 

The DPR field crew collected two samples from each marina in the first and third sampling rounds. The first two sites 

within the marina that were identified for the sampling of copper, zinc, TSS, and BLM-associated parameters were chosen 

as Irgarol sampling sites. 

In the study monitoring plan, sediment samples were originally to be collected from each of the 12 marinas during the third 

sampling round. Due to the inability of field crew to collect sediment samples at these locations, no sediment samples 

were taken for Irgarol/M1 analysis. 

Marina Sites - Toxicity and TIEs 

Water samples were collected for toxicity testing from a subset of marinas. Four samples were taken from the same 12 

marinas selected for Irgarol sampling (again due to shipping constraints) in the second sampling round. A toxicity sample 

was taken from each of the four sampling sites within the marinas that were identified for the sampling of copper, zinc, 

TSS, and BLM-associated parameters. 

TIE procedures were conducted on selected toxic marina water samples. 

Local Reference Sites - Copper, Zinc, TSS, and BLM-Associated Parameters 

Water and sediment samples were taken by boat at the four LRSs outside of the selected marinas. Water samples were 

collected about once a month at each site over a three-month period. 

A limited number of sediment samples were collected in the third sampling round. Recall that the DPR field crew were 

only able to collect sediment samples from three freshwater marina sites (Folsom Lake Marina, Sacramento Marina, and 

Village West Marina). Safety and logistical concerns prevented sediment sampling at all 23 marinas as originally outlined 

in the monitoring plan. When sediment sampling was done, it took place after the collection of water samples at each 

sampling site to minimize contamination of the water samples. 

The field crew determined specific LRSs during the first sampling visit to each marina. The criteria listed under the Site 

Selection Criteria sub-section were used to determine the sites. 

Local Reference Sites - Irgarol, Toxicity, and TIEs 

No samples were taken at LRSs for Irgarol, toxicity, and TIE analysis. 

Sample Collection - Water 

Water samples were collected from approximately one meter below the water surface. Schiff et al. (2007) documented a 

depth-related gradient for copper in marinas with the highest concentrations near the surface. Furthermore, to avoid AFP 

contamination from the sampling vessel itself, samples were taken approximately two meters from the side of the boat. 

11
The water sampling apparatus consisted of a battery-operated peristaltic pump that draws water through polyethylene 

tubing and deposits it directly into a collection container. On the inlet end, the tubing is attached to a long plastic 

extendable pole, which allows for greater freedom of movement and reach. 

For metal samples, U.S. EPA-certified, pre-cleaned 250-ml polyethylene plastic bottles were used. For Irgarol/M1 

samples, U.S. EPA-certified, pre-cleaned 1-L amber glass bottles were used. For TSS and BLM-associated parameter 

samples, 250-ml polyethylene bottles were used. For toxicity samples, 1-L polyethylene containers were used. 

Samples for DCu, DZn, magnesium, calcium, sodium, and potassium, and dissolved organic carbon (DOC) analyses were 

filtered with an in-line 0.45-μm filter and then acidified with Optima® ultra-pure nitric acid to a pH level of < 2.0. 

Samples for sulfate, chloride, and alkalinity analyses were simply filtered (no subsequent acidification). Samples for TCu 

analysis were simply acidified (no filtration prior to acidification). Samples for TSS, Irgarol/M1, and toxicity/TIE analyses 

were not filtered nor acidified. 

Sample Collection - Sediment 

Sediment was collected using a Van Veen® grab sampler. The jaws and doors are coated with Teflon® to prevent 

contamination from the sampler. Each grab satisfied the following criteria to be considered valid: 

• Complete closure of the sampler 

• No evidence of significant sediment washout through the doors 

• Minimum disturbance of the sediment surface 

The overlying water in the sampler was first drained by slightly opening the sampler such that there was minimal 

disturbance of the fine-grained top layer of sediment. The top 2 cm of sediment were then collected with a clean Teflon® 

coated scoop and placed into a 4 oz., U.S. EPA-certified, pre-cleaned polyethylene container. This collection procedure 

was repeated until sufficient sediment had been collected to fill the sample container. 

Field Quality Assurance/Quality Control 

Field blanks and equipment rinse blanks (from here on referred to as rinse blanks) were taken to assess potential 

contamination of water samples in the field. Field blanks were collected by filling sample containers with de-ionized or 

distilled water at the site of collection. Rinse blanks were collected by running de-ionized or distilled water through the 

identical sampling apparatus that was used to collect environmental samples. Rinse blanks were taken after the sampling 

apparatus had been cleaned at least once through the sampling event. 

Sample Handling and Custody 

Since low (parts per billion) concentrations of metals in the water samples are expected in this study, sample collection and 

handling followed U.S. EPA Method 1669–Sampling of Ambient Water for Trace Metals at U.S. EPA Water Quality 

Criteria Levels (U.S. EPA, 1996a). 

All samples were transported in coolers with ice. Glass containers were placed in pre-formed styrofoam packs to minimize 

chance of breakage. After transport, samples were refrigerated and stored at 4o

C until extraction or chemical analysis. 

Irgarol and toxicity samples were immediately shipped to their respective analytical laboratories due to short holding time 

requirements. These samples were carefully packed and shipped (through the United Parcel Service) in styrofoam packs 

and well-insulated coolers with blue ice. Some toxicity samples were picked up directly by toxicity-laboratory personnel. 

Each sample had an accompanying DPR chain-of-custody form, which contains sample collection and identification 

information as well as sampling tracking information. All individuals who come into contact with the sample bottles were 

required to sign and date the form to acknowledge their handling of the samples. 

12
Sample Analysis 

Due to the variety of analyses needed for this study, DPR utilized several laboratories for the analytical work. A summary 

of analytical methods and their citations are provided in the subsequent sub-sections. 

Water Samples - Copper, Zinc, TSS, and BLM-Associated Parameters 

The University of California, Davis, Agriculture and Natural Resources (ANR) Analytical Laboratory analyzed water 

samples for DCu, TCu, DZn, TSS, magnesium, calcium, sodium, potassium, salinity (as chloride), sulfate, and alkalinity. 

Note that magnesium, calcium, sodium, potassium, chloride, sulfate, and alkalinity are also measured as dissolved 

concentrations. 

The ANR Laboratory used the following methods (See Appendix B for complete methods): 

• ANR Method 837 with quantification by Graphite Furnace Atomic Absorption Spectrometry (GFAAS) for DCu. 

• ANR Method 891 with quantification by GFAAS for TCu. 

• ANR Method 870, employing gravimetric analysis following oven drying for TSS. 

• ANR Method 835 for DZn, calcium, magnesium, potassium, sodium with quantification by flame Atomic 

Absorption, Emission Spectrometry (AAS, AES), or Inductively Coupled Plasma Atomic Emission Spectrometry 

(ICP-AES) depending on the analyte. 

• ANR Method 880, an ion chromatography conductivity method for chloride and sulfate 

DPR analyzed water samples for DOC using EPA Method 415.3 (U.S. EPA, 2005). A summary of this method can also be 

found in Appendix B. 

Water Samples - Irgarol/M1 

NOAA’s Hollings Marine Laboratory (HML) in Charleston, South Carolina, analyzed water samples for Irgarol (also know 

as Irgarol 1051 or 2-methylthio-4-tert-butylamino-6-cyclopropyl-amino-s-triazine) and M1 (also known as GS26575 or 2­

methylthio-4-tert-butylamino-6-amino-s-triazine) using a high performance liquid chromatography electro spray ionization 

tandem mass spectrometry method as published in Thomas (2002). A summary of this method is presented in Appendix B. 

Water Samples - Toxicity and TIEs 

The Southern California Coastal Water Research Project’s (SCCWRP’s) laboratory in Westminster, California, tested salt 

and brackish water samples for sub-chronic developmental toxicity on the mussel Mydulis galloprovincialis based on 

methods in EPA/600/R-95/136 (U.S. EPA, 1995). SCCWRP’s complete toxicity method is presented in Appendix C. This 

test measures the reduction in the mussel’s ability to develop fertilized eggs after a 48-hour exposure to water samples. 

Samples with salinity concentrations of less than 30 g/kg were adjusted using hypersaline brine. The tests were conducted 

in glass shell vials containing 10 mL of solution at a temperature of 15°C. Four replicates were tested for each sample. A 

seawater blank was included as negative control. A copper reference toxicant test was included as positive control. 

After a 48-hour exposure period, the embryos were preserved and examined with a microscope to assess normal 

development. The data are presented as percent normal-alive, which was calculated as the number of normal embryos after 

exposure relative to the number of fertilized eggs added at the beginning of the exposure. Samples were considered toxic if 

the difference between mean percent normal development of the sample and the control was statistically significant and if 

the sample’s mean percent normal development was less than 80% when compared to the control. 

SCCWRP also performed follow up TIEs on a subset of toxic samples based on methods from EPA/600/R-96/054 (U.S. 

EPA, 1996b). SCCWRP’s Phase I TIE method is also presented in Appendix C. The toxicity threshold that triggered a 

TIE was 50% abnormal embryo development relative to control. 

Sediment Samples - Copper and Zinc 

UCD ANR analytical laboratory analyzed sediment samples for total copper (TCu) and total zinc (TZn) using ANR method 

13
390, which relies on analysis by atomic absorption spectrometry. In the study monitoring plan, DPR was to analyze sediment 

samples for grain size and total organic carbon (TOC) using DPR method SOPMETH 004.00 (Dietrich, 2005) and SOPMETH 

005.00 (Gunasekara, 2006), respectively. However, DPR decided to not proceed with grain size and TOC analyses due to 

limited sediment samples that were collected. 

Sediment Samples - Irgarol/M1 

In the monitoring plan, NOAA’s HML in South Carolina was to analyze a subset of samples for Irgarol and M1 using an 

accelerated solvent extraction liquid chromatography - tandem mass spectrometry method. However, DPR decided to not 

proceed with the analysis of Irgarol in sediment due to limited sediment samples that were collected. . 

The analytical parameters measured, the methods used, and the method detection limits as reported by the analytical 

laboratories are listed in Table 3. 

Table 3. Summary of Analytical Methods and Detection Limits (BLM-associated parameters presented in grey) 

Analytes 

Analytical Method Method Detection Limit (MDL) or 

Limit of Detection (LOD) 

Dissolved Copper ANR 837 0.1 μg/L 

Dissolved Zinc ANR 835 1.0 μg/L 

Irgarol Thomas, 2002 1.0 ng/L 

M1 Thomas, 2002 1.0 ng/L 

Total Copper ANR 891 0.1 μg/L 

Total Suspended Solids ANR 870 4 mg/L 

Dissolved Organic Carbon EPA 415.3 50 μg/L or 50 mg/L* 

Chloride (Salinity) ANR 880 0.5 mg/L 

Sulfate ANR 880 0.5 mg/L 

Alkalinity ANR 820 2 mg/L 

Magnesium ANR 835 0.5 mg/L 

Calcium ANR 835 0.5 mg/L 

Sodium ANR 835 0.5 mg/L 

Potassium ANR 835 0.5 mg/L 

Total Copper (sediment) ANR 390 1 mg/kg (dry weight) 

Total Zinc (sediment) ANR 390 1 mg/kg (dry weight) 

Toxicity EPA/600/R-95/136 N/A 

Toxicant Identification Evaluation EPA/600/R-96/054 N/A 

* Two possible detection limits depending on the catalyst used. 

The following in-situ field measurements (some of which are required for the BLM) were collected using a variety of 

meters and apparatus: 

• Water Depth (various built-in depth finder of sampling boats) 

• Dissolved Oxygen (DO) (YSI meter, model 85) 

• Electrical Conductivity (EC) (YSI meter, model 85) 

• pH (IQ Scientific Instrument) 

• Temperature (YSI meter, model 85) 

• Turbidity (LaMotte colorimeter, model Smart II) 

During sampling events, information on site-specific activities and factors (e.g., active construction activities, possible 

under water hull-cleaning operations, weather conditions) was recorded. 

Laboratory Quality Assurance/Quality Control 

DPR requires that analytical laboratories must comply with a minimum standard of quality assurance/quality control 

(QA/QC) per DPR Standard Operating Procedure number QAQC.001.00 and study QAPP (DPR, 2006). 

14
Early in the study preparation stage, laboratories validated methods using the appropriate environmental sample matrices 

(e.g., fresh water, salt water, sediment) or they alternately provided documentation that illustrated the accuracy and 

precision of the methods. See Appendix D for method validation results for ANR Laboratory methods. 

For ongoing QA/QC, laboratories employed a variety of QA/QC samples as consistent with the study QAPP. 

Statistical Analysis 

DPR staff generated descriptive or summary statistics to help characterize the results. Various parametric and nonparametric tests were used to determine whether differences between or among groups existed. To explore relationships 

between data groups, correlation analysis was used. MINITAB (Version 15) statistical software for statistical analysis and 

related plots was used. 

The Ryan-Joiner and Anderson-Darling tests were used to test the normality of concentration data. These tests indicated 

that the concentrations of copper, zinc, and the rest of the study analytes were log-normally distributed. Therefore, 

summary statistics were calculated using the log-transformed data. For example, the 95% confidence intervals (CIs) were 

calculated from log-transformed data and then back transformed to original units. Thus, the CIs are frequently asymmetric. 

Similarly, the geometric mean (instead of an arithmetic mean) was the preferred estimate of the central tendency and used 

as the basis for conducting hypothesis testing to determine if differences exist among groups. For simplicity, the geometric 

mean is routinely referred to in this report as the median. Note, however, that many of the boxplots used in this report 

display median lines that denote the 50th percentile value of the untransformed data set. These lines are intended to help 

readers visualize the relative distribution of the untransformed data. The median values that these lines represent are based 

the middle value (or arithmetic mean of the middle two values for an even-numbered data set) of the sorted, untransformed 

data set. Hence, the median values generated from the two approaches may not be identical. In the end, all the 

calculations and statistical tests in this report are based on the geometric mean. 

The four samples taken inside each marina and the four samples taken for each LRS area during each sampling event 

represented sub-samples for that particular marina and LRS area. In contrast, the three sampling events are considered to 

be replication over time. The latter assumption is supported by analyses of variance (ANOVA) results for DCu 

concentrations (and similarly for TCu, DZn, and TSS) over the three summer-month period that indicated no trends or 

major change in marina median concentrations. 

Two ANOVA tests were conducted for DCu, TCu, DZn and TSS. A one-way ANOVA was used to test for the effect of 

water body type (i.e., salt, brackish, and freshwater) on analyte concentration data from the inside of marinas. These 

ANOVAs were conducted with median analyte concentrations from each sampling event within a marina because the 

determination of sub-samples versus true replicates, as discussed above. A second ANOVA was also conducted to test for 

the effect of the factors of individual marina, sampling location (inside versus outside the marinas), and sampling event on 

analyte concentrations. This fully-nested balanced ANOVA was conducted within each water type with marinas as the 

highest nesting factor, followed by inside versus outside (i.e., marina versus LRS), and then sampling event (1st , 2nd, and 

3rd). A level of significance (α) of 0.05 was used for all hypothesis testing. 

Note that results from Tahoe Keys Marina and Coyote Point Marina were omitted from the fully-nested balanced ANOVA 

since samples from only two out of the three events were collected at these two sites. Exclusion of these data allowed the 

ANOVA tests to remain balanced. On rare occasions, one or two sample results were missing for a particular event. When 

this occurred, the missing values were filled in using substitutions based on the averaging of the results of related subsamples from the same event. 

Only a small percentage of results were below detection, thus the method detection limit value was used as a substitute for 

non-detects when mean estimates were needed for graphical display. For hypothesis testing, the value that is 1⁄2 of the 

method detection limit was used as a substitute for non-detects. 

15
Predictive Toxicity Models 

Three computer models, which predict the toxicity of copper based on site-specific water quality data, were used for this 

study. These are the freshwater BLM, the DOC model, and the saltwater BLM. DPR’s staff used the freshwater BLM to 

evaluate freshwater data and the saltwater BLM to evaluate saltwater data. Depending on the site-specific water quality 

characteristics of brackish water sites, their data were used in either the freshwater or saltwater BLM. The DOC model is 

useful for the evaluation of salt and brackish water data. 

Biotic Ligand Model - Freshwater 

In 2007, U.S. EPA issued revised national recommended freshwater aquatic life criteria for copper (U.S. EPA, 2007). The 

revised criteria updated U.S. EPA’s previous hardness-based freshwater criteria, which have not been changed since they 

were established in 1984. The new criteria incorporated the use of the BLM as a developmental tool for site-specific water 

quality criteria. These criteria could potentially be used as future federal or state water quality standards (e.g., via an update 

of the CTR). 

The copper BLM is a model originally developed by Hydroqual, Inc. that utilizes data on site-specific water constituents to 

predict the bioavailability of copper in natural waters and its toxicity to aquatic organisms. It establishes a simplified 

mechanistic framework that was originally based on the toxic effects of copper species on fish gills as the surface ligand. 

Although the BLM makes a number of simplifying assumptions (e.g., the system is in equilibrium, speciation of metals is 

constant over time, and cation effects are limited to competition at the gill site), its usefulness will ultimately be proven by 

its ability to successfully predict toxicity. Early validation of the freshwater model with real-world toxicity data thus far 

has proven the BLM to be a good predictor of toxic effects to fish (U.S. EPA, 2007). In any case, the accounting of the 

effects of various physicochemical characteristics of the water body on copper toxicity is an improvement over the 

previous more limited, hardness-based approach. 

The following site-specific data were entered into the freshwater BLM: temperature, pH, DOC, calcium, magnesium, 

sodium, potassium, sulfate, chloride, and alkalinity. The BLM version that was released in the 2007 U.S. EPA criteria 

update (version 2.2.3) was operational only in the toxicity prediction mode. The metal speciation mode was not available. 

The freshwater BLM has a particular operating range for which it can generate an output (Table 4). 

Table 4. Freshwater BLM Operating Parameters 

Model Input Lower Bound Upper Bound 

Temperature ( ̊C) 10 25 

pH 4.9 9.2 

DOC (mg/L) 0.05 29.65 

Humic Acid Content (%) 10 60 

Calcium (mg/L) 0.204 120.24 

Magnesium (mg/L) 0.024 51.9 

Sodium (mg/L) 0.16 236.9 

Potassium (mg/L) 0.039 156 

Sulfate (mg/L) 0.096 278.4 

Chloride (mg/L) 0.32 279.72 

Alkalinity (mg/L) 1.99 360 

DIC (mmol/L) 0.056 44.92 

Sulfide (mg/L) 0 0 

16
DOC Model - Salt and Brackish Water 

The freshwater BLM was not developed to accurately account for the chemistry of copper in the saline environment. Thus, 

natural waters with higher salinity levels are beyond the acceptable operational range of the freshwater BLM. 

Furthermore, the responses of saltwater and estuarine species to copper must be taken into consideration to determine 

relevant toxic thresholds. Since the saltwater BLM was still in development during the early stage of this report’s 

synthesis, we initially utilized a regression-based DOC model as an interim approach for estimating the potential toxicity 

of copper in marine and estuarine waters. The model has been field validated and is accurate at predicting toxicity (Arnold 

et al., 2005). (Note that an early version of the saltwater BLM has been recently released. Its use and output for this study 

will be discussed in the next sub-section.) 

The DOC model represents a regression-based relationship (power function) of DOC concentrations to DCu EC50 values 

for the mussel Mytilus galloprovincialis exposed to copper in sea water. Model outputs include: 1) an EC50, 2) a final 

acute criterion (FAC), and 3) a final chronic criterion (FCC). Note that Arnold et al., 2005 specifically uses the acronyms: 

FCC and FAC when referring to the DOC model’s calculated toxicity thresholds that were intended to be equivalent to the 

CTR CCC and CMC, respectively. 

Biotic Ligand Model - Salt and Brackish Water 

In 2008, Hydroqual, Inc. released a functional draft saltwater BLM. Although U.S. EPA did not include the saltwater 

BLM with the freshwater BLM in its 2007 revision of the national aquatic life criteria for copper, the saltwater version of 

the model will likely be incorporated into the next revision. U.S. EPA and Hydroqual, Inc. have been discussing the 

technical aspects of the model and the potential for its implementation (R. Arnold, personal communication, 2008). Until 

then, the output of this draft model should be used and interpreted accordingly until it has undergone more extensive 

review and gained formal acceptance from the scientific and regulatory communities. 

At this time, the key output of this model includes predicted copper species and predicted EC50 for both M. 

galloprovincialis and Strongylocentrotus purpuratus (purple sea urchin). Saltwater FAVs, CMC, and CCC are not yet 

generated by the version of the model used for this report. Therefore, the model results that are presented and discussed in 

the saltwater BLM results section will be the predicted site-specific EC50 for M. galloprovincialis. The predicted EC50 

results for S. purpuratus are less relevant for discussion since they are about 2–4 times less sensitive when compared to the 

Mytilus species results. 

The inputs needed to operate the saltwater BLM are identical to those for the freshwater BLM. The operating parameters 

are, however, different (Table 5). Hydroqual, Inc. conducted modeling runs of our data to predict toxicity. 

Table 5. Saltwater BLM Operating Parameters 

Model Input Lower Bound Upper Bound 

Salinity (psu) 26 36 

pH 7.5 8.3 

DOC (mg/L) 0.4 12.0 

17
III. Results 

The analytical results of accuracy and precision measurements for the principal metal analytes (i.e., DCu, TCu, DZn) were 

excellent during method validation (Appendix D). Recoveries for these analytes were in the range of 90–107% and 92– 

105% for the low and high level spikes, respectively. Ongoing batch QA/QC results for all the analytes were almost 

always within accuracy and precision control limits with the exception of one recovery result for the TCu standard 

reference material. 

Copper and zinc were occasionally detected in field and rinse blanks samples (Appendix E). Two of 21 rinse blank 

samples contained relatively high concentrations of DCu and TCu. After potential contaminant sources and pathways were 

considered, it appeared that these occurrences were likely isolated to these two incidents. Furthermore, the potential of a 

similar contamination to environmental samples would have been additionally reduced since the collection of non fieldblank samples also involved a rigorous rinsing with native water prior to the collection of each sample. Overall, our 

analysis of laboratory and field QA/QC data suggested that the study’s chemistry results (i.e., metals and all other analyses) 

are highly reliable and reflective of real world conditions. 

While summary data are presented in this section, a complete tabulation of analytical data is in appendices F–N. Analyte 

concentrations were also compared to applicable water quality standards and toxicity thresholds. Finally, models that 

account for the effect of various factors on copper bioavailability were used to estimate copper toxicity. 

Dissolved Copper 

A total of 346, 96 and 88 water samples were collected from salt, brackish and freshwater sites, respectively. DCu 

concentrations were above the method detection limit (MDL = 0.1 μg/L) in 493 of 517 samples (95%) (Appendix F). The 

range of DCu concentrations for each marina and its associated LRS is shown in Table 6. The sample with the highest 

concentration of DCu (18.4 μg/L) came from one of MdR’s back basins. 

Median DCu concentrations for individual marinas ranged from 0.5 μg/L at Folsom Lake Marina to 13.6 μg/L at MdR’s 

back basins (Table 7). The median concentrations for 21 of 23 marinas were below 7 μg/L. 

The four freshwater marinas exhibited very low to moderate median DCu concentrations (Figure 4). Median 

concentrations for the riverine Sacramento and Village West marinas (3.0 and 3.4 μg/L, respectively) were considerably 

higher than the lake-bound Folsom Lake and Tahoe Keys marinas (0.5 and 0.6 μg/L, respectively). The boxplots for water 

types and regions illustrate the marina median (horizontal line inside each box) as well as the individual sample results 

(circles). 

The four brackish water marinas exhibited low to moderate median DCu concentrations (Figure 5). Among the brackish 

water marinas, median concentrations were highest at Vallejo Marina (3.4 μg/L) and lowest at Pittsburg Marina (2.1μg/L). 

There appears to be some variation in DCu concentrations among brackish water marinas although not as distinctive as in 

the freshwater case. 

The eight San Francisco Bay Area saltwater marinas exhibited low to high median DCu concentrations (Figure 6). Among 

these marinas, the highest median concentration was observed at Loch Lomond Marina (5.8 μg/L) and the lowest median 

concentration was observed at San Francisco Marina (1.1 μg/L). The remaining six Bay Area marinas exhibited median 

DCu concentrations that ranged between 2 and 4 μg/L. 

The seven Central and South Coast saltwater marinas exhibited low to very high median DCu concentrations (Figure 7). 

Among these marinas, DCu concentrations at MdR’s front and back basins (12.4 and 13.6 μg/L) were much higher than 

other marinas in this region. In fact, the median concentrations at MdR were by far the highest in the entire study. 

Alamitos Bay Marina exhibited the lowest median concentration (1.2 μg/L) in this region. The remaining four Central and 

South Coast marinas exhibited median DCu concentrations that ranged between 4 and 7 μg/L. 

The differences in DCu between marinas and their associated LRS were also noteworthy. At all 23 marinas, the median 

DCu concentrations were greater than median concentrations at their associated LRS. This difference is most pronounced 

among the saltwater marinas along the Central and South Coast where marina median concentrations were as high as 57 

18
Range of Dissolved Copper 

Concentrations1 

Range of Total Copper Concentrations1 

Range of Dissolved Zinc Concentrations1 

RanCon

Marina2 Marina LRS3 Marina LRS Marina LRS MaFolsom Lake Marina 0.3 – 0.7 0.2 – 0.5 0.6 – 0.9 0.5 – 0.8 <1.0 – 8.6 <1.0 – 9.1 <4 Tahoe Keys Marina <0.1 – 0.9 <0.1 – 0.7 1.1 – 1.4 0.1 – 0.3 <1.0 – 4.4 <1.0 – 5.0 <4 San Francisco Marina 0.3 – 3.9 <0.1 – 1.0 0.5 – 3.6 <0.1 – 0.7 1.1 – 9.0 <1.0 – 2.2 13 – Alamitos Bay Marina 0.4 – 3.4 <0.1 – 1.2 <0.1 – 4.4 <0.1 – 1.2 2.9 – 22.3 1.6 – 20.3 <4 – Coyote Point Marina 1.7 – 2.6 (n = 8) 1.1 – 1.5 (n = 8) 1.7 – 2.6 (n = 8) 0.7 – 1.2 (n = 8) 2.2 – 8.1 (n = 8) <1.0 – 16.6 (n = 8) 10 – Pittsburg Marina 1.7 – 3.6 1.4 – 1.6 2.4 – 4.9 2.5 – 3.3 1.2 – 7.1 <1.0 – 6.4 <4 – Antioch Marina 1.7 – 2.9 1.4 – 1.7 2.5 – 3.9 2.2 – 2.9 <1.0 – 4.2 <1.0 – 3.1 4 – South Beach Harbor 1.9 – 3.0 0.4 – 1.0 1.1 – 3.2 <0.1 – 1.5 3.3 – 6.2 <1.0 – 2.2 8 – 2Clipper Yacht Harbor 1.3 – 6.6 0.3 – 1.2 0.8 – 9.0 0.6 – 2.3 2.4 – 12.7 <1.0 – 6.3 9 – 2Marina Bay Yacht Harbor 1.7 – 3.6 1.5 – 2.5 (n = 6) 2.0 – 5.7 1.0 – 2.8 (n = 6) 2.9 – 5.3 1.8 – 4.0 (n = 6) 11 – Benicia Marina 1.8 – 3.7 1.5 – 2.2 3.3 – 5.4 2.6 – 9.7 1.1 – 11.7 <1.0 – 2.6 13 – Ballena Isle Marina 1.2 – 4.9 0.8 – 3.7 1.0 – 9.3 0.6 – 4.3 <1.0 – 12.0 1.0 – 8.6 11 – Sacramento Marina 2.2 – 3.7 0.5 – 2.3 2.5 – 5.8 2.0 – 3.0 <1.0 – 7.8 <1.0 – 5.4 <4 – Berkeley Marina 1.8 – 6.8 0.2 – 2.0 2.4 – 7.0 0.4 – 3.2 2.9 – 8.8 <0.1 – 9.5 8 – 2Village West Marina 2.9 – 4.0 1.5 – 2.2 (n = 11) 4.8 – 6.4 2.6 – 4.7 (n = 11) 2.6 – 16.7 1.4 – 6.4 (n = 11) <4 – Vallejo Marina 2.3 – 4.7 1.3 – 1.7 3.1 – 5.6 3.0 – 7.9 3.3 – 9.9 <1.0 – 3.8 10 – Santa Cruz Harbor 3.0 – 5.8 <0.1 – 0.9 4.4 – 7.8 <0.1 8.8 – 22.3 <1.0 – 6.9 12 – Monterey Harbor 3.1 – 7.8 0.1 – 0.5 2.6 – 9.2 <0.1 – 0.3 8.4 – 26.0 <0.1 – 12.0 <4 – Santa Barbara Harbor 2.3 – 8.2 <0.1 – 0.5 3.6 – 8.6 <0.1 6.4 – 22.8 <1.0 – 4.8 10 – Loch Lomond Marina 3.2 – 10.3 1.3 – 2.2 2.8 – 10.6 1.9 – 3.1 4.9 – 13.8 <1.0 – 3.7 10 – Downtown Shoreline Marina 2.4 – 10.9 0.4 – 1.2 2.6 – 11.5 <0.1 – 1.2 9.9 – 40.1 2.1 – 7.6 11 – Marina del Rey FB 8.9 – 16.2 0.3 – 4.7 9.2 – 17.0 <0.1 – 4.6 38.2 – 66.6 <1.0 – 22.1 9 – 2Marina del Rey BB 8.1 – 18.4 0.3 – 4.7 9.0 – 20.2 <0.1 – 4.6 33.3 – 59.5 <1.0 – 22.1 10 – 

Table 6. Range of Metal (μg/L) and Total Suspended Solids (mg/L) Concentrations in Water (sorted by ascending order of marina mconcentrations) 

1 The number of replicates (n) is 12 unless otherwise indicated.2 Marina names are color coded by water type. Green = freshwater, Yellow = brackish water, Blue = saltwater 3 LRS = local reference site 
Table 7. Median Metal (μg/L) and Total Suspended Solids (mg/L) Concentrations in Water (sorted by ascending order of marina m

concentrations) 

2 LRS = local reference site 

Median Dissolved Copper 

Concentrations 

Median Total Copper Concentrations 

Median Dissolved Zinc Concentrations 

MeCon

Marina1 Marina LRS2 Marina LRS Marina LRS MaFolsom Lake Marina 0.5 0.3 0.7 0.6 2.8 2.5 <4 Tahoe Keys Marina 0.6 0.2 1.2 0.2 2.3 2.2 <4 San Francisco Marina 1.1 0.4 0.9 0.2 2.7 1.6 21.Alamitos Bay Marina 1.2 0.3 1.2 0.3 6.7 6.3 13.Coyote Point Marina 2.1 1.3 2.2 1.0 4.6 3.1 17.Pittsburg Marina 2.1 1.5 3.0 2.9 2.9 1.7 7.3 Antioch Marina 2.2 1.5 3.0 2.6 1.9 1.6 6.8 South Beach Harbor 2.2 0.7 1.9 0.4 4.4 1.3 16.Clipper Yacht Harbor 2.4 0.8 2.1 0.9 4.8 2.3 19.Marina Bay Yacht Harbor 2.6 1.7 2.7 1.7 4.1 2.8 13.Benicia Marina 2.7 1.7 4.1 4.5 2.7 1.4 23.Ballena Isle Marina 2.8 1.4 3.3 1.1 5.2 2.4 20.Sacramento Marina 3.0 0.7 4.2 2.4 2.8 2.5 4.3 Berkeley Marina 3.3 0.7 4.0 0.8 4.7 2.2 18.Village West Marina 3.4 1.8 5.6 3.6 5.1 3.2 12.Vallejo Marina 3.4 1.5 4.1 3.9 4.3 1.2 16.Santa Cruz Harbor 4.3 0.3 6.1 0.1 14.3 2.0 23.Monterey Harbor 4.9 0.2 5.3 0.1 18.5 1.7 14.Santa Barbara Harbor 5.7 0.1 5.8 0.1 13.9 1.9 18.Loch Lomond Marina 5.8 1.7 6.3 2.4 7.9 2.0 18.Downtown Shoreline Marina 6.6 0.7 6.4 0.2 20.3 3.9 18.Marina del Rey FB 12.4 1.0 13.3 0.3 49.1 5.6 15.Marina del Rey BB 13.6 1.0 14.7 0.3 50.0 5.6 18.

1 Marina names are color coded by water type. Green = freshwater, Yellow = brackish water, Blue = saltwater 
Figure 4. Distribution of Dissolved Copper Concentrations in Freshwater Marina Areas 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sacramento Marina 

Folsom LRS 

FolsomMarina 

Tahoe Keys LRS 

Tahoe Keys Marina 

4

3

2

1

0

D

Cu Concentrations (μg/

L) 

Figure 5. Distribution of Dissolved Copper Concentrations in Brackish Water Marina Areas 

Vallejo LRS 

Vallejo Marina 

Benicia LRS 

Benicia Marina 

Pittsburg LRS 

Pittsburg Marina 

Antioch LRS 

Antioch Marina 

5

4

3

2

1

D

Cu Concentrations (μg/L) 

   21 
Figure 6. Distribution of Dissolved Copper Concentrations in Saltwater Marina Areas (San Francisco Bay 

Area) 

Coyote Point LRS 

Coyote Point Marina 

Ballena Isle LRS 

Ballena Isle Marina 

S outh Beach LRS 

South Beach Harbor 

San Francisco LRS 

San Francisco Marina 

Berkeley LRS 

Berkeley Marina 

Clipper Y.H. LRS 

Clipper Yacht Harbor 

Loch Lomond LRS 

Loch Lomond Marina 

Marina Bay Y.H. LRS 

Marina Bay Yacht Harbor 

10

8

6

4

2

0

D

Cu Concentrations (μg/L) 

Figure 7. Distribution of Dissolved Copper Concentrations in Saltwater Marina Areas (Central and South 

Coast) 

Alamitos Bay LRS 

Alamitos Bay Marina 

Downtown Shoreline LRS 

Downtown Shoreline Marina 

MdR LRS 

MdR Front Basins 

MdR LRS 

MdR Back Basins 

Santa Barbara LRS 

Santa Barbara Harbor 

Monterey LRS 

Monterey Harbor 

Santa Cruz LRS 

Santa Cruz Harbor 

20

15

10

5

0

D

Cu Concentrations (μg/L) 

  22 
Table 8. Summary of Median Metal Concentrations (μg/L) in Water 

Median DCu Concentrations 

w/ 95% CI 

Median TCu Concentrations 

w/ 95% CI 

Median DZn Concentrations w/ 

95% CI 

Marina Sites LRSs Marina Sites LRSs Marina Sites LRSs 

Saltwater 

Sites 3.3 - 3.7- 4.2 0.5 - 0.6 - 0.7 3.3 - 3.8 - 4.4 0.2 - 0.3 - 0.4 8.0 - 9.2 - 10.7 2.1 - 2.4 - 2.8 

Brackish 

Water Sites 

2.4 - 2.6 - 2.8 1.5 - 1.6 - 1.6 3.3 - 3.5 - 3.8 3.1 - 3.4 - 3.7 2.4 - 2.8 - 3.3 1.3 - 1.5 - 1.7 

Freshwater 

Sites 1.1 - 1.4 - 1.9 0.4 - 0.5 - 0.7 1.7 - 2.2 - 2.9 0.8 - 1.1 - 1.6 2.5 - 3.1 - 3.9 2.1 - 2.6 - 3.2 

All Water 

Sites 2.7 - 3.0 - 3.3 0.6 - 0.7 - 0.8 3.1 - 3.4 - 3.8 0.5 - 0.6 - 0.7 5.4 - 6.2 - 7.0 1.9 - 2.1 - 2.4 

Figure 8. Distribution of Marina Median Dissolved Copper Concentrations by Water Type 

Saltwater LRSs 

Saltwater Marinas 

Brackish Wate r LRSs 

Brackish Water Marinas 

Fre shwater LRSs 

Freshwater Marinas 

14

12

10

8

6

4

2

0

D

Cu Concentrations (

μg/

L) 

  23 
times the LRS median concentrations. Brackish water marinas exhibited the smallest difference with marina median 

concentrations being about twice as high as the LRS median concentrations. 

Overall, median DCu concentrations were 3.0 μg/L for all marina sites and 0.7 μg/L for all LRSs (Table 8). Median 

DCu concentrations in water were 3.7, 2.6, and 1.4 μg/L for salt, brackish, and freshwater marinas, respectively, while 

the respective LRS median DCu concentrations were 0.6, 1.6, and 0.5 μg/L. 

There were significant differences in median DCu concentrations among the three water types (one-way ANOVA, F2, 

64 = 8.90, p < 0.0005), with freshwater marina median DCu concentrations being significantly less than those in salt 

and brackish water marinas (Tukey’s Test, family error rate=0.05) (Figure 8, ). In contrast, there was no significant 

difference between median DCu concentrations in salt and brackish water marinas. In Figure 8, plotted circles 

represent the median value for each marina and the horizontal line inside the box represents the water type median. 

The fully-nested balanced ANOVA showed statistically significant differences between marinas and LRS DCu 

concentrations in saltwater marina areas (fully-nested balanced ANOVA, F14,56 = 22.2, p < 0.0005, Coyote Point 

Marina omitted), brackish water marina areas (fully-nested balanced ANOVA, F4,16 = 36.3, p < 0.0005), and 

freshwater marina areas, (fully-nested balanced ANOVA, F3,12 = 33.9, p < 0.0005, Tahoe Keys Marina omitted). 

Total Copper 

As was the case for DCu, 346, 96, and 88 TCu water samples were collected from salt, brackish, and freshwater areas, 

respectively. TCu concentrations were above the method detection limit (MDL = 0.1 μg/L) in 455 of 517 samples 

(88%), with concentrations ranging up to 20.2 μg/L (Appendix F). The sample with the highest TCu concentration 

came from one of MdR’s back basins (Table 6). 

Median TCu concentrations for individual marinas ranged from 0.7 μg/L at Folsom Lake Marina to 14.7 μg/L at 

MdR’s back basins (Table 7). The median concentrations for 21of 23 marinas were below 7 μg/L. The relative rank 

of marinas, based on the magnitude of their median TCu concentrations, is slightly different than that for DCu. The 

comparison of marina TCu concentrations by water type and region revealed similar patterns to DCu (Figures 9–12) 

with the primary exception of the relationship between the brackish water marinas and their LRSs (Figure 10). 

Figure 9. Distribution of Total Copper Concentrations in Freshwater Marina Areas 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sa cramento Marina 

Folsom LRS 

FolsomMarina 

Tahoe Keys LRS 

Tahoe Keys Marina 

7

6

5

4

3

2

1

0

T

Cu Concentrations (μg/L) 

  24 
Figure 10. Distribution of Total Copper Concentrations in Brackish Water Marina Areas 

Vallejo LRS 

Vallejo Marina 

Benicia LRS 

Benicia Marina 

Pittsburg LRS 

Pittsburg Marina 

Antioch LRS 

Antioch Marina 

10

9

8

7

6

5

4

3

2

T

Cu Concentrations (μg/

L) 

Figure 11. Distribution of Total Copper Concentrations in Saltwater Marina Areas (San Francisco Bay Area) 

Coyote Point LRS 

Coyote Point Marina 

Ballena Isle LRS 

Ballena Isle Marina 

S outh Beach LRS 

South Beach Harbor 

San Francisco LRS 

San Francisco Marina 

Berkeley LRS 

Berkeley Marina 

Clipper Y.H. LRS 

Clipper Yacht Harbor 

Loch Lomond LRS 

Loch Lomond Marina 

Marina Bay Y.H. LRS 

Marina Bay Yacht Harbor 

12

10

8

6

4

2

0

T

Cu Concentrations (μg/L) 

  25 
Figure 12. Distribution of Total Copper Concentrations in Saltwater Marina Areas (Central and South Coast) 

Alamitos Bay LRS 

Alamitos Bay Marina 

Downtown Shoreline LRS 

Downtown Shoreline Marina 

MdR LRS 

MdR Front Basins 

MdR LRS 

MdR Back Basins 

Santa Barbara LRS 

Santa Barbara Harbor 

Monterey LRS 

Monterey Harbor 

Santa Cruz LRS 

Santa Cruz Harbor 

20

15

10

5

0

T

Cu Concentrations (μg/

L) 

Figure 13. Distribution of Marina Median Total Copper Concentrations by Water Type 

Saltwater LRSs 

Saltwater Marinas 

Brackish Wate r LRSs 

Brackish Water Marinas 

Fre shwater LRSs 

Freshwater Marinas 

16

14

12

10

8

6

4

2

0

T

Cu Concentration (ug/

L) 

  26 
Overall, median TCu concentrations were 3.4 μg/L for all marina sites and 0.6 μg/L for all LRSs (Table 8). Median 

TCu concentrations in water were 3.8, 3.5, and 2.2 μg/L for salt, brackish, and freshwater marinas, respectively, while 

the respective LRS median TCu concentrations were 0.3, 3.4, and 1.1 μg/L. 

A test for differences in marina TCu concentrations among the three water types found no significant difference 

between water types, although the significance level was only slightly greater than the cutoff of 0.05 (one-way 

ANOVA, F2, 64 = 3.1, p = 0.051). TCu concentrations tend to be similar among marinas of the three water types 

(Figure 13). 

The TCu concentrations measured within both freshwater and saltwater marinas were significantly higher than those 

measured in the associated LRS (fully-nested balanced ANOVA; saltwater: F14, 56 = 60.8, p < 0.0005, Coyote Point 

Marina omitted; freshwater: F3,12 = 15.8, p < 0.0005, Tahoe Keys Marina omitted) (Figure 13). In contrast, there was 

no significant difference between measurements taken in brackish water marina areas (fully-nested balanced ANOVA, 

F4,16 = 0.4, p = 0.836). 

Median TCu concentrations were almost always higher inside of marinas compared to LRSs. At Benicia Marina, 

however, the median LRS TCu concentration was actually higher than the median marina concentration. At Benicia 

Marina, the corresponding TSS measurements were quite high (median LRS TSS concentration 54.7 mg/L) and could 

potentially serve to explain the high TCu concentrations at the LRSs for Benicia Marina. At Vallejo Marina, where 

the median LRS TSS concentration was also high (55.0 mg/L), the median LRS TCu concentration (3.9 μg/L) was 

almost as high as the median marina TCu concentration (4.1 μg/L). 

There was a strong correlation between DCu and TCu concentrations (Pearson’s r = 0.91, p < 0.0005, Figure 14). 

Overall analysis of dissolved-to-total copper ratios showed that about 92% of total copper is DCu. This percentage is 

consistent with the conversion factors (TCu to DCu) offered in the U.S. EPA’s updated water quality criteria for 

copper (U.S. EPA, 2007). The criteria document suggests that the conversion factors of 0.96 and 0.909 be used for 

freshwater and saltwater, respectively. Thus, the observed correlation is a reflection of the fact that most of the copper 

in the system is dissolved. 

Figure 14. Correlation Plot of Log Dissolved versus Log Total Copper Concentrations 

-3 -2 -1 0 1 2 3

3

2

1

0

-1

-2

-3

log TCu 

log D

Cu 

Pearson’s r = 0.91, p < 0.0005 

  27 
Dissolved Zinc 

A total of 346, 96, and 88 water samples were collected from salt, brackish, and freshwater areas, respectively. DZn 

concentrations were above method detection limit (MDL = 1.0 μg/L) in 465 of 517 samples (90%), with 

concentrations ranging up to 66.6 μg/L (Appendix F). The sample with the highest DZn concentration came from one 

of MdR’s front basins (Table 6). 

Median DZn concentrations for individual marinas ranged from 1.9 μg/L at Antioch Marina to 50.0 μg/L at MdR’s 

back basins (Table 7). Median DZn concentrations were less than 8 μg/L in 17 of the 23 marinas studied. 

The freshwater and brackish water marinas exhibited very low to low median DZn concentrations (Figures 15 and 16). 

DZn concentrations were generally similar among freshwater marinas and among brackish water marinas. 

The eight San Francisco Bay Area saltwater marinas exhibited very low to moderate median DZn concentrations 

(Figure 17). Among these marinas, the highest median concentration was observed at Loch Lomond Marina (7.9 

μg/L) and the low median concentration was observed at San Francisco Marina (2.7 μg/L). The remaining six Bay 

Area marinas exhibited median DCu concentrations that ranged between 4 and 6 μg/L. 

The seven Central and South Coast saltwater marinas exhibited moderate to very high median DZn concentrations 

(Figure 18). Among these marinas, DCu concentrations at MdR’s front and back basins (49.1 and 50.0 μg/L, 

respectively) were by far the highest. Alamitos Bay Marina exhibited the lowest median concentration (6.7 μg/L) in 

this region. The remaining four Central and South Coast marinas exhibited median DCu concentrations that ranged 

between 13 and 20 μg/L. 

Figure 15. Distribution of Dissolved Zinc Concentrations in Freshwater Marina Areas 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sacramento Marina 

Folsom LRS 

FolsomMarina 

Tahoe Keys LRS 

Tahoe Keys Marina 

18

16

14

12

10

8

6

4

2

0

D

Zn Concentrations (μg/

L) 

  28 
Figure 16. Distribution of Marina Dissolved Zinc Concentrations in Brackish Water Marina Areas 

Vallejo LRS 

Vallejo Marina 

Benicia LRS 

Benicia Marina 

Pittsburg LRS 

Pittsburg Marina 

Antioch LRS 

Antioch Marina 

12

10

8

6

4

2

0

D

Zn Concentrations (μg/L) 

Figure 17. Distribution of Marina Dissolved Zinc Concentrations in Saltwater Marina Areas (San Francisco 

Bay Area) 

Coyote Point LRS 

Coyote Point Marina 

Ballena Isle LRS 

Ballena Isle Marina 

S outh Beach LRS 

South Beach Harbor 

San Francisco LRS 

San Francisco Marina 

Berkeley LRS 

Berkeley Marina 

Clipper Y.H. LRS 

Clipper Yacht Harbor 

Loch Lomond LRS 

Loch Lomond Marina 

Marina Bay Y.H. LRS 

Marina Bay Yacht Harbor 

18

16

14

12

10

8

6

4

2

0

D

Zn Concentrations (μg/L) 

  29 
Figure 18. Distribution of Marina Dissolved Zinc Concentrations in Saltwater Marina Areas (Central and 

South Coast) 

Alamitos Bay LRS 

Alamitos Bay Marina 

Downtown Shoreline LRS 

Downtown Shoreline Marina 

MdR LRS 

MdR Front Basins 

MdR LRS 

MdR Back Basins 

Santa Barbara LRS 

Santa Barbara Harbor 

Monterey LRS 

Monterey Harbor 

Santa Cruz LRS 

Santa Cruz Harbor 

70

60

50

40

30

20

10

0

D

Zn Concentrations (μg/

L) 

Figure 19. Distribution of Marina Median Dissolved Zinc Concentrations by Water Type 

Saltwater LRSs 

Saltwater Marinas 

Brackish Wate r LRSs 

Brackish Water Marinas 

Fre shwater LRSs 

Freshwater Marinas 

50

40

30

20

10

0

D

Zn Concentration (ug/

L) 

  30 
Overall, median DZn concentrations in water were 6.2 μg/L for all marina sites and 2.1 μg/L for all LRSs (Table 8). 

Median DZn concentrations were 9.2, 2.8, and 3.1 μg/L for salt, brackish, and fresh water marinas, respectively, while 

the respective LRS median DZn concentrations were 3.6, 1.5, and 2.6 μg/L. 

There were significant differences in DZn concentrations among marinas of the three water types (one-way ANOVA, 

F2, 64 = 15.5, p < 0.0005), with salt water marina DZn concentrations being significantly higher than those for brackish 

and freshwater marinas (Tukey’s Test, family error rate = 0.05) (Figure 19). In contrast, there was no significant 

difference between DZn concentrations in brackish and freshwater marinas. 

At all 23 marina areas, the median marina DZn concentrations were greater than the median LRS concentrations. In 

Figure 18, the median for Alamitos Bay Marina appears to be lower than the median for its LRSs. Note, however, that 

this is an artifact of the two different approaches used to calculate the sample median. Recall that the geometric mean 

median value is the one that is used for data analysis and discussion. 

The difference in marina and LRS DZn concentrations is statistically significant for saltwater marina areas (fullynested balanced ANOVA, F14,56 = 22.9, p < 0.0005, Coyote Point Marina omitted) and brackish water marina areas 

(fully-nested balanced ANOVA, F4,16 = 5.4, p < 0.006) (Figure 19). There is no statistically discernable difference for 

freshwater marina areas (fully-nested balanced ANOVA, F3,12 = 0.3, p = 0.793, Tahoe Keys Marina omitted). 

Total Suspended Solids 

TSS data are frequently useful in improving the understanding of metals data. Thus, these results are evaluated along 

with the metals results. TSS concentrations were above the detection limit (MDL = 4 mg/L) in 465 of 516 samples 

(90%) with concentrations ranging up to 150 mg/L (Appendix F). The sample with the highest TSS concentration in 

this study was from one the Benicia Marina LRS (Table 6). 

Median TSS concentrations for individual marinas ranged from below the detection limit of 4 mg/L at Folsom Lake 

and Tahoe Keys marinas to 23.2 mg/L at Santa Cruz Harbor (Table 7). The median concentrations of 18 of 23 marinas 

were above 12 mg/L. 

The freshwater marinas exhibited very low to low median TSS concentrations (Figure 20). The brackish water 

marinas showed low to moderate median TSS concentrations (Figure 21). The 15 saltwater marinas also showed low 

to moderate median TSS concentrations (Figures 22 and 23). 

TSS concentrations were often higher outside of the marina than inside (Figures 20–23). Since marinas are 

constructed with the primary goal of protecting boats from turbulence caused by wind , currents, and tides, this 

observation seems logical. Median TSS concentrations for LRSs were higher in 13 out of 23 marina areas. 

The two highest median TSS concentrations–54.7 and 55.0 mg/L were documented for the Benicia LRSs and the 

Vallejo LRSs, respectively. These high levels of TSS at these two areas can likely be attributed to particle 

flocculation, which is a common phenomenon in estuarine areas where freshwater meets more saline water. At the 

other two less-saline brackish water LRSs (Pittsburg and Antioch), the TSS ranges are similar to those of the two 

riverine freshwater LRSs (Sacramento Marina and Village West Marina). 

Overall, median TSS concentrations in water were 13.5 mg/L for all marina sites and 17.4 mg/L for all LRSs (Table 

8). Median TSS concentrations in water were 17.5, 11.6, and 5.6 mg/L for salt, brackish, and freshwater marinas, 

respectively, while the respective LRS median TSS concentrations were 17.6, 29.3, and 9.3 mg/L. 

  31 
Figure 20. Distribution of Total Suspended Solid Concentrations in Freshwater Marina Areas 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sacramento Marina 

Folsom LRS 

FolsomMarina 

Tahoe Keys LRS 

Tahoe Keys Marina 

30

25

20

15

10

5

TSS Concentrations (

mg/

L) 

Figure 21. Distribution of Total Suspended Solid Concentrations in Brackish Water Marina Areas 

Vallejo LRS 

Vallejo Marina 

Benicia LRS 

Benicia Marina 

Pitts burg LRS 

Pittsburg Marina 

Antioch LRS 

Antioch Marina 

160

140

120

100

80

60

40

20

0

TSS Concentrations (

mg/L) 

  32 
Figure 22. Distribution of Total Suspended Solid Concentrations in Saltwater Marina Areas (San Francisco Bay 

Area) 

Coyote Point LRS 

Coyote Point Marina 

Ballena Isle LRS 

Ballena Isle Marina 

S outh Beach LRS 

South Beach Harbor 

San Francisco LRS 

San Francisco Marina 

Berkeley LRS 

Berkeley Marina 

Clipper Y.H. LRS 

Clipper Yacht Harbor 

Loch Lomond LRS 

Loch Lomond Marina 

Marina Bay Y.H. LRS 

Marina Bay Yacht Harbor 

45

40

35

30

25

20

15

10

5

TSS Concentrations (mg/L) 

Figure 23. Distribution of Total Suspended Solid Concentrations in Saltwater Marina Areas (Central and South 

Coast) 

Alamitos Bay LRS 

Alamitos Bay Marina 

Downtown Shoreline LRS 

Downtown Shoreline Marina 

MdR LRS 

MdR Front Basins 

MdR LRS 

MdR Back Basins 

Santa Barbara LRS 

Santa Barbara Harbor 

Monterey LRS 

Monterey Harbor 

Santa Cruz LRS 

Santa Cruz Harbor 

35

30

25

20

15

10

5

0

TSS Concentrations (

mg/

L) 

  33 
Figure 24. Distribution of Marina Median Total Suspended Solids Concentrations by Water Type 

Saltwater LRSs 

Saltwater Marinas 

Brackish Water LRSs 

Brackish Water Marinas 

Fre shwaterLRSs 

Freshwater Marinas 

70

60

50

40

30

20

10

0

TSS Concentration (mg/

L) 

There were significant differences in TSS concentrations among marinas of the three water types (one-way ANOVA, 

F2, 64 = 51.6, p < 0.0005), with TSS concentrations from marinas of each water types being significantly different than 

one another (Tukey’s Test, family error rate = 0.05) (Figure 24). Median TSS concentrations in marinas were in the 

order of saltwater > brackish water > freshwater. 

There was no statistical difference in marina and LRS TSS concentrations for saltwater marina areas (fully-nested 

balanced ANOVA, F14, 56 = 0.9, p = 0.601, Coyote Point Marina omitted) (Figure 24). However, there was a statistical 

difference in marina and LRS TSS concentrations for brackish water marina areas (fully-nested balanced ANOVA, 

F4,16 = 17.2, p < 0.0005) and for freshwater marina areas (fully-nested balanced ANOVA, F3,12 = 17.0, p < 0.0005, 

Tahoe Keys Marina omitted). TSS concentrations in brackish and freshwater marinas were lower than TSS 

concentrations in their respective LRSs. 

Correlations of Total Suspended Solids and Dissolved Organic Carbon with Metals 

A correlation analysis was conducted on the TSS, DOC, and metals data to determine the presence of linear 

relationships among the variables. All pair-wise Pearson correlation coefficients and the non-parametric Spearman’s 

rank-correlation coefficients were computed for the log-transformed dataset. 

Across all data (all water types combined), there were no significant correlations between TSS and any of the three 

metal analytes (i.e., DCu, TCu, and DZn). Furthermore, no significant correlations were observed when the data were 

evaluated separately as marina and LRS datasets. 

Within brackish marina areas, a few significant but weak relationships were observed. For example, there was a 

modest linear correlation between TSS and TCu in brackish water marina areas (marina sites + LRSs) (Pearson’s r = 

0.56, p < 0.0005) (Figure 25). A weak positive linear association of TSS-TCu also exists in brackish marinas 

(Pearson’s r = 0.51, p < 0.0005). However, the most notable of the TSS-TCu correlations is the strong positive linear 

association in brackish LRSs (Pearson’s r = 0.84, p < 0.0005) (Figure 25). 

  34 
Figure 25. Correlation of Total Suspended Solids and Total Copper Concentrations in Brackish LRSs 

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

log TCu 

log TSS 

Pearson’s r = 0.84, p < 0.0005 

It is possible that the observed positive relationship between TSS and TCu for brackish water LRSs is reflective of the 

tendency of metals in solution to bind to suspended solids in the water column. Thus, water with high amounts of 

suspended solids will tend to also have high amounts of metals associated with them. Recall that TSS at the Vallejo 

and Benicia LRSs (brackish water sites) exhibited TSS loads that were much higher than any other sites in this study 

(median TSS concentrations > 50 mg/L). 

TSS can also serve to bind DCu and convert it to particulate copper (Morris, 1986; Ackroyd et al., 1986). The more 

TSS there are in a sample, a higher capacity generally exists to lower the DCu concentration in that sample. This 

binding effect may potentially explain the negative linear relationships that are observed for TSS and DCu (Pearson’s r 

= -0.78, p < 0.0005) (Figure 26) and for TSS and DZn (Pearson’s r = -0.64, p < 0.0005) (Figure 27) at the Benicia and 

Vallejo marina areas. Thus, at least for brackish waters, the magnitude of DCu may be significantly influenced by the 

amount of particulate matter that exists in these waters at any given time. By converting dissolved metals to 

particulate metals, suspended solids in natural water systems are moderating bioavailability of copper and other metals 

to aquatic organisms. Note that any significant decreases in TSS concentrations in these brackish marinas could result 

in a lower level of DCu conversion potentially leading to higher concentrations of observed DCu. 

No significant correlations between TSS and metals were observed for saltwater samples. Correlation of freshwater 

TSS and metals data was not conducted since more than 50% of the freshwater TSS data were below detection limits 

(<4 mg/L). 

  35 
Figure 26. Correlation of Total Suspended Solids vs. Dissolved Copper Concentrations at Benicia and Vallejo 

Marina Areas 

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

5.0

4.5

4.0

3.5

3.0

2.5

2.0

log DCu 

log TSS 

Pearson’s r = - 0.78, p < 0.0005 

Figure 27. Correlation of Total Suspended Solids vs. Dissolved Zinc Concentrations at Benicia and Vallejo 

Marina Areas 

0.0 0.5 1.0 1.5 2.0 2.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

log DZn 

log TSS 

Pearson’s r = - 0.64, p < 0.0005 

  36 
Similar to the case of TSS, across all data (all water types combined), there were no significant correlations between 

DOC and any of the three metal analytes (i.e., DCu, TCu, and DZn). Furthermore, no significant correlations were 

observed when the data were evaluated separately as a marina dataset. However, there were weak positive correlations 

between DOC and DCu (Pearson’s r = 0.50, p < 0.0005) and also TCu (Pearson’s r = 0.64, p < 0.0005) for the LRS 

data. 

By water type, a weak correlation was also observed between DOC and DCu in saltwater LRSs (Pearson’s r = 0.48, p 

< 0.0005). Buck et al. (2007) and Kuwabara et al. (1989) both found positive correlations of DOC to DCu 

concentrations in the ambient waters of San Francisco Bay. This association is supported by the finding that in 

ambient water, the majority of DCu exists as a complex with dissolved organic matter (Buck and Bruland, 2005; Hurst 

and Bruland, 2005). This relationship between DOC and DCu is not likely to exist in many saltwater marinas since 

significant input of copper by marina sources greatly disturbs this equilibrium that supposed to exist in ambient water 

areas. Moreover, since DCu concentrations at LRSs could potentially be impacted by copper that is generated from 

the marinas, the DOC-DCu relationship could be weakened. 

No correlations were observed between DOC and metals for brackish or freshwater. 

Irgarol and M1 

Water samples for Irgarol and M1 analysis were taken in the first (July to August) and third (September to November) 

sampling rounds. Forty five out of the anticipated 48 water samples for these analyses were collected from 11 

marinas. Thirty-three samples were from eight saltwater marinas and 12 samples were from three brackish water 

marinas. Complete Irgarol and M1 results are presented in Appendix G. 

Irgarol and M1 were detected in all 45 samples (limit of quantification [LOQ] of 0.1 ng/L or parts per trillion for both 

analytes). Irgarol detections ranged from 12 to 712 ng/L. The highest sample concentration of Irgarol was 

documented at Loch Lomond Marina in San Rafael. M1 detections ranged from 1.6 to 217.1 ng/L. The highest 

sample concentration of M1 was also documented at Loch Lomond Marina. M1 concentrations were always lower 

than Irgarol in the same sample. 

Marina median concentrations were calculated for Irgarol for both sampling rounds (Figure 28). For the first round, 

marina median concentrations were highest at Loch Lomond Marina (539.0 ng/L), followed by MdR back basin #3/4 

(277.6 ng/L), and Berkeley Marina (197.6 ng/L). The lowest marina median concentration was at Benicia Marina 

(30.0 ng/L). For the third round, Irgarol concentrations were noticeably lower. Median concentrations were highest at 

Loch Lomond Marina (148.1 ng/L), followed by Marina Bay Yacht Harbor (111.9 ng/L), and Ballena Isle Marina 

(98.9 ng/L). The lowest marina median concentration was at Alamitos Bay Marina (21.1 ng/L). 

Marina median concentrations were also calculated for M1 (Figure 28). For the first round, marina median 

concentrations were highest at Loch Lomond Marina (189.0 ng/L), followed by MdR back basin #3/4 (104.2 ng/L), 

and Berkeley Marina (75.6 ng/L). For the third round, M1 concentrations (like Irgarol concentrations) were noticeably 

lower. Median concentrations were highest at Loch Lomond (148.1 ng/L), followed by MdR back basin #1/2 (22.0 

ng/L), and MdR back basin #3/4 (22.0 ng/L). 

The overall median Irgarol and M1 concentrations for all marina sites were 66.1 and 15.1 ng/L, respectively (Table 9). 

Median Irgarol concentrations for the first and third sampling rounds were 80.5 and 53.2 ng/L, respectively. 

Table 9. Summary of Median Irgarol and M1 Concentrations (ng/L) in Water Samples (Shown with backtransformed lower and upper 95% Confidence Intervals) 

Analyte 

Overall Median 

Concentration w/ 95% CI 

1st Round (July/August) Median 

Concentration w/ 95% CI 

3rd Round (September/October) 

Median Concentration w/ 95% CI 

Irgarol 50.1 - 66.1 - 87.1 51.9 - 80.5 - 124.8 38.9 - 53.2 - 74.8 

M1 10.3 - 15.1 - 22.0 20.2 - 31.0 - 47.8 4.3 - 6.8 - 10.9 

37 
Figure 28. Marina Median Irgarol and M1 Water Concentrations by Rounds (Round 1 = July/August, Round 3 

= September/October). 

Irgarol Round 1 Irgarol Round 3 M 1 Rou nd 1 M 1 Round 3 

600

500

400

300

200

100

0

Irgarol/

M1 Concentration (ng/L) 

At almost all of the sampling sites, Irgarol and M1 concentrations were higher in the first round as compared to the 

third round. Paired t-tests on log-transformed Irgarol and M1 results yielded statistically significant differences 

between the first and third round of sampling for both Irgarol concentrations (t11 = 2.4, p = 0.036) and M1 (t11 = 13.1, 

p < 0.0005) concentrations. 

ANOVA tests of results also showed that there was a statistically significant difference among marinas (fully-nested 

balanced ANOVA, F11,23 = 5.0, p = 0.005, with MdR grouped into 2 marinas). That is, Irgarol concentrations varied 

considerably among the marinas tested. Results for M1 did not show any statistically significant difference among 

marinas (fully-nested balanced ANOVA, F11,23 = 1.7, p = 0.181, with MdR being categorized as 2 marinas). 

There was a significant log-log correlation between Irgarol and M1 concentrations (Pearson’s r = 0.85, p < 0.0005) 

(Figure 29). This result is expected because M1 is the primary degradate of Irgarol. Assuming that there are no other 

appreciable sources of M1, the amount of M1 that is generated is dependent on the amount of Irgarol that is present 

and on environmental factors. 

For the saltwater sites, the median concentrations for Irgarol and M1 were 83.7 and 20.1 ng/L, respectively. For 

brackish water sites (Benicia, Vallejo, and Pittsburg marinas), the median concentrations for Irgarol and M1 were 33.2 

and 6.8 ng/L, respectively. Based on the results of two-sample t-tests of log-transformed concentration data, saltwater 

marinas exhibited higher concentrations of both Irgarol (t22 = 2.7, p = 0.013) and M1(t22 = 2.2, p = 0.037) as compared 

to brackish water marinas. 

  38 
Figure 29. Correlation of Irgarol vs. M1 Concentrations at Marina Waters 

0 1 2 3 4 5 6

7

6

5

4

3

2

log M1 Concentrations 

log Irgarol Concentrations 

Pearson’s r = 0.85, p < 0.0005 

Total Copper - Sediment 

Twenty three of the anticipated 180 sediment samples were analyzed for TCu (Appendix H). Sediment collection was 

limited to three freshwater marinas: Folsom Lake, Sacramento Marina, and Village West Marina. No sediment was 

collected from the remaining 20 marina sites due to logistical and safety issues. 

At the three sites, DPR’s field crew used a sampling vessel owned by the Central Valley Regional Water Quality 

Control Board (CVRWQCB) with the assistance of its staff. This particular vessel was equipped with a manuallyoperated winch, which allowed field crew to collect sediment samples. Instead of canceling the entire sediment 

collection, DPR chose to collect as many samples as possible using this vessel. 

Sediment TCu concentrations were log-normally distributed, and ranged from 17 to 167 mg/kg. Note that TCu in 

sediment refers to the concentration of total copper in the sediment and not in the interstitial or pore water in the 

sediment. The highest TCu concentration was documented at Sacramento Marina and the lowest was documented at 

an LRS of that marina. For the three marinas where sediment samples were collected, median TCu concentrations in 

sediment were 131.5 mg/kg for the three freshwater marina sites and 48.5 mg/kg for their associated LRSs (Table 10). 

Paired t-tests were used to test the equivalence of marina and LRS sediment TCu concentrations for each marina. 

There were significant differences in TCu concentrations between marina sites and LRSs for all three marina areas: 

Folsom Marina/LRS (t5 = 8.18, p < 0.0005), Sacramento Marina/LRS (t6 = 4.31, p = 0.005), and Village West 

Marina/LRS (t6 = 4.96, p = 0.003). In all cases, TCu concentrations in marina sediment were higher inside the marina 

than in the adjacent LRSs (Figure 30). 

  39 
Table 10. Summary of Median Metal Concentrations (mg/kg dry weight) in Sediment Samples 

Median TCu Concentrations w/ 95% CI Median TZn Concentrations w/ 95% CI 

Marina Sites LRSs Marina Sites LRSs 

3 Freshwater 

Marina Areas 

118.2 - 131.5 - 146.3 36.2 - 48.5 – 64.8 151.3 - 182.5 - 220.2 49.2 - 78.3 - 124.7 

Figure 30. Distribution of Total Copper Concentrations in Marina Area Sediments 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sacramento Marina 

Folsom Lake LRS 

FolsomLake Marina 

180

160

140

120

100

80

60

40

20

0

T

Cu 

Concentrations (

mg/kg dry 

weight) 

Total Zinc - Sediment 

Twenty three of the anticipated 180 sediment samples were collected and analyzed for TZn (Appendix H). 

TZn concentrations ranged from 23 to 273 mg/kg. The highest concentrations were documented at Village West 

Marina and the lowest at an LRS adjacent to the Folsom Lake Marina. For the three marinas where sediment was 

collected, median TZn concentrations in sediment were 182.5 mg/kg for the marina sites and 78.3 mg/kg for the 

associated LRSs (Table 10). 

Paired t-tests were used to test the equivalence of marina and LRS sediment TZn concentrations for each marina. 

There were significant TZn concentration differences between marina and LRS for all three marina areas–Folsom 

Marina/LRS (t5 = 12.56, p < 0.0005), Sacramento Marina/LRS (t6 = 4.28, p= 0.005), and Village West Marina/LRS (t6 

= 6.28, p = 0.001). In all cases, TZn concentrations in marina sediment were higher inside the marina than in the 

adjacent LRSs (Figure 31). 

  40 
Figure 31. Distribution of Total Zinc Concentrations in Marina Area Sediments 

Village West LRS 

Village West Marina 

Sacramento LRS 

Sacramento Marina 

Folsom Lake LRS 

FolsomLake Marina 

300

250

200

150

100

50

0

Concentrations (mg/

kg dry weight) 

Toxicity 

All of the anticipated 48 toxicity samples were collected for toxicity tests on mussel embryo development. One water 

sample (from Pittsburg Marina) was, however, excluded from testing because it was not saline enough to meet 

minimum test requirements. 

Eight of 47 samples (17%) exhibited statistically significant toxicity (Table 11). Seven of the eight toxic samples 

came from Marina del Rey (MdR) in Los Angeles. The remaining toxic sample originated from Marina Bay Yacht 

Harbor (MBYH) in Richmond. The complete toxicity test results are available in Appendix G while the toxicity data 

report from SCCWRP is in Appendix I. 

Table 11. Water Samples with Statistically Significant Toxic Endpoints 

Sample 

Number Location 

Concentration of 

DCu in associated 

metal sample (μg/L) 

Percent Normal-Alive 

Sample 

Mean 

Percent of 

Control 

Standard 

Deviation 

Number of 

Replicates 

111 MBYH #1 1.7 72 88 5.1 4 

118 MdR FB #2 12.5 55 64 11.1 4 

119 MdR BB #3 12.4 61 72 6.9 4 

120 MdR FB #1 13.1 67 79 11.8 4 

121 MdR BB #2 18.4 4 4 1.9 4 

123 MdR BB #4 14.2 66 78 10.8 4 

178 MdR BB #1 11.5 37 44 7.6 4 

179 MdR FB #4 11.9 49 57 7.1 4 

41 
Toxicant Identification Evaluation (TIE) 

Two samples (sample numbers 121 and 179) were tested using Phase I TIE procedures (Appendix I). Both of the 

selected samples were subjected to treatments designed to selectively remove or neutralize classes of compounds 

(Table 12). Treated samples were then retested to determine whether changes in toxicity occurred as a result of the 

treatments. A control sample, using laboratory seawater, was included with each type of treatment to verify that the 

manipulation itself was not causing toxicity. Samples for each TIE manipulation were tested at 100% sample strength. 

Each sample was subjected to four treatments: 

• Ethylenediaminetetraacetic acid (EDTA) addition, which chelates metals and reduces the toxicity caused by them. 

• Sodium thiosulfate (STS) addition, which reduces oxidants and decreases the toxicity of some metals, including 

copper. 

• Centrifugation, which removes particles and reduces the toxicity associated with suspended solids. 

• C-18 column extraction, which removes non-polar organic compounds and reduces the toxicity caused by them. 

Table 12. Toxicant Identification Evaluation Treatment Results 

Sample 

Number Treatment 

Percent Normal-Alive 

Sample Mean 

Percent of 

Control 

Standard 

Deviation 

Number of 

Replicates 

121 Baseline* 1 1 1.1 4 

121 EDTA 91 93 6.8 3 

121 STS 55 57 1.3 3 

121 Centrifuge 6 6 1.5 3 

121 C-18 Column 80 82 3.6 3 

179 Baseline* 14 14 4.1 4 

179 EDTA 92 92 10.4 3 

179 STS 82 83 1.7 3 

179 Centrifuge 37 38 8.3 3 

179 C-18 Column 62 63 54.0 3 

* Results are different from the analogous sample in the previous table since baseline toxicity tests were redone for the TIEs. 

A decrease in sample toxicity after the EDTA and STS treatments suggested that a cationic metal was responsible for 

the majority of the toxicity. Ideally, if metals were the primary cause of the toxicity, the C-18 column treatment would 

have had little effect on the observed toxicity of the baseline samples; however, the intermediate toxicity reduction that 

was observed in the C-18 results suggested that non-polar organics were causing some of the toxicity. Follow up 

discussions with SCCWRP personnel revealed that the C-18 columns used by SCCWRP have been documented to 

also remove a small amount of trace metals along with the non-polar organics. The sequestration of metals by C-18 

columns has been noted in a recent SCCWRP study (Schiff et al., 2007). In that study, the columns were estimated to 

have removed 45% of the copper out of the sample solution. This level of copper removal could account for the 

intermediate reduction of toxicity that was observed for the two C-18 TIE samples. 

Overall, based on results of the TIEs and the complications with the C-18 columns, it is likely that cationic metals are 

the cause of the toxicity. Copper concentrations in these samples were high enough to exceed the toxicity test’s range 

of effects (i.e., EC50’s). Zinc concentrations were below its range of effects. Therefore, we believe that copper is the 

most likely cause of toxicity in the two TIE samples from MdR. 

Comparisons to Water Quality Standards 

Two water quality indicators of AFP pollution (copper and zinc) were compared to available water quality standards. 

There are no applicable water quality standards for Irgarol or M1, however, there are established standards for copper 

and zinc. The most relevant water quality standards for copper and zinc in the State of California are specified in the 

CTR (U.S. EPA, 2000). The CTR contains two standards–the criterion maximum concentration (CMC), which is the 

acute standard defined as the highest concentration of a pollutant to which aquatic life can be exposed for a short 

period of time without deleterious effects, and the criterion continuous concentration (CCC), which is the chronic 

  42 
standard defined as the highest concentration of a pollutant to which aquatic life can be exposed for an extended period 

of time without deleterious effects. For both copper and zinc, the CMC and CCC are defined as a 1-hour average and 

a 4-day average, respectively. 

Considering the specific way the CTR defines compliance assessment for copper and zinc standards and the way the 

samples in this study were taken, a direct comparison of this study’s analytical results to numeric standards may not be 

the most fitting approach from a compliance standpoint. However, since the goal of this study is water quality 

assessment and not regulatory compliance, such a comparison is still informative in determining the relative level of 

metal pollution at a number of sites across California. 

CTR Freshwater CMC and CCC Water Quality Standards - Copper 

Freshwater DCu results were compared to the CTR’s hardness-based CMC and CCC standards to determine if there 

were any exceedances (Appendix J). CMC and CCC equations and conversion factors that are established in the CTR 

were used to calculate the site-specific copper water quality standards (U.S. EPA, 2000) used for the comparison. 

Note that U.S. EPA defines freshwater as water that contains salinity at 1 part per thousand and below at locations 

where this occurs 95% or more of the time. In brackish water areas that are between 1 and 10 parts per thousand in 

salinity, the freshwater criteria could still apply if they are more stringent than the saltwater criteria. Based on these 

definitions, freshwater CCC and CMC were calculated for the following six marinas: Tahoe Keys, Folsom Lake, 

Sacramento, Village West, Antioch, and Pittsburg. Benicia and Vallejo marinas are likely to experience salinity that is 

higher than 10 parts per thousand for most of the year and were thus left out of this determination. 

None of the DCu concentrations in the 135 freshwater samples collected from six different marina areas exceeded the 

acute or chronic CTR standards for copper. 

CTR Freshwater CMC and CCC Water Quality Standards - Zinc 

Freshwater DZn results were compared to the CTR’s hardness-based CMC and CCC zinc standards to determine if 

there were any exceedances (Appendix K). CMC and CCC equations and conversion factors that are established in the 

CTR were used to calculate the site-specific zinc water quality standards (U.S. EPA, 2000) used for the comparison. 

The same salinity considerations as those presented in the previous section for DCu apply here. 

None of the DZn concentrations in the 135 freshwater samples collected from six different marina areas exceeded the 

acute or chronic CTR standards for zinc. 

CTR Saltwater CMC and CCC Water Quality Standards - Copper 

Salt and brackish water DCu results were compared to the CTR’s fixed saltwater CMC and CCC standards to 

determine if there were any exceedances. The CTR states that the saltwater criteria apply at salinities of 10 parts per 

thousand and above at locations where this occur 95% or more of the time. Moreover, the saltwater criteria could also 

apply at salinities between 1 and 10 parts per thousand if these criteria are more stringent than the freshwater criteria. 

Therefore, our evaluation of exceedances of the saltwater standards includes the salt and brackish water marinas as 

classified by this study (see Table 1). Thus, Tahoe Keys Marina, Folsom Marina, Sacramento Marina, and Village 

West Marina were excluded from consideration against saltwater standards. 

For salt and brackish waters, the CTR standards for copper are fixed. The copper CMC is 4.8 μg/L and represents a 

short-term (1-hour) average that is not to be exceeded more than once every three years on the average. The copper 

CCC is 3.1μg/L and represents a 4-day average that is not to be exceeded more than once every three years on the 

average. 

In salt and brackish water areas, DCu concentrations in 16 of 19 marinas exceeded the CCC. The marina areas where 

DCu concentrations did not exceed the chronic standard (and thus not the acute standard either) were Antioch, South 

Beach Harbor, and Coyote Point. DCu concentrations in 10 of the 16 marinas that exceeded the CCC also exceeded 

the CMC. These 10 marinas were Loch Lomond Marina, Clipper Yacht Harbor, Berkeley Marina, Ballena Isle 

  43 
Marina, Santa Cruz Harbor, Monterey Harbor, Santa Barbara Harbor, MdR front basins, MdR back basins, and 

Downtown Shoreline Marina. 

Of the 388 samples taken from the 19 salt and brackish water marina areas, 118 or 30% exceeded the CCC. Of these 

118 samples, 67 had high enough DCu concentrations to also exceed the CMC. Overall, this number is equivalent to 

about 17% of all salt and brackish water samples from this study that exceeded the CMC. Focusing specifically on 

marina samples, 114 of 224 (51%) samples from salt and brackish water marinas exceeded the CCC. Furthermore, 67 

of 224 (30%) of samples from salt and brackish water marinas exceeded the CMC. 

Marina samples taken from MdR had the greatest frequency of CCC and CMC exceedances by far among all the 

marinas. In fact, all of the marina samples (12 of 12 samples) from MdR’s Front and Back Basins were well above the 

CMC. Four marinas exhibited DCu concentrations that were above the CMC in more than half of the samples taken at 

that particular marina. These marinas were Loch Lomond Marina (8 of 12 samples), Monterey Harbor (8 of 12 

samples), Santa Barbara Harbor (9 of 12 samples), and Downtown Shoreline Marina (10 of 12 samples). 

Only 4 of the 118 samples that had DCu concentrations above the CCC were LRS samples. DCu concentrations in 

these samples ranged between 3.4 and 3.8 μg/L. Moreover, three of these four samples came from MdR’s LRS #1, 

which after a thorough evaluation of metals data, appeared to resemble a marina site rather than an LRS. Of the four 

LRSs at MdR, this LRS is the closest to the MdR marina sites. TCu, DCu, and DZn concentrations from MdR LRS #1 

tended to be at an intermediate level between MdR’s eight basin sites and the three LRSs farthest away from the 

marina area. Metal concentrations from MdR’s LRS #2–4 were typically very low. The remaining LRS sample that 

exceeded the CCC was a sample taken from Ballena Isle Marina’s LRS #7. 

Note that site specific objectives (SSOs) that are higher than the CTR are awaiting approval by U.S. EPA (R. Looker, 

personal communication, 2008). The CCC, as proposed in the SSOs, is 6.0 μg/L and the CMC is 9.4 μg/L. Once 

these SSOs are adopted, they will supercede the CTR criteria as the enforceable water quality standards for much of 

San Francisco Bay, where many of the marinas in this study is located. This means that DCu concentrations in these 

marinas will be in compliance with water quality standards more frequently. The establishment of SSOs will make 

this section’s comparison of DCu results with water quality standards somewhat obsolete. An updated comparison 

with the new SSOs will undoubtedly result in a lower frequency of exceedances. 

CTR Saltwater CMC and CCC Water Quality Standards - Zinc 

Salt and brackish water DZn results were compared to the CTR’s fixed saltwater CMC and CCC standards to 

determine if there were any exceedances. For salt and brackish waters, the CTR standards for zinc are also fixed 

values. The zinc CMC is 90 μg/L and represents a short-term (1-hour) average that is not to be exceeded more than 

once every three years on the average. The zinc CCC is 81 μg/L and represents a 4-day average that is not to be 

exceeded more than once every three years on the average. 

CTR saltwater CCC and CMC for zinc were never exceeded in any of the 388 samples taken from the 19 salt and 

brackish water marinas. Even though DZn concentrations at MdR marina sites were some of the highest DZn 

concentrations observed in this study, the highest DZn concentration documented there (66.6 μg/L), did not even 

exceed the CCC. 

Comparisons to Toxicity Benchmark Values 

In certain cases, water quality standards do not exist for comparison with measured concentrations. For example, there 

are currently no numeric standards for metals in sediment and there are no standards for Irgarol and M1. Therefore, 

non-regulatory toxicity thresholds or benchmarks are used instead to provide reference points to help determine the 

potential impacts of measured concentrations. 

Comparison of Sediment Concentrations to NOAA’s Biological-Effects Guidelines - Copper 

Since no numeric sediment quality standards currently exists for metals, sediment TCu concentrations were compared 

to NOAA’s Effects Range Low (ERL) and Effects Range Medium (ERM) concentrations (Long and Morgan, 1991; 

  44 
Long et al., 1995). ERLs and ERMs are screening level tools developed by NOAA to assess the potential impacts of 

sediment pollutants. The ERL value represents the 10th percentile of ranked data for which there was an observed 

effect for a sediment pollutant. The ERM value represents the 50th percentile of the same ranked dataset. 

Concentrations below the ERL are not expected to be toxic to sediment-dwelling organisms. Toxic effects may 

occasionally occur when measured concentrations are between the ERL and ERM thresholds. The likelihood of toxic 

effects increases significantly if a pollutant concentration regularly exceeds the ERM. 

For copper, the ERL is 34 mg/kg dry weight and the ERM is 270 mg/kg dry weight. Of the 23 sediment samples 

taken, TCu concentrations in 22 of the samples exceeded the copper ERL. Even though TCu concentrations from 

inside the marinas are generally higher than TCu concentrations from the LRSs, the LRS concentrations were high 

enough to also exceed the copper ERL. The only sample that did not exceed this threshold was from an LRS 

associated with the Sacramento Marina. None of the sediment samples collected exceeded the ERM. Past placements 

of sediment pollutant data on the SWRCB’s CWA 303d impaired water bodies list have been primarily based on ERM 

exceedances. 

Although no sediment data were generated in this study for salt and brackish water marinas, California’s RWQCBs 

conducted sediment quality assessments in the 1990’s, as part of their California Bay Protection Toxic Clean Up 

Programs. A number of marinas along California’s Central and South Coast (including some that are included in this 

study) did show sediment concentrations that were above the NOAA ERM values (Singhasemanon, 2005). 

Comparison of Sediment Concentrations to NOAA’s Biological Effects Guidelines - Zinc 

NOAA’s ERL and ERM values for zinc are 150 and 410 mg/kg dry weight, respectively. Of the 23 samples taken, 

TZn concentrations in eight of the samples exceeded the zinc ERL. Interestingly, all eight samples were taken from 

marina sites (specifically, from Sacramento Marina and Village West Marina). None of the samples taken from 

Folsom Lake Marina exceeded the ERL. None of the samples exceeded the ERM. 

Comparison of Irgarol Concentrations to Toxicological Endpoints and Aquatic Toxicity Benchmarks 

A variety of toxicological endpoints (e.g., EC50s and LC50s) and aquatic toxicity benchmarks are available in the 

scientific literature for comparison with measured Irgarol concentrations. As an s-triazine herbicide, Irgarol tends to 

be much more toxic to the aquatic flora than to the aquatic fauna. Thus, the toxicity referred to in this sub-section 

focuses specifically on sub-lethal effects on aquatic plants and algae. 

Since Irgarol acts as an inhibitor of photosynthesis, growth inhibition of phytoplankton appears to be one of the most 

commonly-observed effects of Irgarol. For the green algae Navicula pelliculosa, an EC50 of 136 ng/L for growth 

inhibition has been documented for Irgarol (Hughes and Alexander, 1993). DeLorenzo and Serrano (2006) 

documented similar growth-rate inhibition for the marine phytoplankton species Dunaliella tertiolecta at an EC50 of 

270 ng/L. Devilla et al. (2005) determined the EC50 for growth inhibition of Irgarol to be 160 and 250 ng/L for the 

phytoplankton–Synechococcus sp. and Emilinia huxleyi, respectively. Dahl and Blanck (1996) documented a lowest 

observed effects concentration (LOEC) of 63 ng/L for reduced photosynthetic activity in marine periphyton 

communities. They also noted an LOEC of about 250 ng/L where a change in community structure was seen and an 

LOEC of about 1,000 ng/L where a reduction in biomass was observed. 

Irgarol concentrations at many of the marinas in this study are high enough to exceed some of the toxicological values 

cited here. These marinas were Pittsburg Marina, MBYH, Loch Lomond Marina, Berkeley Marina, South Beach 

Harbor, MdR front and back basins, and Downtown Shoreline Marina. In contrast, Irgarol concentrations at Vallejo 

Marina, Benicia Marina, San Francisco Marina, and Alamitos Bay Marina were all below the lowest of these 

toxicological values (63 ng/L). 

Hall et al. (1999) derived an aquatic-plant toxicity benchmark value of 136 ng/L for Irgarol in a ecological risk 

assessment (ERA) for European waters. In our study, Irgarol concentrations in 11 of the 45 samples (24%) collected 

for Irgarol analysis exceeded this plant toxicity benchmark. 

Hall and Gardinali (2004) conducted a similar ERA for U.S. waters (Chesapeake Bay and Southeastern Florida). In 

this study, the authors used a larger aquatic plant toxicity dataset (16 toxicological values) and also included 

  45 
toxicological data, which contained mortality as endpoints. The Irgarol benchmark value that was derived (251 ng/L) 

was higher than the Hall et al. (1999) benchmark of 136 ng/L. Four of the 45 samples (9%) collected for Irgarol 

analysis in our study contained concentrations that were above 251 ng/L. Two of these four samples were collected 

from Loch Lomond Marina. The remaining samples with Irgarol concentrations above 251 ng/L were collected from 

Berkeley Marina and MdR’s back basins. 

In 2007, Hall et al. (2007) generated another benchmark value of 193 ng/L. Comparison of this benchmark yielded the 

same results as the comparison with the 251 ng/L benchmark value from Hall and Gardinali (2004). 

Another aquatic toxicity benchmark known as the Environmental Risk Limit (ERL) is being utilized in the 

Netherlands. This ERL is also based on a plant toxicity dataset and is set at dataset’s 5th percentile value (protective of 

95% of all aquatic plant species). Van Wezel and van Vlaardingen (2004) calculated an ERL for Irgarol in water to be 

24 ng/L. Forty out of 45 samples from our study (89%) contained Irgarol concentrations above the Dutch ERL for 

Irgarol. 

Comparison of M1 Concentrations to Toxicological Endpoints 

M1 is much less toxic to aquatic plants and animals than its parent Irgarol. Hall and Gardinali (2004) established a 

plant aquatic benchmark for M1 at 12,500 ng/L (10th percentile value or protective of 90% of all aquatic plant species) 

based on 8 toxicological values. None of the M1 results from our study exceeded this threshold. The highest M1 

concentration documented in our study was 217 ng/L suggesting very low ecological risk from M1 exposures at these 

sites. 

BLM-Associated Water Quality Data 

Water quality data that are primarily used in the subsequent sections’ metal bioavailability and toxicity prediction 

models (the freshwater BLM, DOC Model, and saltwater BLM) can be divided into two categories: laboratoryanalyzed constituents and in-situ measurements. Unlike the presented results for metals, TSS, toxicity/TIE, and 

Irgarol/M1, the BLM-associated parameters were not summarized and discussed as a narrative. Instead, these 

parameters were simply tabulated to show their ranges and median values on a marina-area specific basis. 

Laboratory-Analyzed Constituents 

Most of the BLM-associated water quality constituents were quantified using laboratory instruments. These 

constituents included alkalinity, calcium, chloride (salinity), dissolved organic carbon, magnesium, potassium, sodium, 

and sulfate (complete results are in Appendix G). Results were within expected ranges for the various water types. 

The marina median and range for each constituent are presented in Table 13 and Table 14, respectively. 

In-Situ Measurements 

In addition to the eight constituents listed in the previous section, two additional water quality constituents–pH and 

temperature were needed to run the BLM. These two constituents were directly quantified in situ (probe placed 

directly in the water) using field water quality meters. Data of several water quality constituents that were not 

necessarily needed for modeling input but were nevertheless collected as general water quality and water type 

indicators were DO, EC, and turbidity. In-situ measurements results are also shown in Appendix G. 

  46 
Table 13. Marina Median Concentrations of Biotic Ligand Model-Related Constituents (in mg/L)1 

DOC 

Alkalinity Calcium Magnesium Potassium Sodium Ch

Marina2 M LRS M LRS M LRS M LRS M LRS M LRS M 

Folsom Lake Marina 1.3 1.2 22.4 22.2 4.7 4.7 1.5 1.5 0.6 0.6 1.5 1.8 

Tahoe Keys Marina 0.9 0.6 45.2 43.3 8.9 8.4 2.3 2.2 1.2 1.7 5.8 5.5 

San Francisco Marina 0.9 1.0 109 109 387 397 1197 1236 369 378 9166 9499 179

Alamitos Bay Marina 0.9 0.9 113 113 433 437 1356 1367 411 413 10489 10595 196

Coyote Point Marina 1.7 1.6 112 111 364 368 1122 1134 348 350 8494 8544 166

Antioch Marina 1.9 1.8 62.7 62.1 18.2 15.3 23.9 16.4 7.9 5.2 150 92.3 2

Pittsburg Marina 1.8 1.7 64.6 65.1 26.4 32.2 47.1 61.8 15.5 20.9 331 437 6

South Beach Harbor 0.9 1.0 108 108 380 388 1176 1203 362 370 9085 9255 174

Clipper Yacht Harbor 1.0 1.1 109 108 386 385 1205 1200 366 367 9392 9444 175

Marina Bay Yacht Harbor 1.1 0.8 104 105 375 373 1150 1148 356 357 8776 8847 171

Benicia Marina 2.0 2.0 78.5 81.7 147 181 411 518 135 167 3282 4077 66

Sacramento Marina 1.4 1.0 57.2 58.3 10.6 10.7 5.4 5.5 0.9 0.9 7.0 7.1 

Ballena Isle Marina 1.4 1.4 112 111 365 367 1127 1130 342 345 8949 8743 167

Village West Marina 3.7 3.6 70.0 71.3 18.2 18.4 8.7 8.8 2.5 2.5 28.9 29.5 3

Vallejo Marina 2.2 2.2 87.4 85.1 210 197 600 563 191 180 4725 4443 93

Berkeley Marina 1.0 1.0 105 106 377 377 1153 1153 356 358 8854 8926 170

Santa Cruz Harbor 1.0 1.0 118 115 429 433 1338 1350 410 414 10398 10430 197

Monterey Harbor 1.0 1.0 115 114 437 436 1363 1361 412 410 10382 10392 197

Santa Barbara Harbor 0.8 0.7 115 113 431 430 1351 1352 407 409 10420 10447 201

Loch Lomond Marina 1.3 1.3 98.5 98.1 325 327 1001 1011 314 314 7741 7803 149

Downtown Shoreline Marina 1.0 1.4 113 116 430 415 1341 1284 409 394 10335 9984 194

Marina del Rey FB 1.0 1.0 117 115 431 430 1359 1358 413 414 10584 10596 197

Marina del Rey BB 1.1 1.0 117 115 428 430 1347 1358 411 414 10590 10596 196

1 Ordered by ascending marina median DCu concentrations. Values > 100 are rounded to the nearest one. Values < 100 are rounded to the nearest tenth. 2 Marina names are color coded by water type. Green = freshwater, Yellow = brackish water, Blue = saltwater. 
Table 14. Ranges of BLM-Related Constituent Concentrations (in mg/L) for Each Marina Area1 

DOC 

Alkalinity Calcium Magnesium Potassium Sodium Ch

Marina2 M LRS M LRS M LRS M LRS M LRS M LRS M Folsom Lake Marina 0.9 ­ 1.6 0.9 ­ 1.6 21 - 26 21 - 24 4.5 ­ 4.9 4.5 ­ 4.9 1.4 ­ 1.6 1.4 ­ 1.5 0.6 ­ 0.6 0.6 ­ 0.6 1.8 ­ 1.9 1.8 ­ 1.9 1.1 

1.

Tahoe Keys Marina 0.7 ­ 1.2 0.5 ­ 0.7 42 - 49 39 - 46 8.9 ­ 9.0 7.8 ­ 8.7 2.3 ­ 2.3 2.0 ­ 2.3 1.2 ­ 1.3 1.6 ­ 1.7 5.6 ­ 6.0 5.1 ­ 5.8 1.5 

1.

San Francisco Marina 0.6 ­ 2.1 0.6 ­ 1.5 107 ­ 113 107 ­ 113 337 ­ 407 387 – 414 1030 ­ 1250 1210 ­ 1280 322 ­ 383 365 ­ 389 7690 ­ 9570 9210 ­ 9830 176

184

Alamitos Bay Marina 0.8 ­ 1.1 0.8 ­ 1.0 110 ­ 115 110 ­ 117 425 ­ 444 429 ­ 449 1340 ­ 1390 1350 ­ 1410 399 ­ 429 399 ­ 438 10200 ­ 10700 10320 ­ 10800 192

204

Coyote Point Marina 1.5 ­ 1.8 1.5 ­ 1.8 110 ­ 114 109 ­ 113 353 ­ 378 354 ­ 381 1080 ­ 1170 1080 ­ 1180 342 ­ 362 337 ­ 363 7680 ­ 9200 7980 ­ 9120 1599

174

Antioch Marina 1.6 ­ 2.3 1.4 ­ 2.4 56 - 73 56 - 70 16.5 ­ 20.4 13.5 ­ 18.3 20.3 ­ 31.0 13.2 ­ 22.4 6.2 ­ 11.7 4.4 ­ 6.9 129 ­ 201 68.0 ­ 137 200 

38

Pittsburg Marina 1.5 ­ 2.3 1.5 ­ 2.3 61 - 72 58 - 80 21.8 ­ 38.7 19.8 ­ 192.1 35.6 ­ 86.8 29.0 ­ 554.6 11.5 ­ 29.5 12.4 ­ 180.7 245 ­ 656 182 ­ 4345 42

13

South Beach Harbor 0.7 ­ 1.5 0.6 ­ 1.5 107 ­ 110 106 ­ 110 371 ­ 392 376 ­ 397 1150 ­ 1210 1170 ­ 1220 351 ­ 370 359 ­ 377 8720 ­ 9310 9020 ­ 9460 171

178

Clipper Yacht Harbor 0.9 ­ 1.5 0.9 ­ 1.5 107 ­ 111 106 ­ 110 374 ­ 403 375 ­ 399 1166 ­ 1250 1170 ­ 1240 358 ­ 376 356 ­ 377 8780 ­ 9790 9250 ­ 9700 1680

181

Marina Bay Yacht Harbor 0.7 ­ 6.4 0.6 ­ 1.2 99 ­ 107 99 ­ 107 369 ­ 382 369 ­ 378 1120 ­ 1170 1120 ­ 1160 341 ­ 373 345 ­ 371 8320 ­ 9349 7860 ­ 9177 1599

178

Benicia Marina 1.7 ­ 2.3 1.6 ­ 2.3 74 - 92 76 - 89 139 ­ 157 161 ­ 202 379 ­ 444 456 ­ 570 129 ­ 141 156 ­ 190 3087 ­ 3520 3590 ­ 4693 603

74

Sacramento Marina 0.9 ­ 1.4 0.2 ­ 1.3 52 - 61 50 - 70 10.2 ­ 11.2 9.7 ­ 13.1 5.0 ­ 5.9 4.8 ­ 7.2 0.9 ­ 1.0 0.9 ­ 1.1 6.3 ­ 7.9 5.6 ­ 9.3 3.6 

4.

Ballena Isle Marina 1.3 ­ 1.6 1.2 ­ 1.6 107 ­ 116 107 ­ 115 357 ­ 373 356 ­ 377 1110 ­ 1150 1110 ­ 1150 333 ­ 350 336 ­ 352 8700 ­ 9140 8640 ­ 9190 1634

180

Village West Marina 3.3 ­ 4.4 3.2 ­ 3.8 59 - 76 62 - 76 15.8 ­ 20.0 16.2 ­ 19.8 7.2 ­ 9.8 7.3 ­ 9.9 2.1 ­ 3.0 2.2 ­ 2.7 24.4 ­ 32.3 25.3 – 32.2 26.

32

Vallejo Marina 1.8 ­ 2.5 1.6 ­ 3.6 84 - 91 78 - 92 200 ­ 222 181 ­ 216 576 ­ 633 523 ­ 614 186 ­ 200 172 ­ 189 4511 ­ 4920 3996 ­ 4730 873

103

Berkeley Marina 0.8 ­ 1.2 0.7 ­ 2.7 99 ­ 109 102 ­ 108 372 ­ 380 364 ­ 385 1130 ­ 1170 1130 ­ 1180 344 ­ 371 347 ­ 366 8152 ­ 9199 8606 ­ 9187 163

178

Santa Cruz Harbor 0.6 ­ 1.7 0.7 ­ 1.9 113 ­ 127 111 ­ 118 413 ­ 447 409 ­ 455 1280 ­ 1380 1280 ­ 1400 393 ­ 432 404 ­ 432 10100 ­ 10700 9900 ­ 10800 1940

200

Monterey Harbor 0.7 ­ 1.6 0.5 – 1.8 111 ­ 120 111 ­ 117 422 ­ 454 419 ­ 456 1300 ­ 1420 1300 ­ 1420 399 ­ 430 393 ­ 425 10100 ­ 10800 9900 ­ 10800 1944

201

Santa Barbara Harbor 0.7 ­ 1.0 0.6 ­ 0.9 112 ­ 119 111 ­ 115 426 ­ 439 413 ­ 439 1330 ­ 1380 1300 ­ 1390 389 ­ 433 391 ­ 437 9927 ­ 11000 10057 ­ 10800 1950

208

Loch Lomond Marina 1.0 ­ 1.7 1.2 ­ 1.6 92 ­ 103 94 ­ 103 313 ­ 344 312 ­ 394 969 ­ 1053 957 ­ 1232 304 ­ 332 297 ­ 379 6970 ­ 8210 7210 ­ 9560 1430

161

Downtown Shoreline Marina 0.9 ­ 1.2 0.9 ­ 2.9 110 ­ 116 109 ­ 128 425 ­ 434 345 ­ 434 1330 ­ 1350 1010 ­ 1360 402 ­ 417 316 ­ 414 10057 ­ 10530 7720 ­ 10500 1897

199

Marina del Rey FB 0.7 ­ 1.1 0.7 ­ 1.7 113 ­ 129 112 ­ 118 421 ­ 437 417 ­ 438 1340 ­ 1390 1320 ­ 1390 401 ­ 421 400 ­ 428 10393 ­ 10700 10400 ­ 10800 1930

204

Marina del Rey BB 1.0 ­ 1.9 0.7 ­ 1.7 114 ­ 120 112 ­ 118 417 ­ 435 417 ­ 438 1320 ­ 1380 1320 ­ 1390 394 ­ 421 400 ­ 428 10367 ­ 11000 10400 ­ 10800 1930

204

1 Ordered by ascending marina median DCu concentrations. Values > 100 are rounded to the nearest one. Values < 100 are rounded to the nearest tenth. 2 Marina names are color coded by water type. Green = freshwater, Yellow = brackish water, Blue = saltwater. 
Predictive Toxicity Models 

Biotic Ligand Model - Freshwater 

Since relatively few of the samples met the operating parameter requirements for the freshwater BLM (Table 4) 

(mainly pertaining to the salinity-related constituents), only 53 samples were analyzed using this model. Most of these 

samples were from the four freshwater sites; however, some of the samples came from the Antioch site, which on 

some occasions contained sodium and chloride concentrations within the BLM operating range. Recall that the 

Antioch Marina has been classified in this study as a brackish water site due to the range of salinity at this location 

over the course of a year. The Pittsburg Marina and sites with greater salinity than Pittsburg had sodium and chloride 

concentrations that were above the operating range of the freshwater BLM. They, however, were evaluated using the 

interim saltwater BLM and will be discussed in a later sub-section. 

Appendix L contains the freshwater BLM results for samples that met the criteria of the operating parameter above. 

The BLM-generated, unit-less, toxic unit (TU) values for freshwater sites ranged from 0.03–9.42. The model 

predicted that seven of 53 freshwater samples evaluated would meet or exceed the 1-TU threshold (Table 15). All 

seven samples were collected from the Sacramento Marina area and the most significant exceedances occurred during 

the second sampling event in August 2006. The BLM output columns shown include: 

• Site and sample information (i.e., station name, sampling date, site, and event) 

• The Final Acute Value (FAV) for copper (calculated from the 4 lowest genus mean acute values adjusted to 

account for site-specific BLM input parameters) 

• CMC (Calculated as 1⁄2 the FAV and rounded to the nearest tenth to facilitate comparison with DCu.) 

• Measured DCu concentrations 

• TU (Measured DCu/CMC. For TU equal to or greater than 1, the model predicts toxicity.) 

The freshwater BLM did not predict acute toxicity from DCu at the following marina areas: Tahoe Keys, Folsom 

Lake, Antioch, Pittsburg, and Village West. Note that at this time, toxicity thresholds generated by the freshwater 

BLM are not enforceable water quality criteria or standards. 

Table 15. Freshwater BLM Output for Samples with Predicted Toxicity (TU ≥ 1) 

Station Name Sampling 

Date Site1

 Event 

FAV CMC Measured 

DCu (μg/L) 

Toxic 

Units 

Sacramento Marina 7/21/2006 1 1 7.3459 3.7 3.7 1.00 

Sacramento Marina 8/16/2006 1 2 0.8178 0.4 3.0 7.50 

Sacramento Marina 8/16/2006 2 2 0.7435 0.4 3.5 8.75 

Sacramento Marina 8/16/2006 3 2 0.6129 0.3 2.8 9.33 

Sacramento Marina 8/16/2006 4 2 0.7626 0.4 2.3 5.75 

Sacramento Marina 8/16/2006 5 2 0.6971 0.3 0.6 2.00 

Sacramento Marina 9/12/2006 8 3 1.4317 0.7 0.7 1.00 1

 Sites 1–4 are marina sites. Sites 5–8 are LRSs. 

The relatively large magnitude of the TU values for the second event suggests that copper concentrations could have 

had a significantly toxic effect on the more sensitive aquatic organisms at that site. BLM input data for the second 

sampling event at Sacramento Marina do not appear to exhibit distinctly different characteristics from data for the first 

sampling event, in which only one sample slightly exceeded the 1-TU threshold, with the exception of the pH data. 

Upon more detailed evaluation, it is likely that the lower pH values recorded during the second sampling event (6.0– 

6.5) compared to those during the first event (7.3–7.7) had a large impact in reducing the FAV and CMC values for 

49 
that event. Lower pH conditions tend to favor the presence of more-bioavailable free copper ions (sometimes referred 

to as labile copper or free dissolved copper), which have greater impact on biotic ligands. At higher pH levels, copper 

tends to be bound with both inorganic (e.g., carbonates) and organic (e.g., fulvic and humic acids) complexes in 

solution and is generally less available to interact with biotic ligands. 

An evaluation of the water quality field data sheet for the second sampling event, however, revealed that the pH levels 

were measured using pH strips (i.e., litmus paper) since the pH meter was not functional that day. This was the only 

time when pH strips were used in the study. Historical pH data from a downstream monitoring station (Sacramento 

River at Hood) showed that pH levels typically ranged between 6.8 and 7.7 during the study’s entire sampling period 

(CDEC, 2008). Thus, the pH readings for the second sampling event is likely to be less accurate than the pH readings 

made using the frequently calibrated pH meters. Consequently, the freshwater BLM’s toxicity predictions for the 

Sacramento Marina for this particular event are likely to be less reliable as well. 

Even if the toxicity predictions for the second sampling event at the Sacramento Marina were considered to be 

unreliable, there are still predictions of toxicity from the first and third sampling event. Even though the predicted 

TUs are relatively low (both at around 1 TU), these predictions suggest that the DCu could be bioavailable enough at 

the Sacramento Marina area to be occasionally toxic to freshwater organisms. 

DOC Model - Salt and Brackish Water 

Appendix M contains the complete results from the DOC model. Table 16 shows the FCC and FAC output ranges for 

the 19 salt and brackish water marinas evaluated. Applicable saltwater CTR CCC/CMC and range of measured DCu 

concentrations are also shown for comparison. The DOC-model generated FCC and FAC values are threshold values 

that are used to predict toxic effects. They are not enforceable water quality objectives or standards. 

Table 16. DOC Model FCC and FAC Output Ranges (μg/L) 

Station Name FCC Range FAC Range CTR CCC1 CTR CMC1 Range DCu 

Alamitos Bay Marina 3.0–3.8 4.7–5.9 3.1 4.8 ND–3.6 

Antioch Marina 4.4–6.0 6.9–9.4 3.1 4.8 1.4–3.0 

Ballena Isle Marina 4.0–4.8 6.3–7.5 3.1/6.0 4.8/9.4 0.1–5.0 

Benicia Marina 4.8–5.9 7.5–9.3 3.1/6.0 4.8/9.4 ND–3.7 

City of Berkeley 3.0–6.5 4.6–10.2 3.1/6.0 4.8/9.4 0.2–7.2 

Clipper Yacht Harbor 3.1–4.7 4.8–7.3 3.1/6.0 4.8/9.4 0.3–6.6 

Coyote Point Marina 4.6–5.2 7.2–8.0 3.1/6.0 4.8/9.4 1.0–2.6 

Downtown Shoreline Marina 3.3–6.9 5.1–10.7 3.1 4.8 0.4–10.9 

Loch Lomond Marina 3.6–5.0 5.6–7.8 3.1/6.0 4.8/9.4 1.3–10.3 

Marina Bay Yacht Harbor 2.8–11.0 4.3–17.1 3.1/6.0 4.8/9.4 1.3–3.6 

Marina del Rey Back Basin 3.6–5.3 5.6–8.2 3.1 4.8 8.1–18.4 

Marina del Rey Front Basin 2.9–5.0 4.5–7.8 3.1 4.8 0.3–16.2 

Monterey Harbor 2.5–5.1 3.9–7.9 3.1 4.8 ND–7.8 

Pittsburg Marina 4.5–6.0 7.1–9.3 3.1 4.8 1.4–3.2 

San Francisco Marina 2.6–5.5 4.1–8.7 3.1/6.0 4.8/9.4 ND–3.9 

50
Station Name FCC Range FAC Range CTR CCC1 CTR CMC1 Range DCu 

Santa Barbara Harbor 2.6–3.7 4.0–5.7 3.1 4.8 ND–8.2 

Santa Cruz Harbor 2.7–4.9 4.2–7.6 3.1 4.8 ND–5.8 

South Beach Harbor 2.7–4.6 4.2–7.1 3.1/6.0 4.8/9.4 0.4–3.0 

Vallejo Municipal Marina 4.7–7.8 7.3–12.1 3.1/6.0 4.8/9.4 1.3–4.7 

1

 CTR’s CCC and CMC for the San Francisco Bay Area Marinas will be replaced by Site Specific Objectives pending U.S. EPA approval. Once 

approved, the new chronic and acute water quality objectives for these marinas will be 6.0 and 9.4 μg/L, respectively. 

Out of the 412 salt and brackish water samples that had DOC results, 86 contained high enough concentrations of DCu 

to equal or exceed the DOC-model predicted FCC and 54 of those also exceeded the FAC. Both FCC and FAC 

exceedances are highlighted in Appendix M. The majority of the samples that exceeded toxicity thresholds came from 

Central and South Coast marinas, most notably MdR’s front and back basins, Downtown Shoreline Marina, Santa 

Barbara Harbor, Monterey Harbor, and Santa Cruz Harbor. Bay Area marinas also had occasional exceedances, 

namely Loch Lomond Marina, Clipper Yacht Harbor, Berkeley Marina, and Ballena Isle Marina. 

Eighty five of the 86 samples that exceeded the lower FCC threshold came from inside of marinas. Only one LRS 

sample exceeded this same threshold. This sample came from MdR’s LRS #1, which has been noted as an LRS site 

with characteristics that make it more like a marina site. 

In comparison to the CTR’s fixed CCC and CMC for the same sites, the DOC model FCCs and FACs are generally 

higher. Thus, measured DCu concentrations tend to exceed CTR standards more frequently than DOC-model 

predicted toxicity thresholds. Occasionally, the opposite is true. This is most noticeable at San Francisco Marina, 

Santa Cruz Harbor, Monterey Harbor, and Santa Barbara Harbor. This phenomenon is likely a reflection of the 

relatively low DOC concentrations at these locations. Due to the potential of DOC concentrations to fluctuate at each 

site, a particular concentration of measured DCu could exceed FCCs and FACs one day and be below them the next. 

A comparison of the Bay Area marinas’ FCCs and FACs to water quality standards needs to be done with the 

understanding that site specific objectives (SSOs) could be adopted that would result in the CCC being raised to 6.0 

μg/L and the CMC to 9.4 μg/L. Subsequently, DCu concentrations in Bay Area marinas will likely exceed the FCCs 

and FACs more frequently than the SSOs. 

Since the study’s toxicity tests involved the exact same test species, toxic endpoint, and exposure period as the toxicity 

tests on which the DOC model’s function is based, it is possible to directly compare model-predicted results to our 

toxicity results. A comparison of the results from the DOC model with the study’s actual toxicity results showed good 

agreement in 42 of 47 outcomes. In other words, the DOC model predicted acute toxicity when statistically significant 

toxicity was observed in toxicity tests, or it predicted that no acute toxicity would occur for samples with no observed 

toxicity. Five inconsistent outcomes were, however, observed. The model predicted acute toxicity at one Loch 

Lomond Marina site, one MdR back basin site, and two Downtown Shoreline Marina sites when no acute toxicity was 

observed for samples tested from these sites. In one case, in a Marina Bay Yacht Harbor sample, the model predicted 

no toxicity and yet low level acute toxicity was observed in the actual sample. Overall, the DOC model predicted 

water column toxicity well (89% success rate) when compared to actual result of acute toxicity tests on coppersensitive M. galloprovincialis. When the model and toxicity results disagree, it is due to the tendency of the model to 

over-predict toxicity rather than under-predict. 

51
Biotic Ligand Model - Salt and Brackish Water 

The output of the saltwater BLM is presented in Appendix N. Saltwater BLM results that were greater than one toxic 

unit (TU) are presented in Table 17. Sixty seven of 373 samples (for which output could be calculated) or 18% of the 

samples contained a high enough concentration of DCu to reach or exceed the 1-TU threshold. Key BLM output 

columns shown include: predicted EC50 for M. galloprovincialis, observed DCu concentrations, and TUs. Note that 

the TUs calculated for the saltwater BLM in Table 17 and Appendix N are based on the predicted EC50’s for M. 

galloprovincialis and not on the CMC as was the case for the freshwater BLM. In this case, exceedances over one TU, 

as determined by the saltwater BLM, suggests that adverse risks to M. galloprovincialis may exist from the exposure 

of copper. At this time, the predicted EC50’s for M. galloprovincialis that are generated by the saltwater BLM are not 

enforceable water quality criteria or standards. 

Table 17. Saltwater BLM Output for Samples with Predicted Toxicity (TU ≥ 1) 

Station Name Date Site Event 

Water 

Type 

Observed 

DCu (μg/L) 

Predicted EC50 

M. galloprovincialis 

Toxic 

Units 

Berkeley Marina 7/24/2006 1 1 Salt 6.8 5.2 1.31 

Berkeley Marina 9/18/2006 1 3 Salt 3.8 3.2 1.19 

Downtown Shoreline Marina 8/1/2006 1 1 Salt 7.4 3.9 1.90 

Downtown Shoreline Marina 8/1/2006 2 1 Salt 6.3 3.9 1.62 

Downtown Shoreline Marina 8/1/2006 3 1 Salt 7.7 4.7 1.64 

Downtown Shoreline Marina 8/29/2006 1 2 Salt 5.3 5.3 1.00 

Downtown Shoreline Marina 8/29/2006 2 2 Salt 7.4 4.3 1.72 

Downtown Shoreline Marina 8/29/2006 3 2 Salt 8.9 4.2 2.12 

Downtown Shoreline Marina 9/26/2006 1 3 Salt 10.9 4.6 2.37 

Downtown Shoreline Marina 9/26/2006 2 3 Salt 9.5 4.7 2.02 

Downtown Shoreline Marina 9/26/2006 3 3 Salt 8.6 4.2 2.05 

Downtown Shoreline Marina 9/26/2006 4 3 Salt 6 4.8 1.25 

Marina del Rey Back Basin 8/2/2006 1 1 Salt 18.4 4.8 3.83 

Marina del Rey Back Basin 8/2/2006 2 1 Salt 17.9 4.5 3.98 

Marina del Rey Back Basin 8/2/2006 3 1 Salt 14.5 4.5 3.22 

Marina del Rey Back Basin 8/2/2006 4 1 Salt 14.1 4.8 2.94 

Marina del Rey Back Basin 8/30/2006 1 2 Salt 11.5 4.5 2.56 

Marina del Rey Back Basin 8/30/2006 2 2 Salt 18.4 4.6 4.00 

Marina del Rey Back Basin 8/30/2006 3 2 Salt 12.4 4.7 2.64 

Marina del Rey Back Basin 8/30/2006 4 2 Salt 14.2 4.6 3.09 

Marina del Rey Back Basin 9/27/2006 1 3 Salt 12.7 4.6 2.76 

Marina del Rey Back Basin 9/27/2006 2 3 Salt 13.3 4.6 2.89 

Marina del Rey Back Basin 9/27/2006 3 3 Salt 8.1 8.1 1.00 

Marina del Rey Back Basin 9/27/2006 4 3 Salt 12 4.3 2.79 

Marina del Rey Front Basin 8/2/2006 1 1 Salt 16.2 4.8 3.38 

Marina del Rey Front Basin 8/2/2006 2 1 Salt 14.5 4.6 3.15 

Marina del Rey Front Basin 8/2/2006 3 1 Salt 14.9 4.4 3.39 

Marina del Rey Front Basin 8/2/2006 4 1 Salt 13.8 3.2 4.31 

Marina del Rey Front Basin 8/2/2006 5 1 Salt 4.7 4.2 1.12 

Marina del Rey Front Basin 8/30/2006 1 2 Salt 13.1 4.5 2.91 

Marina del Rey Front Basin 8/30/2006 2 2 Salt 12.5 4.2 2.98 

Marina del Rey Front Basin 8/30/2006 3 2 Salt 11.8 4.5 2.62 

52
Station Name Date Site Event 

Water 

Type 

Observed 

DCu (μg/L) 

Predicted EC50 

M. galloprovincialis 

Toxic 

Units 

Marina del Rey Front Basin 8/30/2006 4 2 Salt 11.9 5 2.38 

Marina del Rey Front Basin 9/27/2006 1 3 Salt 12.2 4.3 2.84 

Marina del Rey Front Basin 9/27/2006 2 3 Salt 11.5 3.9 2.95 

Marina del Rey Front Basin 9/27/2006 3 3 Salt 8.9 4.6 1.93 

Marina del Rey Front Basin 9/27/2006 4 3 Salt 10 4.1 2.44 

Monterey Harbor 8/10/2006 1 1 Salt 6.3 4.7 1.34 

Monterey Harbor 8/10/2006 2 1 Salt 7.1 4.4 1.61 

Monterey Harbor 8/10/2006 3 1 Salt 4.6 3.9 1.18 

Monterey Harbor 8/10/2006 4 1 Salt 4.8 4.1 1.17 

Monterey Harbor 9/8/2006 1 2 Salt 3.7 3 1.23 

Monterey Harbor 9/8/2006 2 2 Salt 3.7 3.2 1.16 

Monterey Harbor 10/19/2006 3 3 Salt 7.8 6.7 1.16 

Loch Lomond Marina 7/25/2006 1 1 Salt 10.3 5.8 1.78 

Loch Lomond Marina 7/25/2006 2 1 Salt 8.4 5.8 1.45 

Loch Lomond Marina 7/25/2006 3 1 Salt 7.2 5.7 1.26 

Loch Lomond Marina 7/25/2006 4 1 Salt 8 5.5 1.45 

Loch Lomond Marina 8/22/2006 1 2 Salt 7.7 7.5 1.03 

Loch Lomond Marina 9/19/2006 1 3 Salt 6.7 4.5 1.49 

Santa Barbara Harbor 8/3/2006 2 1 Salt 6.5 3.6 1.81 

Santa Barbara Harbor 8/3/2006 3 1 Salt 6.6 4.2 1.57 

Santa Barbara Harbor 8/3/2006 4 1 Salt 4.6 3.3 1.39 

Santa Barbara Harbor 8/31/2006 1 2 Salt 5.6 3.5 1.60 

Santa Barbara Harbor 8/31/2006 2 2 Salt 6.9 3.2 2.16 

Santa Barbara Harbor 8/31/2006 3 2 Salt 8.2 3.4 2.41 

Santa Barbara Harbor 8/31/2006 4 2 Salt 7.4 3.3 2.24 

Santa Barbara Harbor 9/28/2006 1 3 Salt 4.5 4.3 1.05 

Santa Barbara Harbor 9/28/2006 2 3 Salt 7.1 4.1 1.73 

Santa Barbara Harbor 9/28/2006 3 3 Salt 6 4 1.50 

Santa Barbara Harbor 9/28/2006 4 3 Salt 5.4 4.6 1.17 

Santa Cruz Harbor 8/9/2006 3 1 Salt 4.3 3.5 1.23 

Santa Cruz Harbor 9/7/2006 1 2 Salt 5.1 2.7 1.89 

Santa Cruz Harbor 9/7/2006 2 2 Salt 5.8 3 1.93 

Santa Cruz Harbor 9/7/2006 3 2 Salt 4.4 3.1 1.42 

Santa Cruz Harbor 9/7/2006 4 2 Salt 4.5 2.9 1.55 

Clipper Yacht Harbor 8/25/2006 3 2 Salt 6.6 4.4 1.50 

For samples that equaled or exceeded the toxicity threshold, TUs ranged from 1.00–4.36. The majority of the samples 

that exceeded the M. galloprovincialis site-specific predicted EC50s were from Central and South Coast marinas. This 

observation is similar to the DOC model results. MdR’s front and back basins had the most occurrences of samples 

that reached or exceeded one TU. The magnitude of TU values appeared to also be highest at MdR. TUs for MdR 

were often 3–4 times the Mytilus sp. EC50 values. Bay Area marinas also had occasional exceedances, but these 

occurrences were primarily isolated to Loch Lomond Marina and Berkeley Marina. 

Sixty six samples with predicted toxicity were collected from marina sites and one sample was collected from an LRS. 

This sample is the same sample (MdR LRS #1) that exceeded the DOC model’s toxicity threshold. As previously 

53
asserted, this LRS site has characteristics that are more like a marina site than an LRS. Overall, it is clear that water 

samples from saltwater marinas have the potential to be acutely toxic to M. galloprovincialis. 

Predictions were fairly consistent between the saltwater BLM (i.e., exceedances of M. galloprovincialis EC50) and the 

DOC model (i.e., exceedances of FCCs or FACs). That is, both models tend to predict acute toxicity or the lack of 

toxicity for the same sample. This is not entirely surprising since DOC, which is a major determinant in the 

bioavailability of copper in saline water, is an input in both models. Note that chronic toxicity cannot be compared 

here since the draft saltwater BLM used for this report does not currently generate a CCC output. 

The saltwater BLM predicted acute toxicity more frequently than the DOC model. There were 14 occasions where the 

saltwater BLM predicted the exceedance of an acute toxicity threshold and the DOC model did not. In contrast, there 

were only two occasions where the DOC model predicted the exceedance of an acute toxicity threshold and the 

saltwater BLM did not. This suggests that the DOC model is more conservative than the saltwater BLM in predicting 

acute toxicity. There is an assumption made here that both models’ toxicity thresholds (i.e., FACs and BLM predicted 

EC50’s) are similar enough to allow this comparison. 

Since site-specific CCCs and CMCs are not yet available from the saltwater BLM, it is not possible to accurately 

compare the current model output with the CTR’s water quality criteria. However, considering the importance of 

DOC in mediating the bioavailability of copper, it is likely that the comparison of the saltwater BLM criteria to the 

existing CTR water quality criteria will be similar in result to the comparison of the DOC model’s FACs and FCCs to 

the same criteria. In other words, the saltwater BLM-predicted CCCs and CMCs are likely to be higher than the fixed 

acute (3.1 μg/L) and chronic (4.8 μg/L) CTR standards for many sites. However, at sites where DOC tends to be low 

(i.e., San Francisco Marina, Santa Cruz Harbor, Monterey Harbor, and Santa Barbara Harbor), it is possible that the 

saltwater BLM-generated criteria could be lower than the existing standards. 

A subset of the saltwater BLM-predicted toxicity results were directly compared to the study’s toxicity results. A 

comparison of samples that had both predicted and actual toxicity results showed 35 of 40 matching outcomes. That 

is, the saltwater BLM predicted acute toxicity when statistically significant toxicity was observed in toxicity tests, or it 

predicted that no toxicity would occur for samples with no observed toxicity. Five contradicting outcomes were 

observed. The model predicted acute toxicity at a Loch Lomond Marina site, one MdR Back Basin site, and two 

Downtown Shoreline Marina sites when no acute toxicity was observed in these samples. In one case, for a Marina 

Bay Yacht Harbor sample, the model predicted no toxicity and yet acute toxicity was observed in the sample. 

Interestingly, these five anomalous results are identical to those observed between the DOC model predictions and 

actual toxicity results. 

Overall, the saltwater BLM predicted EC50’s for M. galloprovincialis were generally in good agreement with actual 

toxicity (88% success rate). Like the DOC model, however, the saltwater BLM also tends to over-predict toxicity on 

occasions. This tendency can be seen when toxicity results were plotted against the BLM-predicted TUs (Figure 32). 

Note the red vertical line on the graph, which denotes the 1-TU threshold. Interestingly, significant abnormal 

development (which includes larval mortality) does not appear to take place until the predicted TU is greater than 2. 

This suggests that toxicity predictions by the saltwater BLM, particularly at levels between 1 and 2 TUs, may not 

always translate to notable toxicity to M. galloprovincialis in laboratory tests. With this in mind, of the 67 samples 

that were above the 1-TU threshold, only 29 of them were also above the 2-TU threshold. These 29 samples were 

collected from four marinas–Santa Barbara Harbor, MdR back basins, MdR front basins, and Downtown Shoreline 

Marina. Thus, if this subset of samples were analyzed for toxicity to M. galloprovincialis, they would likely exhibit 

statistically significant toxicity. Future interpretation and linkage of BLM toxicity predictions to actual toxicity results 

should consider this observation. 

54
Figure 32. BLM-Predicted Toxic Units (Observed DCu/Predicted M. galloprovincialis EC50) vs. Observed 

Toxicity 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

10 

Percent Abnormal Development 

0.1 1 

Log Toxic Units 

IV. Discussion 

Occurrence and Magnitude 

DCu, TCu, and DZn were frequently detected. The high frequency of detection is not surprising considering (1) the 

ubiquitous nature of copper and zinc (2) that monitoring was conducted in marina areas, which have historically been 

known to exhibit elevated concentrations of metals, and (3) the relatively low detection limits used for quantification. 

Water Column – Copper 

DCu concentrations in salt and brackish water marinas were frequently higher than those found in freshwater marinas. 

This difference is statistically significant. Marinas in California’s South Coast exhibited low to very high median DCu 

concentrations among results for all the marinas. MdR’s front and back basins had the two highest median 

concentrations at 12.4 and 13.6 μg/L, respectively. The highest sample concentration of DCu was detected in two 

samples from the back basins of MdR. Both samples contained 18.4 μg/L of DCu. Downtown Shoreline Marina had 

the third highest median concentration in the study at 6.6 μg/L. The remaining South Coast marina, Alamitos Bay 

Marina, exhibited a relatively low median DCu concentration of 1.2 μg/L. This marina is located only a few miles 

from Downtown Shoreline Marina and is similar in size. This deviation, however, could potentially be created by the 

dilution effect caused by the routine pumping of a high volume of water from the marina for cooling use at a nearby 

power-generating facility (M. Sandoval, personal communication, 2007). During peak pumping periods, a volume that 

is roughly equivalent to the capacity of Alamitos Bay could be emptied out from the basin. 

Elevated DCu concentrations in South Coast marinas have also been documented in two recent studies. In 2005, 

SCCWRP conducted a monitoring study of 12 yacht basins in four Southern California harbors (Dana Point Harbor, 

Oceanside Harbor, Mission Bay, and San Diego Bay) as part of SDRWQCB’s Harbor Monitoring Program 

(SCCWRP, 2006). DCu concentrations ranged from below the detection limit to 21.0 μg/L with a mean concentration 

of 7.0 μg/L. In 2006, the Santa Ana RWQCB conducted a metal monitoring study in LNB. DCu concentrations in the 

marinas sampled ranged from 1.3–11 μg/L with a mean concentration of 4.2 μg/L. The marinas investigated in both of 

55
these studies are located farther south along the California coast than the southernmost site in this DPR study. If the 

copper results from the three studies were pooled together, they would show that elevated DCu concentrations are 

common among California South Coast marinas. 

The three Central Coast marinas in this study exhibited moderately high median DCu concentrations. Santa Barbara 

Harbor, Monterey Harbor, and Santa Cruz Harbor had median concentrations of 5.7, 4.9, and 4.3 μg/L, respectively. 

These median concentrations are the fifth through seventh highest values documented among the 23 marinas studied. 

The highest DCu concentration documented among these three Central Coast marinas was detected in a sample from 

Santa Barbara Harbor at 8.2 μg/L. 

Eight Bay Area marinas were examined in this study and most of them exhibited moderate median DCu concentrations 

between 2.1–3.3 μg/L. The higher-end exception to this range is Loch Lomond Marina in San Rafael where the 

median concentration was 5.8 μg/L. The lower-end exception is San Francisco Marina where the median 

concentration was 1.1 μg/L. 

The brackish water marinas–the Vallejo, Benicia, Pittsburg, and Antioch marinas–exhibited low to moderate median 

DCu concentrations of 3.4, 2.7, 2.2 and 2.1 μg/L, respectively. The freshwater marinas–the Village West, Sacramento, 

Folsom Lake, and Tahoe Keys marinas–exhibited moderate to very low median concentrations of 3.4, 3.0, 0.5, 0.6 

μg/L. We observed the lowest median DCu concentrations in this study at the two lake marinas at Tahoe Keys and 

Folsom Lake. 

The median TCu and DCu concentrations for each marina tend to be similar in magnitude. That is, marinas that 

exhibit high DCu concentrations tend to also exhibit high TCu concentrations. So, the ranking of marinas from the 

lowest to the highest median TCu concentrations closely resembles the ranking for DCu. This observation is largely 

explained by the fact that the TCu measurement accounts for the DCu load as well as the particulate copper load. U.S. 

EPA derived a recommended multiplier of 0.91 as a conversion factor of DCu from TCu in saltwater environments 

(U.S. EPA, 2007). The freshwater multiplier is 0.96 (Stephan, 1995). In other words, DCu tends to make up over 

90% of TCu. The overall ratio of DCu to TCu for this study is 0.92, which is a good indicator that DCu and TCu were 

within the expected range in this study. Despite this relationship between the DCu and TCu, statistical analysis (oneway ANOVA tests) of TCu data only showed a marginal difference between the TCu concentrations of salt and 

freshwater marinas. 

Water Column – Zinc 

DZn concentrations were generally 2–4 times larger than DCu and TCu concentrations in the same sample. 

Consequently, median DZn concentrations were usually higher than those for DCu or TCu at each marina. MdR’s 

front and back basins exhibited the two highest median DZn concentrations at 49.1 and 50.0 μg/L, respectively. The 

highest DZn concentration documented in the study was detected in one of the front basins of MdR at the 

concentration of 66.6 μg/L. The lowest marina median DZn concentrations are often associated with the fresh and 

brackish water samples. The difference between DZn concentrations in saltwater marinas and DZn concentrations in 

brackish and freshwater marinas is statistically significant. 

The ranking of lowest to highest median DZn concentrations is also very similar to the analogous ranking for DCu and 

TCu. The primary exception was Alamitos Bay which ranked much higher for zinc than copper. Note, however, that 

the LRSs at Alamitos Bay Marina (median DZn of 6.3 μg/L) exhibited similar DZn concentrations as the marina sites 

(median DZn of 6.7 μg/L). Thus, the flushing activity that is unique to this marina would not necessarily serve to 

effectively dilute the DZn load in the marina. This could explain why DZn concentrations are still relatively high 

compared to DCu and TCu concentrations in Alamitos Bay Marina. 

Overall, larger saltwater marinas tend to rank highly for metal concentrations. Smaller saltwater marinas and fresh and 

brackish water marinas tend to make up the intermediate and lower rankings. This observation suggests that factors 

relating to water types and marina size may play a major role in determining the magnitude of copper and zinc in 

marina waters. 

As previously noted for Alamitos Bay Marina, site specific hydrological factors (i.e., flushing) appear to be an 

influential factor (Katz, 1998) and may serve to explain why Alamitos Bay Marina (affected by industrial pumping) 

and San Francisco Marina (located at the mouth of San Francisco Bay) exhibited rather low median DCu and TCu 

56
concentrations relative to their size. Poor flushing may also explain why Loch Lomond and Ballena Isle marinas, 

which are both smaller marinas, exhibited higher concentrations of copper and zinc when compared to larger marinas 

in the Bay Area. 

Water Column – Irgarol/M1 

The biocide Irgarol and its primary degrade M1 were detected in all 45 samples. Irgarol and M1 concentrations 

ranged from 12–712 and 2–217 ng/L, respectively. The highest Irgarol and M1concentrations were found in a sample 

from Loch Lomond Marina in San Rafael. M1 concentrations were also always lower than Irgarol in the same sample. 

M1 concentrations also correlated well with Irgarol at each site reaffirming the parent compound-degradate 

relationship between the two analytes. 

Irgarol and M1 concentrations were higher in saltwater marinas than brackish water marinas. Similar to our evaluation 

of copper, Irgarol concentrations in a particular marina are likely dictated by the amount of use in that marina and the 

level of site-specific flushing at that location. DPR’s product data show that 68 products (including several 

manufacturing use products) are registered with DPR (DPR, 2005b). Almost all of the end-use products are for AFP 

use with boat hulls being the most common application site. 

Irgarol and M1 concentrations were higher in the first versus the third sampling round. Assuming that all of the 

Irgarol in the water column originated from AFP sources, then the difference between the two sampling rounds can be 

attributed to the differences in the release of Irgarol from boat hulls (whether through leaching or underwater hullcleaning) during the time of sample collection in the first round versus the third round. Many factors could have an 

influence on the release rates between the two rounds including use, elapsed time from application and hull cleaning, 

and seasonal changes in flushing and water temperature. 

In 2005, Sapozhnikova et al. (2007) conducted an assessment of Irgarol and M1 in San Diego Bay Area marinas. 

Irgarol and M1 concentrations in that study ranged from 1–304 ng/L and 1–68 ng/L, respectively. In 2006, Hall et al. 

(2007) quantified Irgarol and M1 concentrations in 15 California marina areas. Alamitos Bay Marina, MdR, Ballena 

Isle Marina, and Berkeley Marina were also studied in Hall et al. (2007). Irgarol and M1 concentrations in this study 

ranged from 1–339 ng/L and below detection to 74 ng/L, respectively. Hall et al. (2007) also assessed local reference 

sites in addition to marina sites. Both Irgarol and M1 concentrations were much lower at local reference sites than at 

marina sites. This supports our previous assertion that the main source of Irgarol and M1are from AFP use. 

Sediment – Copper and Zinc 

Only 23 of the anticipated 180 sediments samples were taken in this study. These samples were taken from three 

freshwater marinas. For TCu, both the highest (169 mg/kg dry weight) and lowest concentrations (17 mg/kg dry 

weight) were detected at Sacramento Marina and one of its associated LRSs, respectively. For TZn, the highest 

concentration (273 mg/kg dry weight) was detected at Village West Marina and the lowest concentration (23 mg/kg 

dry weight) was detected at an LRS of Folsom Marina. The availability of sediment metals data, particularly those 

from studies where sediment samples are not depth-composited, is very limited for freshwater marinas in California. 

Sediment metals data that do exist are mainly from saltwater marinas. Pap (2004) documented the range of sediment 

TCu concentrations in four Bay Area marinas (Ballena Isle, Loch Lomond, Berkeley, and Corinthian Club) to be 38.2– 

151 mg/kg dry weight. Sediment TZn concentrations in the Pap (2004) study ranged from 82.7–219 mg/kg dry 

weight. These ranges are fairly similar to the ranges observed for TCu and TZn at the three freshwater marinas in our 

study. Orange County Coast Keeper’s study in LNB documented sediment TCu concentrations in the range of 16-365 

mg/kg dry weight and TZn concentrations in the range of 69–692 mg/kg dry weight (Orange County Coast Keeper, 

2007). These ranges from LNB marinas are somewhat higher than the ones observed in Pap (2004) and our study. 

Comparison to Water Quality Standards or Biological Effects Thresholds 

Copper 

The CTR contains the enforceable water quality standards for copper in California. These standards have been in 

place for California since 2000 when U.S. EPA promulgated them in the absence of a state-established water quality 

standards. 

57
The comparison of freshwater DCu results to the CTR’s hardness-based CCC (chronic) and CMC (acute) standards for 

copper suggested that DCu concentrations in freshwater marina areas (marinas sites and LRSs) were not likely to 

represent a significant risk to freshwater aquatic organisms. Both the CTR chronic and acute standards for DCu in 

freshwater were never exceeded in the 135 DCu samples evaluated from six different marinas and associated LRSs. 

Note that some samples from two of the brackish water marinas contained low enough salinity that it was possible to 

evaluate them with the freshwater standards. 

DCu results from the study’s salt and brackish water marina areas were also compared to the CTR fixed saltwater 

standards. Of the 388 combined salt and brackish water samples, DCu concentrations in 30% and 17% of them 

exceeded the chronic and acute copper standards, respectively. Almost all of the samples that exceeded CTR 

standards were marina samples. For marina samples only, the DCu concentrations in 51% and 30% of the salt and 

brackish water marina samples combined exceeded the chronic and acute standards, respectively. 

DCu concentrations in 16 of 19 marina areas exceeded the chronic saltwater CTR standard of 3.1 μg/L. The marina 

areas where DCu concentrations did not exceed the chronic standards were Antioch Marina, South Beach Harbor, and 

Coyote Point Marina. Copper concentrations in 10 of the 16 marina areas that exceeded the CCC also exceeded the 

CMC standard of 4.8 μg/L. These 10 marina areas were Loch Lomond, Clipper Yacht Harbor, Berkeley Marina, 

Ballena Isle Marina, Santa Cruz Harbor, Monterey Harbor, Santa Barbara Harbor, MdR front basins, MdR back 

basins, and Downtown Shoreline Marina. 

As defined by the CTR, exceedances of CTR saltwater standards indicate that concentrations of DCu are high enough 

to present a significant risk to the aquatic organisms and their uses. The exceedance of CTR standards for copper has 

historically been used to support the declaration of beneficial use impairments by the San Diego RWQCB. Therefore, 

water bodies across the state that exceed these standards can certainly be placed on the CWA 303(d) impaired water 

bodies list by other RWQCBs as well. TMDL development is a possibility for all water bodies that are on the CWA 

303(d) list. The adoption of a site-specific objectives for the San Francisco Bay will, however, likely impact the future 

303(d) listing process for Bay Area marinas. 

Zinc 

The CTR also established specific water quality standards for zinc. None of the 135 freshwater DZn samples collected 

in this study exceeded the CTR’s hardness-based freshwater chronic and acute standards for zinc. Moreover, none of 

the 388 DZn samples from salt and brackish water marinas contained high enough DZn concentrations to exceed the 

CTR’s saltwater fixed chronic and acute standards of 81 and 90 μg/L, respectively. Therefore, DZn concentrations in 

all the marina areas in this study were not likely to present an adverse risk to freshwater aquatic organisms. 

Irgarol/M1 

There are no water quality standards for Irgarol or M1. Thus, a comparison of sample concentrations to various 

biological effect thresholds is a more informative way to put Irgarol and M1 data into perspective. Since Irgarol acts 

as an inhibitor of photosynthesis, growth inhibition of phytoplankton appears to be one of its most commonlyobserved biological effects. Irgarol concentrations at some of the marinas in this study are high enough to exceed 

some of the toxicological values for growth and reproduction inhibition endpoints. Thus, there are instances where 

simple risk quotients (EC50/observed concentration) would be greater than one. 

For a more complete assessment of potential risks, it is more informative to compare environmental concentrations to 

aquatic benchmark values than to EC50 or LC50 values. Hall et al. (1999) derived an aquatic-plant toxicity benchmark 

value for Irgarol in an ecological risk assessment (ERA) for European waters. The benchmark threshold represents the 

10th percentile of the available and accepted plant toxicity dataset that contained nine toxicological values. For this 

particular assessment, the benchmark value was calculated to be 136 ng/L. When compared with measured 

environmental concentrations (i.e., 90th percentile value of various station types), varying levels of ecological risks 

were established with risks in marinas being the highest among the station types. Hall et al. (1999) determined that 

Irgarol concentrations at 24% of the marina sites and 10% of all the sites evaluated exceeded the 136 ng/L benchmark. 

In our study, Irgarol concentrations in 11of the 45 samples (also 24%) collected for Irgarol analysis exceeded this 

same plant toxicity benchmark. 

58
Similar ERAs were conducted by Hall and Gardinali (2004) and Hall et al. (2007). Both these ERAs used a larger 

aquatic plant toxicity dataset (16 toxicological values) than the 1999 assessment and also included LC50 data. The 

Irgarol benchmark values that were derived in Hall and Gardinali (2004) and Hall et al. (2007) were 251 ng/L and 193 

ng/L, respectively. Four of the 45 samples (9%) collected for Irgarol analysis in our study contained Irgarol 

concentrations that were above these higher and more robust benchmarks. In all three of these ERAs, ecological risks 

from Irgarol were assessed to be generally low. However, the ERAs found that risks in marina areas were notably 

higher. 

For additional perspective, an aquatic toxicity benchmark known as the Environmental Risk Limit (ERL) (not to be 

mistaken with the identical abbreviation that stands for Effects Range Low that is used in the context of sediment 

quality guideline) is used in the Netherlands. The Dutch Irgarol ERL of 24 ng/L is also based on a plant toxicity 

dataset and is set at dataset’s 5th percentile value (protective of 95% of all aquatic plant species). Recall that the 

benchmark values use by Hall et al. (1999) and Hall and Gardinali (2004) utilized the 10th percentile value. The more 

protective percentile value used for the ERL and the difference in the toxicity dataset analyzed resulted in a 

significantly lower benchmark value. Forty out of 45 samples from our study (89%) contained Irgarol concentrations 

above the Dutch ERL for Irgarol. 

Considering the overlap of measured marina Irgarol concentrations with phytoplankton and periphyton EC50 ranges 

and aquatic benchmark values, there appears to be risk from Irgarol exposure to these assemblages in some of the 

saltwater marinas in this study. However, at this point, it is not clear what biological and/or ecological impacts are 

occurring due to Irgarol exposure in these marinas. A more focused study on the impact of Irgarol to marina 

biological assemblages should reveal if ERA predictions are representative of real-world conditions. 

M1 is considerably less toxic to aquatic organisms than Irgarol. As a consequence, its aquatic benchmark value 

(12,500 ng/L) is much higher than that for Irgarol. None of the samples in this study contained M1 concentrations that 

were close to this benchmark. Thus, ecological risks from M1exposure to aquatic plants in the marinas in this study 

are extremely low. 

Marina versus Local Reference Site 

Water Column 

Copper and zinc concentrations were frequently higher in marina samples than in LRS samples taken from adjacent 

areas. For DCu, this difference is statistically significant for marinas in all three water types. For TCu, this difference 

is statistically significant in saltwater and freshwater marinas. For DZn, the difference is statistically significant for 

salt and brackish water marinas. The discrepancy between marina and LRS concentrations of copper and zinc strongly 

support the assertion that sources of metals from inside of marinas and/or factors that are related to the residence time 

and flushing dynamics (as compared to LRSs) are mainly responsible for the observed difference. 

Two basic factors could potentially explain why elevated metal concentrations often exist in many of the marinas in 

this study. Metals loading from the marina’s confines could serve as a localized source, which could distinctly elevate 

water column and sediment concentrations of copper and zinc in the immediate vicinity of the marina. Moreover, 

since the primary basis of marina placement, construction, and design is to provide shelter and protection for mooring 

boats from wind, currents, and tides, marina areas tend to be poorly flushed compared to the larger body of water with 

which they are associated. 

In 2005, DPR conducted a review of California copper monitoring studies that could have relevance to the evaluation 

of copper AFP pollution. The review showed that a number of studies had documented elevated concentrations of 

copper (as well as zinc) in marina water and sediment versus the local background levels (Singhasemanon, 2005). 

Two large marina studies that were recently conducted in LNB and along California’s South Coast (sampling sites 

between Dana Point Harbor and the U.S./Mexico border) also documented this phenomenon (Orange County Coast 

Keeper, 2007; Schiff et al., 2007). 

Marina-related metal studies are typically conducted during dry periods to help reduce the interference of metal 

contaminants associated with rain runoff. Storm discharge data from the LNB metals study documented high metal 

concentrations in runoff water that discharged directly into several marinas (Orange County Coast Keeper 2007). 

59
During periods of wet weather, it is likely that metal loads introduced via runoff and sediment disturbance and 

transport overshadow metal sources from within the marina. 

When LRS and marina metal concentrations are not statistically different as in the cases of TCu in brackish water 

marina areas and DZn in freshwater marina areas, this suggests that loading might not be high enough in the marinas 

to be distinct from the LRSs or that site-specific flushing characteristics are good enough to keep metal concentrations 

low. For the case of TCu in brackish water marina areas, the LRS median concentrations ranged from 73–110% of the 

associated marina median TCu concentrations. Based on the strong correlation of TCu to TSS in brackish water 

marina areas and based on the relationships established in the Equilibrium Partitioning Theory, the high TCu 

concentrations observed in the LRSs are likely linked to the high TSS concentrations that co-occur there. For DZn, the 

lack of statistical difference in freshwater marina areas may potentially be explained by the lack of loading within the 

marinas. This will be further explored in the next sub-section. 

Sediment 

It is difficult to draw clear conclusions from the sediment data considering that only three freshwater marina areas 

were sampled and that sediment from salt and brackish water marina areas were not collected and analyzed. Sediment 

concentrations of TCu and TZn in the three freshwater marinas sampled were statistically higher than the 

concentrations in their respective LRSs. Results from Pap (2004), which looked at five S.F. Bay Area (saltwater) 

marina areas, also showed higher sediment metal concentrations inside of marinas versus outside. The same two 

factors that likely explain the differences in water column concentrations may also play a major role in this observed 

difference. The loading and marina flushing dynamics may be somewhat different for metals in the sediment versus 

metals in the water column. For example, higher sediment concentrations of total metals in marinas may be an artifact 

of greater association of particulate metals with finer sediment particles and organic matter that is typically more 

prevalent inside of marinas compared to the more well-flushed LRSs, where the sediment is likely to be larger-grained. 

Marina Sources of Copper 

Wood Preservatives 

There are many possible metal sources within a marina. Copper-containing wood preservatives that are used in the 

marina environment represent a potential copper source. Marina survey responses that were provided to DPR by 

marina managers (Appendix A) suggested that pilings and other marina-associated structures that are continually or 

occasionally immersed in the water could leach wood preservatives into marina waters. Creosote, copper borate azol 

(CBA), chromated copper arsenate (CCA), Copper-Green®, and ammoniacal copper zinc arsenate (ACZA) were 

identified as being historically or currently used in a few of the marinas. Benicia Marina, Vallejo Marina, Berkeley 

Marina, Ballena Isle Marina, South Beach Harbor, and Santa Barbara Harbor also reported that there were creosotetreated piles in their marinas, particularly in association with older construction. Considering that creosote is a product 

of coal tar distillation, the leachate of concern from creosote use is polyaromatic hydrocarbons (PAHs), and does not 

contain copper in any appreciable amount (U.S. EPA, 2003). 

CBA, CCA, Copper-Green®, and ACZA, however, do leach copper and have the potential to contribute to copper 

levels in the marinas. Berkeley Marina reported that limited replacement piles for wood and all other wood treated at 

the marina are treated with CBA. Antioch Marina reported that some of its wood is treated with CCA although none 

of the wood is submerged. Copper-Green®, a copper naphthenate compound, is reportedly used for the treatment of 

the ends of pressure-treated wood in Monterey Harbor. The survey response suggests, however, that there is limited 

use of wooden materials in the marina area and that the treated materials are not submerged in marina waters. Lastly, 

Santa Barbara Harbor reported that wooden walers, stringers, and deck boards at the marina are treated with ACZA. 

Again, these are above-water uses, and continuous and direct leaching into the water column is unlikely although 

contamination associated with rain events or structural washing and rinsing is still possible. 

Considering the wood preservative use information that were submitted by marina managers via the marina survey, it 

is unlikely that copper-treated wood had a significant direct influence on the water column concentrations of copper 

observed during the study. Note that survey responses also suggest that the recent trend in marina construction 

appears to be in incorporating more concrete and pressure-treated wood (without added preservatives) on newer 

structures. 

60
Background Load 

A sizable portion of the copper load in the marina’s water column can be attributed to the local background load of 

copper. For example, at the Ballena Isle Marina area, the measured median DCu concentrations were 2.8 μg/L in the 

marina and 1.4 μg/L for the LRSs. Assuming that the copper load measured at the LRSs originated from non-marina 

sources and not the marina itself, then local background sources of copper from San Francisco Bay could have 

contributed as much as 50% of the load characterized at Ballena Isle Marina. An inverse perspective to this is that 

copper sources within Ballena Isle Marina caused the copper concentrations to be twice as high as the local 

background level. Thus, if loading from all the marina sources suddenly ceases, then the marina concentrations of 

copper should approach those of the LRS concentrations. Since the background metal load of each marina area is 

unique, it was necessary to take LRS samples at all the marinas. This observation also assumes that the copper 

concentrations measured at the LRSs are a good approximation of the local water body’s background load. 

Marina Sediment 

The marina’s underlying sediment also represents a potential intermediary source despite the popular view that it 

simply acts as a sink for heavy metals. A sizable portion of copper in the marina sediment could have originated from 

sources within the marina as well as from sources external to the marina that are deposited via (1) wet deposition (rain 

runoff and flows generated from rain events), (2) dry deposition, and (3) deposition via currents or wave action. 

Once deposited, sediment-bound copper can be reintroduced into the water column via diffusion or particle 

resuspension and subsequent desorption. In fact, desorption from resuspended particles could be a significant source 

of DCu to the water column as demonstrated by Gee and Bruland (2002) for the southern portion of S.F. Bay. In the 

Gee and Bruland (2002) study, even with the accounting of regional point-source discharges, a significant portion of 

DCu in the South Bay was still attributable to DCu that was released from suspended bay sediment. Thus, a 

significant net movement of copper from the sediment to the water column is therefore possible. 

Before generally transposing the finding for South S.F. Bay to the marina environment, it is necessary to consider the 

differences between the open bay environment and the typical marina environment. South S.F. Bay is a large water 

body with a significantly large sediment-water column surface area for diffusion and partitioning interaction. 

Moreover, the S.F. Bay is shallow and relatively vulnerable to perturbations from the wind and water movement. The 

scale of sediment-water column interface in a marina is much smaller and the marina sediment bed is, by design, much 

more protected from perturbations compared to the exposed sediment in the open bay. Furthermore, there is a 

potentially substantial source of copper in the marina (i.e., boat antifouling paints that are designed to leach copper 

directly into the water column) to factor into the source calculation. 

So, although the marina sediment may not appear to be the chief contributor to the copper concentrations in the marina 

water column, it does not mean that contributions from the sediment are necessarily fixed or that sediment 

contributions will remain small. Since copper concentrations in the water column and sediment are continually 

seeking a state of equilibrium, sediment desorption could become a more significant pathway for copper release into 

the water column if copper concentrations in the marina sediment are high enough and/or if loading from the other 

marina inputs to the water column sharply declined. This potential shift in contributions was specifically noted as a 

peer review comment in the SIYB TMDL (SDRWQCB, 2005). 

Note that the background load of copper in the LRS should, to some extent, account for copper that is released through 

the sediment diffusion and resuspension process, at least for the larger water body in which the marina resides. In this 

way, the magnitude of sediment contributions may be partially accounted for by the LRS results. 

Antifouling Paints 

AFPs that are applied to boat hulls are one of the most commonly identified major sources for copper in the marina 

environment (Jones and Bolam , 2007; Srinivasan and Swain, 2007; Pap 2004; Hall and Anderson, 1999; Matthiessen 

et al., 1999; Hall et al., 1998; Katz, 1998, Goh and Chou, 1997; McPherson and Peters, 1995; Claisse and Alzieu, 

1993; Young et al., 1979). 

There are over 170 copper AFP products (including a few manufacturing use products) currently registered with DPR 

in 2007. However, reporting of AFP applications (with the exception of TBT since it is a restricted use pesticide) is 

61
not required by DPR. Therefore, it is difficult to develop a good understanding of the use pattern and use amount for 

copper or any other types of non-TBT AFPs. 

Researchers have attempted to estimate copper loading from AFP sources. In 1979, SCCWRP estimated that as much 

as 180 metric tons of copper were applied annually to boat hulls in Southern California (Young et al., 1979). 

Furthermore, it estimated that this load represented at least twice the amount that enters the Southern California coastal 

waters from storm runoff and aerial deposition. At that time, TBT AFPs were still commonly used. Thus, this 

particular copper AFP use estimate undoubtedly represents a smaller fraction than what is being used in recent years. 

With the additional restrictions put on TBT AFPs in the U.S. and abroad, copper AFPs have become the most popular 

AFP for recreational boats (Young et al., 1979; Claisse and Alzieu, 1993). Today, more than 95% of AFPs registered 

for sale and use in California contain copper biocides. 

In recent years, U.S. EPA has generated a more contemporary estimate of copper loading from AFP use in the Lower 

Newport Bay (LNB) area, which harbors approximately 10,000 boats. The Agency estimated that boat AFPs 

contribute more than 62,000 lbs. of copper (via passive leaching and underwater hull cleaning) into LNB waters 

annually (U.S. EPA, 2002). U.S. EPA believed that this load could account for as much as 80% of all copper input 

into LNB. 

Two copper source loading studies for San Diego Bay were conducted by the U.S. Navy and by private researchers in 

the late 1990s (Johnson et al., 1998; PRC, 1997). Both of these studies concluded that AFPs accounted for the 

majority of DCu loading to the Bay. For SIYB, SDRWQCB estimated that passive leaching and underwater hull 

cleaning of boat hulls painted with copper AFPs combine to contribute 98% to the basin’s copper load (SDRWQCB, 

2005). 

Field observations and anecdotal evidence show that AFPs are regularly used on boats that operate in salt and brackish 

waters. The use of copper AFPs is necessary to combat continual hull-fouling pressures (particularly hard fouling) 

that exist in the salt and brackish aquatic environment. Moreover, the largest marinas in California tend to be located 

in saltwater areas. Thus, we expected to see the highest copper concentrations in saltwater marinas. The eight highest 

marina median DCu concentrations are from saltwater marinas. The marinas with the largest assemblages of boats 

(i.e., MdR front and back basins) exhibited the greatest marina median DCu and TCu concentrations. And although 

two saltwater marinas–San Francisco and Alamitos Bay marinas–exhibited very low DCu concentrations, the 

hydraulic flushing regimes at these sites may serve to explain this deviation. 

Since hard fouling is not a major issue in freshwater, boats in freshwater areas do not need to rely as heavily on copper 

AFPs to protect their hulls. Therefore, freshwater marinas, particularly those that are in landlocked water bodies, tend 

to have very few boats with hulls painted with AFPs. Also, marinas in freshwater tend to be smaller (contain less slips 

for mooring) especially when compared to their saltwater counterparts. Moreover, boats in freshwater marinas may 

not spend as much time in the water due to trailering practices and the relative ease of dry storage. Considering these 

potential loading factors, we expected to see the lowest copper concentrations in freshwater marinas. The two lake 

marinas in our study–Tahoe Keys and Folsom Lake–exhibited the two lowest median DCu concentrations by far 

among all the marinas. 

In our study, when all the marina DCu data were separated by water types and compared, DCu concentrations in salt 

and brackish water marinas were statistically higher than DCu concentrations in freshwater marinas. This finding is 

consistent with our observations of AFP use and water types since AFP use appears to be more prevalent among boats 

that are moored in salt and brackish waters. 

It is important to note, however, that boats that are moored in some freshwater marinas may occasionally spend time in 

salt or brackish water during their use. So, in some cases, AFPs are employed on these boats. Some of the boats 

observed at Sacramento and Village West marinas had AFPs on their hulls. The use of AFPs there is likely a 

reflection of the periodic operation of these boats in the adjacent saline waters of S.F. Bay and beyond. This may 

explain why these two freshwater marinas exhibited moderate marina median DCu concentrations and why 

concentrations of DCu and TCu were also distinctly higher inside of these two marinas compared to their LRSs. 

There are likely other noteworthy sources of copper that exist within the marina environment. In the end, until an 

exhaustive source identification and copper mass loading study in the marina environment can be done, it will be 

difficult to precisely allocate loading from all the various marina sources. Such a study may perhaps identify 

62
additional sources. In 2007, MEC Environmental initiated a monitoring study of various marina-associated pollutants 

(including metals) in a protected water area prior to the construction and operation of a marina facility (R. Schottle, 

personal communication, 2007). The findings from this study should provide a clearer picture on the effects of boatassociated pollutants on existing background conditions. 

At this time, considering (1) the study results for DCu and TCu, (2) the available scientific literature, (3) field 

observations and anecdotal evidence (including responses from the marina survey), (4) the consideration of other 

likely sources of copper in the marina environment, and (5) the predominant use of copper AFPs compared to other 

types of AFPs in California, it is likely that boat AFPs are the most significant source of copper in salt and brackish 

water marinas, particularly during periods of dry weather. 

The level of AFP contribution will vary from marina to marina due to site-specific factors. However, it is likely that 

contributions of AFPs to the marina’s water column load of copper is highest in (1) saltwater marinas where AFP use 

is high, (2) larger marinas where there is a high number of AFP-painted vessels, and (3) locations where contributions 

from non-AFP marina sources are relatively low. In particular, the concentrations of copper in the LRSs of many of 

the marina areas suggest that the background load of copper must always be considered when marina concentrations of 

copper are evaluated, as these loads could represent a sizable portion of what is observed in the marina. 

Marina Sources of Zinc 

Antifouling Paints 

Zinc is a common marina pollutant found at elevated concentrations in marina waters and sediments (Singhasemanon, 

2004). Some AFPs contain and leach zinc. The sole zinc biocide, contained in AFPs sold and use in California, is 

zinc pyrithione (ZnPt2 or trade name Zinc Omadine®). This chemical is used as a booster biocide, which supplements 

the antifouling effects of a product’s primary biocide to be effective on a wider range of fouling pests. Thus, ZnPt2 

usually makes up a lower percentage of an AFP product than the primary active ingredient. There are 25 AFP 

products (including several manufacturing-use products) that contain ZnPt2 currently registered with DPR for use in 

California. 

Zinc oxide is also a non-biocidal component in AFPs; however, the water quality implications of this relatively waterinsoluble compound have not been well studied. Therefore, at this time, it is difficult to estimate or evaluate the 

contribution of zinc oxide from leaching or underwater hull cleaning activities to the overall marina water column or 

sediment zinc load. Future evaluations of zinc in marinas should involve the determination of contributions of zinc 

oxide from AFP use. 

Wood Preservatives 

One wood preservative, ACZA, contains zinc. However, only the marina at Santa Barbara Harbor reported ACZA use 

on some of its above-water structures. Therefore, the very limited use of zinc-based wood preservative makes them an 

unlikely major source of zinc to the marina environment. 

Sacrificial Anodes 

Zinc is also used in saltwater areas as sacrificial anodes that are attached to boat hulls and other metal surfaces that 

come into contact with saltwater. Zinc anodes protect less-corrodible metals (e.g., copper) from decay while 

sacrificing themselves in the process resulting in a continual release of zinc ions into the surrounding water. 

Aluminum-based anodes are preferred for anodic protection in brackish waters, and magnesium-based anodes in 

freshwater. In our study, DZn concentrations tend to be high in saltwater marinas, particularly the larger marinas 

along the Central and South Coast. Brackish and freshwater marinas tend to exhibit lower DZn concentrations. This 

difference in DZn concentrations among water types (saltwater > fresh and brackish water) was statistically 

significant. Therefore, the magnitude of DZn concentrations in the study marinas is consistent with the general use 

pattern of zinc anodes. Bird et al. (1996) and Matthiessen et al. (1999) established that sacrificial zinc anodes are the 

major source on zinc in the marina environment. Moreover, both studies found that emissions from sacrificial anodes 

63
are so great that they appear to be a major influence on zinc levels in the larger water body in which marinas are a part 

of. 

At this time, considering (1) the study results for DZn, (2) the available scientific literature, (3) field observations and 

anecdotal evidence (including responses from the marina survey), (4) the consideration of other likely sources of zinc 

in the marina environment, and (5) the limited use of zinc AFPs in California, it is likely that sacrificial anodes are the 

most significant source of zinc in saltwater marinas during periods of dry weather. In California’s saltwater marinas, 

the contribution of AFPs to the marina zinc load is likely to be smaller than the contribution from ubiquitous zinc 

anodes. For fresh and brackish water marinas, the significance of zinc anodes to the marina load of zinc is likely to be 

smaller due to boaters’ reliance on non-zinc sacrificial anodes. Regardless of the relative ranking of the sources of 

zinc in marinas across different water types, the current zinc load from the combined applications and sources of zinc 

in the marina environment is not significant enough to result in a water column concentration that exceeds water 

quality standards. 

Toxicity/Toxicant Identification Evaluation 

Toxicity tests were conducted to assess the potential adverse impacts of the water samples to pre-selected test 

organisms and toxic endpoints. Toxicity tests are common tools that are used by agencies, including those that 

regulate water quality (i.e., RWQCBs), to determine if biological impairments exist. 

Eight of the 47 water samples were toxic to copper-sensitive mussel embryo development bioassays. Seven of these 

eight toxic samples were collected from MdR. The DCu concentrations in these seven samples ranged from 11.5–18.4 

μg/L. These concentrations are within the range of documented EC50 values from similar toxicity tests as compiled 

and discussed in Arnold et al. (2005b). Thus, these toxicity results were within the expected concentration range. The 

toxicity that was observed in the sample that came from MBYH, however, was more surprising. The DCu 

concentration in the MBYH sample in question was only 1.7 μg/L; however, statistically significant toxicity (albeit 

relatively low) was observed. It is possible that a false positive or type I error occurred in the analysis of this sample. 

Two of the toxic samples from MdR were selected for TIE tests. The tests determined that a trace metal (most likely 

copper) was the likely cause of toxicity in these samples. Considering the TIE results and the high DCu 

concentrations of the all the samples taken at MdR, persistent toxicity to M. galloprovincialis may exist in the waters 

of MdR. The regular exceedances of the CTR acute copper standard at this location also strongly suggest that there 

may be a significant risk to aquatic organisms at MdR. If biological and ecological impacts from copper are to be 

studied, MdR would be an excellent candidate location based on the frequent occurrences and high magnitude of DCu 

and TCu concentrations observed there. DCu concentrations documented at MdR are even higher than the 

concentrations documented at SIYB in San Diego Bay (SDRWQCB, 2005). 

Although toxicity samples were also collected from Pittsburg, Benicia, Vallejo, Loch Lomond, Berkeley, San 

Francisco, South Beach Harbor, Downtown Shoreline, and Alamitos Bay marinas, none of the samples taken from 

these locations exhibited any statistically significant toxicity. The five highest DCu concentrations outside of the 

MBYH and MdR samples were 8.9, 7.7, 7.4, 5.3, and 5.3 μg/L. Thus, DCu concentrations as high as 8.9 μg/L did not 

induce a response in the copper sensitive toxicity test. Although the water chemistry of each sample, particularly those 

factors that regulate the bioavailability of copper, must be taken into consideration (e.g., DOC, pH), it is remarkable to 

see that DCu concentrations that are close to two times higher than the CTR saltwater acute standard were not toxic in 

the copper-sensitive M. galloprovincialis test. This observation confirms the protective nature and intent of the CTR 

standards that were originally derived from U.S. EPA’s aquatic life criteria development process. 

In this study, we specifically considered the toxicity of copper to a mussel species. However, recent studies suggest 

that low parts-per-billion concentrations of dissolved copper can negatively impact olfactory sensory neurons and 

predator avoidance behaviors of salmonids that are critical for their survival (McIntyre et al., 2007; Pyle and Mirza, 

2007; McPherson et al., 2004). Therefore, elevated copper concentrations observed in many marinas in this study, 

even at levels that may not affect mussel species, could potentially be hazardous to salmonids. Future consideration 

of the aquatic impacts of copper must certainly evaluate this aspect of toxicity. 

64
Predictive Copper Toxicity Models 

Since it is impractical and expensive to conduct toxicity tests on all the study samples, predictive toxicity models 

offered an alternate way to generate information on potential biological impacts. The predictive toxicity models (i.e., 

the freshwater Biotic Ligand Model [BLM], saltwater BLM, and Dissolved Organic Carbon [DOC] model) employed 

in this study focused on the potential effects of copper on aquatic test organisms. These models are notable in that 

they all take into account site-specific water quality characteristics that affect the bioavailability of copper to affect 

aquatic organisms. 

The freshwater BLM has already been adopted by U.S. EPA as the recommended approach to calculate the national 

ambient water quality criteria for copper (U.S. EPA, 2007). U.S. EPA is considering the applicability of the saltwater 

BLM as the basis for an update to its saltwater criteria. California’s CTR standards were based on an older version of 

the federal criteria. Ultimately, U.S. EPA considers the current BLM-based approach to be more scientifically sound 

than the previous approach, which did not adequately integrate the impacts of bioavailability. Moreover, the revised 

criteria should replace the need for resource intensive site-specific adjustments for freshwater using the water effect 

ratio. 

For our study, the freshwater BLM predicted that seven of 53 freshwater samples would exceed the 1-TU threshold. A 

closer evaluation of these results strongly suggested that only two of these seven toxicity predictions were reliable (see 

the Freshwater BLM Results Section). In comparison, DCu concentrations from all 53 samples evaluated by the 

model never exceeded the CTR freshwater CCC (chronic) and CMC (acute) standards. Thus, the freshwater BLM 

predicted that some risk (albeit low) to aquatic life exists due to the exposure to copper, while existing standards 

suggested that aquatic organisms are fully protected. 

We also used a DOC-based model, as presented in Arnold et al. (2005) to evaluate the potential toxicity of salt and 

brackish water samples. Out of the 412 salt and brackish water samples evaluated, 86 (21%) contained high enough 

concentrations of DCu to equal or exceed the DOC-model predicted FCC and 54 of those also exceeded the FAC. 

Measured DCu concentrations tend to exceed CTR standards more frequently than DOC-model predicted toxicity 

thresholds. Occasionally, the opposite is true. This is most noticeable at San Francisco Marina, Santa Cruz Harbor, 

Monterey Harbor, and Santa Barbara Harbor. This phenomenon is most likely a reflection of the relatively low DOC 

concentrations that occur at these locations during our sampling events. Overall, the DOC model predicted water 

column toxicity well (89% success rate) when compared to actual toxicity test results from this study. 

A draft saltwater BLM was recently completed for use on salt and brackish water samples. At this time, the model 

was only able to generate predicted EC50 thresholds for M. galloprovincialis and S. purpuratus. It was not possible to 

generate CCC and CMC thresholds with which a more complete evaluation of risk to aquatic organisms could be 

made. However, the comparison of predicted acute TUs for M. galloprovincialis to actual toxicity results still allowed 

us to factor in the site-specific bioavailability of copper to determine if samples could be toxic to a copper-sensitive 

test organism and endpoint. Sixty seven of 373 samples (for which output could be calculated) or 18% of the samples 

contained a high enough concentration of DCu to reach or exceed the 1-TU threshold. Recall that since the acute 

CMC thresholds could not be calculated, a comparison of saltwater BLM results to CTR standards is not possible. 

Overall, the saltwater BLM predicted EC50’s for M. galloprovincialis were in good agreement with actual toxicity 

(88% success rate). A comparison was not done for S. purpuratus since toxicity tests were not conducted on them. 

The fact that about 98% of all the samples for which toxicity was predicted were marina samples strongly suggests that 

the existing load of copper (representing the combined sources within a marina) in many of the study’s marinas is high 

enough to pose a risk to copper-sensitive aquatic organisms. Risks for the LRSs, which were immediately adjacent to 

the marinas, appear to be much lower. Thus, the study of copper toxicity and impacts of copper to aquatic 

communities in marinas should be an important area for future research. 

Scientific validation of these models with data from other studies has, thus far, been strong. Overall, agreement of 

model prediction to our toxicity results is also very good. Our comparison of the DOC model’s and the saltwater 

BLM’s predicted toxicity to actual toxicity results did show that these models tend to slightly over-predict toxicity. 

However, considering that the use of these models provide an inexpensive way to estimate toxicity and a more precise 

prediction of biological effects by accounting for bioavailability, these models appear to be reliable tools in the future 

evaluation of the potential ecological impacts from copper exposure. 

65
V. Conclusions 

The extensive monitoring of marina areas showed that California marinas have elevated concentrations of DCu, TCu, 

and DZn. Higher water column concentrations of these metals tend to occur in larger saltwater marinas such as those 

along the State’s Central and South Coast. Lower concentrations were found in marinas that are located in freshwater 

lakes or in areas that are well-flushed. 

Despite being frequently elevated compared to background levels, DCu and DZn concentrations in freshwater marinas 

were always below CTR freshwater standards for copper and zinc. Saltwater DZn concentrations were also always 

below CTR saltwater zinc standards. However, in many salt and brackish water marinas, DCu concentrations 

regularly exceed CTR water quality standards that have been established for the protection of aquatic life. Toxicity 

tests showed that marina water samples were sometimes disruptive to the normal development of mussel embryos. An 

evaluation of two of the toxic samples pointed to high concentrations of DCu as the most likely cause of the observed 

toxicity. Predictive toxicity models, which accounted for bioavailability, determined that many of the marina samples 

collected were likely to be toxic to copper-sensitive aquatic organisms. 

Our source evaluation and the consideration of past research on metal sources in marinas suggest that for salt and 

brackish water marinas, AFPs are likely a major source of copper, particularly during periods of dry weather. The 

contribution of AFPs to the total marina copper load, however, will vary from marina to marina depending on the 

magnitude of each water body’s background load and other marina sources. In brackish water marinas and many San 

Francisco Bay Area marinas where the background load of copper tends to be high, AFP use contributes less to the 

marina copper load than in Central and South Coast marinas where the background load tends to be low. Nonetheless, 

copper that is generated from leaching of AFPs and underwater hull cleaning of these paints clearly contribute to the 

exceedance of copper water quality standards. 

Considering that water in 16 of the marinas in our study exceeded water quality standards for copper, it is possible that 

these areas will be placed on the CWA 303(d) impaired water bodies list. Moreover, recent studies of copper in Lower 

Newport Bay and along California’s South Coast (Dana Point to the U.S./Mexico border) have shown that many of the 

marinas in these areas also exceeded the same standards. Therefore, it is possible that more than 40 California marinas 

could be placed on the CWA 303(d) list due to elevated DCu concentrations. Placement on this list could eventually 

result in TMDL development. 

In recent years, there has been an increase in the registration of AFP products with ZnPt2 as the primary active 

ingredient. If ZnPt2 products gain a larger foothold on the AFP market (e.g., as a replacement for copper AFPs), 

contributions of zinc AFPs to the marina zinc load will increase and potentially lead to zinc-related toxicity. Future 

evaluations of ecological risks due to zinc AFP use should not rely solely on the comparison of water column or 

sediment concentrations of zinc to standards and guidelines, but also on the spatial and temporal distribution of the 

biocide ZnPt2 and its trans-chelation product CuPt2. CuPt2 is more stable and more toxic to aquatic organisms than 

ZnPt2. Conversion of ZnPt2 to CuPt2 tends to occur in natural water where copper concentrations are high like in some 

marinas. 

At this time, U.S. EPA continues to refine its risk characterizations for antimicrobial uses of copper oxide and ZnPt2. 

Once this characterization is complete, the Agency will evaluate its risk management options and announce its risk 

management decisions. U.S. EPA is also developing discharge permits for recreational and commercial vessels. 

Discharges from AFP use are currently included in these permits. Overall, these federal activities may potentially 

have a significant impact on how States, including California, regulate and use copper and zinc AFPs in the future. 

66
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