COPPER SPECIATION IN HIGHWAY 

STORMWATER RUNOFF AS RELATED 

TO BIOAVAILABILITY AND TOXICITY 

TO ESA-LISTED SALMON 

Final Report 

SPR 663 

COPPER SPECIATION IN HIGHWAY STORMWATER 

RUNOFF AS RELATED TO BIOAVAILABILITY AND 

TOXICITY TO ESA-LISTED SALMON 

SPR 663 

by 

Jeffrey A. Nason, Ph.D. 

Peter O. Nelson, Ph.D. 

Don J. Bloomquist 

Matthew S. Sprick 

School of Chemical, Biological and Environmental Engineering 

Oregon State University 

for 

Oregon Department of Transportation 

Research Section 

200 Hawthorne Avenue SE, Suite B-240 

Salem OR 97301-5192 

and 

Federal Highway Administration 

400 Seventh Street S.W. 

Washington, DC 20590 

APRIL 2011 

Technical Report Documentation Page 

1. Report No.

 FHWA-OR-RD-11-11 

2. Government Accession No. 3. Recipient’s Catalog No. 

 

5. Report Date

 April 2011 

4. Title and Subtitle

Copper Speciation in Highway Stormwater Runoff as Related to 

Bioavailability and Toxicity to ESA-Listed Salmon 6. Performing Organization Code

 

7. Author(s)

Jeffrey A. Nason, PhD, Peter O. Nelson, PhD, Don J. Bloomquist, and 

Matthew S. Sprick 

8. Performing Organization Report No. 

10. Work Unit No. (TRAIS)

 

9. Performing Organization Name and Address

 School of Chemical, Biological and Environmental Engineering 

 101 Covell Hall 

 Oregon State University 

 Corvallis, OR 97331-2409 

11. Contract or Grant No.

 SPR 663 

13. Type of Report and Period Covered 

 Final Report 

12. Sponsoring Agency Name and Address

 Oregon Department of Transportation 

 Research Section and Federal Highway Administration 

 200 Hawthorne Ave. SE, Suite B-240 400 Seventh Street, SW 

 Salem, OR 97301-5192 Washington, DC 20590-0003 14. Sponsoring Agency Code

 

15. Supplementary Notes 

16. Abstract

The objectives of this study were to 1) identify the effects of site location, storm hydrology, and water quality parameters 

on the concentration of dissolved copper (Cu2+diss) in Oregon highway runoff; 2) establish an analytical technique suitable 

for the determination of copper speciation in highway stormwater runoff; 3) compare analytically determined free ionic 

copper (Cu2+free) concentrations in highway stormwater runoff with modeled concentrations; and 4) develop a qualitative 

understanding of where and when copper toxicity has the most potential to be problematic for receiving waters. 

In this study, stormwater runoff from an urban high annual average daily traffic(AADT) site had consistently higher event 

mean concentrations of measured Cu2+diss and Cu2+free than the non-urban sites with lower AADT. First flush samples 

displayed consistently higher concentrations of both Cu2+diss and Cu2+free. A modified Competitive Ligand ExchangeCathodic Stripping Voltammetry (CLE-ACSV) technique utilizing salicylaldoxime as an added ligand can be used to 

determine the speciation of copper in highway stormwater runoff. Analytical results from composite stormwater samples 

suggest that dissolved copper in highway stormwater runoff is largely complexed by organic matter (typically > 99.9%), 

and that very little of the copper in stormwater is bioavailable; the concentrations of Cu2+free were generally several orders 

of magnitudes below levels found to inhibit olfaction in Endangered Species Act listed fish species. Elevated Cu2+diss

levels proved to be the greatest indicator of high Cu2+free concentrations. 

Urban sites with AADT and first flush samples characterized by elevated concentrations of Cu2+diss are of the greatest 

concern with respect to elevated free ionic copper concentrations. Available dissolved organic matter models in Visual 

MINTEQ overestimate Cu2+free concentrations when compared to analytically determined Cu2+free concentrations. This 

imparts a conservatism that makes these models potentially useful for regulatory purposes. 

17. Key Words

Copper, speciation, dissolved, stormwater, ESA, salmon, 

bioavailability, ligand, ion 

18. Distribution Statement

Copies available from NTIS, and online at 

http://www.oregon.gov/ODOT/TD/TP_RES/ 

19. Security Classification (of this report)

 Unclassified 

20. Security Classification (of this page)

Unclassified 

21. No. of Pages

130 + appendices 

22. Price

Technical Report Form DOT F 1700.7 (8-72) Reproduction of completed page authorized Printed on recycled paper 

i 
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS 

APPROXIMATE CONVERSIONS F

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By 

LENGTH LENGTH

 in inches 25.4 millimeters mm mm millimeters 0.039 inche ft feet 0.305 meters m m meters 3.28 feet  yd yards 0.914 meters m m meters 1.09 yards  mi miles 1.61 kilometers km km kilometers 0.621 miles 

AREA AREA

 in2 square inches 645.2 millimeters squared mm2 mm2 millimeters squared 0.0016 squar ft2 square feet 0.093 meters squared 

m2 m2 meters squared 10.764 squar

 yd2 square yards 0.836 meters squared 

m2 m2 meters squared 1.196 squar

 ac acres 0.405 hectares ha ha hectares 2.47 acres  mi2 square miles 2.59 kilometers squared km2 km2 kilometers squared 0.386 squar

VOLUME VOLUME

 fl oz fluid ounces 29.57 milliliters ml ml milliliters 0.034 fluid  gal gallons 3.785 liters L L liters 0.264 gallon ft3 cubic feet 0.028 meters cubed 

m3 m3 meters cubed 35.315 cubic 

 yd3 cubic yards 0.765 meters cubed 

m3 m3 meters cubed 1.308 cubic 

 NOTE: Volumes greater than 1000 L shall be shown in m3. 

MASS MASS

 oz ounces 28.35 grams g g grams 0.035 ounce lb pounds 0.454 kilograms kg kg kilograms 2.205 pound T short tons (2000 lb) 0.907 megagrams Mg Mg megagrams 1.102 short 

TEMPERATURE (exact) TEMPERATURE (exa

 °F Fahrenheit (F-32)/1.8 Celsius °C °C Celsius 1.8C+32 Fahre*SI is the symbol for the International System of Measurement 

ii 
ACKNOWLEDGEMENTS

First and foremost, we would like to acknowledge the Oregon Department of Transportation for 

the opportunity to work on this challenging and interesting problem. The financial support of 

this project allowed the education and training of two M.S. students (Don Bloomquist and 

Matthew Sprick) and the procurement of analytical equipment that will facilitate future research 

in the area of aquatic chemistry in the Nason Lab. Several individuals were instrumental in the 

completion of this work. We would like to thank Kathy Motter from the OSU Institute for Water 

and Watersheds Collaboratory for her help with dissolved organic carbon measurements, Andy 

Ungerer from the W.M Keck Collaboratory for Plasma Spectrometry at OSU for assistance with 

trace metals analysis, and Mohammad Azizian for assistance with general laboratory procedures 

and troubleshooting. We thank John Westall, Neal Slezynski, Kristen Buck, and Ken Bruland 

for discussions regarding the voltammetric technique employed in this work. In addition, 

collaboration with engineers at Herrera Environmental Consultants to collect composite samples 

from the Portland, Wemme and Bend sites was extremely helpful. We appreciate the input and 

feedback from the Technical Advisory Committee (Miguel Estrada, William Fletcher, Devin 

Simmons, William VanPeeters, Chris Woods, Jeffrey Smith, and Pamela Porter) and are 

especially thankful to Matthew Mabey at ODOT for his patience, guidance and support 

throughout this process. 

DISCLAIMER

This document is disseminated under the sponsorship of the Oregon Department of 

Transportation and the United States Department of Transportation in the interest of information 

exchange. The State of Oregon and the United States Government assume no liability of its 

contents or use thereof. 

The contents of this report reflect the view of the authors who are solely responsible for the facts 

and accuracy of the material presented. The contents do not necessarily reflect the official views 

of the Oregon Department of Transportation or the United States Department of Transportation. 

The State of Oregon and the United States Government do not endorse products of 

manufacturers. Trademarks or manufacturers’ names appear herein only because they are 

considered essential to the object of this document. 

This report does not constitute a standard, specification, or regulation. 

iii 
iv 
COPPER SPECIATION IN HIGHWAY STORMWATER RUNOFF AS 

RELATED TO BIOAVAILABILITY AND TOXICITY TO ESA-LISTED 

SALMON 

TABLE OF CONTENTS 

GLOSSARY OF TERMS........................................................................................................... XI

1.0 INTRODUCTION..................................................................................................................1

1.1 OBJECTIVES......................................................................................................................1 

1.2 APPROACH ........................................................................................................................2 

2.0 BACKGROUND ....................................................................................................................3

2.1 COPPER TOXICITY...........................................................................................................3

2.1.1 Salmonids .....................................................................................................................5 

2.2 CONCENTRATION AND SPECIATION IN NATURAL WATERS...............................5 

2.3 COPPER IN STORMWATER ............................................................................................7 

2.3.1 Primary Sources ...........................................................................................................7 

2.3.2 Concentration, Partitioning, and Speciation in Stormwater .........................................8 

2.4 FACTORS AFFECTING COPPER IN RUNOFF ..............................................................9 

2.4.1 First Flush.....................................................................................................................9 

2.4.2 Hydrologic Effects .....................................................................................................10

2.4.3 Traffic Effects.............................................................................................................11 

2.4.4 Other Effects...............................................................................................................11 

2.5 COPPER SPECIATION THEORY...................................................................................12 

2.5.1 Analytical Techniques for Quantifying Free Ionic Copper........................................12 

2.5.1.1 CLE-ACSV ..........................................................................................................15

2.5.2 Determining Copper Speciation .................................................................................16 

2.5.3 Previous Work with Salicylaldoxime.........................................................................17 

2.6 STATISTICS .....................................................................................................................20 

2.6.1 Inferences and Assumptions.......................................................................................20 

2.6.2 Sample Comparison ...................................................................................................21 

2.6.3 Multiple Linear Regression (MLR)............................................................................22 

2.6.4 Modeling ....................................................................................................................23 

2.7 CHEMICAL EQUILIBRIUM MODELS..........................................................................24 

3.0 METHODS ...........................................................................................................................27

3.1 STORMWATER SAMPLING ..........................................................................................27 

3.1.1 Site Descriptions.........................................................................................................27 

3.1.1.1 Dixon Outfall ......................................................................................................27

3.1.1.2 I-5 Convention Center.........................................................................................28

3.1.1.3 Bend ....................................................................................................................29

3.1.1.4 Wemme................................................................................................................30

3.1.2 Sampling Criteria .......................................................................................................31 

v 
3.1.3 Sampling Equipment/Setup........................................................................................31 

3.1.4 Field Sampling Procedures.........................................................................................34 

3.1.4.1 Dixon Outfall ......................................................................................................34

3.1.4.2 Herrera Sites.......................................................................................................35

3.2 LABORATORY METHODS............................................................................................35 

3.2.1 Sample Separation......................................................................................................36

3.2.2 Cleaning Procedures...................................................................................................37 

3.2.2.1 General Containers/Labware .............................................................................37

3.2.2.2 Organic Carbon Bottles......................................................................................38

3.2.2.3 Organic Carbon Septum Caps............................................................................38

3.2.2.4 Voltammetric Teflon Cup....................................................................................38

3.2.3 Total Suspended Solids (TSS)....................................................................................38 

3.2.4 pH and Conductivity ..................................................................................................39 

3.2.5 Alkalinity....................................................................................................................39 

3.2.6 Cations........................................................................................................................39 

3.2.7 Anions ........................................................................................................................40 

3.2.8 Total Dissolved Solids (TDS) ....................................................................................40 

3.2.9 Dissolved Organic Carbon (DOC) .............................................................................40 

3.2.10 Copper Speciation Measurements (CLE-ACSV).......................................................41 

3.2.10.1 Reagents..............................................................................................................41

3.2.10.2 Determination of  CuSAx ......................................................................................41

3.2.10.3 Total Dissolved Copper ......................................................................................43

3.2.10.4 Sample Speciation...............................................................................................43

3.2.10.5 CLE-ACSV Instrument Settings ..........................................................................44

3.2.10.6 Speciation Calculations for Stormwater.............................................................45

3.3 STATISTICS .....................................................................................................................45 

3.4 VISUAL MINTEQ MODEL PARAMETERS..................................................................46 

4.0 RESULTS AND DISCUSSION ..........................................................................................49

4.1 TOTAL AND DISSOLVED COPPER .............................................................................49 

4.1.1 Site Comparison .........................................................................................................49 

4.1.2 The First Flush Effect.................................................................................................55

4.1.3 Relationships with Hydrologic Parameters................................................................57 

4.1.4 Modeling Copper Concentrations with Water Quality Parameters............................60 

4.1.5 Intra-Storm Variations in Copper Concentration .......................................................65 

4.2 COPPER SPECIATION IN COMPOSITE SAMPLES ....................................................68 

4.2.1 Determination of CuSAx  ............................................................................................68 

4.2.2 Method Verification ...................................................................................................69 

4.2.3 Analytical Procedures and Data Analysis ..................................................................69 

4.2.3.1 Copper Titrations................................................................................................70

4.2.3.2 Determination of Natural Ligand Concentration and Strength..........................71

4.2.3.3 Detection Windows .............................................................................................72

4.2.4 Copper Speciation Results..........................................................................................74 

4.2.4.1 Ligand Concentration .........................................................................................76

4.2.4.2 Conditional Stability Constants ..........................................................................78

4.2.4.3 Free ionic copper Concentrations ......................................................................79

vi 
4.2.4.4 Dixon Outfall Speciation Results........................................................................85

4.2.4.5 I-5 Speciation Results .........................................................................................85

4.2.4.6 Bend Speciation Results......................................................................................85

4.2.4.7 Wemme Speciation Results .................................................................................86

4.2.4.8 Comparison to Previous Studies.........................................................................86

4.2.5 Intrastorm and First Flush Results..............................................................................87 

4.2.5.1 Ligand Concentrations in Dixon Outfall Samples..............................................89

4.2.5.2 Free Ionic Copper Concentrations in First Flush and Discrete Samples ..........92

4.2.6 Chemical Equilibrium Modeling................................................................................93 

4.2.6.1 Speciation Modeling with the Gaussian DOM Model ........................................93

4.2.6.2 Site Comparisons ................................................................................................93

4.2.6.3 The First Flush Effect .........................................................................................95

4.2.6.4 Intra-Storm Copper Variations...........................................................................97

4.2.6.5 Comparison Chemical Equilibrium Modeling with CLE-ACSV Results ............99

5.0 CONCLUSIONS ................................................................................................................101

5.1 FUTURE WORK.............................................................................................................103

5.2 SIGNIFICANCE..............................................................................................................104 

6.0 REFERENCES...................................................................................................................107

APPENDICES........................................................................................................................... A-1

A.1 QUALITY ASSURANCE/QUALITY CONTROL ....................................................... A-1 

A.2 DIXON OUTFALL STORM DATA.............................................................................. A-3 

A.3 COMPILED STORMWATER DATA......................................................................... A-13 

A.4 MINTEQ SPECIATION DATA................................................................................... A-20 

A.5 S-PLUS MATRIX PLOTS ........................................................................................... A-25 

A.6 COMPOSITE STATISTICS......................................................................................... A-45 

A.6.1 Paired t-Tests......................................................................................................... A-51 

A.6.1.1 10μM SA vs 2μM SA Tests..............................................................................A-51

A.6.1.2 Modeled Cufree Concentrations vs Experimental ..........................................A-52

A.6.2 Other t-Tests .......................................................................................................... A-53 

A.6.3 Linear Regression Models..................................................................................... A-55 

A.6.4 Intra Storm Samples .............................................................................................. A-60 

vii 
LIST OF FIGURES 

Figure 2.1: Example free ionic copper equilibrium in natural waters with modeled stability constants; taken 

from Luider, et al. (2004).....................................................................................................................................4

Figure 2.2: Gaussian distribution model and its important parameters (Susetyo, et al. 1991).....................................25

Figure 3.1: Dixon site aerial view................................................................................................................................28

Figure 3.2: Portland/I-5 site aerial view ......................................................................................................................29

Figure 3.3: Bend site aerial view .................................................................................................................................30

Figure 3.4: Wemme site aerial view ............................................................................................................................31

Figure 3.5: Portland/I-5 site pipe installation ..............................................................................................................32

Figure 3.6: Wemme site pipe installation ....................................................................................................................32

Figure 3.7: Bend site pipe installation .........................................................................................................................33

Figure 3.8: Dixon Outfall site pipe installation ...........................................................................................................34

Figure 3.9: Sample processing flowsheet ....................................................................................................................36

Figure 4.1: Summary of dissolved copper concentrations in composite samples collected at the four sampling 

sites. ...................................................................................................................................................................52

Figure 4.2: Summary of dissolved organic carbon concentrations in composite samples collected at the four 

sampling sites.....................................................................................................................................................52

Figure 4.3: Summary of total copper concentrations in composite samples collected at the four sampling sites .......53

Figure 4.4: Relationship between dissolved copper concentrations in composite samples and annual average 

daily traffic for all four sampling sites ...............................................................................................................54

Figure 4.5: Relationship between dissolved copper concentrations in composite samples and annual average 

daily traffic for the three non-urban sites ...........................................................................................................55

Figure 4.6: Summary of dissolved copper concentrations in composite and first flush samples collected at Dixon 

Outfall ................................................................................................................................................................56

Figure 4.7: Summary of dissolved organic carbon concentrations in composite and first flush samples collected 

at Dixon Outfall .................................................................................................................................................56

Figure 4.8: Relationship between dissolved copper concentrations in composite samples and antecedent dry 

period at all four sampling sites .........................................................................................................................58

Figure 4.9: Relationship between dissolved copper concentrations in composite samples and total event rainfall 

at all four sampling sites ....................................................................................................................................58

Figure 4.10: Relationship between dissolved copper concentrations in composite samples and rainfall duration 

at all four sampling sites ....................................................................................................................................59

Figure 4.11: Relationship between dissolved copper concentrations in composite samples and rainfall intensity 

at all four sampling sites ....................................................................................................................................59

Figure 4.12: Relationship between dissolved copper concentrations in first flush samples and antecedent dry 

period at Dixon Outfall ......................................................................................................................................60

Figure 4.13: Relationship between dissolved copper and dissolved organic carbon in all samples ............................62

Figure 4.14: Relationship between dissolved copper and alkalinity in all samples.....................................................62

Figure 4.15: Relationship between total copper concentrations and total suspended solids in all samples.................64

Figure 4.16: Relationship between total copper concentrations and dissolved organic carbon in all samples ............64

Figure 4.17: Relationship between dissolved and total copper concentrations in all samples.....................................65

Figure 4.18: Variation of dissolved copper and dissolved organic carbon concentrations over the course of a 

single storm at the Dixon Outfall site.................................................................................................................66

Figure 4.19: Variation of dissolved organic carbon concentrations throughout the course of all storms collected 

at the Dixon Outfall site. Progress through the storm is represented as the normalized cumulative volume....67

Figure 4.20: Variation of dissolved copper concentrations throughout the course of all storms collected at the 

Dixon Outfall site. Progress through the storm is represented as the normalized cumulative volume..............67

Figure 4.21: Variation of  CuSAx with hardness ........................................................................................................69

Figure 4.22: Triplicate voltammagrams of the Wemme 3/24/09 composite sample containing 2 μM SA and 100 

nM of added copper ...........................................................................................................................................70

Figure 4.23: Copper titration of Wemme composite sample from 3/24/09 containing 2 μM SA................................71

Figure 4.24: Non-linear least squares fit of the titration data from the Wemme 3/24/09 composite sample 

containing 2μM SA to a Langmuir-type expression, describing complexation of copper with the organic 

ligands in the sample..........................................................................................................................................71

viii 
Figure 4.25: Copper titrations of the Dixon 2/06/09 composite sample at two detection windows (2 and 10 μM 

SA).....................................................................................................................................................................72

Figure 4.26: Non-linear least squares fit of copper titration data from the Dixon Outfall 2/06/09 composite 

sample containing 2 μM SA...............................................................................................................................73

Figure 4.27: Non-linear least squares fit of copper titration data from the Dixon Outfall 2/06/09 composite 

sample containing 10 μM SA.............................................................................................................................73

Figure 4.28: Summary of total ligand concentrations determined in composite samples collected at the four 

sampling sites using the 10 μM SA detection window ......................................................................................77

Figure 4.29: Relationship between total ligand concentration and dissolved organic carbon content of composite 

samples collected at all for sampling sites .........................................................................................................77

Figure 4.30: Determined stability constants for the CuL complex ..............................................................................78

Figure 4.31: Effect of hardness on conditional stability constants (empty circles are high hardness Wemme 

samples) .............................................................................................................................................................79

Figure 4.32: Summary of free ionic copper concentrations calculated in composite samples collected at the four 

sampling sites.....................................................................................................................................................80

Figure 4.33: Concentrations of total dissolved, and free ionic copper in composite stormwater samples 

* indicates samples where [Cu2+diss] > [LT]; a “worst case” [Cu2+free] was estimated as [Cu2+diss] − [LT]...........82

Figure 4.34: Effects of dissolved copper on free ionic copper concentrations ............................................................83

Figure 4.35: Effects of hardness on free ionic copper concentrations (open circles represent high hardness 

Wemme samples)...............................................................................................................................................83

Figure 4.36: Effects of DOC on free ionic copper concentrations...............................................................................84

Figure 4.37: Ligand concentrations for first flush and composite samples at Dixon Outfall ......................................89

Figure 4.38: Comparison between [LT] values for first flush and composite samples ................................................90

Figure 4.39: Relationship between [LT] and DOC for Dixon Outfall samples............................................................91

Figure 4.40: Relationship between [LT] and hardness for Dixon Outfall samples ......................................................92

Figure 4.41: Modeled free ionic copper concentrations in composite samples ...........................................................94

Figure 4.42: Modeled percentage of dissolved copper present as free Cu2+ in composite samples.............................95

Figure 4.43: The first flush effect on free ionic copper ...............................................................................................96

Figure 4.44: The first flush effect on Percent free ionic copper ..................................................................................97

Figure 4.45: Intra-storm free ionic copper and Cu-DOM............................................................................................98

Figure A.1: Dixon Outfall 11/8/08 storm data...........................................................................................................A-4

Figure A.2: Dixon Outfall 11/20/08 storm data.........................................................................................................A-5

Figure A.3: Dixon Outfall 12/1/08 storm data...........................................................................................................A-6

Figure A.4: Dixon Outfall 2/6/09 storm data.............................................................................................................A-7

Figure A.5: Dixon Outfall 2/23/09 storm data...........................................................................................................A-8

Figure A.6: Dixon Outfall 3/14/09 storm data...........................................................................................................A-9

Figure A.7: Dixon Outfall 4/12/09 storm data.........................................................................................................A-10

Figure A.8: Dixon Outfall 5/13/09 storm data.........................................................................................................A-11

Figure A.9: Dixon Outfall 10/13-10/14/09 storm data ............................................................................................A-12

Figure A.10: Data from Herrera sites ......................................................................................................................A-14

Figure A.11: Data from Herrera sites, continued.....................................................................................................A-15

Figure A.12: Dixon Outfall composite and first flush data......................................................................................A-16

Figure A.13: Dixon Outfall composite and first flush data, continued....................................................................A-17

Figure A.14: Dixon Outfall grab and flow-weighted sample data...........................................................................A-18

Figure A.15: Dixon Outfall grab and flow-weighted sample data, continued .........................................................A-19

Figure A.16: Visual MINTEQ raw copper speciation data .....................................................................................A-21

Figure A.17: Dissolved copper – water quality parameters matrix plot ..................................................................A-25

Figure A.18: Total Copper – water quality parameters matrix plot.........................................................................A-26

Figure A.19: Dissolved copper – hydrologic variables matrix plot.........................................................................A-26

Figure A.20: Modeled Free ionic copper – water quality parameters matrix plot ...................................................A-27

Figure A.21: Modeled Percent Free ionic copper – water quality parameters matrix plotx contains the relevant 

S-Plus output from the statistics analysis. Annotations denoting the nature of the analysis and any 

conclusions drawn accompany the output......................................................................................................A-28

ix 
x 

LIST OF TABLES 

Table 2.1: Water quality parameters influencing copper toxicity...........................................................................4

Table 2.2: Freshwater copper concentrations and speciation..................................................................................6

Table 2.3: Marine water copper concentrations and speciation..............................................................................6

Table 2.4: Total copper in stormwater from previous studies.................................................................................8

Table 2.5: Dissolved copper in stormwater from previous studies..........................................................................8

Table 2.6: Summary of analytical techniques for quantifying free ionic copper .................................................13

Table 2.7: Advantages and disadvantages for copper speciation analytical techniques......................................14

Table 2.8: Literature alpha values for Cu-SA complex..........................................................................................18

Table 2.9: A list of copper speciation data determined in seawater in previous studies through CLEACSV and SA ...................................................................................................................................................18

Table 2.10: A list of copper speciation data determined in freshwater in previous studies through 

CLE-ACSV and SA..........................................................................................................................................19

Table 3.1: Concentrations used for cation calibration curves ...............................................................................39

Table 3.2: VA 797 Instrument settings ....................................................................................................................44

Table 3.3: Gaussian model parameters....................................................................................................................47

Table 3.4: NICA-Donnan model parameters ..........................................................................................................47

Table 3.5: SHM parameters......................................................................................................................................47

Table 4.1: Summary of dissolved constituent concentrations in composite stormwater samples.......................50

Table 4.2: Summary of total constituent concentrations in composite stormwater samples ..............................51

Table 4.3: MLR variable selection results for dissolved copper (Cu2+diss) ............................................................61

Table 4.4: MLR variable selection results for total copper (Cu2+tot) .....................................................................63

Table 4.5: Log alpha values for salicylaldoxime (all values at I = 0.05M, pH 6.80).............................................68

Table 4.6: Ligand characteristics, free ionic copper concentrations and related water quality parameters 

for composite samples collected at all sites ....................................................................................................75

Table 4.7: Speciation results for first flush, intrastorm, and composite samples from Dixon Outfall...............88

Table 4.8: Comparison of experimentally determined and modeled free ionic copper concentrations.............99

Table A.1: Cation and DOC blank measurements ...............................................................................................A-1

Table A.2: Anion blank measurements..................................................................................................................A-1

Table A.3: Matrix spike percent differences .........................................................................................................A-2

Table A.4: Coefficient of variation analysis and sample measurement notes ....................................................A-2

Table A.5: Summary of cation-DOM complexes determined for each model and sample (1 of 2).................A-22

Table A.6: Summary of cation-DOM concentrations calculated in each model and sample (2 of 2) ............A-23

Table A.7: Summary of free ionic copper concentrations and copper complexation calculated for each 

model and sample.........................................................................................................................................A-24
GLOSSARY OF TERMS 

Cu2+

diss Dissolved copper 

ADP Antecedent Dry Period 

AADT Annual Average Daily Traffic 

ANOVA Analysis of Variance 

ASV Anodic Stripping Voltammetry 

BLM Biotic Ligand Model 

CI Confidence Interval 

CLE-ACSV Competitive Ligand Exchange-Cathodic Stripping Voltammetry 

Complex A compound consisting of a metal and nonmetal (ligand) molecule 

Cu2+

free Copper ion not bound to any ligand, bioavailable form 

DDI Ultrapure Distilled De-Ionized water 

DI Laboratory grade De-Ionized water 

DGT Diffusive Gradient in Thin Films 

DMT Donnan Membrane Technique 

DOC Dissolved Organic Carbon 

DOM Dissolved Organic Matter 

EDTA Ethylenediaminietetraacetic acid 

EMC Event Mean Concentration 

EPA Environmental Protection Agency 

ESA Endangered Species Act 

FIAM Free Ion Activity Model 

GIME Gel Integrated Microelectrodes 

HMDE Hanging Mercury Drop Electrode 

IC Ion Chromatography 

ICP-AES Inductively Coupled Plasma Atomic Emission Spectrometry 

ICP-MS Inductively Coupled Plasma Mass Spectrometry 

ICP-OES Inductively Coupled Plasma Optical Emission Spectrometry 

ISE Ion Selective Electrode 

K ́CuL Conditional Stability Constant for Cu-(organic) ligand complex 

Ligand A molecule, ion, or compound that is bonded to a metal atom 

LT Organic ligand concentration 

NICA-Donnan Humic model based on the combination of the Non-Ideal 

Competitive Adsorption and Donnan humic models 

NMFS National Marine Fisheries Services 

NOM Natural Organic Matter 

NURF National Urban Runoff Program 

ODOT Oregon Department of Transportation 

PLM Permeation Liquid Membranes 

S Sensitivity (slope) of CLE-ACSV method 

SA Salicylaldoxime (added ligand used in CLE-ACSV in this study) 

SCP Stripping Chronopotentiometry 

xi 
xii 

SHM Stockholm Humic Model 

TDS Total Dissolved Solids 

TSS Total Suspended Solids 

UV Ultraviolet 
1.0 INTRODUCTION 

Recent research has shown that very low concentrations of dissolved copper can inhibit the 

olfactory system of salmon listed as threatened and endangered under the U.S. Endangered 

Species Act (ESA). Highway stormwater runoff is a source of copper to surface waters inhabited 

by ESA-listed species. Informed by this research, the National Marine Fisheries Service 

(NMFS) is more likely to determine in their Biological Opinions (permits issued as part of the 

consultations performed under Section 7 of the ESA) that transportation projects are “Likely to 

Adversely Affect” the ESA-listed fish. The delivery of transportation projects is tied to these 

consultations and Biological Opinions; project timelines can be altered and costly stormwater 

treatment systems may be required. In natural waters, only a fraction of the dissolved copper 

(consisting primarily of ionic and weakly complexed species) is bioavailable and toxic to aquatic 

species. At present, it is unclear how the total dissolved copper is partitioned between ionic and 

complexed forms in highway stormwater runoff and how that influences copper toxicity. 

The overall objective of this study was to develop a fundamental framework for estimating the 

likely impact of copper in highway stormwater runoff that discharges to surface receiving waters 

inhabited by ESA-listed fish species in the State of Oregon. This guidance will allow ODOT to 

predict when, where, and to what extent copper toxicity is likely to be a problem and will inform 

NMFS in their assessment of the risks associated with transportation projects. Measurement of 

copper speciation and the concentrations of other constituents that influence copper toxicity are 

keys to this analysis and therefore were the focus of this work. 

1.1 OBJECTIVES 

The primary objectives of this work were as follows: 

1) Identify the effects of site location, storm hydrology, and water quality parameters on 

the concentration of dissolved copper in Oregon highway runoff; 

2) Develop an analytical technique for the determination of copper speciation in 

highway stormwater runoff; 

3) Compare analytically determined free ionic copper concentrations in highway 

stormwater runoff with modeled concentrations; and 

4) Develop a qualitative understanding of where and when copper toxicity has the most 

potential to be problematic. 

1 
2 

1.2 APPROACH 

The foundation of this work was an extensive stormwater sampling effort. Composite 

stormwater samples were collected from four diverse sites in Oregon. First flush and flowweighted samples were collected from one site in Corvallis to examine variability within 

individual storms. All composite and first flush samples were analyzed for an extensive array of 

constituents. Other grab and selected flow-weighted samples were analyzed for trace metals and 

dissolved organic carbon (DOC), at a minimum. Storm data was collected throughout each 

sampled storm. 

Objective 1 was achieved through statistical analyses of highway runoff samples gathered during 

the sampling effort. These analyses were aimed at identifying significant differences in copper 

concentrations between different sites and sample types, as well as determining important 

variables controlling or correlating with copper concentrations. Objective 2 was accomplished 

through the development of a competitive ligand exchange–adsorptive cathodic stripping 

voltammetry technique suitable for use in stormwaters. The method was then used to determine 

the free ionic copper concentration in stormwater samples. Objective 3 was completed through 

chemical equilibrium modeling of each stormwater sample using three dissolved organic matter 

(DOM) models available in Visual MINTEQ (Gaussian, Non Ideal Competitive AdsorptionDonnan, and Stockholm Humic Model) and subsequently comparing the modeled and 

experimental results. For Objective 4, the results of the analytical and modeled copper 

speciation and water quality determinations were analyzed for trends and differences between 

sites and sample type. 

The remainder of this report is organized as follows: 

 Chapter 2 describes background information and a review of literature on the topics of 

copper speciation chemistry, copper toxicity, analytical techniques, studies on copper 

speciation, relevant statistical assumptions, and the chemical equilibrium models used to 

compare with experimental results; 

 Chapter 3 discusses the sampling procedure, methods for determining the range of 

chemical constituents, materials and methods used for copper speciation in stormwater, 

evolution of the methodology, and statistical procedures used to analyze the results; 

 Chapter 4 describes the copper speciation results, correlation of free ionic copper with 

water quality parameters, the comparison of modeled and experimental results, and the 

importance therein; and 

 Chapter 5 is a discussion of the implications of the results of this study and 

recommendations for future work. 
2.0 BACKGROUND 

2.1 COPPER TOXICITY 

Though other copper oxidation states exist, Cu2+ is the most stable and abundant oxidation state 

in aqueous environments (USEPA 1980; 2007). The total concentration of copper (henceforth 

Cu2+

tot) in aqueous environments consists of the particulate, dissolved, and colloidal forms. 

Particulate copper is less prevalent in aquatic systems than dissolved copper (henceforth Cu2+

diss) 

or colloidal copper, and can be a source or sink for Cu2+

diss, depending on the conditions present 

(Sigg and Behra 2005; USEPA 2007; Wells, et al. 1998). In this study, Cu2+

diss is operationally 

defined as the fraction of copper that passes through a 0.45 μm pore size filter. In natural aquatic 

systems a substantial fraction of copper thus defined as dissolved is actually associated with 

organic colloidal particles (Wells, et al. 1998; Sigg and Behra 2005; USEPA 2007). Copper 

which is truly dissolved in natural waters is present as either a free ion (henceforth referred to as 

Cu2+

free) or complexed with organic or inorganic ligands (compounds and elements that form 

coordination complexes with the metal). The toxicity of copper is directly dependent on its 

bioavailability to organisms; in general, bioavailability is limited to Cu2+

free and weakly 

complexed copper (Brooks, et al. 2007; Luider, et al. 2004; Sigg and Behra 2005; USEPA 

2007). 

In the past, both Cu2+

tot and Cu2+diss have been utilized as primary indicators of copper toxicity. 

Although elevated Cu2+

diss and Cu2+tot concentrations can suggest elevated toxicity, the complex 

aquatic chemistry of Cu2+

free prevents any straightforward correlations between Cu2+tot or Cu2+diss

and Cu2+

free. A number of other water quality parameters affect copper toxicity: temperature, the 

presence of natural ligands (inorganic and organic), cation concentrations (hardness causing 

cations, in particular), and pH (Luider, et al. 2004; USEPA 2007). The effects of these 

parameters are summarized below in Table 2.1; a helpful diagram showing this concept is 

provided in Figure 2.1. Of particular importance is the presence of natural organic ligands 

(categorized in the table as dissolved organic carbon, DOC) that often outcompete inorganic 

ligands for Cu2+

free in aquatic systems and form strong complexes (Buck and Bruland 2005). 

The concentration and binding strength of organic ligands are critical factors in determining 

copper toxicity (Bruland, et al. 2000; Linton, et al. 2007; Luider, et al. 2004; Plöger, et al. 2005; 

Sigg and Behra 2005; USEPA 2007). 

3 
Table 2.1: Water quality parameters influencing copper toxicity 

CONSTITUENT 

EFFECT OF INCREASED 

CONSTITUENT 

CONCENTRATION ON 

COPPER TOXICITY 

MECHANISM 

Alkalinity (HCO3

−

, CO3

2−

) Decrease Increased complexation of Cu2+free 

DOC Decrease Increased complexation of Cu2+free 

Hardness (Ca2+, Mg2+, Fe2+, 

etc.) Decrease 

Competition with Cu2+free for 

adsorption sites on the organism 

Indicator of increased inorganic 

ligand concentrations (i.e., 

increased complexation of Cu2+free) 

Competition with Cu2+free for DOM 

sites 

H+ (decreasing pH) Increase 

Competition with Cu2+free for 

ligands 

Increased solubility (Cu2+diss) 

Dissolved copper Increase Increased Cu2+free

Anions (NO3

−

, SO4

2−

, PO4

3−

, 

S2−

, Cl−, etc.) Decrease Increased complexation of Cu2+free 

Temperature Increase Increased solubility (Cu2+diss) 

Figure 2.1: Example free ionic copper equilibrium in natural waters with modeled stability constants; taken 

from Luider, et al. (2004) 

Importantly, none of these parameters (including Cu2+

diss) can be used exclusively to determine 

copper toxicity; rather, toxicity is determined through direct measurement of Cu2+

free and the 

relative strength of copper complexes with ligands (Brooks, et al. 2007) or through the use of 

detailed chemical equilibrium models such as Free Ion Activity Models (FIAMs) or Biotic 

4 
Ligand Models (BLMs) (Bryan, et al. 2002; Luider, et al. 2004; USEPA 2007). Though the 

equilibrium models are useful, further refinement is necessary to describe the complexation of 

copper with natural organic matter (NOM) and various biotic ligands, and to verify modeling 

results using analytical data. To date, no attempts have been made to verify such models when 

applied to stormwater. 

2.1.1 Salmonids 

Most relevant to this study is the toxicity of copper in highway stormwater runoff to ESA-listed 

salmonid species. Recent research by Sandahl, et al. (2007) has shown that low concentrations 

(2-5 μg/L) of Cu2+

diss can impair the olfactory system of juvenile Coho salmon, one of several 

ESA-listed fish species. Damage to the chemosensory system reduces the ability of fish to 

navigate and avoid predators, likely increasing mortality. Much of the research on low-level 

copper toxicity to salmonid species has been summarized in a recent white paper by researchers 

at NMFS and the USGS (Hecht, et al. 2007). In short, the authors report an 8-57% reduction in 

predator avoidance behavior at Cu2+

diss concentrations ranging from 0.18 to 2.1 μg/L above 

background (≤ 3 μg/L) levels. Furthermore, impaired olfaction resulting from short-duration 

(~10 min) exposure can last for minutes to weeks, depending on the dose. 

A study by McIntyre, et al. (2008) examined the effects of water quality parameters on 

chemosensory deprivation in Coho Salmon. The authors found that increasing both water 

hardness (0.2-1.6 mM Ca) and alkalinity (0.2-3.2 mM HCO3

-

) only slightly decreased the 

inhibitory effects of copper. Increasing DOC (0-6 mg/L) showed a much greater capacity for 

protecting salmon from copper toxicity. Specifically, 19% of all surface waters collected for the 

study had enough DOC to reduce sub-lethal copper toxicity by half, and 2% of the sites collected 

in the Willamette basin had enough DOC (over 6 mg/L) to eliminate copper toxicity altogether. 

The relative ameliorating effects of hardness, alkalinity, and DOC were similar in a recent study 

examining copper’s toxicity on the mechanosensory system of zebrafish (Linbo, et al. 2009). 

Though highway runoff is well recognized as a significant contributor to copper in natural 

waters, no attempts have yet been made to characterize copper speciation as a function of these 

water quality parameters. 

2.2 CONCENTRATION AND SPECIATION IN NATURAL WATERS 

The U.S. Environmental Protection Agency (EPA) has established hardness dependent water 

quality criteria for acute (1 hr) and chronic (96 hr) exposure to Cu2+

diss. With an assumed 

hardness of 100 mg/L as CaCO3, the acute criteria is 13 μg/L, and chronic criteria is 9 μg/L for 

freshwater (USEPA 2008). In marine water the acute criteria is 4.8 μg/L and the chronic criteria 

is 3.1 μg/L. Currently, there are no regulations for Cu2+

free in either fresh or marine waters. 

Oregon freshwater and marine exposure limits are identical to federal standards. 

Bowen (1985) reported typical Cu2+

diss concentrations for freshwater between 0.20-30 μg/L and 

for marine water between 0.03 and 0.23 μg/L. There have been a number of studies quantifying 

both Cu2+

diss and Cu2+free. In all cases where both parameters were reported, Cu2+free was only a 

small fraction of the Cu2+

diss largely due to complexation with organic ligands. The data from 

these studies are presented below in Table 2.2 (for freshwater) and Table 2.3 (for marine water). 

5 
Table 2.2: Freshwater copper concentrations and speciation 

STUDY SITE Cu2+

diss

(μg/L)(a)

Cu2+

free (μg/L)(a)

DOC 

(mg/L) pH 

Bryan, et al. (2002) Rivers and 

Lakes/Pools NR 6.4×10−4

 to 6.4 4.4 to 26.7 4.0 to 8.1 

Plöger, et al. (2005) 

High Altitude 

Lakes (Runoff 

Supplied) 

0.06 to 0.1 5×10−8 to 2.5×10−7 0.3 to 2.3 5.6 to 7.6 

Sigg, et al. (2006) Swiss Lakes and 

Rivers 1.3 to 2.0 10−3

 to 4×10−3 NR 2

(b)

Monticelli, et al.

(2004) Glacial Streams 0.19 to 0.45 5.1×10−6

 to 2.0×10−5 NR 7.5 to 8.2 

Averyt, et al. (2004) New Zealand 

Alpine Lakes 0.034 to 0.54 3.2×10−12 to 0.25 NR 5.9 to 9.0 

Hardwater 

Lakes and 

Rivers 

0.32 to 2.0 6.4×10−9 to 2×10−6 NR 7.5 to 8.5 

“typical” 

conditions 0.64 to 6.4 6.4×10−7

 to 6.4×10−5 2.8 NR 

Sigg & Behra 

(2005) 

New England 

Rivers NR 6.4×10−4

 to 6.4×10−2 NR 5.2 to 7.9 

Pei, et al. (2000) Arve River 0.13 to 0.8 (c) 0.08 to 0.1 NR NR 

NR – values not reported 

(a) 1 μg/L of Cu is equivalent to 1.57×10−8

 M 

(b) samples were acidified 

(c) from Cu2+tot 

Table 2.3: Marine water copper concentrations and speciation 

STUDY SITE TYPE Cu2+

diss

(μg/L)(a)

Cu2+

free (μg/L)(a)

DOC 

(mg/L) pH 

Hurst & Bruland 

(2005) Estuarine 1.0 to 2.5 4×10−4

 to 4×10−3 2.5 to 5.0 3.5(b)

Ndungu, et al. 

(2005) Estuarine 1.6 to 2(c) 6.4×10−3

 to 0.05 3.0 to 4.1 7.7 to 8.5 

Twiss & Moffett 

(2002) Coastal Marine 0.4 to 6.4 4×10−5

 to 0.024 NR 7.5 to 8.2 

Bruland, et al. 

(2000) Coastal Marine 0.8 2.5×10−6

 to 2.5×10−5 NR 7.9 

Buck & Bruland 

(2005) Coastal Marine 0.13 to 3.2 2×10−8

 to 3.2×10−6 2.5 to 4.8 7.7 to 8.6 

Eriksen, et al. 

(2001) Coastal Marine 3.2 to 20 6.4×10−5

 to 3.4×10−4 NR 2

(b)

NR – values not reported 

(a) 1 μg/L of Cu is equivalent to 1.57×10−8

 M 

(b) samples were acidified 

(c) from Cu2+tot 

 

Oregon freshwater and marine exposure limits are identical to federal standards. A collection of 

Oregon data collected from July 1977 to October 2006 showed Cu2+

diss concentrations ranging 

from <0. 3 μg/L to 40.2 mg/L with a median value of <10 μg/L, and Cu2+

tot ranging from <0.05 

μg/L to 570 mg/L with a median value of 12 μg/L (ODEQ 2007). According to the recent 

303(d) list submitted to the EPA, eleven Oregon surface water sites are currently listed as 

impaired with respect to Cu2+

diss (ODEQ 2008). In a study focusing on trace elements in the 

Willamette River Basin, Cu2+

diss concentration ranged from <0.5 to 4.6 μg/L (14 sites sampled) 

6 
and the Cu2+

tot ranged from <0.5 to 11 μg/L (23 sites sampled) (Anderson, et al. 1996). The 

USGS online water quality database (USGS 2007) reveals the presence (but not quantity) of 

copper at 75 of the 98 sites sampled; the quantified data reported values ranging from <1 to 20 

μg/L. 

2.3 COPPER IN STORMWATER 

Urban stormwater runoff is an important non-point source of many contaminants present in 

aquatic environmental systems. In 1983, the EPA’s National Urban Runoff Program (NURP) 

examined the effects of urban stormwater runoff on receiving water quality (Athayde, et al. 

1983). Among the main conclusions of this project was that heavy metals are the most prevalent 

priority pollutant found in urban stormwater, and that the effect stormwater runoff had on 

receiving waters was highly site-specific. The results of the NURP showed that copper was 

prevalent in highway stormwater runoff and that it was a potential source of toxicity to aquatic 

organisms (Athayde, et al. 1983). Specifically, copper was detected in 91% of the NURP 

samples and had a median Event Mean Concentration (EMC) of 34 μg/L (Athayde, et al. 1983). 

The end-of-pipe total copper concentrations exceeded the EPA’s acute freshwater criteria (~10-

40 μg/L, depending on the hardness of the water) 47% of the time. This value is comparable to 

the 36-43% of sites that exceeded the ambient standards in a more recent study performed by the 

Oregon Association of Clean Water Agencies (Strecker, et al. 1997). In another study, copper 

and zinc caused 90% of the toxicity in assays in which various aquatic species were exposed 

directly to roadway stormwater runoff (Kayhanian, et al. 2007). These results show that copper 

is not only commonly found in highway runoff, but that it also often exceeds existing standards 

for acute toxicity in aquatic species. However, most existing standards are conservative 

estimates based on Cu2+

tot loadings, and do not account for the speciation of copper in the runoff. 

2.3.1 Primary Sources 

The primary source of copper in highway stormwater runoff is brake pad wear. Brake pads can 

contain anywhere from 0-20% copper by mass (Rosselot 2006a). A 2006 study in the San 

Francisco Bay area estimated that brake pad linings released approximately 0.58 mg copper per 

vehicle per km driven (Rosselot 2006a). Another study found brake linings to be the dominant 

source of copper in highway runoff (Legret and Pagotto 1999). Brake pad wear also contributes 

significantly to atmospheric copper concentrations (Rosselot 2006a). Atmospheric deposition is 

an especially important source of copper in runoff from urban areas (Sabin, et al. 2005). Dry 

deposition has been found to be the dominant mechanism in dry climates, while wet deposition 

becomes increasingly important in wet climates (Sabin, et al. 2005). Wu and coworkers (1998) 

found deposition to contribute 30-50% of the copper in highway runoff loadings. Other sources 

of copper to stormwater runoff include: engine oil, combustion of lubricating oils, roof/gutter 

runoff, building siding corrosion, fertilizer, pesticides, industrial releases, and wet and dry 

deposition (Davis, et al. 2001; Kim and Fergusson 1994; Makepeace, et al. 1995; Rosselot 

2006b). 

7 
2.3.2 Concentration, Partitioning, and Speciation in Stormwater 

Typical total copper concentrations in stormwater range from below the detection limit (often 1 

μg/L) to several hundred μg/L, with medians in the 10-40 μg/L range. Dissolved copper 

concentrations measured below detection limits more often than Cu2+

tot (Bannerman, et al. 1996; 

Strecker, et al. 1997); concentrations ranged from below the detection limit to ~100 μg/L, with 

median values of 3-12 μg/L (Bannerman, et al. 1996; Driscoll, et al. 1990; Harrison, et al. 

1997; Kayhanian, et al. 2003; Strecker, et al. 1997; USEPA 1983; WERF, et al. 2007). The 

Kayhanian, et al. (2003) study focused specifically on runoff generated from highways and 

showed a median for Cu2+

tot of 20.2 μg/L (ranging from 1-9500 μg/L) and a median for Cu2+diss

of 9.9 μg/L (ranging from 1-121 μg/L). Table 2.4 and Table 2.5 below show the copper 

concentrations from some significant stormwater studies. 

Table 2.4: Total copper in stormwater from previous studies 

STUDY LOCATION NUMBER OF 

SITES 

RANGE OF Cu2+tot

(μg/L) 

MEDIAN Cu2+tot

(μg/L) 

Kayhanian, et al. 

(2003) 

California 83 1-9500 20.2 

Bannerman, et al. 

(1996) 

Wisconsin 14 <3-210 18 

USEPA (1983) Nationwide 51 4-349(a) 34 

Driscoll, et al. (1990) Nationwide 22 9-120(b) 39 

WERF, et al. (2007) Nationwide 122 1.3-874.5(c) 20.1(c)

Harrision, et al. 

(1997)

(d)

Oregon 8 1-45 8 

Strecker, et al. (1997) Oregon 51 <1-250 11.5 

(a) Based on 90% confidence intervals at specific sites 

(b) Based on 90% data values from urban and non-urban sites 

(c) Based on high/low/median mean site values from the 122 sites studied 

(d) Urban surface water sites heavily influenced by stormwater 

Table 2.5: Dissolved copper in stormwater from previous studies 

STUDY RANGE OF Cu2+dis

(μg/L) 

MEDIAN Cu2+dis

(μg/L) 

MEDIAN % 

DISSOLVED(a)

Kayhanian, et al. (2003) 1-121 9.9 49.1% 

Bannerman, et al. (1996) <3-33 5 27.8% 

WERF, et al. (2007) 1.5-45.5(b) 11.6(b) 57.8% 

Harrision, et al. (1997)

 (c) 1-21 3.5 43.8% 

Strecker, et al. (1997) <1-110 4 34.8% 

(a) Based on quotient of dissolved median Cu to total median Cu, for each study 

(b) Based on high/low/median mean site values from the 122 sites studied 

(c) Urban surface water sites heavily influenced by stormwater 

As shown above, copper is typically found in both the particulate and dissolved fractions of 

stormwater runoff (Dean, et al. 2005; Grant, et al. 2003; Prestes, et al. 2006). The dissolved 

8 
fraction is most commonly between 30 and 70% of the total copper in runoff, as indicated in 

Table 2.5 and by Breault and Granato (2000). However, the dissolved fraction can approach 

100% during snowmelt events (Breault and Granato 2000). The dissolved fraction is of 

immediate concern to aquatic species, though the particulate-bound fraction can be released into 

the environment as well. Tuccillo (2006) found that copper in runoff was predominantly 

attached to particles > 5 μm or in the dissolved phase (here defined as passing through a 10 kDa 

filter). 

Few studies have examined the speciation of dissolved copper in stormwater. Dean and 

coworkers (2005) examined the aqueous phase metal speciation using a chemical equilibrium 

model (MINTEQ) and found that copper speciation varied with storm event hydrology and, in 

some cases, varied over the course of individual storms. Generally, Cu-DOM (dissolved organic 

matter) and CuCO3 species were found to be the most prevalent, but Cu2+

free was a significant 

species in their models (about 8-40%). Factors affecting the speciation included: rainfall 

intensity, rainfall pH, concentration of copper, concentration of ligands, and alkalinity. 

2.4 FACTORS AFFECTING COPPER IN RUNOFF 

2.4.1 First Flush 

Heavy metals, total suspended solids (TSS), and other stormwater pollutants often exhibit a “first 

flush” effect at the beginning of a storm. Though it has been defined in different ways, the first 

flush effect can be most generally defined as more mass of a pollutant being washed off during 

the beginning of a storm than is washed off during the end – i.e., the first portion of a storm 

flushes most of the pollutants that were collected on the road surface prior to the storm. 

Mathematically, this effect can be described as follows: 

( )/ 1

( )/

mt M

vt V



where m(t) and v(t) represent the cumulative mass of pollutant and the cumulative runoff volume 

washed off at any given time, t, respectively, while M and V represent the total mass of pollutant 

and total runoff volume for the entire storm, respectively. 

As this ratio increases, it indicates an increasingly pronounced first flush. Noting a first flush 

can be helpful in determining what best management practices (BMPs) may be useful in 

controlling stormwater runoff (Sansalone and Cristina 2004). Using the above criteria, Flint and 

Davis (2007) found that a first flush of Cu2+

tot occurred in 79% of the storms studied. 

The first flush effect lends itself to certain types of storms more than others. Storms of short 

duration and relatively constant rainfall intensity (mass-limited) have exhibited the first flush 

effect more than other storms (Barrett, et al. 1998). These mass-limited hydrologic events result 

in pollutant delivery that is disproportionately higher towards the beginning of the event (Dean, 

et al. 2005; Deletic and Maksimovic 1998; Sansalone and Buchberger 1997). Low intensity 

hydrologic events are considered “flow-limited” and result in pollutant delivery that is roughly 

9 
proportional to the storm hydrograph (Dean, et al. 2005; Sansalone and Buchberger 1997). As 

such, flow-limited events often do not exhibit first flush behavior. Sansalone and Buchberger 

(1997) showed a first flush for Cu2+

diss in flow-limited and mass-limited events; evidence of a 

first flush for particulate-bound species was not so well-defined. 

The first flush effect has also been witnessed with respect to toxicity. In a study involving 

various aquatic species and pollutants, over 40% of the toxicity typically stemmed from the first 

20% of discharged runoff volume (Kayhanian, et al. 2007). This study also showed that toxic 

effects were rarely observed in organisms exposed to composite samples, even when a storm 

exhibited strong first flush behavior. 

Snowmelt runoff events can also affect the amount of copper in runoff. Snowmelt events are 

typically less intense than rainfall events, but extend for longer durations. However, due to the 

ground being either frozen or saturated, more runoff results from snowmelt events than would be 

expected for a similar rain event (Driscoll, et al. 1990). Total median metal concentrations have 

been shown to increase by a factor of ~2.5 for snowmelt events (as compared to rainfall events) 

(Driscoll, et al. 1990). However, the receiving water impacts of snowfall events may be 

mitigated by the effects of dilution due to the large volume of runoff. 

2.4.2 Hydrologic Effects 

Total event rainfall and runoff volume have been hypothesized to correlate to the event mean 

concentration (EMC) of a contaminant, either by causing increased wash-off of pollutants or by 

increased dilution. The EMC is the measurement most often used to describe the amount of a 

pollutant washed off during a given storm. The EMC is defined as the flow-weighted average 

concentration of the pollutant over the course of one full hydrologic event. Mathematically, it 

can be defined as follows: 

0

0

() ()

( )

T

T

C t Q t dt

EMC

Q t dt







where C(t) is the concentration of the pollutant at time t, Q(t) is the flow rate at time t, and T is 

the duration of the entire runoff event. 

This equation can be similarly represented in summation form for discrete data points. Although 

these values are useful, EMCs give no information on how the concentration of a pollutant varies 

with respect to time – i.e., the concentration of a pollutant at a given point in time during the 

event may be higher or lower than the EMC for the entire event. 

Reports studying the relationship between total rainfall and pollutant EMCs typically found weak 

negative correlations, indicating the effect of dilution may be dominant (Driscoll, et al. 1990; 

Kayhanian, et al. 2003; USEPA 1983). Similarly, Kayhanian, et al. (2003) found increasingly 

intense rainfall did not correlate significantly with Cu2+

tot and correlated negatively with Cu2+diss

EMCs. This result indicates that rainfall intensity is closely tied to total event rainfall – meaning 

10 
that the effects of dilution were more important than any increased wash-off effect caused by 

more intense rainfall. However, as noted earlier, increasingly intense storms often have a more 

pronounced first flush. These results suggest that although the EMC from an intense storm may 

be lower than that of a calmer storm, the copper concentration early in the intense storm may be 

higher than it would be at any point during a calmer storm. 

2.4.3 Traffic Effects 

The average daily traffic (ADT) has been shown to influence the amount of copper found in 

runoff. However, this effect is generally very broad and a poor predictor of pollutant 

concentrations. In one study, urban highways (classified as ADT > 30,000 vehicles/day) had a 

median of 54 μg/L Cu2+

tot while non-urban highways showed a median of 22 μg/L in stormwater 

runoff (Driscoll, et al. 1990). There was little correlation found between copper concentrations 

and ADT beyond sites that are considered urban or non-urban. This finding was echoed in a 

study by Kayhanian, et al. (2003) where they found a significant difference between sites that are 

considered urban (same ADT criteria as above) and non-urban, but there was no significant 

difference distinguishing between sites with ADTs ranging from 30,000 to over 200,000 

vehicles/day. Urban highways, when compared to each other at different ADT volumes, often 

do not exhibit a significant difference in Cu2+

tot EMC. Other traffic-related variables, such as 

traffic during storms and braking intensity, have rarely been studied, but may also alter copper 

concentration in highway runoff. 

2.4.4 Other Effects 

Antecedent Dry Period (ADP), the period of time between the end of one storm and the start of 

the next one, has been correlated to pollutant EMCs in previous studies. Longer ADPs would be 

expected to allow more pollutants to build up on the roadway prior to being washed off by the 

next storm. Previous studies in California and Nevada reported that dissolved pollutant loads on 

streets may reach a steady-state value within approximately 1-2 weeks, after which the mass of 

pollutants deposited onto the surface does not increase (Soller, et al. 2005). Therefore, ADPs 

significantly longer than 2 weeks may not yield significantly different dissolved pollutant loads 

than storms with 1-2 week ADPs. Some studies have shown longer ADPs to have a significant 

positive correlation with higher EMCs (Kayhanian, et al. 2003; Prestes, et al. 2006). However, 

other studies have shown that the influence of ADP on pollutant EMCs may be substantially 

diminished by other transportation processes (natural and vehicular-induced wind, material pickup by tires) that influence pollutant build-up during the dry period (Irish, et al. 1995). 

Primary land use in areas surrounding highways has also been thought to affect pollutant 

concentrations. The NURP did not find any statistically significant variations in EMCs between 

land use categories, outside of the differences between urban and non-urban categories (Athayde, 

et al. 1983). The report concluded that if land use category does have an effect, it seems to be 

eclipsed by storm-to-storm variability. Kayhanian, et al. (2003) determined that land use 

category (with the exception of industrial and mixed-use) did not have a significant correlation to 

Cu2+

tot EMCs, especially when other variables (event rainfall, ADP, cumulative precipitation, 

and ADT) were taken into account; higher than average copper concentrations were found in 

industrial and mixed land use areas. 

11 
The NURP also did not find any consistent relationship between copper EMCs and location 

(Athayde, et al. 1983). Additionally, significant differences in copper concentrations found in 

geographically distinct locations are likely due to other parameters (e.g., ADP, urban/non-urban 

location, rainfall characteristics). 

2.5 COPPER SPECIATION THEORY 

As mentioned above, copper can exist in three different “compartments” in aqueous 

environments: particulate, colloidal, and dissolved. This can be summarized simply as: 

             2 2 2 2

tot part col CuCuCuCu diss (2-1) 

where [Cu2+

tot] refers to the total concentration of copper within the system consisting of 

particulate [Cu2+

part], colloidal [Cu2+col], and dissolved [Cu2+diss] copper. 

Within each environmental compartment, copper can exist in many different forms or “species.” 

Cu2+

diss presents the most acute threat to aquatic species due to its bioavailability, although some 

dissolved species are more bioavailable/toxic than others. Therefore, the accurate determination 

of the speciation of copper in the dissolved form is essential for assessing the potential toxicity of 

a given water. 

In the aqueous environment, Cu2+

free forms complexes with many inorganic and organic ligands. 

When present, copper preferentially binds to organic ligands (Buck and Bruland 2005). The total 

concentration of Cu2+

diss in the natural environment can be described by the following mass 

balance equation (Campos and van den Berg 1994): 

       

       

  



 

















 







  

n

free

CuOH

i

i free

n

CuX i

diss free iCuL free

H

Cu

CuX

CuCu CuL

n n

2

2

2 2 2

' ''

''

 



(2-2) 

where  is the total dissolved copper concentration including free ionic copper ( ) 

and copper bound with organics, 

 2 Cudiss

2 Cu free

Li

 and inorganics,  Xi.  CuL  and  CuX  represent the 

conditional stability constants for each of the organic and inorganic copper-ligand complexes, 

respectively.  CuOH  is the acidity constant for stormwater. is the stoichiometric coefficient for 

the given ion. 

n

2.5.1 Analytical Techniques for Quantifying Free Ionic Copper 

Analytical techniques typically used to quantify copper in water (such as Inductively Coupled 

Plasma Mass Spectrometry) are not capable of measuring Cu2+

free without a separation step. 

With the rising concern of copper toxicity in natural aquatic environments, a number of 

analytical techniques have been developed for measurement of metal speciation in natural 

environments. These techniques and instruments include: 

12 
 Diffusive Gradient in Thin Films (DGT) 

 Donnan Membrane Technique (DMT) 

 Permeation Liquid Membranes (PLM) 

 Ion Selective Electrodes (ISE) 

 Anodic Stripping Voltammetry (ASV) 

 Gel Integrated Microelectrodes (GIME) 

 Stripping Chronopotentiometry (SCP) 

 Competitive Ligand Exchange/Adsorptive Cathodic Stripping Voltammetry (CLEACSV) 

Each technique can be characterized as either a dynamic- or equilibrium-based technique, 

although some techniques can be employed in either mode. Dynamic techniques recognize and 

account for the kinetics of copper binding reactions. As such, these techniques are characterized 

by measuring labile Cu2+ rather than Cu2+

free. A complex is considered labile if it is prone to 

change, or likely to undergo one. Lability is an operationally defined characteristic that depends 

on the technique’s response and accumulation time (Sigg, et al. 2006). In general, labile Cu2+

includes Cu2+

free and Cu2+ weakly complexed with organics and inorganics. 

Equilibrium techniques typically determine Cu2+

free after allowing the solution to equilibrate 

with, or without, an added ligand. Equilibrium techniques are commonly used to determine 

chemical species distribution and are essential to understanding the fate of constituents in 

aqueous systems; however, it is important to note the possible kinetic dependence of the 

reactions (Scally, et al. 2003). Table 2.6 and Table 2.7 below summarize the important 

characteristics and advantages and disadvantages for each of these techniques. 

Table 2.6: Summary of analytical techniques for quantifying free ionic copper 

DETECTION LIMIT 

(μg/L)(a) TECHNIQUE MODE IN 

SITU FRESHWATER MARINE WATER 

DGT Dynamic Yes 8.3×10−2 5.8×10−2(b)

DMT Dynamic or 

Equilibrium Yes 7.3×10−4(b) Not Available 

PLM Dynamic or 

Equilibrium Yes 6.4×10−5(b) 5.7×10−2(c)

ISE Equilibrium Yes 6.4×10−5(d) 6.4×10−5(d)

ASV Dynamic Yes 5×10−3 5×10−3

GIME Dynamic Yes 6.4×10−3

 1.3×10−2

SCP Dynamic No 2.5×10−3(c) 0.7 

CLE-ACSV Equilibrium No 2.9×10−8(c) <1.3×10−6(e)

Notes: 

(a) 1 μg/L of Cu is equivalent to 1.57×10−8

 M 

(b) long preconcentration times (Parthasarathy, et al. 2001) 

(c) lowest reported value (detection limit not reported) 

(d) using metal ion buffers 

(e) calculated based on the reported detection limit of Cu(SA)x of 0.1 nM and the competition 

strength of the binding ligand (Campos and van den Berg 1994). 

13 
Table 2.7: Advantages and disadvantages for copper speciation analytical techniques 

TECHNIQUE ADVANTAGES DISADVANTAGES 

DGT(a)

 No power supply required 

 Determines disassociation rate of weak 

copper complexes 

 Long accumulation time (days) to measure 

low metal concentrations 

 Measures labile Cu2+, not Cu2+free

DMT(b)  Simultaneous metal determination 

 Requires long equilibration times (1-4 days) 

in equilibrium mode to measure low metal 

concentrations 

 HCO3

-

, CO3

2-, and OH-

 can complex with 

Cu2+free and cause measurement errors 

PLM(c)

 Provides time-averaged copper 

concentrations 

 Long accumulations time (hours) required 

 Only used in one previous study on copper 

speciation 

ISE(d)

 Simple operation 

 Measures activity of Cu2+free as opposed 

to molar concentration 

 Membrane fouling by organics 

 Cross contamination 

 Slow response time at low ion concentrations 

 Declining performance over the lifespan of 

the electrode 

ASV(e)

 Simultaneous metal determination 

 Can be combined with competitive 

ligand techniques to characterize copperorganic complex strengths 

 Electrode fouling by organics 

 Measures labile Cu2+, not Cu2+free

GIME(f)

 Simultaneous metal determination 

 Gel layer prevents electrode fouling and 

provides qualitative information relative 

concentrations of mobile and colloidal 

copper-ligand complexes 

 Use in traditional metal titrations shows 

artifacts at high copper concentrations 

 Measures labile Cu2+, not Cu2+free

SCP(g)

 Oxidation step allows for determination 

of metal:ligand ratio 

 Used in media with high organic content 

 Must account for oxygen concentration in 

sample 

CLE-ACSV(h)

 Used in many previous speciation 

studies 

 Highly sensitive 

 Determines binding strength of copper 

with natural ligands in the sample 

 Metal titrations are time-consuming 

 Sensitive to surfactants in natural waters 

 Oversaturation of Cu-ligand complexes at 

the mercury drop is possible 

References: 

(a) (Meylan, et al. 2004; Scally, et al. 2003; Sigg, et al. 2006; Twiss and Moffett 2002) 

(b) (Kalis, et al. 2007; Sigg, et al. 2006; Temminghoff, et al. 2000; Weng, et al. 2001; Weng, et al. 2005) 

(c) (Ndungu, et al. 2005; Sigg, et al. 2006; Zhang, et al. 2007) 

(d) (De Marco, et al. 2007; Eriksen, et al. 2001) 

(e) (Bruland, et al. 2000; Buck and Bruland 2005; Howell, et al. 2003; Hurst and Bruland 2005) 

(f) (Pei, et al. 2001; Pei, et al. 2000; Sigg, et al. 2006) 

(g) (Pei, et al. 2001; Pei, et al. 2000; Serrano, et al. 2007; Sigg, et al. 2006; Town 1998) 

(h) (Bruland, et al. 2000; Eriksen, et al. 2001; Miller and Bruland 1997; Rue and Bruland 1995; Sigg, et al. 

2006; Twiss and Moffett 2002) 

14 
In general, equilibrium techniques have lower detection limits than dynamic techniques. 

Furthermore, many techniques require long equilibration or preconcentration/accumulation times 

to achieve the values stated in the table (e.g., DGT, DMT, PLM, GIME and ISE). CLE-ACSV is 

the most sensitive analytical method, with detection limits more than an order of magnitude 

lower than the closest competitor. Detection limits reported in Table 2.6 are conservative 

estimates based on low concentrations of the competing ligand and a deposition time of 1 min. 

CLE-ACSV outperformed several other techniques (DGT, ISE, GIME, PLM, SCP, and DMT) in 

terms of detection limits for free metal concentrations (Sigg, et al. 2006; Xue and Sunda 1997). 

Another major advantage of CLE-ACSV is that it can be used to determine the relative strength 

of the naturally occurring Cu-ligand complexes. Because copper speciation in stormwater has 

not been analytically examined, this added capability is quite valuable. Determination of binding 

strengths between copper and organic matter present in stormwater will aid in verifying results 

generated using equilibrium models. These advantages made CLE-ACSV an attractive choice 

for the current study. 

The primary disadvantage of CLE-ACSV is its inability to be used in situ. The advantages of 

dynamic in situ techniques are the lack of sample preparation and the ability to account for the 

kinetic aspects of copper complexation. That being said, the techniques that can be used in situ

often require deployment/accumulation or equilibration times that are longer than typical storm 

event durations. Because copper speciation has been shown to vary over the course of an 

individual storm and copper olfactory toxicity to juvenile salmonids can occur over short 

exposure times, use of these in situ techniques will not yield the temporal resolution necessary to 

experimentally verify these effects. Furthermore, many of the alternative techniques have high 

detection limits (relative to CLE-ACSV), are prone to electrode fouling by colloids and NOM, 

and are unable to measure Cu2+

free alone. On these merits, it was determined that CLE-ACSV 

was the most appropriate technique for use in this study. 

2.5.1.1 CLE-ACSV 

CLE-ACSV has been used to quantify both free metal ion concentrations and the relative 

binding strengths of several transition metals with natural ligands (Rue and Bruland 

1995; Miller and Bruland 1997; Buck and Bruland 2005; Sigg, Black, et al. 2006). CLEACSV consists of three primary steps. First, a competing ligand, La , is added to a natural 

water sample and allowed to equilibrate; during equilibration, some of the dissolved Cu2+

complexes with the added ligand (see Equation (2-3)). The variables and are the 

stoichiometric coefficients and molecular charge, respectively. 

m n

       mn

a m

K n

free LmCu a LCu CuLa   

 

2    .........  2

(2-3) 

Second, the complex is adsorbed onto a hanging mercury drop electrode; it is this 

pre-concentration step that results in extremely low detection limits (Wang 1985). 

Finally, Cu2+ is reduced to Cu0

 by cathodically scanning the potential (towards more 

negative values). The induced current is proportional to the amount of Cu2+ adsorbed 

onto the mercury drop. By titrating the original sample (and the added competitive 

ligand) with copper, the Cu2+

free concentration and the relative binding strengths of 

CuLa

15 
copper with the naturally present ligands can be determined. Selection of the added 

ligand depends on the metal ion of interest, the electroactivity of the metal-ligand 

complex, and the ability of the complex to adsorb to mercury (Rue and Bruland 1995). 

The primary compound adsorbed by the mercury drop is in the form . The 

instrument response can be modeled as the following equation: 

 0

a m Cu L

p a  m i S Cu L      (2-4) 

where is the current measured by the instrument and is a proportionality constant 

(sensitivity) of the method. 

pi S

2.5.2 Determining Copper Speciation 

In order to determine the speciation of copper in an aquatic system, Equation (2-4) needs to be 

solved. Techniques such as inductively coupled plasma spectroscopy can be used to determine 

the Cu2+

diss concentration; ion chromatography and alkalinity titrations can measure inorganic 

ligand concentrations; and many stability constants are tabulated in literature (Martell and Smith 

1995). The remaining unknowns are the concentration and binding strength of the organic 

ligands. These are determined with CLE-ACSV. Once determined, Equation (2-2) can be 

solved for the Cu2+

free concentration. 

As described above, CLE-ACSV requires the addition of an added ligand that binds with copper 

to form an electroactive complex. There are multiple ligands that have been used for copper 

speciation, including: catechol (van den Berg 1984), tropolone (Donat and van den Berg 1992), 

ethylenediamine (Scarano, et al. 1990), 8-hydroxyquinoline (van den Berg 1986), and 

salicylaldoxime (Buck and Bruland 2005; Campos and van den Berg 1994; Kogut and Voelker 

2001; Monticelli, et al. 2004; Plöger, et al. 2005). Of these ligands, salicylaldoxime (SA) is the 

most sensitive (to copper) and thus leads to greater signal responses; SA also allows a larger 

detection range of complex strengths (  CuL  ) (Campos and van den Berg 1994). For these 

reasons, SA was selected as the added ligand in the present work. 

In the presence of SA, Equation (2-2) becomes: 

         

 

2 2

2 2

n

n

n

diss CuL i CuX i

i i

n

CuOH CuSAx

Cu Cu L Cu X Cu

Cu H Cu

 

 

 

 

         

            

 



2

(2-5) 

This competition between SA, inorganic ligands, Xi , and organic ligands, , can be described 

in an equilibrium equation also: 

Li



16 
 

    L i    CuL i

' ' 

        free i i x Cu X XCu     CuX 2 ' ' 

      x SA    CuSAx  SACU  '

(2-6) 

 As shown in Equation (2-5), condition-specific alpha values for the complex CuSA,  CuSAx , are 

essential components in determining the speciation of copper.  CuSAx , as defined by Campos 

and van den Berg (1994), is: 

2 ' ''

2   CuSA CuSA X K SA SA CuSA

'          





(2-7) 

Where is the concentration of unbound SA in solution and ' SA  KCuSA  and  CuSA2  are 

conditional stability constants for the complex CuSA+

 and Cu(SA)2, respectively. In actuality 

CLE-ACSV is thought to measure the complex Cu(HSA)2, but this compound is commonly 

referred to in literature as simply Cu(SA)2. Henceforth these terms will be considered 

interchangeable. 

2.5.3 Previous Work with Salicylaldoxime 

Salicylaldoxime has been used for copper speciation studies in rain water, seawater, and fresh 

water (Buck and Bruland 2005; Campos and van den Berg 1994; Kogut and Voelker 2001; 

Monticelli, et al. 2004; Plöger, et al. 2005; Witt and Jickells 2005). Campos and van den Berg 

(1994) first proposed use of SA in copper speciation due to its greater sensitivity to copper (3-4 

fold greater) compared with previous added ligands (tropolone, 8-quinolinol, and catechol) and 

its detection window that is centered in between the detection limits of tropolone and 8-

quinolionol. 

Alpha values for CuSA were calculated in sea water conditions with varying salinity 

(representing estuaries) and in glacier water with varying calcium concentrations. Table 2.8

shows a list of determined  CuSAx values. Table 2.9 and Table 2.10 include a list of copper 

speciation results obtained with SA; all but Monticelli and coworkers use alpha values 

determined by Campos and van den Berg. Ligand concentrations and binding strengths are 

represented by Lx  and respectively, where x is a demarcation for weak and strong ligand 

classes in the Buck and Bruland study. Cu2+

diss concentrations range from 3.1 to 49.6 nM for 

seawater studies; 2.9 to 6.3 nM for glacier supplied streams; and 1.4 to 80.1 nM in rainwater 

studies. Cu2+

free concentrations were generally low, the highest (10−12.2 M) was from a 

continental rainstorm and the lowest (10−15.5 M) was from the Grizzly Bay, San Francisco (Bay). 

KCuLx

17 
Table 2.8: Literature alpha values for Cu-SA complex 

SA 

CONCENTRATION 

(μM) 

log αCuSAx STANDARD 

DEVIATION 

CONDITIONS 

2 4.71a 0.03 

10 5.87a 0.09 

25 6.60a 0.07 

Seawater with Salinity of 1 

psu, pH 8.35 

2 3.85a 0.20 

25 5.83a 0.20 

Seawater with Salinity of 

35 psu, pH 8.35 

2 3.94b 0.02 

10 4.81b 0.01 0.4 ppm Calcium, pH 7.68 

25 5.19b 0.01 0.4 ppm Ca, pH 7.68 

25 5.16b 0.01 4 ppm Ca, pH 7.68 

25 4.75b 0.01 20 ppm Ca, pH 7.68 

References: 

a) (Campos and van den Berg 1994) 

b) (Monticelli, et al. 2004) 

Table 2.9: A list of copper speciation data determined in seawater in previous studies through CLE-ACSV 

and SA 

SITE [Cu2+diss] 

(nM) 

Log [Cu2+free] 

(M) 

Log KCuL1

(KCuL2) 

[L1] ([L2]) 

(nM) 

CONDITIONS 

Dumbarton Bridge 

(Jan 03)a

33.7 -14.0 13.9 (12.1) 48 (135) Salinity: 21.7 psu 

Dumbarton Bridge 

(Mar 03)a

27.0 -14.0 14.3 (12.6) 29 (78) Salinity: 22.8 psu 

Redwood Creek (Jan 3)a 26.4 -13.5 13.4 (12.7) 25 (75) Salinity: 22.9 psu 

Redwood Creek 

(Mar 03)a

25.0 -13.6 13.9 (12.9) 27 (66) Salinity: 23.6 psu 

San Bruno Shoals 

(Jan 03)a

22.9 -13.5 13.1 (12.2) 42 (85) Salinity: 22.9 psu 

San Bruno Shoals 

(Mar 03)a

27.1 -13.3 13.8 (12.3) 31 (64) Salinity: 25.6 psu 

Yerba Buena Island 

(Jan 03)a

18.9 -13.5 12.9 (12.1) 58 (75) Salinity: 21.1 psu 

Yerba Buena Island 

(Mar 03)a

17.9 -13.3 14.0 (12) 22 (48) Salinity: 26.4 psu 

San Pablo Bay (Jan 03)a 25.0 -14.5 14.0 (12.4) 68 (78) Salinity: 13.0 psu 

San Pablo Bay (Mar 

03)a

20.3 -13.8 13.6 (12.6) 45 (55) Salinity: 17.3 psu 

Grizzly Bay (Jan 03)a 27.7 -15.5 13.5 265 Salinity: 0.0 psu 

Grizzly Bay (Mar 03)a 49.6 -15.5 14.0 (12.8) 247 (133) Salinity: 0.5 psu 

Mediterraneanb 3.1 nr 13.3 10.8 Tested at 2 μM SA 

Atlanticb 1.55 nr 13.1 4.9 Tested at 2 μM SA 

References: 

nr: value not reported 

a) (Buck and Bruland 2005), all samples are seawater at pH 8.2 

b) (Campos and van den Berg 1994), all samples are seawater at pH 8.35

18 
Table 2.10: A list of copper speciation data determined in freshwater in previous studies through 

CLE-ACSV and SA 

SITE [Cu2+diss] 

(nM) 

Log [Cu2+free] 

(M) 

Log KCuL1

(KCuL2) 

[L1] ([L2]) 

(nM) 

CONDITIONS 

Rutor 1a 4.3 nr 12.5 10.2 Distance from source: 0 

km 

Rutor 2a 6.3 nr 12.9 9.1 0.50 km 

Rutor 3a 6.9 nr 12.7 11.6 0.96 km 

Rutor 4a 5.2 nr 12.4 16.0 1.64 km 

Rutor 5a 5.6 nr 12.9 11.9 2.56 km 

Rutor 6a 2.9 nr 12.5 9.6 4.04 km 

Rutor 7a 4.8 nr 12.5 13.4 7.67 km 

Rutor 8a 3.6 nr 12.5 15.7 10.06 km 

Rutor 9a 3.1 nr 12.7 10.6 14.03 km 

Redo (Nov 00)b 1.4 -15.1 14.0 20.2 

Redo (May 01)b 1.4 -14.8 13.9 13.3 

Redo (Sep 01)b 1.7 -14.8 13.8 20.8 

Altitude: 2,235m 

Ladove Pleso (Sep 00)b 1.2 -15.0 13.8 18.5 Altitude: 2,057m 

Ladove Pleso (Jul 01)b 1.0 -14.4 13.5 12.2 

Event #446c 11.1 nr 15.5 10.5 Mixed storm 

Event #478c 22.0 -13.8 14.0 35.2 Mixed storm 

Event #488c 80.1 -12.2 13.8 82.2 Continental storm 

Event #493c 23.5 -14.1 14.5 32.4 Continental storm 

Event #494c 10.3 nr nr nr Marine storm 

Event #496c 23.6 -15.3 15.8 31.1 Marine storm 

Event #534c 78.5 nr 14.0 64.5 Mixed storm 

Event #540c 11.4 -13.2 14.0 13.1 Mixed storm 

Event #553c 10.2 nr 14.0 6.5 Marine storm 

Event #554c 55.1 nr 13.9 30.4 Mixed storm 

Event #559c 2.1 -15.2 14.7 8.3 Continental storm 

Event #562c 49.8 nr 14.0 12.5 Mixed storm 

Event #563c 21.0 -14.5 15.5 23.1 Mixed storm 

Event #564c 26.0 -13.3 13.9 32.9 Mixed storm 

Event #567c 37.8 nr 14.7 34.6 Mixed storm 

Event #568c 23.0 nr nr nr Mixed storm 

Event #570c 1.4 -15.1 15.1 2.9 Mixed storm 

Event #572c 12.9 nr 15.0 9.2 Mixed storm 

Event #575c 7.0 nr nr nr Continental storm 

Event #577c 10.0 nr 16.1 6.2 Mixed storm 

Event #578c 4.4 nr 14.5 3.8 Marine storm 

Event #580c 3.4 -14.7 14.2 13.3 Mixed storm 

Event #582c 2.5 -14.6 14.1 9.7 Marine storm 

References: 

nr: value not reported 

a) (Monticelli, et al. 2004), all samples glacier water at pH 7.68 

b) (Plöger, et al. 2005), all samples from high altitude lakes at pH 7.8 

c) (Witt and Jickells 2005), all samples are rainwater at pH 7.8 

19 
2.6 STATISTICS 

Statistical tools are vital to analyzing and interpreting data sets. Improper use of statistics can 

lead to faulty interpretations of data. Some common statistical procedures, as well as their 

inherent assumptions and limits of inference are discussed in the following section. 

2.6.1 Inferences and Assumptions 

There are two primary types of statistical studies: randomized experiments and observational 

studies. In a randomized experiment, the researcher is able to randomly assign treatments to 

different groups in a study. An example of a randomized experiment would be a medical study 

where patients are randomly assigned to take two different types of medicine. In an 

observational study, the researcher has no control over the treatment of groups in a study. The 

present work is an example of an observational study – we have no control over how much 

rain/traffic/etc. is applied to the stormwater samples. This distinction is important in regards to 

the scope of inference. It is impossible to draw causal conclusions in an observational study 

from statistical analysis alone. There is always a possibility of confounding variables – variables 

that are associated to both group assignment and the outcome – affecting the measured outcome 

of a study. This study may only determine differences in groups and associations with 

explanatory variables. Significant outcomes may support, but cannot prove, causation (Ramsey 

and Schafer 2002). 

Using the t-tools to analyze data from different sample groupings requires meeting three 

assumptions: 

1. Samples must be drawn from normally-distributed populations. 

2. The standard deviations of the sample measurements from different groups must be 

equal. 

3. The observations within a sample group are independent, and the sample groups are 

independent of each other. 

Data transformations can help in achieving normal distributions and equal variance. 

Additionally, the t-tools are robust in detecting departures from normality, provided the sample 

size is large enough (Ramsey and Schafer 2002). Potentially most problematic of these 

assumptions in a stormwater study is that of independence. The samples should be independent 

from each other, as a departure from this would mean that a measured outcome (response 

variable) is dependent on another measured outcome and not solely on explanatory variables. 

There is a possibility of serial correlation of samples taken at minimal temporal differences. 

There is also a possibility for spatial correlation between samples. 

Linear regression is used when the groups in a study are not discrete, but continuously 

distributed over a range of values. The group becomes the explanatory variable, while the 

observation means become the response variable. As their name suggests, the explanatory 

variables aim to quantify the response variable. Simple linear regression is defined by a single 

explanatory variable, while multiple linear regression (MLR) is defined by two or more 

20 
explanatory variables predicting a single response variable. The assumptions necessary in a 

simple linear regression model are similar to those described for comparison between sample 

groups. They are: 

1. The plot of response means as a function of the explanatory variable is a straight line. 

2. The deviation of responses at different values of the explanatory variable is equal across 

all levels of the explanatory variable. 

3. The subsets of responses at different explanatory variable values are normally distributed. 

4. The response must be independent of other responses – its mean value may only be 

predicted as a function of explanatory variables. 

In terms of MLR, ideally, the variance of a response variable should be constant over all levels 

of predictor variables. 

2.6.2 Sample Comparison 

To determine the level of significance between sample types or locations, the means of the 

groups of interest must be compared. To do this, a test statistic must be quantified – in this case 

a difference between the mean values of each sample group. Hypothesis testing then addresses 

the question of the level of significance in the difference of the mean of the groups. A typical 

hypothesis test might be concerned with proving or disproving the null hypothesis that the 

difference in mean measurements between two groups is zero. This analysis produces a p-value, 

which is a probability value that measures the uncertainty associated with the measured test 

statistic. 

The t-tools are useful in terms of comparing samples. Using the t-tools requires computing a test 

statistic (t-statistic). The calculation of the t-statistic is shown in the equation below (Ramsey 

and Schafer 2002). 

2 1 2 1

2 1

( )[ (

( )

Y Y Hypothesized value for t statistic

SE Y Y

     )]   

In the above equation, Y2 and Y1 represent the sample means for groups 2 and 1, respectively; 

2 and 1 represent the true means for groups 2 and 1, respectively; and 2 1 SE Y Y (  ) is the 

standard error of the difference between the two sample means. The hypothesized value for 

2  1 is zero, when testing the null hypothesis of no difference between the means of the 

groups. 

In an observational study, a permutation distribution of the t-statistic is built to calculate the pvalue. This permutation distribution represents the measurement of the test statistic if the groups 

were randomized (and of a proper size) (Ramsey and Schafer 2002). The proportion of these 

randomized groupings that meet or exceed the observed test-statistic (i.e., the difference of 

means) yields a p-value. Since the “true mean” of any sample measurement can never be 

21 
quantified, and the actual measurements are only representative of that true mean, there can 

never be 100% certainty that there is a difference in the means of sample groups. For example, a 

p-value of 0.043 signifies a 4.3% possibility that the true mean of different groups is the same, 

and conversely, a 95.7% chance that there is a difference in the true means of the groups. A twosided t-test examines the possibility of a t-statistic being either higher or lower than the null 

hypothesis value (typically zero), while a one-sided t-test is only concerned with one of those 

possibilities. 

Confidence intervals expressing the difference in means are constructed in the general format 

expressed in the following equation below: 

2 1 2 1 () () Y Y M SE Y Y   

Here, 2 1 (Y Y )represents the estimate of difference between groups 2 and 1, 2 1 SE Y Y (  ) is the 

standard error of that estimate, and M is a multiplier. In the comparison between two samples, 

M is a percentile of the t-distribution based on the degrees of freedom and the prescribed 

confidence level. 

Multiple comparisons can be done on samples collected from many different groups. If 

comparisons between groups are planned prior to the study, the researcher should control for 

individual (pair-wise) confidence levels. If comparisons are unplanned, the researcher should 

control for an overall (family-wise) confidence level. In planned, pair-wise, comparisons, the 

above equation may be used with a t-value as the multiplier. Family-wise comparisons control 

for an overall confidence level for all comparisons (i.e., an overall confidence level that all 

comparisons between any two groups are significant). Family-wise comparisons use a variety of 

different procedures to determine the value for M , including Tukey-Kramer, Scheffe, and 

Neuman-Keuls, among others (Ramsey and Schafer 2002). 

2.6.3 Multiple Linear Regression (MLR) 

Simple linear regression models are of the form displayed in the equation below (Ramsey and 

Schafer 2002): 

0 1 {| } YX X    

The meaning of this equation is that the mean of the response variable, Y , can be predicted by a 

linear relationship with the explanatory (or predictor) variable, X . The two statistical 

parameters, 0 and 1, represent the intercept and slope of this model, respectively. 1 is the 

parameter of most concern, as it quantifies the relationship between the response and explanatory 

variables. MLR differs from simple linear regression by the inclusion of two or more 

explanatory variables, an example of an MLR model is shown below: 

1 2 0 11 2 2 { | , , ... , } ...  YXX X X X X n n        n

22 
In this equation, the parameters 1thru n quantify the relationship of explanatory variables 

thru with the response variable Y , respectively. Other forms of this equation are possible 

in MLR. For example, equations involving squared terms of an explanatory variable or 

multiplicative “interaction” terms of explanatory variables are also possible. Similar to sample 

comparison, t-statistics can be used to quantify statistical parameters (

X1 Xn

 -values). These  -

values are still associated with p-values measuring the confidence of a particular  being 

different from zero. 

2.6.4 Modeling 

Building an MLR model is helpful in determining the importance of explanatory variables. One 

common way to do this is by using a one-way analysis of variance (ANOVA) F-test. In this test, 

two models are compared: the full and reduced models. The full model contains more 

explanatory variables than the reduced model. The fitting of these two models to a response 

variable produces an F-statistic, as defined below: 

[ ( ) ( )] /[ ( ) (

( )/ ( )

res res

res

SS red SS full df red df full F

SS full df full

  )] 

The values are the residual sums of squares produced by a model. Large residual sum of 

squares measurements imply a high degree of variation between measured and modeled values. 

The degrees of freedom for each model ( ) refers to the number of statistical parameters 

(

res SS

df

 -values) subtracted from the number of observations. Notations for red and full refer to the 

reduced and full models being tested, respectively. Therefore, the number of degrees of freedom 

from the reduced model is always less than that of the full model. The F-statistic that is 

calcuated produces a p-value based on its specific F-distribution. A large F-statistic produces a 

small p-value. A small p-value implies a low probability that the statistical parameter(s) 

included in the full model, as opposed to the reduced model, are zero. Statistical parameters can 

be analyzed one-by-one with ANOVA. This is different from the t-test, which produces p-values 

for the inclusion of a parameter after all other parameters have been accounted for. 

Sometimes many different explanatory variables may have an unknown association to a 

particular response variable. In this situation, model variable selection can help determine key 

parameters in a model. A good MLR model should not be biased or overfit. A biased model 

does not contain enough predictor variables and therefore does not accurately account for effects 

of the explanatory variables in the model. An overfit model has problems of lack of power or 

precision (due to too few degrees of freedom), and multicollinearity – when two or more 

predictors have a linear relationship between each other. When two predictors have a linear 

relationship with each other, they are not simultaneously significant in determining the response 

variable. 

There are various statistics that can be calculated to compare models. R2

 and adjusted R2 can be 

used in model selection, but both of these statistics favor models with too many variables 

(Ramsey and Schafer 2002). More commonly used variable selection statistics include the Cp 

23 
statistic, Akaike’s Information Criterion (AIC), and Schwarz’s Bayesian Information Criterion 

(BIC). All of these statistics involve terms which account for the variability in the model, as 

well as “penalties” for models using too many predictors (Ramsey and Schafer 2002). 

There are various approaches to model variable selection. Stepwise regression involves 

selectively adding or subtracting explanatory variables until the model is optimized. Stepwise 

regression can be done in forwards, backwards, or both-way directions. Forwards regression 

begins with a minimum model and adds variables one-by-one until the model cannot be further 

improved by the addition of more variables (based on some specified statistic, like Cp). 

Backwards regression starts with a full model – one which contains all possible predictors – and 

removes predictors one-by-one until the model cannot be improved. Two-way stepwise 

regression selectively adds or subtracts explanatory variables from a specified starting point. 

Another approach is to use best subsets variable selection. All possible predictors in the model 

are combined in all possible ways and a model-fitting statistic is calculated (most commonly Cp) 

for each combination. The user then decides on the best fit model. 

Making inferences about coefficients found from the dataset used for variable selection should 

be avoided. Often in MLR, explanatory variables are correlated, and interpreting their regression 

coefficients can be difficult. The central of difficulty of interpreting regression coefficients in 

large models is that a single explanatory variable typically doesn’t change while all other 

explanatory variables stay constant – one predictor usually won’t change in isolation (Ramsey 

and Schafer 2002). Variable selection techniques are useful in situations where many 

explanatory variables exist, and quantifying the effect of each is less important than noting that 

there is an effect. 

2.7 CHEMICAL EQUILIBRIUM MODELS 

Chemical speciation calculations involve solving simultaneous sets of equilibrium and mass 

balance equations; such problems are tedious and difficult to solve by analytical or graphical 

procedures. Computer programs, such as Visual MINTEQ or MINEQL+, have been developed 

to rapidly solve aquatic equilibrium problems. Users supply information regarding the makeup 

of the aquatic system, and the appropriate set of equations is solved numerically within the 

model that contains a database of thermodynamic information. 

As stated previously, complexation reactions between organic ligands and cations are much more 

difficult to describe than those with inorganic ligands. Dissolved organic matter (DOM) consists 

of a collection of large organic molecules containing a variety of functional groups and 

conformations. Even the general characteristics of DOM in natural waters vary by region 

(Dobbs, et al. 1989). Therefore, accurately quantifying the ability of DOM to complex cations is 

difficult. 

There are two general approaches to modeling DOM-cation interactions: discrete ligand models 

and continuous distribution models. In the discrete ligand approach, a small number of ligands 

(usually five or fewer) are defined to represent binding sites on DOM (HydroGeoLogic and 

Allison Geoscience Consultants 1998). Most notable of the discrete ligand models are those 

developed by Tao (1992), Tipping and Hurley (1992), Westall (1995), and Gustafsson (2001). 

24 
Continuous distribution models express the heterogeneity of binding sites by correlating site 

abundance and binding affinity. The most commonly used continuous distribution models are 

the NICA-Donnan model (Benedetti, et al. 1995) and the Gaussian DOM model (Susetyo, et al. 

1991). A number of DOM models are available in the Visual MINTEQ program; these include: 

1) Gaussian Model 

2) Nonideal Competitive Adsorption (NICA)-Donnan Model 

3) Stockholm Humic Model (SHM) 

Gaussian DOM modeling assumes concentrations of individual ligands of a complex DOM 

mixture to be normally distributed with respect to their logK values (HydroGeoLogic and Allison 

Geoscience Consultants 1998). Site preference for specific metals varies from DOM to DOM, 

but each DOM molecule is assumed to have the same number of sites available (thus site 

distribution is split amongst metals) (Grimm, et al. 1991). Binding strengths of the different sites 

can be modeled as uni-, bi-, or tri-modal Gaussian distributions. Mathematically, the uni-modal 

Gaussian DOM model is described by the following equation: 

2

Log( ) exp 0.5 Log( ) 2

L M Mi

i M

C K

C d 

  

             

K

In this equation, is the concentration of binding sitei , is the total concentration of ligands, Ci CL

Mi K is the stability constant for a specific metal M binding to ligand site i ,  M is the mean of a

Log( i

ll 

KM ) values, and  is the standard deviation of the distribution. These parameters are 

visually presented in Figure 2.2. As the figure notes, at an arbitrarily high number of total 

binding sites ( ), is equal to (8σ)/imax (Susetyo, et al. 1991). ma i x d Log( ) KM

Figure 2.2: Gaussian distribution model and its important parameters (Susetyo, et al. 1991) 

25 
The NICA-Donnan model is a combination of the NICA cation-humic binding model and the 

Donnan nonspecific humic binding model (Kinniburgh, et al. 1996). The NICA model assumes 

that all sites are monodentate (one cation per site, irrelevant of charge), and that only two types 

of sites exist: carboxylic and phenolic (Benedetti, et al. 1995). The sites themselves are assumed 

to be continuous (Benedetti, et al. 1995). Non-ideality is accounted for on an ion-specific basis 

(Benedetti, et al. 1995). The Donnan model is included to account for electrostatic effects by 

assuming that humic material can be modeled as electrically neutral with a specific volume and 

average electrostatic potential called the Donnan potential (Kinniburgh, et al. 1996). Ionic 

strength has been shown to affect Cu-humic binding, but the magnitude of the effect may be due 

more to differing types of DOM (fulvic acid from Suwannee River, purified peat humic acid, 

etc.) (Kinniburgh, et al. 1996). 

Unlike the NICA-Donnan and Gaussian models, the SHM is based on discrete metal/proton 

binding sites (Gustafsson 2001). Additionally, cations can form bidentate as well as 

monodentate complexes. SHM is primarily an empirically based model that assumes the NOM 

are impermeable spheres. The Basic Stern Model (BSM) is utilized to correct for electrostatic 

effects. Both SHM and NICA-Donnan model showed little effect of ionic strength on copper 

speciation and an underestimation of Cu2+/H+

 exchange stoichiometry (Gustafsson 2001; 

Kinniburgh, et al. 1996). 

26 
3.0 METHODS 

3.1 STORMWATER SAMPLING 

Samples analyzed for this study included a variety of samples collected by the OSU 

Environmental Engineering lab, as well as composite samples collected and sent to OSU by 

Herrera Environmental Consultants. While the present study focuses on copper speciation in 

highway runoff, Herrera was working on another ODOT-funded study to characterize highway 

runoff water quality. Grab, first flush, and flow-weighted samples were collected at the Dixon 

Outfall site in Corvallis, Oregon from October 2008 through October 2009; and flow-weighted 

composite samples were collected and sent to the OSU lab by Herrera Environmental 

Consultants from March 2008 through May 2009. These composite samples arrived from sites 

in Portland, Wemme, and Bend, Oregon. These three sites are henceforth referred to as the 

“Herrera sites”. All aerial views of the sampling sites shown in the Site Descriptions section 

were found using Google Maps (Google 2009). The annotated stars indicate the approximate 

sampling location. 

3.1.1 Site Descriptions 

3.1.1.1 Dixon Outfall 

Dixon Outfall is located off of Highway 20 just north of downtown Corvallis (mile point 

0.75). The annual average daily traffic (AADT) of this site is approximately 8,000 

(ODOT 2008). Three storm drains, located on the southeast curb of Highway 20, feed a 

12” concrete outfall pipe which drains to Dixon Creek just upstream of where it 

discharges to the Willamette River. The approximate street area feeding this site is 451 

m

2

 (4,850 ft2 or 0.11 acres). Precipitation measurements were taken on site with an ISCO 

674 rain gauge. Figure 3.1 displays an aerial view of the area surrounding the Dixon 

Outfall site. The immediate vicinity of the site is residential, though the Corvallis 

Wastewater Treatment Plant is located nearby. 

27 
Figure 3.1: Dixon site aerial view 

3.1.1.2 I-5 Convention Center 

This site is located in Portland in a manhole in the Oregon Convention Center Exhibitor 

Parking area just west of NE 1st Avenue. The site has an AADT of approximately 

130,000 and drains directly to the Willamette River through a 36” concrete pipe. The 

drainage area is approximately 9.4 hectares (23.1 acres) and covers a 1.55 km (0.96-mile) 

stretch of the I-5 corridor. Precipitation measurements were taken approximately 0.5 

miles from the site at the Portland Fire Bureau Rain (55 SW Ash Street) (Herrera 

Environmental Consultants 2008-2009). Figure 3.2 displays an aerial view of the area 

surrounding the Portland/I-5 site and emphasizes the urban nature of this site in 

comparison to the other sampling sites. 

28 
Figure 3.2: Portland/I-5 site aerial view 

3.1.1.3 Bend 

The Bend site is located on the north side of Highway 20, about 1.2 miles east of the 

Highway 20/Highway 97 intersection. The AADT of this site is approximately 24,000. 

A detention pond collects the roadway runoff via an 18” corrugated plastic pipe. The 

drainage area is approximately 0.6 hectares (1.4 acres) and covers a 0.19 km (0.12-mile) 

stretch of Highway 20. Precipitation measurements were obtained from a rain gauge 

owned by the City of Bend located across the street from the site. Figure 3.3 displays an 

aerial view of the area surrounding the Bend sampling site. As can be seen in this figure, 

the Bend site is not rural in the strictest sense. The highway AADT and the proximity to 

large areas of pervious, natural surfaces are both consistent with it being classified as 

non-urban. 

29 
Figure 3.3: Bend site aerial view 

3.1.1.4 Wemme 

The Wemme site is an outfall located approximately 0.45 miles northwest of the Welches 

Road interchange north of Highway 26. The AADT of this site is approximately 12,000. 

The outfall is a 24” diameter, tar-lined corrugated steel pipe that drains to the Sandy 

River through a grass field. The drainage area is approximately 8.7 hectares (21.5 acres) 

and covers 1.36 miles of Highway 26. Precipitation measurements were taken from the 

Welches rain gauge (CW 6318), part of the Citizen Weather Observer Program (CWOP). 

Figure 3.4 displays an aerial view of the area surrounding the Wemme site. The 

immediate vicinity is forested and distant from any urban centers. 

30 
Figure 3.4: Wemme site aerial view 

3.1.2 Sampling Criteria 

Storm events had to meet certain criteria for the data to be considered valid. These preset criteria 

differed between the Dixon Outfall site and the Herrera sites. At Dixon Outfall, a storm event 

was considered valid if there was over 0.1” of rain occurring over a 72-hour period preceded by a 

72-hour dry period receiving less than 0.1” of rain (USEPA 1992). Typically, the rainfall 

accumulated in periods of less than 24 hours. Forecasts and expected rainfall amounts were 

taken from the National Weather Service website (www.weather.gov). Antecedent dry periods 

were calculated from data at the MAR754 weather station provided by APRSWXNET and found 

at the Weather Underground website (www.wunderground.com). 

At the Herrera sites, a storm event was considered valid if 0.15” of rain occurred over a 24-hour 

period preceded by a dry period receiving less than 0.04” of precipitation over six hours. The 

Herrera samples also specified a minimum storm duration of one hour and defined the end of a 

storm as a six-hour period receiving less than 0.04” of precipitation following the event. 

3.1.3 Sampling Equipment/Setup 

At all sites, flow-weighted samples were collected. All three Herrera sites had similar sampling 

setups. A Sigma 950 Bubbler Flow Meter measured water level in a pipe (converted to flow 

with Manning’s Equation) and triggered a portable autosampler (Sigma 800 at I-5 and Bend, 

31 
Sigma SD900 at Wemme) to take 100 ml sub-samples at a preset flow increment into one 

composite sample. A 3/8” inner diameter Teflon tube was used for the intake line. Figure 3.5, 

Figure 3.6, and Figure 3.7 show the in-pipe installations of the intake and bubbler tubes for the 

Portland, Wemme, and Bend sampling sites, respectively (pictures courtesy of Herrera). 

Figure 3.5: Portland/I-5 site pipe installation 

Figure 3.6: Wemme site pipe installation 

32 
Figure 3.7: Bend site pipe installation 

At the Dixon Outfall site, an ISCO 6712 Autosampler was used to collect samples into 24 

separate bottles. An ISCO 730 Bubbler module and ISCO 674 Rain Gauge were both connected 

to the Autosampler. The bubbler reported water levels to the nearest 0.001 ft and the rain gauge 

recorded every 0.01” of rain. The intake line was a 25-foot, 3/8” inner diameter Teflon tube. 

The intake line was connected to a small piece of tygon tubing equipped with a Teflon-coated 

strainer on the end to collect samples. A 12” Thel-mar V-notch weir was placed at the end of the 

pipe to create submerged conditions. These conditions allowed for accurate level measurement 

and created a sufficient stormwater volume from which to collect samples. This installation is 

shown in Figure 3.8. 

33 
Figure 3.8: Dixon Outfall site pipe installation 

Measured flow levels were converted to flowrates using the V-Notch Weir equation (Walkowiak 

2008). This was programmed into the sampler with an 8-point calibration curve, as it was shown 

to give more reliable results than the manufacturer-programmed V-notch weir equation. Data for 

level, precipitation, and sampling events was typically recorded in 1-minute data intervals. ISCO 

Flowlink (v. 5.1) software was used to upload the data to a computer. Storm data was analyzed 

and graphed using Microsoft Excel. 

3.1.4 Field Sampling Procedures 

3.1.4.1 Dixon Outfall 

Prior to sample collection at the Dixon Outfall site, the Teflon intake tubing was rinsed 

successively with acid and distilled and deionized (DDI) water (Barnstead NANOpure II 

system). 2 L of 10% HCl were drained through the tube followed by 2 L of 10% HNO3. 

Finally, the tube was rinsed out with at least 2 L of DDI water before being taken into the 

field. Sample intake volume calibration was performed in the field with laboratory grade 

deionized (DI) water. The bubbler level was calibrated at zero in the field also using DI 

water. 

The sampling program consisted of two parts: an initial level-triggered program, and a 

flow-dependent program. The first part of the program was designed to take one 950-mL 

sample at the first level measured above 0.12”. This part of the program collected the 

34 
“first flush” samples from the storms. The second part of the program was based only on 

flow-pacing and initiated directly after the first part completed. This part of the program 

produced discrete 950-mL samples at points throughout the storm after a certain volume 

of flow passed by. Flow pacing for this sampling was determined based on the expected 

rainfall and the desired number of bottles to be filled – i.e., larger storms would be paced 

at larger flow volumes. 

Sampling bottles were uncapped with gloved hands directly prior to sampler setup. The 

caps were stored in a new re-sealable plastic zipper bag until they were needed to recap 

the bottles. The central cavity in the autosampler was filled with ice to keep the samples 

cool. After sample collection, the sample bottles were recapped with gloved hands 

immediately after the sampling program was stopped. Field blanks – consisting of DDI 

water sampled through the Teflon intake tube and into an open container inside the 

sample – were taken for some storms. 

3.1.4.2 Herrera Sites 

At the Herrera sites, autosamplers and other field equipment were installed semipermanantly, as opposed to the Dixon Creek installation, which was set up for each 

sampling event. Immediately following equipment installation, field personnel calibrated 

sample aliquot volumes. One week after the field equipment was installed field 

personnel visited each site to confirm that it was installed correctly and functioning as 

designed. The field equipment was checked routinely to gather data, replace batteries 

(when necessary), visually inspect system components, and perform calibration checks as 

necessary (Herrera Environmental Consultants 2008). 

Once a potential storm event was targeted for sampling, field personnel visited each 

station to verify the proper function of the autosampler, install a clean 15.2-liter 

polyethylene carboy, add crushed ice surrounding the carboy, and begin the sampling 

program. Flow-pacing for the composite sample was determined by plotting projected 

rainfall totals on a rainfall-runoff rating curve, generated for each monitoring site after a 

sufficient amount of data was collected. After each targeted storm event, field personnel 

returned to the sampling site to verify the proper function of the sampling equipment and 

upload the sample collection data from the automated samplers to a laptop computer or a 

proprietary data transfer device. Carboys were then removed from the autosampler, 

shaken to homogenize the sample, and divided into laboratory bottles. 

3.2 LABORATORY METHODS 

The goals for this project included quantifying copper speciation and examining correlations for 

copper and copper speciation with other water quality parameters, storm characteristics, and site 

characteristics. In order to accomplish these objectives, a variety of analyses were performed on 

each sample. The flowchart shown in Figure 3.9 displays the parameters measured and the 

aliquot volumes needed for each determination. The first step in this process was separating the 

sample into ‘total’ and ‘dissolved’ subsets. Subsequently, measurements were made for total 

suspended solids (TSS), pH, conductivity, alkalinity, cations (both major cations and metals), 

35 
anions, total dissolved solids (TDS), dissolved organic carbon (DOC), and Cu2+

free (speciation). 

These procedures are detailed in sections 3.2.1 through 3.2.10. 

Figure 3.9: Sample processing flowsheet 

3.2.1 Sample Separation 

Samples collected for this study included discrete samples collected at the Dixon Outfall site in 

Corvallis and flow-weighted composite samples collected and sent to the OSU lab by Herrera 

Environmental Consultants. These composite samples arrived from sites in Portland, Wemme, 

and Bend; these sites are henceforth referred to as the “Herrera sites.” All sampling sites are 

detailed in Section 3.1.1. Samples received from the Portland and Wemme sites were separated 

from the bulk sample at Herrera’s Portland office. Herrera engineers first obtained enough of the 

36 
stormwater samples to analyze for their stormwater characterization study prior to pouring off an 

aliquot for our analysis. This separation typically occurred within one hour of sample collection 

at the Portland site and two hours for the Wemme site. Samples collected from the Bend site 

were separated within two hours of initial collection. The Herrera composite samples were 

delivered to the OSU lab in a cooler with ice through the mail within 24 hours of initial 

collection. Samples from Dixon Outfall were taken directly from the field to the OSU lab after 

collection. All samples were processed within 24 hours of receipt in the lab. 

Clean containers and instruments were used for each step in sample analysis. Labware cleaning 

procedures are detailed in Section 3.2.2. Composite samples from Dixon Outfall were formed 

from discrete flow-weighted samples in the lab. Individual flow-weighted samples were 

thoroughly mixed and an aliquot was drawn out and pipetted into a new, clean container for the 

composite sample. The final volume of the composite sample was 800-1000 mL. The volume of 

the aliquots varied based on the number of flow-weighted samples taken for a specific storm. 

Snub-nosed volumetric pipets (to avoid excluding solids) were used to draw these aliquots. The 

pipet(s) were rinsed thoroughly with DDI water between uses. 

Upon arrival, raw samples were immediately analyzed for total suspended solids (see Section 

3.2.3). Approximately 60 mL of the raw sample was taken for analysis of total cations. In this 

work, “total” cations refers to the concentration of cations released after an acid addition of 1% 

(v/v) ultrapure HNO3 (Aristar Ultra). The acid treatment facilitates mobilization of any cations 

in solid form (adsorbed or pure solid) and dropped the pH below 2. This represents a total 

concentration of potentially environmentally available cations. These samples equilibrated in a 

covered beaker for over an hour. 

The aliquots for both the dissolved and total samples were vacuum-filtered with 0.45 μm filters 

(Pall Supor 450). Filters were stored in 1% ultrapure HNO3 for at least one day prior to use. 

Filters were rinsed with 250 mL of DDI water before use. The filtrate was collected in 500- or 

250- mL vacuum flasks. 

After filtering, the bulk dissolved samples were stored in 1000- or 500-mL HDPE containers. 

Aliquots for dissolved cations and anions were transferred into appropriately sized containers, 

acidified (1% ultrapure HNO3 addition), and stored in the dark at 4 °C until analysis. Aliquots 

for determination of DOC were transferred into appropriately prepared glass DOC bottles. Once 

the alkalinity was measured for the dissolved sample, the remainder was stored in a freezer (-20o 

C) awaiting speciation testing. Aliquots for total cations were transferred directly from the 

vacuum flasks to appropriately sized containers. 

3.2.2 Cleaning Procedures 

3.2.2.1 General Containers/Labware 

Majority of labware was cleaned in successive acid baths and DDI water. Labware 

cleaned in this process includes glassware, HDPE containers (including autosampler 

bottles), and Teflon bottles (used for copper speciation measurements). This cleaning 

procedure is detailed below: 

37 
1) Particulates were removed from labware by rinsing and scrubbing with DI water. 

2) Labware was submerged in a 10% (v/v) HCl bath for at least 18 hours. 

3) Labware was taken out of the HCl bath and put into a 10% HNO3 bath for at least 

18 hours. Both acid baths were made with reagent grade acid and changed out 

every four months. 

4) Upon removal from the HNO3 bath, labware was rinsed off with DI water and 

submerged into a DDI bath for at least 30 minutes. 

5) Each piece of labware was rinsed out three times with DDI water. Where 

applicable, containers were immediately capped after being rinsed out. 

Otherwise, labware was allowed to dry upside-down and subsequently covered 

with Parafilm. 

3.2.2.2 Organic Carbon Bottles 

After use, organic carbon bottles were rinsed with DI water. The bottles were then 

soaked in a 10% HCl bath for at least 24 hours. Upon removal from the acid bath, the 

bottles were rinsed thoroughly with DI water and allowed to dry. Finally, the bottles 

were ashed at 550oC in a muffle furnace for at least two hours and subsequently stored in 

a closed container. 

3.2.2.3 Organic Carbon Septum Caps 

After use, the organic carbon bottle septum caps were rinsed twice with DDI water and 

stored in a re-sealable plastic zipper bag filled with DDI water for at least 24 hours. At 

the end of this time, they were rinsed three more times with DDI water and allowed to 

dry. They were then stored in a re-sealable plastic zipper bag. 

3.2.2.4 Voltammetric Teflon Cup 

The Teflon cup used in speciation measurements was filled with 1% Ultrapure HNO3

until needed. Prior to use in voltammetric determinations, the cup was emptied and 

rinsed thoroughly with DDI water. 

3.2.3 Total Suspended Solids (TSS) 

TSS was measured in accordance with Standard Method 2540D (APHA, et al. 2005). Typically, 

aliquots of 80-110 mL of the sample were used in this determination. TSS determinations were 

performed in triplicate. 

38 
3.2.4 pH and Conductivity 

75-90 mL of dissolved sample was used to measure initial pH, conductivity and alkalinity. 

Initial pH was measured with a VWR sympHony probe and an Accumet AR50 control panel. 

The pH probe was calibrated daily with pH buffers of 4, 7, and 10 (BDH General). Conductivity 

was measured on the same control panel with an Accumet conductivity probe. Conductivity was 

standardized with a 970 μS/cm solution. Typically, pH and conductivity determinations were 

done in duplicate. 

3.2.5 Alkalinity 

The same 75-90 mL aliquot of dissolved sample used for pH and conductivity measurements was 

used for alkalinity determination. Alkalinity was measured with a Gran Titration using 0.01 M 

(0.02 N) sulfuric acid. The theory behind this method is described in Stumm and Morgan 

(1996). 

3.2.6 Cations 

Total and dissolved cation concentrations were analyzed with Inductively Coupled Atomic 

Emission Spectrometry (ICP-AES) (Standard Method 3120), and either Inductively Coupled 

Plasma Mass Spectrometry (ICP-MS) (Standard Method 3125) or Inductively Coupled Plasma 

Optical Emission Spectrometry (ICP-OES). All cation concentrations were measured within 6 

months of sample collection. ICP-AES was used to quantify Ca, Mg, Fe, Pb, Cd, Cu, Zn, K, Ni, 

Na at levels above 50 ppb. 10 mL of sample was required for each ICP-AES measurement and 

samples were run in triplicate. Samples were stored in HDPE or glass containers. ICP-MS was 

used for Pb, Cd, Cu, Zn, and Ni below the 50 ppb concentration. ICP-OES was used for Fe, Cu, 

Pb, Cd, Ni, and Zn between the 1 ppb (higher for Pb, Ni, and Cd) and 200 ppb levels. 5 mL of 

sample was required for either ICP-MS or -OES and samples were run in triplicate. Samples 

measured on the ICP-MS or -OES were stored and capped in single-use BD Falcon 

polypropylene conical test tubes. 

Standards were made from single element ICP Standards (BDH; Aristar). For the ICP-MS, an 

internal standard was added to each sample (2 ppb Indium). DDI blanks with a 1% ultrapure 

HNO3 addition were used with ICP-MS, -OES, and -AES. Multiple-point calibration curves for 

cation measurements were constructed from known standards at the concentrations listed in 

Table 3.1. 

 

Table 3.1: Concentrations used for cation calibration curves 

TEST METHOD UNITS CONCENTRATIONS 

ICP-AES mg/L (ppm) 0.05, 0.08, 0.16, 0.50, 1.0, 5.0 

ICP-OES μg/L (ppb) 1.0, 5.0, 10, 20, 50, 100, 200 

ICP-MS μg/L (ppb) 0.01, 0.05, 0.20, 0.50, 1.0, 10, 50 

For ICP-AES measurements, a 15 mg/L standard for Ca, Mg, and Na was also used in 

calibration. Due to some curvature in intensity versus concentration plots over the entire range 

of concentrations measured, high- and low-range calibration curves were used for each element. 

39 
Low-range calibrations measured values up to and including 0.5 mg/L, while high-range 

calibrations measured at 0.5 mg/L and above. At minimum, a blank and check standard were 

tested every 50 samples (including standards). For some tests, the entire set of standards was remeasured at the end of a sample run. Hardness was calculated from the measured Ca and Mg 

concentrations in accordance with Standard Method 2340 (APHA, et al. 2005). 

For ICP-OES measurements, a 50 ppb check standard was measured every 17 samples. For ICPMS measurements, the entire calibration curve was re-measured at the end of a sample run. 

Method blanks consisted of DDI water that underwent the filtration process. 

3.2.7 Anions 

Anions were measured following Standard Method 4110 (APHA, et al. 2005). 2 mL of the 

dissolved samples were stored in small HDPE containers and refrigerated prior to anion 

determinations. These samples were analyzed for nitrate, nitrite, sulfate, phosphate, and chloride 

(the major anions in stormwater) using a Dionex Ion Chromatograph (DX500). Each sample 

was measured as a 10x dilution and at full strength in order to elucidate any peak area 

suppression that occurred. All measurements were performed in triplicate. A set of standards 

made from dry chemical stock were measured alongside the samples. The standards ranged from 

50 ppb to 20 ppm. Due to the large range of concentrations, the standard curves were separated 

into two sets – a high set used to determine concentrations from 1 ppm to 20 ppm, and a low set 

covering 50 ppb to 1 ppm. Blanks consisted of DDI water and method blanks consisted of DDI 

carried through the filtering procedure. 

3.2.8 Total Dissolved Solids (TDS) 

Total Dissolved Solids (TDS) were calculated from dissolved anion and cation concentrations. 

The mass concentration of each constituent (anion and cation) was reduced to a molar 

concentration and then summed. Equation (3-1) below shows the general formula used. Here, 

Ci and Ai represent individual cation and anion species, respectively, while x represents the 

charge on the species. 

i

x x

i

i i

TDS C A             (3-1) 

3.2.9 Dissolved Organic Carbon (DOC) 

Dissolved organic carbon (DOC) determinations were made on a Shimadzu TOC-VCSH total 

organic carbon analyzer at Oregon State’s Institute for Water and Watersheds Collaboratory 

following Standard Method 5310 (APHA, et al. 2005). Samples were tested for organic carbon 

within a week of their receipt in the lab. Samples were stored in the dark at 4°C prior to analysis. 

Typically, samples were run at a 5X or 10X dilution with DI water from the Collaboratory. 

Calibration curves were constructed from standards of 0, 0.2, 0.5, 1.0, 2.0, and 5.0 mg/L of 

organic carbon. Standards were made at the Collaboratory with potassium hydrogen phthalate as 

the source of organic carbon. 

40 
3.2.10 Copper Speciation Measurements (CLE-ACSV) 

An alteration of the CLE-ACSV method described in Campos and van den Berg (1994) was used 

for determination of  CuSAx . A modified version of CLE-ACSV method from Buck and Bruland 

(2005) was utilized for copper speciation. Important differences in the CLE-ACSV methodology 

developed for seawater and the methodology developed in this study for stormwater are the 

selection of pH and ionic strength. 

For both  CuSAx determinations and copper speciation experiments the pH was adjusted to 6.80 to 

reflect ambient pH for stormwater in this study (6.78±0.3). This change was made in order that 

ligand concentrations, associated stability constants, and the Cu2+

free concentrations would be as 

similar as possible to actual values while still allowing for comparison between the sites.  CuSAx

values are specific to pH and ionic strength and therefore a constant pH (and ionic strength) was 

important to maintain across samples. 

The dependency of  CuSAx values on ionic strength required an ionic strength adjustment for the 

stormwater samples to a (quasi) constant value to compensate for the highly variable ion 

concentrations from storm to storm and site to site. A minimum ionic strength value of 0.05 M 

was chosen to mitigate varied ionic strengths ranging from 0.003 to 0.016 M. A second result of 

increasing ionic strength is an enhancement to the sensitivity of the method. This adjustment 

boosted the sensitivity in stormwater grab samples in preliminary studies by ~3 times. 

3.2.10.1 Reagents 

All samples tested with CLE-ACSV were buffered to pH 6.8 with a 1M PIPES 

(Piperazine-N,N'-bis(2-ethanesulfonic acid)) standard made with PIPES (J.T. Baker) in 

DDI. Minor pH adjustments were made with ultrapure NH4OH (BDH Aristar Ultra) and 

HNO3 (BDH Aristar Ultra) diluted in DDI. Ionic strength adjustments were done with a 

5 M NaCl standard made with ultrapure NaCl (Alfa Aesar Puratronic) in DDI. Copper 

and Calcium standards were made from ICP standards (BDH Aristar Plus, EMD 

Certipur) diluted with DDI where necessary. 1 mM ethylenediaminieteraacetic acid 

(EDTA) standards were made from Na2EDTA (J.T. Baker) in DDI; 50 and 500 μM 

EDTA standards were made from subsequent dilutions with DDI. 10 mM SA standards 

were made with Salicylaldoxime (Alfa Aesar) in 100% Methanol (BDH); 1 mM SA 

standards were made by dilution with DDI and changed biweekly. 

3.2.10.2 Determination of  CuSAx

 CuSAx was determined by calibration through titration with a known ligand. Identical to 

Campos and van den Berg (1994), ethylenediaminetetraacetic acid (EDTA) was selected 

as the known ligand of choice. A bulk solution was made containing DDI, 100 nM of 

copper, 5 mM PIPES, varying calcium concentrations (1, 10, 50 ppm) and enough NaCl 

to adjust the ionic strength to 0.05 M. Clean Teflon bottles were conditioned by leaving 

10 mL aliquots of this solution to soak for 30 minutes. After conditioning, a second 10 

mL aliquot of the solution was placed in each conditioned bottle with a variable addition 

41 
of EDTA (0 to 6 μM) and allowed to equilibrate overnight. SA was added 15 minutes 

before each sample was analyzed in a Teflon voltammetric cup that had been previously 

conditioned with SA.  CuSAx was determined using the following equation (Campos and 

van den Berg 1994): 

  ' '

1

  C EDTA u Cu Cu

CuSAx

X

X





          (3-2) 

Where,  Cu '

is the alpha coefficient for inorganic complexes, described in full by 

Equation (3-3);  CuEDTA is the alpha coefficient for the copper-EDTA complex, described 

by Equation (3-4); and X is the ratio describing the reduction in peak current, , in the 

presence of EDTA described by Equation 

pi

(3-5) (Campos and van den Berg 1994). 

       

    















  





i

n

free

free CuOH

n

CuX X i n ' Cu

H

Cu

Cu n

2

2

'   '1 ' (3-3) 

 CuEDTA  K EDT CuED   TA  A  (3-4) 

Where is the conditional stability constant for the copper-EDTA complex for a 

specific pH and ionic strength; 

KCuEDTA 

EDTA represents the concentration of EDTA that is not 

bound by copper.  CuEDTA was determined in Visual MINTEQ by iterating  CuSAx values 

until a best fit between experimentally measured and modeled peak ratios was reached. 

Previous studies assumed     EDTA  EDTA T  , but this was not a reasonable assumption 

because the  T

EDTA used in these titrations was on the same order of magnitude as the 

copper additions. 

 

 

2

2 0

S Cu SA

S Cu SA

 

,0

p

p

i

X

i

    (3-5)    

Where   , 2 S Cu SA      2 0

 SA  

 S Cu represent the peak response of the solution with and 

without EDTA, respectively. 

 CuSAx was calculated from the peak ratio range of 0.1 to 0.8. This range eliminated any 

potential problems in distinguishing the signal from background noise at a low peak ratio 

(high EDTA concentration). Applying this criterion to all determinations provided 

consistency as well. 

42 
3.2.10.3 Total Dissolved Copper 

Dissolved copper concentrations,   2 Cudiss , for copper speciation were measured with ICPOES/MS. Previous studies have used UV digested water and CLE-ACSV to determine 

ambient copper concentrations. Preliminary tests in this study using CLE-ACSV on UV 

digested stormwater resulted in under estimations of Cu2+

diss concentrations in 

comparison to ICP measurements. The source of this difference was most likely 

refractory organics complexing copper. 

3.2.10.4 Sample Speciation 

Typically, 130 mL of each dissolved sample was set aside for copper speciation and 

placed in clean Teflon bottles in 10 mL aliquots. Teflon bottles were allowed to 

equilibrate with copper additions (0 to 600 nM), NaCl, and 5 mM of PIPES for two 

hours. SA additions (resulting in 2 or 10 μM final concentrations) were made 15 minutes 

before being transferred to a Teflon voltametric cup and analyzed. 

The first (of two) samples containing no added copper were used to condition the 

voltametric cup and electrodes. The Teflon cup and instrument were also conditioned 

(warmed-up) with a solution containing 10 or 25 μM of SA and 5 mM PIPES until a 

baseline response of 1-3 nA was reached. All Teflon bottles used for speciation tests 

were conditioned for 30 minutes with 10 mL of DDI and an addition of copper equivalent 

to test conditions. 

Samples under analysis were subject to the following steps: (1) 5 min nitrogen purging 

step to remove all oxygen while stirring at 600 rpm; (2) 10 second quiescent 

equilibration; and (3) a potential of −0.15 V was applied for 1 min (while stirring) to aid 

adsorption of CuSA2 onto the mercury drop. Finally, the potential was scanned from 

−0.00 V to −0.60 V and a current proportional to the concentration of reduced Cu2+ (to 

Cu0

) was produced. The peak current that centered about −0.14 V was plotted against 

added copper concentration for each sample in the titration. 

The information from the titration was used to solve Equation (2-5). For clarity purposes 

Equation (2-5) is simplified and reproduced here: 

          

diss CuCu free CuL CuXi CuSAx

2 2 (2-5)

 2 Cudiss 



 represents the entirety copper in the system, including added copper in the 

titration.  represents the free ionic copper in the titration test (distinctly different 

from free ionic copper in the sample at normal conditions). 

2 Cu free

CuL is the concentration of 

copper bound to organic ligands in the system. CuXi represents the entirety of copper 

bound to inorganic ligands in the system. CuSAx  represents the amount of copper 

bound to SA. CuSAx  was determined with Equation (3-6); where is the instrument pi

43 
response (current) and is the sample sensitivity. The sensitivity is equal to the linear 

portion of the titration plot and is described in more detail in Section 

S

4.2.3.1. 

  p

x

i

CuSA

S  (3-6) 

 2 Cu free  was calculated with Equation (3-7). 

   

CuSAx Cu

x

free

CuSA (3-7) 

  2 Cu

CuXi was calculated with Equation (3-8). 

       2 1 i  Cu Cu free CuX (3-8) 

CuL was calculated with Equation (2-5). 

3.2.10.5 CLE-ACSV Instrument Settings 

Table 3.2 lists the settings for the VA 797 Computrace from Metrohm for all CLE-ACSV 

tests. 

Table 3.2: VA 797 Instrument settings 

PARAMETER VALUE UNITS 

Drop Settings 

Drop Size 9 -- 

Drop Method HMDE -- 

Deposition Settings

Deposition Potential -0.15 volts (V) 

Deposition Time 60 seconds (s) 

Equilibration Time 10 s 

Sweep Settings 

Start Potential -0.00 V 

End Potential -0.60 V 

Pulse Amplitude 0.05 V 

Pulse Time 0.035 s 

Voltage Step 0.004 V 

Voltage Step Time 0.2 s 

Sweep Rate 0.02 V/s 

Other Settings 

Stir Rate 600 rpm 

44 
3.2.10.6 Speciation Calculations for Stormwater 

Ligand concentrations,LT , and conditional stability constants, , were determined 

through non-linear least squares fits of Equation 

KCuL '

(3-9). This equation is a version of the 

Langmuir equation (Gerringa, et al. 1995). 

    

  



  2

2

'1

'

CuL free

CuL free

T CuK

CuK

LCuL (3-9) 

For       2

T CuL tot , free ionic copper in stormwater samples was calculated iteratively 

with Equation (3-10). This equation was derived through solving Equation (3-11) for 

 free

2 Cu . 

   

 

  











' 2

2

2

'1

'

CuL free

TCuL

Cu

diss

free

CuK

LK

Cu Cu



(3-10) 

 

  ' ' Cu2 L

CuL K

free

CuL   (3-11) 

3.3 STATISTICS 

The S-Plus® 8.0 statistical software was used for all statistical analyses. A 95% confidence level 

was used for all analyses. Therefore, for significance to be shown, the p-value of a test should be 

less than 0.05. 

Comparisons between sample groupings were made based on composite samples from different 

sites, first flush and composite samples from Dixon Outfall, and inter-site differences in 

speciation results. These comparisons were planned, so individual confidence levels are 

acceptable. Differences between sites were quantified by Fisher’s Least Significant Difference 

(LSD) multiple comparison procedure. This procedure uses an F-test to first determine if any 

differences between any two sites are significant. If the p-value from this F-test is large (here, 

>0.05), no individual significant differences are determined. If the p-value is <0.05, the 

procedure continues by evaluating pair-wise differences using the t-tools. 

Inferences on first flush samples were made with a simple linear regression model involving only 

a first flush indicator variable. An indicator variable is a binary term that indicates the group 

association of a particular measurement. Indicator variables are 1 when their specific association 

is met, and 0 in all other cases. In this case, the indicator was one for first flush samples and zero 

otherwise. Site indicator variables were also used in the MLR analysis examining the effects of 

45 
hydrologic parameters on Cu2+

diss in composite samples. The results of a simple linear regression 

using a single indicator variable are identical to a two-sample t-test. 

Model variable selection was done using backwards stepwise regression. Site indicator variables 

were used in the analysis of hydrologic parameters. S-Plus uses the Cp statistic as a measure of 

goodness-of-fit (Insightful Corporation 2007) in stepwise regression. The Cp statistic is shown 

in the equation below. 

  

2

22

full

full

p pnpC







In this equation, is the number of regression coefficients, is the sample size, is the 

residual mean square of the model being considered, and is the residual mean square of the 

full model. 

p n

full

2 

2 

3.4 VISUAL MINTEQ MODEL PARAMETERS 

Visual MINTEQ, version 2.5.2 was used to model chemical equilibrium speciation. This version 

of the software is based on MINTEQA2 version 4.0. This software uses activity corrections 

based on the Davies equation. Unimodal Gaussian DOM distributions were used to model DOM 

interactions in Visual MINTEQ. This was done to match previous work done by Dean, et al. 

(2005) examining metal speciation in stormwater. This model only examines the carboxylic 

binding sites. Default parameters from Visual MINTEQ were used in modeling cation-DOM 

interactions. The default μ values for the various DOM interactions with other species were 

taken from research by Susetyo, et al. (1991), which examined metal-humic interactions using 

Lanthanide Ione Probe Spectroscopy on Suwanee River DOM. 

All measured dissolved concentrations chemical constituents were input into the program by the 

user. DOM was accounted for by entering in the measured concentration of DOC. The pH was 

set constant at the value analytically measured in the sample. Solids were not allowed to 

precipitate. None of the thermodynamic parameters in Visual MINTEQ’s database were altered 

from their default values. The ionic strength of the solution was not set constant, but rather 

calculated by the program. Charge imbalance calculated by the program was typically 10-20%. 

Model comparisons to analytical results for composite samples were modeled in Visual 

MINTEQ using three different DOM models: Gaussian, NICA-Donnan, and Stockholm Humic 

Model (SHM). pH, Na, and Cl concentration of the models were adjusted to reflect experimental 

test conditions (pH 6.80, ionic strength of 0.05M). Default model parameters were used in all 

models; a concise list is shown in Table 3.3, Table 3.4, and Table 3.5. 

46 
Table 3.3: Gaussian model parameters 

Parameter Value 

DOM Component DOM1 

DOC Concentration varied 

Fixed Database Values selected 

Speciation Based Values unselected 

Table 3.4: NICA-Donnan model parameters 

Parameter Value 

Parameter File genFA.npf.txt 

Mol H+ dissociating groups, type 1 0.00588 

Mol H+ dissociating groups, type 2 0.00186 

Donnan Vol. Parameter 0.57 

Width of Distribution, type 1 0.59 

Width of Distribution, type 2 0.7 

nH1 0.66 

nH2 0.76 

Table 3.5: SHM parameters 

Parameter Value 

Parameter File Typicalfa.mpf.txt 

Stern layer capacitance 2 

Spherical radius 0.75 

Site density 1.2 

Gel fraction parameter 0.72 

Conc. Type B sites 30 

Central log K of type A groups -3.51 

Central log K of type B groups -8.81 

Delta-pKA 3.48 

Delta-pKB 2.49 

Log KC 0.8 

File for SHM site settings Default.MSI.txt 

47 
48 
4.0 RESULTS AND DISCUSSION 

4.1 TOTAL AND DISSOLVED COPPER 

This study focuses on the speciation of copper in highway runoff and factors that may affect the 

presence and form of copper from roadways draining into surface waters. From the literature 

review, some of the factors likely to influence the presence of copper in runoff samples include 

AADT, Urban site classification, ADP, the First Flush Effect, Total Rainfall, and Rainfall 

Intensity. Additionally, water quality parameters will be examined for correlations with both 

total and free ionic copper. Of these parameters, DOC and TSS are known to complex and 

adsorb copper, respectively (Allen and Hansen 1996; Buck and Bruland 2005; Gerringa, et al. 

1998; Lee, et al. 2005; Stead-Dexter and Ward 2004). Due to the ability of natural organic 

matter (NOM) to strongly complex copper, as discussed in Section 2.1, both dissolved copper 

and DOC (a surrogate measure for NOM) have been examined in some of the statistical analyses. 

For the purposes of data analysis, the concentrations of most constituents and hydrologic 

variables have been log-transformed. These transformations are common in stormwater studies 

(Burton, Jr. and Pitt 2002; Kayhanian, et al. 2003), have been used for copper concentrations 

(Kayhanian, et al. 2003), and they provided consistently better statistical distributions for most 

measurements in this study. For clarity, dissolved copper will be referred to as Cu2+

diss, and total 

copper will be referred to as Cu2+

tot throughout this section. 

4.1.1 Site Comparison 

The overall results from the composite samples from all sites are shown in Table 4.1 (dissolved 

samples) and Table 4.2 (total samples). In general, the I-5 site showed consistently higher 

concentrations of all stormwater constituents. Also significant is the fact that the Wemme 

samples from 1/6/2009 and 3/16/2009 had unusually high conductivity, as well as abnormally 

high concentrations of magnesium (and therefore, hardness) and chloride. These outliers are 

likely due to the addition of magnesium chloride to the roadway surface as a de-icing chemical. 

The two Wemme samples in question were removed from the data set when examining 

correlations between copper and the above listed parameters due to their artificially high values 

of conductivity and hardness. Only two samples were received from the Bend site, which limits 

the inferences that can be made for that site. 

49 
Table 4.1: Summary of dissolved constituent concentrations in composite stormwater samples 

SITES I-5 DIXON WEMME BEND 

NUMBER OF 

SAMPLES 5 8 7 2 

PARAMETER 

(UNITS) RANGE MEDIAN RANGE MEDIAN RANGE MEDIAN RANGE MEDIAN 

General Water 

Quality 

 pH 6.24 – 

7.15 6.92 6.26 – 

7.31 6.63 6.41 – 

7.04 6.80 6.66 – 

6.86 6.76 

 Conductivity 

(μS/cm)

55.2 – 

141 83.9 16.3 – 48.2 23.5 18.2 – 1065 206.5 77.4 – 152 115 

 Alkalinity 

(mg/L as 

CaCO3) 

13.8 – 

29.9 18.41 4.19 – 10.0 6.29 3.62 – 14.1 8.84 9.68 – 10.1 9.89 

 Hardness 

(mg/L as 

CaCO3)

22.7 – 

43.4 30.9 5.24 – 

12.2 8.25 9.61 – 

477 95.9 27.3 – 

72.8 50.1 

 TSS (mg/L) 23.1 – 

118 89.9 14.5 – 162 26.1 26.6 – 117 61.8 85.8 – 241 163 

 DOC (mg/L) 5.88 – 

9.97 7.00 1.81 – 

10.8 4.51 1.54 – 

5.97 2.50 5.16 – 

5.8 5.48 

Major Cations 

 Calcium 

(mg/L)

5.96 – 

11.6 9.14 1.62 – 3.51 2.46 1.64 – 21 10.9 2.72 – 4.27 3.50 

 Magnesium 

(mg/L) 

1.14 – 

3.51 1.78 0.29 – 0.83 0.45 0.62 – 103 16.5 4.98 – 15.1 10.0 

 Sodium 

(mg/L) 

1.61 – 

5.14 2.38 0.83 – 2.50 1.17 0.56 – 9.62 1.68 1.17 – 1.6 1.39 

 Potassium 

(mg/L) 

0.56 – 

2.53 1.49 0.33 – 0.85 0.62 bdl – 0.52 0.35 0.48 – 0.74 0.61 

 Iron (mg/L) bdl bdl bdl – 

0.059 0.031 bdl – 

0.009 bdl bdl – 

0.24 NA 

Anions

 Chloride 

(mg/L) 

2.13 – 

16.7 3.52 0.77 – 3.27 1.14 2.42 – 489 40.3 17.5 – 55.8 36.6 

 Nitrate (mg/L) 1.95 – 

5.85 3.86 0.58 – 

2.51 1.25 0.37 – 

1.28 0.47 0.77 – 

1.96 1.36 

 Nitrite (mg/L) 0.1 – 

0.3 0.2 bdl bdl bdl bdl bdl bdl 

 Phosphate 

(mg/L) bdl bdl bdl bdl bdl bdl bdl bdl 

 Sulfate (mg/L) 3.33 – 

6.86 6.19 0.32 – 1.28 0.75 0.43 – 12.9 2.18 1.42 – 2.47 1.94 

Trace Metals 

 Copper (μg/L) 12.8 – 

22.7 17.3 1.78 – 13.2 4.12 2.30 – 5.20 3.19 5.21 – 8.95 7.08 

 Cadmium 

(μg/L) 

0.06 – 

0.25 0.19 bdl – 1.52 bdl bdl – 0.14 bdl bdl – 0.08 NA 

 Nickel (μg/L) 0.76 – 

1.46 1.35 bdl – 

9.58 0.95 0.12 – 

4.32 1.10 1.15 – 

4.95 3.05 

 Lead (μg/L) 0.10 – 

0.64 0.30 bdl – 

84.3 bdl bdl – 

1.8 0.22 bdl – 

0.42 NA 

 Zinc (μg/L) 43.8 – 

193 60 10.6 – 45.4 31.7 12.0 – 70.7 23.9 28.7 – 63.5 46.1 

50 
Table 4.2: Summary of total constituent concentrations in composite stormwater samples 

SITES I-5 DIXON WEMME BEND 

NUMBER OF 

SAMPLES 5 8 7 2 

PARAMETER 

(UNITS) RANGE MEDIAN RANGE MEDIAN RANGE MEDIAN RANGE MEDIAN 

Major Cations 

 Calcium 

(mg/L)

6.66 – 

14.1 9.67 1.80 – 

3.96 2.98 2.81 – 

21.3 11.2 4.40 – 

5.44 4.92 

 Magnesium 

(mg/L) 

1.26 – 

4.14 1.87 0.38 – 

1.14 0.55 0.76 – 

106 15.8 5.97 – 

14.5 10.2 

 Sodium 

(mg/L) 

1.56 – 

5.88 2.17 0.66 – 2.83 1.10 0.55 – 8.90 1.52 1.18 – 1.49 1.34 

 Potassium 

(mg/L) 

1.52 – 

3.79 1.59 0.30 – 0.89 0.55 0.12 – 0.51 0.26 0.51 – 0.77 0.64 

 Iron (mg/L) 0.53 – 

1.68 0.87 bdl – 

1.85 0.54 0.52 – 

1.77 0.77 0.44 – 

1.69 1.07 

Trace Metals 

 Copper 

(μg/L) 

36.8 – 

60.9 40.9 4.74 – 

26.2 9.91 2.09 – 

21.9 9.08 21.8 – 

25.8 23.8 

 Cadmium 

(μg/L) 

0.37 – 

0.74 0.68 bdl – 

1.75 bdl bdl – 

0.16 bdl bdl – 

0.12 NA 

 Nickel 

(μg/L) 

1.91 – 

3.92 2.84 bdl – 10.7 2.10 0.51 – 7.30 1.30 8.40 – 8.50 8.45 

 Lead (μg/L) 11.0 – 

31.3 15.8 bdl – 101 6.86 2.01 – 25.5 10.4 8.52 – 27.6 18.0 

 Zinc (μg/L) 106 - 

219 162 26.1 – 

189 62.7 31.2 – 

138 58.6 148 – 

150 149 

The Fisher’s LSD simultaneous comparison procedure was used to analyze the differing copper 

concentrations in the composite samples at individual 95% confidence intervals. Figure 4.1

displays Cu2+

diss concentrations as a function of site. The I-5 samples consistently contain more 

Cu2+

diss than composite samples from other sites. Indeed, all differences in Cu2+diss

concentrations between I-5 and other sites were significant (p<0.05). The median concentration 

of Cu2+

diss in I-5 composites was: 3.78 times higher than Dixon Outfall composites, with a 95% 

confidence interval (CI) of 2.18-6.58 times the amount. Median Cu2+

diss concentrations at I-5 

were also 5.33 times higher than Wemme composites (3.03-9.38, 95% CI), and 2.40 times higher 

than Bend composites (1.07-5.40, 95% CI). Additionally, Bend composites showed a median of 

2.2 times more Cu2+

diss than Wemme samples (1.02-4.82, 95% CI). All other comparisons were 

not significant. 

Figure 4.2 shows that somewhat similar results were found in terms of DOC at the different sites. 

The median concentration of DOC at the I-5 site averaged 1.99 times higher than the median 

Dixon Outfall concentration (1.15-3.44, 95% CI) and 2.92 times higher than the median Wemme 

concentration (1.67-5.13, 95% CI). DOC in stormwater can be derived from both natural 

biologic material and anthropogenic materials (i.e. petroleum based products). All other 

comparisons of DOC concentrations by site were not significant. The limited number of samples 

from the Bend site likely plays a role in the inability to determine any significant differences in 

DOC concentration with other sites. 

51 
Bend Dixon I-5 Wemme

Site

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Dissolved Copper (Composite Samples)

Figure 4.1: Summary of dissolved copper concentrations in composite samples collected at the four sampling 

sites. 

Bend Dixon I-5 Wemme

Site

0.0

0.2

0.4

0.6

0.8

1.0

LogDOC (DOC in mg/L)

DOC (Composite Samples)

Figure 4.2: Summary of dissolved organic carbon concentrations in composite samples collected at the four 

sampling sites 

52 
Figure 4.3 shows the results for Cu2+

tot across the four sites. Similar trends were seen with 

Cu2+

tot as were witnessed with both Cu2+diss and DOC. The median concentration of Cu2+tot at I-5 

was 4.62 times higher than Dixon Outfall composites (2.26-9.46, 95% CI) and 5.36 times higher 

than Wemme composites (2.57-11.22, 95% CI). Bend composites showed a median Cu2+

tot

concentration 2.82 times higher than Wemme composites (1.03-7.71, 95% CI). All other 

comparisons were not significant 

Bend Dixon I-5 Wemme

Site

0.0

0.5

1.0

1.5

LogCu (Cu in ppb)

Total Copper (Composite Samples)

Figure 4.3: Summary of total copper concentrations in composite samples collected at the four sampling sites 

The higher concentrations of copper (both total and dissolved) at the I-5 site compared to the 

others could be due to a variety of factors. Possibly most important among these factors is 

AADT (since the primary source of copper on highways is brake pads) and urban site 

association. Using the roadway classification of Kayhanian, et al. (2003) based on AADT levels, 

only the I-5 site would be classified as urban (specifically medium-high urban) with an AADT of 

130,000. All other sites would be classified as non-urban, with AADT levels below 30,000. 

MLR modeling of Cu2+

diss as a function of both the I-5 site and AADT showed that only one of 

these variables is necessary in the model, as they are not simultaneously significant. 

In an attempt to focus on the effect of AADT independently from urban association, the 

composite samples from just the Dixon, Wemme, and Bend sites were examined. The Cu2+

diss

measurements plotted as a function of AADT from all sites and from only the non-urban sites are 

displayed in Figure 4.4 and Figure 4.5, respectively. The MLR model of the non-urban sites 

shows insufficient evidence to establish an association between AADT and Cu2+

diss (p>0.05, two 

53 
sided t-test). From this study’s dataset, it is only possible to establish that the I-5 site shows 

higher copper concentrations. The individual effects of AADT and urban site association cannot 

be conclusively determined with the data presented here. 

The lack of a good correlation between traffic and copper concentrations is not surprising, given 

the differing conclusions reached by previous researchers. Previous research by Kayhanian, 

et al. (2003) showed that oil & grease was the only runoff pollutant (including copper) to have a 

strong correlation with AADT. Driscoll, et al. (1990) demonstrated a weak, positive, correlation 

between AADT and copper with a low R2

 of 0.139. Though significant differences in pollutant 

concentrations have been demonstrated between sites deemed urban and non-urban, based on 

AADT, there has been little research to suggest any continuous relationship between pollutant 

concentrations and traffic density. While the present study also supports that conclusion, the 

Driscoll and Kayhanian studies collected many more samples at sites with varying AADT levels 

and therefore reached their conclusions with greater certainty. 

0 20000 40000 60000 80000 100000 120000 140000

AADT (vehicles/day)

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Copper vs AADT (Composite Samples)

Figure 4.4: Relationship between dissolved copper concentrations in composite samples and annual average 

daily traffic for all four sampling sites 

54 
9000 14000 19000 24000

AADT (vehicles/day)

0.1

0.3

0.5

0.7

0.9

1.1

LogCu (Cu in ppb)

Dissolved Cu vs AADT (No I-5 samples)

Figure 4.5: Relationship between dissolved copper concentrations in composite samples and annual average 

daily traffic for the three non-urban sites 

4.1.2 The First Flush Effect 

Figure 4.6 and Figure 4.7 display the first flush effect for Cu2+

diss and DOC (respectively) at 

Dixon Outfall. Both Cu2+

diss and DOC showed significantly higher concentrations in first flush 

samples than in composite samples (p<<0.05, two sided t-test). On average, first flush samples 

showed 4.01 times the concentration of Cu2+

diss found in composite samples (2.20-7.32, 95% CI). 

In terms of DOC, first flush samples showed an average of 4.75 times the amount found in 

composite samples (2.83-7.96, 95% CI). The similar correlations of Cu2+

diss and DOC with the 

first flush effect suggest a possibility that the Cu2+

diss present in the stormwater is bound to DOM 

in the Dixon Outfall samples. 

The first flush effect witnessed at the Dixon Outfall site is unsurprising, considering how welldocumented the effect is (Flint and Davis 2007; Han, et al. 2006; Sansalone and Buchberger 

1997). The previously noted studies examined an overall mass first flush – meaning higher 

copper mass loadings in runoff, while our study only examines the difference in concentrations. 

Han and coworkers (2006) noted strong first flush behavior for DOC, the most pronounced of a 

variety of pollutants, including copper. That study also found a strong correlation between the 

mass first flush ratios of both DOC and Cu2+

tot, which potentially implies a strong correlation 

between the two parameters. The observation of a first flush for copper suggests that best 

management practices which control early-storm runoff are likely to decrease the overall effect 

of copper in receiving waters. 

55 
Composite First Flush

Sample Type

0.0

0.5

1.0

1.5

LogCu (Cu in ppb)

Dissolved Copper: The First Flush Effect

Figure 4.6: Summary of dissolved copper concentrations in composite and first flush samples collected at Dixon 

Outfall 

Composite First Flush

Sample Type

0.0

0.5

1.0

1.5

LogDOC (DOC in mg/L)

DOC: The First Flush Effect

Figure 4.7: Summary of dissolved organic carbon concentrations in composite and first flush samples collected 

at Dixon Outfall 

56 
4.1.3 Relationships with Hydrologic Parameters 

The possible effect of hydrologic parameters on copper concentrations could potentially assist in 

determining what kinds of storms are likely to cause high copper concentrations in receiving 

waters. To examine the effect of hydrologic variables on Cu2+

diss in composite samples, a 

backwards stepwise regression procedure was used. The reduced model contained only the site 

indicator variables, while the full model contained site indicator variables as well as variables 

accounting for ADP, total rainfall, and rainfall intensity. Rainfall duration was dropped from the 

analysis due to its covariation with total rainfall. The graphs displaying the direct relationships 

between copper and hydrologic variables are shown in Figure 4.8 through Figure 4.11. No 

apparent relationship arises on inspection of these figures. The MLR analysis confirms the lack 

of any significant relationship between Cu2+

diss and these hydrologic parameters after the effect 

of different sites is accounted for – none of the hydrologic parameters were retained in the final 

model. However, ADP might be expected to affect first flush concentrations more than 

composite copper concentrations. This relationship is shown in Figure 4.12 and was examined 

with the Dixon Outfall samples modeling Cu2+

diss only as a function of ADP (since all first flush 

samples are only from Dixon Outfall). No significant statistical association between Cu2+

diss and 

ADP was found in first flush samples (p>0.05, two sided t-test). 

As noted earlier, the relationship between either total rainfall or rainfall intensity and pollutant 

EMCs is often weakly negative. Previous studies that revealed pollutant correlations to 

hydrologic parameters (Driscoll, et al. 1990; Kayhanian, et al. 2003; USEPA 1983) examined 

many more samples than the present study. The lack of any significant correlation found in the 

present study may be due to a relatively small sample size. This fact may also have contributed 

to a lack of significant correlation between copper and ADP in both composite and first flush 

samples. Many factors outside of ADP affect pollutant transport on roadways, so to quantify a 

direct relationship between concentration and ADP with high confidence would require many 

samples. Longer ADPs have been shown to have a positive correlation with pollutant EMCs in 

other studies, though that relationship cannot conclusively be shown here. The samples gathered 

in this study were primarily from storms in the fall, winter, and spring, and were marked by 

relatively low ADPs. The infrequency of summer storms (i.e., long ADPs) would have provided 

data over a much greater range of ADPs. However, their unpredictability (and infrequency) 

makes sampling these summer storms difficult in terms of practicality. 

57 
0 50 100 150 200 250 300 350

ADP (hrs)

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Dissolved Copper (Composite Samples)

Figure 4.8: Relationship between dissolved copper concentrations in composite samples and antecedent dry 

period at all four sampling sites 

-1.15 -0.90 -0.65 -0.40 -0.15 0.10 0.35

Log(Total Rainfall) (Rainfall in inches)

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Dissolved Copper (Composite Samples)

Figure 4.9: Relationship between dissolved copper concentrations in composite samples and total event rainfall 

at all four sampling sites 

58 
0.7 0.9 1.1 1.3 1.5 1.7

Log(Rainfall Duration) (Rainfall Duration in hrs)

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Dissolved Copper (Composite Samples)

Figure 4.10: Relationship between dissolved copper concentrations in composite samples and rainfall duration 

at all four sampling sites 

-2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8

Log(Rainfall Intensity) (Rainfall Intensity in in/hr)

0.1

0.5

0.9

1.3

LogCu (Cu in ppb)

Dissolved Copper (Composite Samples)

Figure 4.11: Relationship between dissolved copper concentrations in composite samples and rainfall intensity 

at all four sampling sites 

59 
100 150 200 250 300 350

ADP (hrs)

0.9

1.1

1.3

1.5

LogCu (Cu in ppb)

Dissolved Copper (First Flush Samples)

Figure 4.12: Relationship between dissolved copper concentrations in first flush samples and antecedent dry 

period at Dixon Outfall 

4.1.4 Modeling Copper Concentrations with Water Quality Parameters 

To examine the effect of water quality parameters on the concentration of Cu2+

diss in stormwater 

samples, backwards stepwise regression was employed. This variable selection process differs 

from the analysis in section 4.1.3 as this process is concerned with using measured 

concentrations of constituents in the water samples to predict the concentration of copper. The 

previous analysis looked at parameters which might affect the concentrations of many different 

constituents, like site association and total rainfall. Since the present MLR analysis did not 

involve comparing different sites or sample types, but rather the presence of constituents in any 

water sample, all fully quantified samples were examined. In all, 39 samples of various types 

(grab, first flush, flow-weighted, composite) and from all four sites were used in the data set. 

The predictor variables that were examined in the full model were pH, alkalinity, hardness, 

conductivity, DOC, and TSS. The final model reached in the analysis kept all of the water 

quality predictor variables with the exception of conductivity. Table 4.3 summarizes the results 

of this analysis. 

60 
Table 4.3: MLR variable selection results for dissolved copper (Cu2+diss) 

Parameter MLR Relationship w/ Cu2+diss p-value 

pH negative 0.0238 

Alkalinity positive <0.0001 

Hardness negative 0.0321 

Conductivity not significant not reported 

DOC positive <0.0001 

TSS positive 0.1168 

The most significant variables in predicting Cu2+

diss were DOC and alkalinity, both having pvalues <0.0001 (two-way t-test). Looking at a matrix plot of all the predictor variables (see 

Appendix A.5), hardness and alkalinity were well-correlated to each other. Therefore, only one 

of these terms is of great significance to predict Cu2+

diss in the model (in this case, alkalinity). 

This association may explain the counterintuitive negative term for hardness in the model in that 

the hardness term is only accounting for variation not captured by alkalinity. The weak negative 

association with pH is reasonable, as acidic runoff would be more likely to bring particulate 

copper into solution. The relatively low p-value may be indicative of the fact that the pH of 

stormwater samples is approximately neutral and does not vary greatly – so other effects are 

more pronounced. 

The strong positive relationship between DOC and copper has been observed in other studies 

(Martinez and McBride 1999; Romkens and Dolfing 1998) and highlights the ability of large, 

natural organic matter (NOM) macromolecules to increase the concentration of Cu2+

diss in 

solution through complexation. Looking at Figure 4.13, an order of magnitude increase of 

Cu2+

diss concentrations at the ppb level are correlated with an order of magnitude increase of 

DOC at the ppm level. This suggests that while NOM does bind copper, the binding sites are not 

common moieties on the NOM molecules. Hoffman, et al. (2007) suggested that copper is 

bound to uncommon binding sites or sites with special conformation within the macromolecule. 

The Hoffman research concluded that bulk NOM characteristics, such as aromaticity and 

elemental ratios, are not very important in determining the ability of NOM to bind trace metals. 

Though relatively few sites are likely able to bind copper within a NOM molecule, NOM is often 

present in great excess compared to copper. 

Graphs showing the DOC- Cu2+

diss and alkalinity- Cu2+diss correlations can be seen in Figure 4.13

and Figure 4.14, respectively. The association of these two parameters with increased copper 

levels does not necessarily imply causation. The association of Cu2+

diss with DOC or alkalinity 

may only demonstrate the presence of many pollutants, including copper, in the runoff samples. 

However, the simultaneous significance of these two parameters in predicting Cu2+

diss means 

they are accounting for distinct effects in the samples. If both of these parameters only 

accounted for a positive variation with overall increased pollutant concentrations in stormwater 

(including copper), they would not both have been found to be so significant in the final model. 

DOC and alkalinity are also more easily quantified than trace metals but may provide some 

information on the amount of Cu2+

diss during a runoff event. 

61 
0.05 0.30 0.55 0.80 1.05 1.30 1.55

LogDOC (DOC in mg/L)

-0.1

0.4

0.9

1.4

LogCu (Cu in ppb)

Dissolved Cu vs DOC (all samples)

Figure 4.13: Relationship between dissolved copper and dissolved organic carbon in all samples 

0.6 0.8 1.0 1.2 1.4

Log(Alkalinity) (Alkalinity in mg/L as CaCO3)

-0.1

0.4

0.9

1.4

LogCu (Cu in ppb)

Dissolved Cu vs Alkalinity (all samples)

Figure 4.14: Relationship between dissolved copper and alkalinity in all samples 

62 
Determining a model for Cu2+

tot in samples was done in a similar manner to the model-building 

process for Cu2+

diss. The predictor variables that were examined in the full model were hardness, 

DOC, and TSS, and the analysis was done on the same 39 samples examined in the Cu2+

diss

model. Table 4.4 below summarizes the results of this analysis. 

Table 4.4: MLR variable selection results for total copper (Cu2+tot) 

Parameter MLR Relationship w/ Cu2+tot p-value 

Hardness not significant not reported 

TSS positive <0.0001 

DOC positive <0.0001 

The final model showed significant positive associations of Cu2+

tot with both TSS and DOC 

(p<0.0001). The direct Cu2+

tot -TSS and Cu2+tot -DOC relationships are shown in Figure 4.15 and 

Figure 4.16, respectively. The matrix plot for this MLR model showed covariation between 

hardness and TSS. So again, only one of these terms is necessary in the final model. 

Since metals are either deposited on the roadway as TSS or are bound to other particles, TSS is 

an important parameter in predicting the presence of metals in stormwater samples (Herngren, et 

al. 2005). Although the correlation of TSS with Cu2+

tot is unsurprising, the direct relationship 

between Cu2+

tot and TSS does not appear particularly strong. The absence of TSS as an 

important variable in the Cu2+

diss model may indicate that other water quality parameters, 

primarily DOC and alkalinity, may facilitate the dissolution of copper. 

As with Cu2+

diss, DOC is still an important variable in predicting Cu2+tot concentrations, likely 

due to copper’s affinity for NOM. The simultaneous significance of both TSS and DOC in this 

model is reasonable: a) DOC remains an important predictor variable, as it accounts for much of 

the variation in copper in the dissolved fraction of the samples; and b) TSS becomes an 

important predictor, as it accounts for any copper which remained adsorbed to solids in the 

sample. These two fractions are summed in the measurement of the total samples. 

Since Cu2+

diss predictably correlates with Cu2+tot (see Figure 4.17), another MLR model was 

constructed to see if DOC or alkalinity had any effect on Cu2+

diss concentrations after the 

concentration of Cu2+

tot present was accounted for. This model revealed that, after accounting 

for the effect of Cu2+

tot, DOC was still a significant predictor of Cu2+diss (p<0.0001, F-test). 

Alkalinity showed an insignificant association with Cu2+

diss after Cu2+tot had been accounted for 

in the model (p=0.19, F-test). These findings suggest that while both alkalinity and DOC are 

associated with Cu2+

diss, only the relationship with DOC could potentially be causative. The 

inclusion of alkalinity in the Cu2+

diss model may only be indicative of the presence of Cu2+tot. 

This is a reasonable conclusion – DOM has a strong affinity for copper and is likely to cause 

particulate copper (part of the Cu2+

tot measurement) to partition into the dissolved phase. 

63 
0.9 1.4 1.9 2.4

LogTSS (TSS in mg/L)

0.0

0.5

1.0

1.5

LogCu (Cu in ppb)

Total Copper vs TSS (all samples)

Figure 4.15: Relationship between total copper concentrations and total suspended solids in all samples 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

LogDOC (DOC in mg/L)

0.0

0.5

1.0

1.5

LogCu (Cu in ppb)

Total Copper vs DOC (all samples)

Figure 4.16: Relationship between total copper concentrations and dissolved organic carbon in all samples 

64 
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75

Log(Tot Cu) (Tot Cu in ppb)

-0.1

0.4

0.9

1.4

Log(Diss Cu) (Diss Cu in ppb)

Dissolved Cu vs. Total Cu

Figure 4.17: Relationship between dissolved and total copper concentrations in all samples 

4.1.5 Intra-Storm Variations in Copper Concentration 

Flow-weighted samples collected throughout 9 different storms were examined at the Dixon 

Outfall site. These flow-weighted samples were measured for dissolved trace metals and DOC. 

An example of the data collected is shown in Figure 4.18, for the 10/13 to 10/14/09 storm at 

Dixon Outfall. This figure shows higher concentrations of Cu2+

diss and DOC present in samples 

analyzed early in a storm as opposed to those collected later in the storm’s progression. 

Furthermore, the drop in concentrations of both copper and DOC after the first flush is 

precipitous at first, and more gradual as the storm continues. This trend was consistent in other 

storms as well. This figure also suggests a weak flush occurring subsequent to a pause in the 

storm. For example, the concentrations of Cu2+

diss and DOC slightly increased from the 2nd to 

the 3rd samples from this storm; between these two samples there was a 4-hour break in the 

storm. This short dry period may have allowed a small amount of pollutant accumulation on the 

roadway, which resulted in the jump in Cu2+

diss and DOC concentrations present in the 3rd

sample. This plot also agrees with the strong correlation of copper and DOC discussed 

previously. In all cases, a relative increase or decrease in DOC between two flow-weighted 

samples has a corresponding increase or decrease in Cu2+

diss. 

The intra-storm data was analyzed with simple linear regressions, modeling Cu2+

diss or DOC 

concentration as a function of Vi/Vtot, or ‘normalized cumulative volume.’ Vi/Vtot is a ratio 

which represents the cumulative volume of runoff when sample i was taken divided by the total 

runoff volume produced by the storm. This measure can be applied to all storms and is used here 

to show the overall progress of a storm. Both Cu2+

diss and DOC concentrations were found to 

65 
significantly decrease over the course of individual storms (p<0.05, two sided t-tests). Graphs of 

these results are shown in Figure 4.19 and Figure 4.20. 

Though used in this simple analysis, Vi/Vtot is by no means an accurate predictor of Cu2+

diss. The 

primary weakness of this parameter is that it does not reveal the magnitude of a storm. A sample 

collected at a given Vi/Vtot within a large storm would be expected to have more dilute 

concentrations of pollutants than another sample with the same Vi/Vtot from a small storm. 

Additionally, there may not be a linear relationship between Vi/Vtot and Cu2+

diss. The most 

important result of this analysis is the significant decrease in copper and DOC throughout the 

course of a storm, which is also apparent in the figures. 

Figure 4.18: Variation of dissolved copper and dissolved organic carbon concentrations over the course of a 

single storm at the Dixon Outfall site 

66 
0.1 0.3 0.5 0.7 0.9

Vi/Vtot

0.0

0.4

0.8

1.2

LogDOC (DOC in mg/L)

DOC throughout storms

Figure 4.19: Variation of dissolved organic carbon concentrations throughout the course of all storms collected 

at the Dixon Outfall site. Progress through the storm is represented as the normalized cumulative volume. 

0.1 0.3 0.5 0.7 0.9

Vi/Vtot

-0.1

0.3

0.7

1.1

1.5

LogCu (Cu in ppb)

Dissolved Copper throughout storms

Figure 4.20: Variation of dissolved copper concentrations throughout the course of all storms collected at the 

Dixon Outfall site. Progress through the storm is represented as the normalized cumulative volume. 

67 
4.2 COPPER SPECIATION IN COMPOSITE SAMPLES 

This section details the application of the methodology described in Chapter 3 to determine the 

speciation of copper in 21 composite stormwater samples collected from the Bend, Wemme, I-5, 

and Dixon Outfall sampling sites. First, the competition strength of the added ligand (SA) was 

determined; this consisted of determining the value of CuSAx  as described previously. CuSAx 

values determined in this study are presented and contrasted with previously reported values for 

natural waters. Second, the analytical procedures and data analysis procedures used for 

determining copper speciation are discussed using one stormwater sample as an example. 

Speciation results are discussed on a site-by-site and a parameter-by-parameter basis. Third, the 

correlations of free ionic copper and organic ligand concentrations with aggregate water quality 

parameters are examined. Finally, free ionic copper concentrations determined in this study are 

compared to those predicted using three different Visual MINTEQ DOM models. 

4.2.1 Determination of CuSAx  

As described in Chapter 3, the binding strength of the added ligand is characterized through the 

side-reaction constant CuSAx  ; values of the constant determined in this study are tabulated in 

Table 4.5. Alpha values decreased with increasing calcium concentration (see Figure 4.21), 

suggesting that calcium was competing with copper for SA binding sites. 

Table 4.5: Log alpha values for salicylaldoxime (all values at I = 0.05M, pH 6.80) 

[SA] [Ca] Hardness log CuSAx

a

μM mg/L mg/L CaCO3

1 2.5 --b

2 10 25 4.22±0.06 

50 125 3.66±0.10 

1 2.5 5.81±0.11 

10 10 25 4.37±0.09 

50 125 3.79±0.14 

Notes: 

(a) Alpha values were reported with one standard deviation. 

(b) Alpha value for 2μM SA at 1ppm Ca was omitted due to noisy instrument response/plots and 

low instrument response in the blank. 

These results confirm earlier finding by Monticelli, et al. (2004) and Campos and van den Berg 

(1994). Differences between the calculated alpha values for this study and previous studies can 

be attributed to pH and ionic strength. Alpha values reported in this study are greater than those 

reported by Monticelli, et al. (2004). The differences between the results can be attributed to 

ionic strength differences. Ionic strength increases lead to increases in instrument response and 

stability constants, resulting in greater alpha values. The instrument response effects of ionic 

strength are reduced (though not eliminated) due to the lower pH used in this study. The 

decreased pH used in this study led to decreased HSA−

 concentrations and a corresponding 

decrease in CuSAx  . 

68 
Differences between the alpha values determined at SA concentrations of 2 and 10 μM in this 

study were not as great as expected. Previous studies reported alpha values calculated at 

different SA concentrations as more distinct. The reasons for this discrepancy are unknown. 

Figure 4.21: Variation of  CuSAx with hardness 

4.2.2 Method Verification 

Preliminary speciation experiments were performed on Willamette River water in order to verify 

the method. Results from these experiments were very positive, producing copper titration 

curves (see below) with sensitivity (0.8 nA/nM Cu) and appearance close to those reported by 

Campos and van den Berg (0.86 nA/nM Cu). To test the effects of surfactants on the method, a 

range of Triton X concentrations were added to Willamette samples. Interpreting the titration 

with the internal slope (sensitivity), ligand information was recovered up to 3mg Triton X/L (but 

not at 6 mg/L). Therefore, speciation results from samples containing high concentrations of 

surfactants will likely not be accurate. 

4.2.3 Analytical Procedures and Data Analysis 

For the purpose of illustration, the analytical procedure and analysis of the data from one 

representative composite sample collected at the Wemme site (3/24/09) is discussed in detail to 

demonstrate the procedure that was followed and the results that were obtained. These same 

procedures were utilized for all stormwater samples. Figure 4.22 is an example of a 

voltammagram (stripping potential vs. instrument response) obtained from one of the many 

10 mL aliquots analyzed from the Wemme site sample. Curves like these were produced as Cu2+

was reduced to Cu0

 at the mercury electrode after adsorbing onto the drop as CuSA2. This 

specific figure represents a 100 nM Cu addition during a titration in which the added copper 

concentration was varied from 0 to 200 nM. Copper peaks in this study were typically centered 

69 
at −0.14 volts (−0.12 to −0.18 volts). This is in contrast to the work of Campos and van den 

Berg, where peaks were centered at approximately −0.36 volts. Lower copper reduction 

potentials compared to the Campos and van den Berg study are due to pH differences (Campos 

and van den Berg 1994; Wang 1985). 

Figure 4.22: Triplicate voltammagrams of the Wemme 3/24/09 composite sample containing 2 μM SA and 100 

nM of added copper 

4.2.3.1 Copper Titrations 

Figure 4.23 is the titration curve produced for the Wemme sample with 2 μM of SA. As 

copper is added to the sample, the concentration of the electroactive complex CuSA2

increases, leading to a rise in measured peak current ( ). Eventually, with continuing 

copper additions, the sites on the natural organics in the sample become saturated with 

copper. At this point, the titration curve becomes linear due to the fact that additional 

copper is bound solely by SA; the slope of this region is equal to the sample sensitivity 

( ). As shown previously, the sensitivity is used to determine [

pi

S CuSAx ] using Equation 

(3-6). As described in Chapter 3, using this internal slope can correct for signal 

suppression due to surfactants (Kogut and Voelker 2001). Figure 4.23 illustrates the 

sensitivity determination for the Wemme sample. 

70 
Figure 4.23: Copper titration of Wemme composite sample from 3/24/09 containing 2 μM SA 

4.2.3.2 Determination of Natural Ligand Concentration and Strength 

All speciation data were fit to Equation (3-9) following the Langmuir non-linear curve 

fitting method described in Section 3.2.10.6. Figure 4.24 illustrates how the ligand 

concentration and stability constant information is obtained from this procedure. The 

total concentration of ligands (i.e., sites), [ ], calculated for the Wemme 3/24/09 sample 

was 161.5 nM; the conditional stability constant for the Cu-Ligand complex, , was 

1010.92. 

LT

KCuL 

Figure 4.24: Non-linear least squares fit of the titration data from the Wemme 3/24/09 composite sample containing 

2μM SA to a Langmuir-type expression, describing complexation of copper with the organic ligands in the sample 

71 
4.2.3.3 Detection Windows 

The competitive strength of the added ligand( )  CuSAx can be adjusted through the 

variation of the added SA concentration. Ligands of different strengths can be selectively 

outcompeted by varying the competition strength of the added ligand (thereby varying the 

concentration of SA). This phenomenon can be utilized to obtain ‘snap shots’ of 

different natural ligand populations and their respective stability constants. Bruland, 

et al. (2000) have termed these analyses ‘detection windows.’ Ligand information within 

the same window can be compared readily, whereas it is impossible to quantitatively 

compare that same information across windows (Bruland, et al. 2000; Buck and Bruland 

2005). Two detection windows were used in this study, 2 and 10 μM SA, to provide 

information on strong and weak ligands in the system. Figure 4.25 is a comparison of the 

titration curves obtained using the two detection windows for the Dixon Outfall 2/06/09 

composite sample. 

Figure 4.25: Copper titrations of the Dixon 2/06/09 composite sample at two detection windows (2 and 10 μM SA) 

Applying the Langmuir non-linear curve fitting method to both titrations for the 2/06/09 

Dixon composite sample leads to two separate ligand concentrations and stability 

constants. The lower (2 μM SA) detection window reveals a ligand concentration of 335 

nM and a stability constant of 1011.26. The higher (10 μM SA) window reveals a lower 

ligand concentration, 68.4 nM, and a higher stability constant, 1012.80. Comparison 

between each detection window for the five samples tested with both SA levels reveals 

that ligand concentrations decrease and stability constants increase with the shift from 2 

to 10 μM SA. This behavior is reflected in previous studies in natural waters (Bruland, et 

al. 2000, Buck and Bruland 2005). Figure 4.26 and Figure 4.27 present non-linear least 

squares fits of the titration data from the Dixon Outfall 2/06/09 composite sample to 

Equation (3-9). 

72 
Figure 4.26: Non-linear least squares fit of copper titration data from the Dixon Outfall 2/06/09 composite 

sample containing 2 μM SA 

Figure 4.27: Non-linear least squares fit of copper titration data from the Dixon Outfall 2/06/09 composite 

sample containing 10 μM SA 

The high detection window for this study (10 μM SA) led to titration curves that became linear at 

low added copper concentrations. This behavior is due to the high Cu2+

diss concentrations 

relative to the number of available natural ligand sites in some stormwater samples. The ligand 

73 
sites in samples with high Cu2+

diss concentration were (mostly) filled with copper before the 

titration started, essentially truncating the titration curves and eliminating the curved portion. In 

these situations, the resulting Langmuir curve is truncated and little information about stability 

constants is provided at low concentrations (see 2 Cu free Figure 4.27 for an example). Other 

(erroneous) fitted data sets had decreasing trends (negative slopes) with increasing 

concentration and/or negative CuL values for the entire range of concentrations. These 

results are discussed below in the Wemme site overview. 

2 Cu free

2 Cu free

4.2.4 Copper Speciation Results 

74 



Total ligand concentrations and conditional stability constants determined for all composite 

samples are listed in Table 4.6. Water quality parameters (excluding pH and temperature) 

known to affect either free ionic copper concentrations or copper toxicity are supplied alongside 

the speciation results. represents the total dissolved copper concentration; [ ] 

represents the natural organic ligand concentrations; 

 2 Cudiss LT

KCuL  is the stability constant associated 

with those ligands; represents uncomplexed, or free ionic copper; and ‘Cu 

Complexation’ is the percentage of Cu2+

diss that was complexed by organic ligands in the sample. 

Cu2+

free concentrations were calculated using Equation 

  2

free

  LT 

Cu

Cudiss

2

(3-10), but where samples had 

 then a ‘worst case’ scenario was assumed and Cu2+

free concentrations were 

approximated as   . Stormwater samples that fell into this category were re-tested at 

2 μM SA when sample volume allowed. Lowering the detection window usually resulted in a 

more representative picture of the organic ligands. In most of these cases, the lower detection 

window revealed the presence of a population of weaker ligands, implying that the ligands in 

these samples were weaker and easily outcompeted by 10 μM of SA. 

     2 LT Cudiss 



Differences between ligand concentrations and stability constants were significant for the two 

detection windows at 2 μM and 10 μM SA. The mean ligand concentration determined in the 10 

μM SA detection window was 187.2 nM less than the mean ligand concentration determined 

with 2 μM SA (−325.2 to −49.1 nM, 95% CI). The mean (log) conditional stability constant 

value determined in the 10 μM SA window was 1.1 log units more than the mean (log) stability 

constant determined with 2 μM SA (0.6 to 1.7 log units, 95% CI). The differences in stability 

constants and ligand concentrations between detection windows is corroborated by Buck and 

Bruland (2005). It is important to recognize that this analytical procedure assumes that there is a 

single population of ligands with a defined binding strength. In reality, the DOM in the 

stormwater samples is a heterogeneous mixture of organic molecules that have different binding 

strengths. This “fitting” of the data to a single population of ligands is the reason for the 

differences in the ligand concentrations and binding strengths determined at the different 

detection windows. For this reason, only ligand information (total concentration and stability 

constant) from a single detection window can be compared (Buck and Bruland 2005). 

Calculated Cu2+

free concentrations are independent of the detection window; thus, calculated 

Cu2+

free concentrations determined at the 2 μM and 10 μM SA detection windows can be 

compared. 
Table 4.6: Ligand characteristics, free ionic copper concentrations and related water quality parameters for composite sample

Site Storm SA [Cu2+diss] DOC Hardness Alkalinity [LT] log K′CuL log [Cu2+fr

μM μg/L nM mg/L mg/L as CaCO3 mg/L as CaCO3 nM M 

12-29-08 10 5.21 82.00 5.16 27.30 9.68 173.58 11.65 −11.69 03-16-09 10 8.95 140.86 5.80 72.84a 10.1 114.15 11.89 −7.57e 

Bend 03-16-09 2 8.95 140.86 5.80 72.84a 10.1 432.73 10.54 −10.85 

10-06-08 10 22.70 357.26 7.00 30.85 18.41 316.01 11.14 −7.38e 11-02-08 10 17.80 280.14 9.50 30.15 19.07 97.96 c −6.74e 11-03-08 10 12.80 201.45 5.88 19.58 13.78 190.54 12.02 −7.96e 11-03-08 2 12.80 201.45 5.88 19.58 13.78 382.99 11.08 −11.03 11-20-08 10 17.30 272.27 6.90 24.95 15.15 162.56 11.62 −6.96e 

I-5 

11-20-08 2 17.30 272.27 6.90 24.95 15.15 206.19 c −7.18e 11-20-08 10 4.43 69.72 4.77 8.37 4.45 98.20 12.59 −12.20 12-01-08 10 1.78 28.01 1.81 5.75 4.19 111.05 12.28 −12.75 02-06-09 10 4.80 75.54 4.24 12.18 10.02 68.30 12.80 −8.14e 02-06-09 2 4.80 75.54 4.24 12.18 10.02 334.78 11.26 −11.80 02-23-09 10 3.81 59.96 2.62 8.13 6.91 128.64 12.56 −12.62 03-14-09 10 2.50 39.35 2.08 5.24 4.30 105.21 12.75 −12.97 04-12-09 10 7.88 124.02 4.79 7.51 6.66 132.69 13.49 −12.34 05-13-09 10 13.20 207.74 10.78 8.85 5.91 211.04 12.97 −11.11 

Dixon Outfall 

10-13-09 10 3.28 51.62 4.90 9.42 8.01 78.11 12.06 −11.77 11-20-08 10 3.19 50.2 3.69 9.05 4.36 99.57 12.61 −12.60 12-01-08 10 2.30 36.20 1.54 13.60 3.91 66.77 12.58 −12.50 01-06-09 10 5.20 81.84 2.56 148.41a 9.59 64.82 11.68 −7.77e 01-06-09 2 5.20 81.84 2.56 148.41a 9.59 179.47 10.94 −11.02 03-11-09 10 3.84 60.43 5.97 476.59a,b 14.09 239.89 10.40 −10.86 03-16-09 10 1.90 29.90 2.04 95.94 8.84 d03-24-09 10 3.02 47.53 2.50 136.34a 9.98 d03-24-09 2 3.02 47.53 2.50 136.34a 9.98 161.47 10.92 −11.30 

Wemme 

05-05-09 10 3.10 48.79 1.96 9.61 3.62 56.71 13.06 −12.27 

Notes: (a) Mg outside upper limit of ICP-AES calibration (b) Ca outside upper limit of ICP-AES calibration (c) KCuL was unable to be quantified because Langmuir curve at low [Cu2+free] values was not captured in detection window 

(d) Calculated CuL values are not positive or slope for curve was negative (e) [Cu2+diss] > [LT] therefore free ionic copper concentration is best estimated by [Cu2+diss] − [LT] 

75 
What follows is an examination of the ligand characteristics and Cu2+

free concentrations 

determined using the CLE-ACSV method. In addition to summarizing the differences in 

ligand properties and Cu2+

free concentrations in composite samples at different sites, the 

influence of water quality parameters on ligand properties and Cu2+

free concentrations are 

examined. These parameters (alkalinity, hardness, dissolved copper concentrations, 

dissolved organic carbon concentrations) were discussed in detail in Section 2.1. For the 

reasons outlined above, ligand concentrations and stability constants are compared across 

sites only at the 10μM SA detection window. 

Four extremely high hardness samples collected at the Wemme site were not included in 

statistical calculations (1/06/09, 3/11/09, 3/16/09, and 3/24/09), due to the fact that cation 

concentrations were outside the calibration region and potentially a seasonal outlier. 

However, these sites are plotted with a different symbol (open circle) to indicate where 

they fall in the trends. Cu2+

free concentrations from I-5 (10/06/08, 11/02/08, 11/20/08) 

were not used in the statistical analysis, because the analytical window did not fully 

capture the ligands (see below for further discussion). 

4.2.4.1 Ligand Concentration 

Figure 4.28 compares the total ligand concentrations determined in composite 

samples collected at each site. The median ligand concentration determined in I-5 

composite samples is 2.92 times (1.03 to 3.72, 95% CI) greater than that found in 

composite samples from the Wemme site; other site-wise comparisons did not 

reveal statistically significant differences. Ligand concentrations varied widely 

(64.8 to 432.7 nM) and indicate that stormwater is a highly heterogeneous mixture 

that varies in composition from storm to storm and from location to location. 

This heterogeneity includes variability in the type and concentration of organic 

ligands present in the runoff. Potential sources of ligands are biological material 

(leaves, pollen, bacteria, etc.) and anthropogenic derived material (industrial 

chemicals, petroleum products, partially combusted hydrocarbons, pesticides, 

etc.). Wemme, Dixon Outfall, and Bend are all sites that would receive both 

types of organic material, whereas the urban isolation of the I-5 site may result in 

petroleum products, combustion byproducts and industrial chemicals dominating 

the mixture. 

76 
Figure 4.28: Summary of total ligand concentrations determined in composite samples collected at the 

four sampling sites using the 10 μM SA detection window 

Ligand concentration is positively correlated with DOC (p = 0.005). As expected, 

ligand increases as the mass of organics in the system increases. Corroborating 

the relationship between Cu2+

diss and DOC in Section 4.1.4, large differences 

between the magnitude of DOC and ligand concentrations indicate that cation 

binding sites consist of a small portion of organic molecules present in 

stormwater. Figure 4.29 shows the relationship between DOC and ligand 

concentration. 

 

Figure 4.29: Relationship between total ligand concentration and dissolved organic carbon content of 

composite samples collected at all for sampling sites 

77 
4.2.4.2 Conditional Stability Constants 

Figure 4.30 compares the conditional stability constants for the Cu-Ligand 

complexes determined in the composite samples collected at each site. There are 

significant differences in ligand site binding strengths between Dixon and the I-5 

site. The median value determined at Dixon Outfall site exceeds that from 

the I-5 site by 1.1 log units (0.1 to 2.1 log units, 95% CI); no other site-wise 

comparisons revealed statistically significant differences. Within samples 

collected at each site, the conditional stability constants were widely variable. 

This variation can be attributed to the likely difference in type of organic matter 

present in each sample. For example, one difference could be the relative 

concentrations of ligands from anthropogenic sources and biogenic sources. This 

difference is most likely the cause for the significance between the Dixon (varied 

ligand sources) and I-5 (primarily anthropogenic sources) sites. Composite 

samples collected at the Wemme site were characterized by an unusually high 

range of ligand strengths; this may be due to capturing a wide variety of biogenic 

ligands from different biota, in addition to anthropogenic ligands. Ligand sources 

at Dixon may have been limited to a few major biotic sources due to a small 

(relative to the other sites) collection area. 

KCuL 

Figure 4.30: Determined stability constants for the CuL complex 

As shown in Figure 4.31, there is a negative relationship between and 

hardness (p = 0.002). At high hardness concentrations, calcium and magnesium 

outcompete copper for binding sites on the ligands. As a result, fewer copper 

atoms are associated with the organic matter at a given total Cu2+

diss

concentration, leading to a decrease in the conditional stability constant. 

KCuL 

78 
Figure 4.31: Effect of hardness on conditional stability constants (empty circles are high hardness 

Wemme samples) 

4.2.4.3 Free ionic copper Concentrations 

The Cu2+

free concentation can be calculated from the ambient Cu2+diss

concentration, the total ligand concentration, and the conditional stability constant 

as described in Section 3.2.10.6. Results from individual samples shown in Table 

4.6 reveal that Cu2+

free concentrations were typically quite low (< 1 ng/L) in 

stormwater samples. Furthermore, in the majority of cases, the copper was 

greater than 99.99% complexed by organics in solution. Figure 4.32 compares 

the Cu2+

free concentrations determined in composite samples at each site using a 

10 μM SA detection window. Cu2+

free concentrations in composite samples 

collected from the I-5 site appear to be significantly higher than all other sites. 

However, statistical comparisons can not be made due to the fact that the 

measured copper concentrations exceeded the total ligand concentrations in all of 

the I-5 samples analyzed in the 10 μM SA detection window, leading to an 

incomplete characterization of the ligands in the sample. 

As described above, when large concentrations of Cu2+

diss and relatively low 

concentrations of dissolved organic carbon were present in samples, virtually all 

of the natural ligands were filled with copper at the start of the titrations and 

added copper immediately began to complex with the added ligand (SA). As a 

result, the speciation analysis determined a relatively small concentration of 

strong binding ligands in those samples. For all five I-5 composite samples, and 

one sample each from Bend, Dixon Outfall and Wemme, the Cu2+

diss

concentration exceeded the total ligand concentration, and a “worst-case” estimate 

of Cu2+

diss had to be made. 

79 
Figure 4.32: Summary of free ionic copper concentrations calculated in composite samples collected at 

the four sampling sites 

As outlined in Chapter 2, the presence of Cu2+

diss (more specifically, Cu2+free) can 

inhibit the olfactory senses of juvenile salmonids. Different studies have reported 

different effects levels. Sandahl, et al. (2007) determined a negative effect on the 

olfactory system of juvenile Coho salmon at a nominal concentration of 2 μg/L of 

added Cu2+

diss (added as CuCl2, so virtually all of the Cu2+diss was in the free ionic 

form). The background Cu2+

diss concentration of the fish hatchery water used in 

the Sandahl study measured 0.3 μg/L and the measured concentration for the 2 

μg/L Cu2+

diss addition was 1.9±0.4 μg/L. Hecht, et al. (2007) determined a 

benchmark concentration of 0.59-2.1 μg/L of Cu2+

diss above background 

concentrations (defined as ≤ 3μg/L) to have a significant effect on the olfaction 

of unexposed juvenile salmon. In addition, McIntyre, et al. (2008) have shown 

that the presence of alkalinity, hardness and dissolved organic carbon acts to 

reduce the neurotoxic effects of Cu2+

diss. Due to the uncertainty in the background 

concentration of copper used in the Hecht, et al. study, we have adopted the 

nominal 2 μg/L (10−7.5 M) concentration of Cu2+

free as a benchmark for potential 

toxicity in the following discussion. 

Cu2+

diss and Cu2+free concentrations for each composite sample are compared to the 

juvenile Coho salmon toxicity level in Figure 4.33. The Cu2+

diss concentrations 

exceed the toxicity threshold of 2 μg/L in all but one case. However, analysis of 

copper speciation (and complexation by organics in particular) reveals that in all 

samples that were fully characterized (i.e.,     Cudiss  LT

2 ), copper was > 99.9% 

complexed by organics in the stormwater. As a result, calculated concentrations 

of Cu2+

free ranged from 10−5

 to 10−3 μg/L, well below the effect threshold of 

80 
2 μg/L identified by Sandahl, et al. (2007). Of the composite samples where the 

10 μM SA detection window (and in one case the 2 μM SA detection window) 

resulted in a worst-case estimate of the Cu2+

free concentration, only three samples 

exceeded the toxicity threshold and all three samples were from the I-5 site. Even 

the worst case estimates of Cu2+

free concentrations at the 10 μM SA detection 

window for the 3/16/2009 Bend sample, the 2/6/2009 Dixon sample, the 

11/3/2008 I-5 sample and the 1/6/2009 Wemme sample were below the toxicity 

threshold. In all but one of these samples, the use of a lower detection window (2 

μM SA) revealed large concentrations of weaker ligands and allowed a better 

estimate of Cu2+

free. 

Clearly, these results indicate that the vast majority of Cu2+

diss is complexed with 

organics present in highway stormwater runoff. Only samples collected at the 

urban I-5 site had Cu2+

free concentrations that exceeded 2 μg/L, and those 

estimates were worst-case scenarios based on incomplete characterization of the 

natural ligands. The complexation of copper by organic matter likely renders the 

copper non-bioavailable to salmonids and would provide some protection against 

copper toxicity. The results make clear that it is important to consider more than 

the total or dissolved concentrations of metals in stormwater. The general trends 

of increased pollution at the urban I-5 site (in terms of total and dissolved copper) 

do appear to translate to the speciation results, indicating that high-traffic, urban 

areas are at the greatest risk for copper toxicity. 

81 
Figure 4.33: Concentrations of total dissolved, and free ionic copper in composite stormwater samples 

* indicates samples where [Cu2+diss] > [LT]; a “worst case” [Cu2+free] was estimated as [Cu2+diss] − [LT] 

The three samples that exceeded the toxicity threshold were all collected from the 

I-5 site, suggesting that copper toxicity is more likely to be a problem at urban 

sites where Cu2+

diss concentrations are higher. The three exceeding samples had at 

least 70 nM more of Cu2+

diss, higher hardness, and higher DOC than the one I-5 

sample that did not exceed the toxicity level. However, as mentioned previously, 

these three I-5 samples may not have been fully characterized and therefore the 

Cu2+

free concentrations are likely lower than the worst-case scenario reported here. 

Although no other samples at any of the sites exceeded the toxicity level, there 

was a pattern within the sites between high Cu2+

free concentrations and other water 

quality parameters. The 12/29/08 Bend sample with the highest Cu2+

free

concentration had higher hardness and Cu2+

diss concentrations than the only other 

Bend sample. The 3/11/09 Wemme sample with the highest Cu2+

free

concentrations had the highest hardness, DOC concentration, and highest Cu2+

diss

concentrations (except for the 3/24/09 sample) when compared to other Wemme 

samples. Examining these trends further shows that Cu2+

free concentrations are 

positively correlated with Cu2+

diss concentrations (p < 0.0001), hardness (p-value 

is 0.004), DOC (p < 0.002), and alkalinity (p < 0.0001). These relationships are 

reflected in Figure 4.34, Figure 4.35, and Figure 4.36, respectively. 

82 
Figure 4.34: Effects of dissolved copper on free ionic copper concentrations 

Figure 4.35: Effects of hardness on free ionic copper concentrations (open circles represent high 

hardness Wemme samples) 

83 
Figure 4.36: Effects of DOC on free ionic copper concentrations 

The relationship between Cu2+

diss and Cu2+free is expected – as Cu2+diss increases, so 

should the species that stem from it. An increase in hardness corresponds with an 

increase in major cations that can bind with ligand sites on NOM. In turn, this should 

lower the available number of sites for copper to bind and increase the amount of free 

ions in solution. The effects of alkalinity were expected to be negative due to the 

increased complexation of copper by carbonate species, but the co-variation of alkalinity 

with hardness likely masks those effects. 

If Cu2+

free concentrations were the only indicator for the toxicity of copper, then these 

results suggest that waters containing high hardness and Cu2+

diss concentrations are more 

likely to have toxic levels of Cu2+

free. However, in order for copper to be toxic to 

organisms, it must first successfully bind with ‘biotic ligand’ sites on the organism. In 

high hardness waters calcium and magnesium compete with copper for these biotic sites, 

just as they do for sites on the natural organic ligands. Therefore, the effects of hardness 

on copper toxicity are likely more complicated than pictured here. Regardless, elevated 

Cu2+

diss concentrations in stormwater are a cause for concern, and may be the best single

indicator for Cu2+

free and toxicity levels. 

The positive relationship between Cu2+

free and DOC was not expected. An increase in 

DOC corresponds with an increase in ligand sites, and therefore Cu2+

free concentrations 

should decrease due to increased binding with organics. The positive relationship 

between DOC and Cu2+

diss (i.e., the fact that more highly polluted waters contain more 

copper and more DOC) likely overwhelms the relationship between DOC and ligand 

concentration. There is a weak negative correlation between DOC and KCuL  that may 

contribute to an overall decrease in the strength of CuL complexes as DOC increases. 

The co-variation of dissolved organic carbon with Cu2+

diss, the influence of hardness 

causing cations, and the recognition that not all DOC is equivalent in terms of its ability 

to bind copper make generalization of the relationships between dissolved organic carbon 

and Cu2+

free concentrations difficult. Analytical speciation, like that performed in this 

84 
study, and chemical equilibrium modeling (discussed in the next section) are the 

preferred methods for characterizing the effects of dissolved organic carbon on Cu2+

free

concentrations. 

4.2.4.4 Dixon Outfall Speciation Results 

A 10 μM SA detection window was appropriate for characterization of the ligands 

in all but one sample (2/06/09) from Dixon Outfall. Cu2+

diss was 99.99% 

complexed with organic ligands in all Dixon Outfall samples. Cu2+

free

concentrations were correspondingly low and did not surpass 2 μg/L. Hardness 

concentrations in the Dixon samples were lower than other sites, which may have 

contributed to a decrease in calcium/magnesium competition with copper for 

ligand sites. values for Dixon were higher than those at I-5, indicating that 

sources of DOC at the Dixon site provided stronger copper complexation. 

KCuL 

4.2.4.5 I-5 Speciation Results 

High Cu2+

diss concentrations in the I-5 samples led to a truncation of the titration 

curves. As such, I-5 sites were characterized by low ligand concentrations 

relative to ambient Cu2+

diss concentrations within the 10 μM SA detection 

window. These low ligand concentrations led to Cu2+

free concentration to be 

calculated as a worst case scenario in three (of four) samples. Two samples, 

11/03/08 and 11/20/08, had sufficient volume to reanalyze at a 2 μM SA detection 

window. Analysis at the lower detection window revealed the presence of a 

population of lower strength ligands in both samples. The ligand concentration 

measured at 2 μM SA in the 11/20/08 sample was still lower than the Cu2+

diss

concentration. High Cu2+

diss concentrations in the 11/20/08 sample truncated the 

titration and Langmuir curve and didn’t allow determination of the stability 

constant. 

Stability constants for the organic ligands at the I-5 site were lower than those at 

Dixon Outfall. DOC concentrations in I-5 samples were also higher than those at 

Dixon and Wemme. This suggests that anthropogenic DOC at the I-5 site, such as 

petroleum and industrial chemicals, forms only weak(er) complexes with copper. 

34.97% or more of the Cu2+

diss in the samples was complexed with the organic 

ligands. However, for all of the samples analyzed at the 10 μM SA detection 

window, this was a worst-case estimate. All but one of the worst-case estimates 

for Cu2+

free exceeded 2 μg/L. Hardness concentrations at the I-5 site were not 

excessively high (as compared to those at Wemme). DOC concentrations were 

the highest of all the sites, but Cu2+

free concentrations exceeded all other sites 

based on the worst-case estimates. These results suggest that the elevated Cu2+

diss

concentration at the I-5 site were the leading cause of differences in Cu2+

free

concentrations. 

4.2.4.6 Bend Speciation Results 

Through analysis at 10 μM of SA, ligand concentration and stability constants at 

Bend were not significantly different from the other sites. One sample (3/16/09) 

contained more Cu2+

diss than ligand sites. Further investigation with the 2 μM SA 

85 
window showed that weaker ligands were present. Overall, at least 99.99% of 

Cu2+

diss in both samples was complexed with organic ligands. The collection of 

more samples for Bend would allow a fuller characterization of the site; as it 

stands, the low number of samples allows for limited conclusions. 

4.2.4.7 Wemme Speciation Results 

KCuL  values at Wemme varied widely, ranging from 1010.40 to 1013.06, indicating 

that a variety of organic material types were present in the stormwater. A high 

influx of magnesium and chloride in the stormwater occurred during mid- to late 

winter. This hardness spike is due to road salt (MgCl2) additions to aid driving in 

ice/snow. Organic ligands in stormwater collected at Wemme were characterized 

well in a 10 μM SA detection window. Of the samples successfully measured, 

only one (3/11/09) fell below 99.99% copper complexation. Cu2+

free

concentrations were higher than in Dixon Outfall samples, but otherwise were 

low, not surpassing 2 μg/L in any samples. Extremely high hardness (476.6 mg/L 

as CaCO3) corresponded to the elevated Cu2+

free concentration in the 3/11/09 

storm. 

KCuL  and [ could not be quantified for two samples (3/24/09 and 3/16/09) at the 

10 μM SA window due to misleading results for their Langmuir curve fits. The 

Langmuir curve fit to the 3/24/09 sample reached a plateau briefly before 

declining. This formed an overall negative slope for the Langmuir isotherm. This 

negative slope was caused by the sensitivity decreasing as the titration progressed. 

Approach of electrode saturation was the likely cause of this drop in sensitivity. 

The 3/16/09 sample resulted in negative calculated values for CuL. From 

Equation 

L]

(3-5), an overestimation of [CuSAx] can lead to negative CuL 

concentrations if [CuSAx] exceeds [Cu2+

diss]. Overestimation of [CuSAx] can 

occur in the presence of sufficient surfactants; as titration sensitivity decreases 

with increasing surfactant concentration, leading to an overestimation of [CuSAx] 

(see Equation (3-6)). Using an internal slope (sensitivity) can correct for this 

suppression (Kogut and Voelker 2001), but tests on Willamette River water 

revealed that correction for surfactants has a limit. Negative CuL concentrations 

indicate an extreme case of surfactant suppression. 

4.2.4.8 Comparison to Previous Studies 

Mean Cu2+

diss concentrations in stormwater samples were typically greater than 

those measured in previous fresh/seawater studies; in some cases, Cu2+

diss

concentrations were orders of magnitude larger than those in typical surface 

waters. These trends were not surprising, given the increased levels of pollutants 

in stormwater with respect to surface freshwater and rainwater. Mean Cu2+

free

concentrations were also generally greater than those measured in previous 

freshwater/seawater studies. No free copper concentrations in the fresh/seawater 

studies exceeded 2 μg/L. Differences in pH and ionic strength prevent any 

comparison of and ligand concentrations between the studies. KCuL 

86 
87 

Buck and Bruland (2005), noted that the copper complexation capacity of one of 

their sites at Dumbarton Bridge was near 108 nM of Cu2+

diss – over double the 

measured concentration of Cu2+

diss. The implication of these results is the 

possibility that fresh/seawater sites may have substantial capacity to complex 

copper spikes and/or point source pollution from stormwater. Therefore, it is 

important to examine the characteristics of the receiving waters (e.g, organic 

matter type and concentration) to fully evaluate the potential for copper toxicity. 

Elevevated dissolved and Cu2+

free concentrations are likely to be higher at the 

source of the pollution, but will be diluted as the stormwater mixes with the 

surface water. Furthermore, copper may re-equilibrate with the ligands present in 

the surface water. All of these processes must be considered when evaluating the 

potential for copper toxicity from highway stormwater runoff. What the results 

from this study indicate is that in the stormwater itself (similar to many natural 

waters), there is a large complexation capacity, and the majority of the copper is 

bound with organic matter. 

4.2.5 Intrastorm and First Flush Results 

The speciation results determined with 10 μM SA for all samples (grab, first flush, and 

composite) collected at the Dixon site are presented in Table 4.7; as in Table 4.6, water 

quality parameters are also included. In general, speciation analysis of the first-flush and 

grab samples was hampered by the high Cu2+

diss concentrations and elevated levels of 

other pollutants (e.g., surfactants, etc.) in these samples, which created problems for the 

analytical method. The majority of first flush and intra-storm samples did not yield 

reliable information for conditional stability constants or Cu2+

free concentrations; those 

that did included the first flush and intra-storm samples collected on 11/20/2008, and the 

#4 and #20 samples from the 10/13/2009 storm. These samples had low hardness, 

Cu2+

diss, and DOC concentrations. Conversely, the first flush samples that did not yield 

copper speciation information had high Cu2+

diss concentrations (roughly equivalent or 

exceeding I-5 composite samples), which led to saturated ligands in the 10 μM SA 

detection window. Those first flush samples with sufficient volume were also tested at 2 

μM SA, but analyses did not yield additional information due to negative CuL 

concentrations or negatively sloped Langmuir equation fits. This issue was likely due to 

a marked increase in surfactants or other compounds in the first flush samples that 

interfered with the mercury electrode. 
Table 4.7: Speciation results for first flush, intrastorm, and composite samples from Dixon Outfall 

Storm Type SA [Cu2+diss] DOC Hardness Alkalinity [LT] log K′CuL 

 2  log Cufree 

μM μg/L nM mg/L mg/L as CaCO3 mg/L as CaCO3 nM M 

11/20/2008 Composite 10 4.43 69.72 4.77 8.37 4.45 98.2 12.59 −12.20 11/20/2008 First Flush 10 9.02 141.96 12.52 19.1 7.09 158 12.45 −11.49 11/20/2008 #3+4 10 2.55 40.13 2.59 6.13 3.18 43.4 13.08 −11.99 

12/1/2008 Composite 10 1.78 28.01 1.81 5.75 4.19 111.05 12.28 −12.75 12/1/2008 First Flush 10 16.43 258.58 14.2 41.04 13.56 216 12.91 −7.37e 

2/6/2009 Composite 10 4.8 75.54 4.24 12.18 10.02 68.3 12.80 −8.14 e 2/6/2009 Composite 2 4.8 75.54 4.24 12.18 10.02 334.78 11.26 −11.80 2/6/2009 First Flush 10 12.2 192.01 15.57 63.11 20.58 137 c −7.26 e 

2/23/2009 Composite 10 3.81 59.96 2.62 8.13 6.91 128.64 12.56 −12.62 2/23/2009 First Flush 10 20.9 328.93 18.45 47.06 18.55 285 c −7.36 e 3/14/2009 Composite 10 2.5 39.35 2.08 5.24 4.3 105.21 12.75 −12.97 3/14/2009 First Flush 10 17.5 275.42 16.02 52.23b 9.75 224 11.91 c −7.29 e 4/12/2009 Composite 10 7.88 124.02 4.79 7.51 6.66 132.69 13.49 −12.34 4/12/2009 First Flush 10 35.6 560.28 27.44 27.24 10.21 535 13.10 c −7.60 e 5/13/2009 Composite 10 13.2 207.74 10.78 8.85 5.91 211.04 12.97 −11.11 5/13/2009 First Flush 10 30.5 480.01 37 34.27 12.4 479 12.92 c −9.00 e 5/13/2009 #4 10 24.5 385.58 26.08 20.87 9.27 413 12.88 −11.70 9/29/2009 First Flush 10 10.3 162.10 16.75 28.29 6.4 118.69 12.56 c −7.36 e 9/29/2009 #2 10 7.28 114.57 9.79 11.15 4.35 40.6 12.98 c −7.13 e 

10/13/2009 Composite 10 3.28 51.62 4.9 9.42 8.01 78.11 12.06 −11.77 10/13/2009 First Flush 10 11.5 180.99 15.79 28.1 11.2 36.7 c −6.84 e 10/13/2009 #4 10 4.58 72.08 5.26 8.66 9.9 93.1 12.13 −11.54 10/13/2009 #5 10 5.2 81.84 8.58 16.45 13.7 37.3 12.29 c −7.35 e 10/13/2009 #11 10 2.37 37.30 4.25 7.53 7.58 35.7 12.59 −8.81 e 10/13/2009 #17 10 0.97 15.27 2.81 5.54 6.34 12 12.65 −8.49 e 10/13/2009 #20 10 1.36 21.40 3.6 7.55 5.73 61.2 12.01 −12.28 

Notes: (a) Mg outside upper limit of ICP-AES calibration (b) Ca outside upper limit of ICP-AES calibration (c) KCuL was unable to be quantified or suspect because Langmuir curve at low Cu2+free values was not captured in detection window 

(d) Calculated CuL values are not positive or slope for curve was negative (e) [Cu2+diss] > [LT] therefore Cu2+free concentration is best estimated by [Cu2+diss] − [LT] 

88 
4.2.5.1 Ligand Concentrations in Dixon Outfall Samples 

Organic ligand concentrations determined for the first flush samples exceeded the 

composite concentrations at Dixon Outfall in all but one sample (10-13-09). On 

average, the first flush samples had ligand concentrations 1.8 times higher than 

the composite samples for the same storm (1.09 to 2.98 times, 95% CI). This 

difference is significant (p = 0.0286). Figure 4.37 and Figure 4.38 compare the 

ligand concentration within each storm and the overall differences between first 

flush and composite samples, respectively. The higher organic ligand 

concentrations in the first flush samples are related to the elevated DOC 

concentrations relative to the composite samples. 

Nov 20 08 Dec 01 08 Feb 06 09 Feb 23 09 Mar 14 09 Apr 12 09 May 13 09 Oct 13 09

Dixon Outfall Storm Date

0

100

200

300

400

500

[L] (nM)

First Flush

Composite

[L]: The First Flush Effect

Figure 4.37: Ligand concentrations for first flush and composite samples at Dixon Outfall 

89 
First Flush Composite

Sample Type

1.5

1.9

2.3

2.7

log([L]) ([L] in nM)

[L]: The First Flush Effect

Figure 4.38: Comparison between [LT] values for first flush and composite samples 

As was the case with composite samples, [LT] was positively associated with 

DOC (p = 0.0006). On average, a doubling in DOC was associated with a 1.61-

fold increase in [LT] (1.26 – 2.06, 95% CI). This relationship between [LT] and 

DOC in first flush and composite samples is more significant and greater in 

magnitude than with composite samples alone. This implies that organics 

collected at the beginning of a storm may have a greater number of copper 

binding sites available. 

90 
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

LogDOC (DOC in mg/L)

1.0

1.5

2.0

2.5

Log([L]) ([L] in nM)

[L] vs DOC

Figure 4.39: Relationship between [LT] and DOC for Dixon Outfall samples 

Figure 4.40 shows the relationship between [LT] and hardness for all the Dixon 

Outfall samples. Considering the log-transformation, [LT] was positively 

associated with hardness (p = 0.0034). On average, a doubling in hardness was 

associated with a 1.60-fold increase in [LT] (1.19 – 2.16, 95% CI). Hardness 

should not be directly related to [LT]; therefore the positive association is likely 

due to major cations (Ca, Mg, etc) and organic matter (DOC) being flushed into 

the stormwater together. 

91 
0.7 0.9 1.1 1.3 1.5 1.7 1.9

LogHardness (Hardness in mg/L as CaCO3)

1.0

1.5

2.0

2.5

Log([L]) ([L] in nM)

[L] vs Hardness

Figure 4.40: Relationship between [LT] and hardness for Dixon Outfall samples 

4.2.5.2 Free Ionic Copper Concentrations in First Flush and Discrete 

Samples 

Calculated Cu2+

free concentrations in 6 of the 9 first flush samples exceeded 

2 μg/L. In each case, the Cu2+

free concentration was a worst case estimate, due to 

the fact that the ligands were saturated at the 10 μM SA detection window. 

Attempts to characterize the samples at the 2 μM SA detection window were 

inhibited by the signal suppression by surfactants or other constituents in the 

samples. Only 2 of the 8 discrete samples collected during individual storms 

exceeded the toxicity threshold. Again, these estimates were worst case estimates 

obtained at the 10 μM SA detection window. In every sample where the ligands 

were fully characterized at the 10 μM SA detection window, the Cu2+

free

concentrations were well below the toxicity threshold. 

As with the composite samples, trends in the Cu2+

free concentrations tracked well 

with the relative concentrations of Cu2+

diss present in the samples. First flush 

samples had significantly more Cu2+

diss than the composite samples and discrete 

samples collected over the course of individual storms. These elevated Cu2+

diss

concentrations were likely the reason that the ligands in these samples could not 

be fully characterized. Based on these results it does appear that the risk of 

copper toxicity from the release of highway stormwater at the beginning of a 

storm poses a greater risk than event mean concentrations, or runoff discharged 

later in a storm. However, it is likely that even in those samples, a substantial 

fraction (greater than that reported in Table 4.7) of the copper is bound with 

organic matter in these samples. Unfortunately, the heterogeneous nature of the 

92 
dirtier first-flush samples prevented accurate characterization of the stormwater 

organics in this study and limits the ability to make firm statements about the 

likely toxicity of first flush samples. 

4.2.6 Chemical Equilibrium Modeling 

Speciation modeling using Visual MINTEQ was performed on all samples for which 

concentrations of all cations (including trace metals), anions, and DOC were quantified. 

Initially, all 48 such samples were modeled using the Gaussian DOM Model; these 

included 22 composite samples from the various sites, 8 first flush samples, 7 grab 

samples, and 10 flow-weighted samples from Dixon Outfall. Subsequently, more 

sophisticated DOM models (NICA-Donnan and SHM) were utilized to examine the 

speciation in composite samples. Results from the chemical equilibrium models were 

compared with analytical determinations of Cu2+

free. This analysis was limited to the 

composite samples because of the larger uncertainty associated with the analytical 

determination of speciation in the first flush and flow-weighted samples at the Dixon 

Outfall site. 

4.2.6.1 Speciation Modeling with the Gaussian DOM Model 

Results of the speciation analysis using the Gaussian DOM model predicted that 

the concentrations of the cupric ion (Cu2+), the modeled Cu-DOM complex, 

CuOH+

 and CuCO3 accounted for over 99% of the copper species in all but one 

sample (Wemme 3/11/09). The Cu-DOM complex was typically the most 

abundant species, accounting for a median of 86.5% of copper species in the 

modeled stormwaters. Modeled Cu2+

free concentrations ranged from 0.086 to 2.55 

μg/L with a median of 0.53 (1.36-40.1 nM, median of 8.33 nM). Cu2+

free

accounted for a range of 2.4- 35% of the Cu2+

diss in the samples, with a median of 

8.4%. Samples with elevated concentrations of Cu2+

free, and higher percent 

Cu2+

free coincided with high hardness concentrations, especially at the Bend and 

Wemme sites. As mentioned previously, higher hardness at these sites coincided 

with the application of MgCl2 road salts. The effect of hardness on copper 

speciation is more thoroughly examined in subsequent sections. Complete results 

from the copper speciation modeling effort are provided in Appendix A.4. 

Overall, the results of the Gaussian DOM modeling suggested that Cu2+

free

concentrations were quite low and that the majority of Cu2+

diss was bound with 

organic matter in highway stormwater runoff. Comparing the modeled Cu2+

free

concentrations with the 2 μg/L toxicity criterion, only one of the 48 samples 

showed an exceeding Cu2+ concentration (the I-5 10/6/08 composite). 

4.2.6.2 Site Comparisons 

Figure 4.41 presents the modeled Cu2+

free concentrations at the four sites. For 

reference, the 2 μg/L toxicity threshold equates to a log[Cu2+

free] value of 1.5. 

The trends in modeled Cu2+

free are similar to the trends in measured Cu2+diss at the 

four sites. As with Cu2+

diss, the I-5 site showed consistently higher concentrations 

of Cu2+

free than the other three sites. The similarity in these plots underscores the 

direct relationship between Cu2+

diss and Cu2+free. Furthermore, results suggest that 

93 
the elevated DOC concentrations in the I-5 samples were not high enough to 

increase in the copper complexation capacity and keep the Cu2+

free concentrations 

low. The most obvious difference between Figure 4.1 (Cu2+

diss concentrations) 

and Figure 4.41 (modeled Cu2+

free) is the elevated and highly varied 

concentrations of Cu2+

free in the Wemme samples. The peak Cu2+free of the 

Wemme samples approaches I-5 levels, while the lowest concentration of Cu2+

free

is below some Dixon Outfall samples. Reasons for these trends are discussed 

below. 

Bend Dixon I-5 Wemme

Site

0.5

0.7

0.9

1.1

1.3

1.5

LogCu2+ (Cu2+ in nM)

Modeled Cu2+ By Site (Composite Samples)

Figure 4.41: Modeled free ionic copper concentrations in composite samples 

Although not as important as the overall concentration of Cu2+

free, examining the 

percentage of Cu2+

free provides insight into instances where copper toxicity might 

become a problem. Figure 4.42 displays the percentage of Cu2+

free in the 

composite samples at the four sites. Modeling results predict that samples from 

Bend, I-5, and Dixon Outfall have about 9% of the Cu2+

diss in the free ionic form, 

while the Wemme subset displays a wide range of Cu2+

free percentages with a 

much higher median. From these data, it is evident that the Wemme samples 

exhibited a much larger and wider distribution of percent Cu2+

free. Further 

analysis shows that four of the Wemme composite samples contained high levels 

of hardness, and this significantly affected the copper speciation of these samples. 

On average, these four samples contained six times the concentration of calcium 

and over 20 times the amount of magnesium compared to the other three Wemme 

composite samples. The increased calcium and magnesium concentrations 

resulted in hardness that was 10 or more times greater than the other Wemme 

94 
composites and caused an increase in the percent of Cu2+

free in solution. 

Examining the species distribution of DOM in the speciation model agrees with 

this assessment. The high hardness Wemme samples did not exhibit vastly 

different copper or DOC concentrations in comparison to the low hardness 

Wemme samples. The high hardness samples contained an average of 0.63 μg/L 

more copper and 0.87 mg/L more DOC. However, Mg-DOM made up 6.2-15.2% 

of the DOM species in the high hardness samples, but only 0.8-2.4% in the low 

hardness samples. Magnesium, along with a small contribution from calcium, 

occupied binding sites on the modeled organic molecules and reduced the free 

DOM from 76-79% in the low hardness samples to 66-68% in the high hardness 

samples, thereby reducing the potential for DOM to complex Cu2+

free. 

Bend Dixon I-5 Wemme

Site

0

10

20

30

Percent Cu2+

Percent Cu2+ (Composite Samples)

Figure 4.42: Modeled percentage of dissolved copper present as free Cu2+ in composite samples 

4.2.6.3 The First Flush Effect 

The first flush effect on modeled Cu2+

free is similar to the first flush effect 

witnessed on Cu2+

diss concentrations in Section 4.1.2. Figure 4.43 compares 

Cu2+

free concentrations in first flush and composite samples for the 8 storms 

collected at the Dixon Outfall site. In all cases, first flush samples were predicted 

to have higher levels of Cu2+

free than their respective composite samples. The 

percentage of Cu2+

diss present as Cu2+free in the two sample types is shown in 

Figure 4.44. Interestingly, a lower percentage of Cu2+

free exists in first flush 

samples as compared to composite samples. The factors that most affect copper 

species distribution in these samples are DOC and hardness. First flush samples 

95 
are characterized by high concentrations of most runoff constituents, including 

hardness (Ca and Mg), DOC, and Cu2+

diss. The fact that lower percentages of 

Cu2+

free are present in first flush samples reveals that, in the model, the increased 

concentration of DOM overwhelms the effects of increased concentrations of 

divalent cations, which compete with Cu2+

free for binding sites on organics. 

Therefore, it is likely that the increased Cu2+

free seen in Figure 4.43 is attributable 

to higher Cu2+

diss concentrations, and not competition from calcium and 

magnesium. 

Nov 20 08 Dec 01 08 Feb 06 09 Feb 23 09 Mar 14 09 Apr 12 09 May 13 09 Oct 13 09

Storm

0.5

0.7

0.9

1.1

1.3

LogCu2+ (Cu2+ in nM)

First.Flush

Composite

Cu2+: The First Flush Effect

Figure 4.43: The first flush effect on free ionic copper 

96 
Nov 20 08 Dec 01 08 Feb 06 09 Feb 23 09 Mar 14 09 Apr 12 09 May 13 09 Oct 13 09

Storm

0

5

10

15

Percent Cu2+

First.Flush

Composite

Percent Cu2+: The First Flush Effect

Figure 4.44: The first flush effect on Percent free ionic copper 

4.2.6.4 Intra-Storm Copper Variations 

Runoff samples from the 10/13-10/14/09 storm at Dixon Outfall were fully 

quantified and modeled for their speciation using the Gaussian DOM model. The 

results of that analysis are summarized in Figure 4.45. The storm hydrograph is 

shown in the primary vertical axis, while concentrations of Cu2+

free, Cu-DOM, and 

the baseline salmonid Cu2+

free toxicity levels reported from Sandahl (2007) (2 

μg/L, 31.5 nM) are on the secondary vertical axis. The first flush Cu-DOM 

concentration was 162 nM but not shown due to space constraints. The 

concentration of CuOH+ ranged from 0.4 nM (in the 5th and 6th samples) to 6.50 

nM (in the 3rd sample) while the concentration of CuCO3 ranged from 0.4 nM (in 

the 5th sample) to 3.3 nM (in the 1st sample); these were also not graphed due to 

space constraints. 

None of the samples eclipsed the 2 μg/L toxicity level. The trend in modeled 

Cu2+

free concentrations was similar to that of measured dissolved concentrations 

shown in Figure 4.18. The similarity of Figure 4.18 and Figure 4.45 underscores 

the relationship between DOC, Cu2+

diss, and Cu2+free. The highest concentration of 

Cu2+

free was found in the first flush sample, and the concentration generally 

decreased over the course of the storm. The breaks in this storm (e.g., between 

approximately 19:00 and 23:00) that appeared to result in small intra-storm 

flushes of Cu2+

diss also produced slight increases in Cu2+free, notable in the 3rd and 

6th samples collected in this storm. The decrease in Cu2+

free over the course of this 

storm supports the idea that highway runoff early in a storm is potentially more 

97 
toxic to aquatic life than runoff later on in the storm. However, in this case, even 

in the first flush, Cu2+

free concentrations are predicted to be very low. 

Figure 4.45: Intra-storm free ionic copper and Cu-DOM 

The Gaussian DOM model is based on a single type of well-characterized DOM 

molecule (Suwanee Fulvic Acid) and may not be an accurate representation of 

DOM in stormwater. Furthermore, the Gaussian distribution of binding site 

number to binding site strength (as outlined in Section 2.7) may or may not 

accurately predict copper-DOM binding in these samples. For these reasons, 

analytically determining copper speciation in stormwater is important. 

Comparisons of analytically determined and modeled Cu2+

free concentrations are 

discussed in the following section. 

98 
4.2.6.5 Comparison Chemical Equilibrium Modeling with CLE-ACSV Results 

The Gaussian DOM model utilized in the above analysis is a relatively simple 

organic binding model. For the purposes of comparing model results with 

analytically determined speciation, two more sophisticated DOM models (the 

NICA-Donnan model and the Stockholm Humic Model) were utilized in addition 

to the Gaussian DOM model. Again, stormwater samples were modeled using 

Visual MINTEQ. For this analysis, modeling was performed at the same 

conditions as the CLE-ACSV determinations; that is, pH 6.80, and elevated Na 

and Cl concentrations. Table 4.8 compares the modeled Cu2+

free concentrations 

with analytical measurements. 

 

Table 4.8: Comparison of experimentally determined and modeled free 

ionic copper concentrations 

Sample Measured 

log 

[Cu2+free] 

(M) 

Gaussian 

log 

[Cu2+free] 

(M) 

NICADonnan 

log 

[Cu2+free] 

(M) 

SHM 

log 

[Cu2+free] 

(M) 

Bend Site 

12-29-09 −11.69 −7.93 −10.10 −10.41 

03-16-09a −10.85 −7.59 −9.16 −9.77 

I-5 Site 

10-06-08 −7.38 −7.18 −8.96 −9.40 

11-02-08 −6.74 −7.40 −9.50 −9.84 

11-03-08a −11.08 −7.47 −9.41 −9.77 

11-20-08a −7.18 −7.34 −9.24 −9.57 

Dixon Site 

11-20-08 −12.20 −7.99 −10.31 −10.58 

12-01-08 −12.75 −8.14 −10.29 −10.64 

02-06-09a −11.80 −7.87 −9.96 −9.83 

02-23-09 −12.62 −7.90 −9.74 −10.17 

03-14-09 −12.97 −8.07 −9.93 −10.39 

04-12-09 −12.34 −7.80 −9.62 −10.11 

05-13-09 −11.11 −7.74 −9.96 −9.13 

10-13-09 −11.77 −8.13 −10.57 −10.70 

Wemme Site 

11-20-08 −12.60 −8.08 −10.38 −10.67 

12-01-08 −12.50 −7.94 −9.81 −10.24 

01-06-09a −11.02 −7.45 −9.15 −9.60 

03-11-09 −10.86 −7.64 −10.20 −10.22 

03-24-09a −11.30 −7.87 −9.97 −10.23 

05-05-09 −12.27 −7.94 −9.78 −10.18 

Note: 2 μM SA Results 

Rigorous statistical comparisons between modeled and experimentally determined 

Cu2+

free concentrations cannot be made due to the unknown error associated with 

the models. However, all three DOM models overestimate the Cu2+

free

concentration determined by CLE-ASCV by one to three orders of magnitude. 

The Gaussian DOM model consistently predicted the highest Cu2+

free

concentrations, while the Stockholm Humic Model predicted the smallest 

99 
concentrations of Cu2+

free. In general, results from the NICA-Donnan and 

Stockholm Humic Model were similar. For the majority of samples the primary 

copper species were: 

          DOMCu Cufree CuCO3  CuOH 2 (4-1) 

As discussed above, the Wemme sample collected on 3/24/09 was unique in that 

Cu2+

free was the most abundant species in the Gaussian model due to Ca and Mg 

competing with copper for DOM. Overall, the trend in modeled species 

concentrations in Equation (4-1) agrees with experimental results. 

Despite the fact that modeled Cu2+

free concentrations overestimate the 

concentrations determined by CLE-ACSV, general trends in the modeled results 

reflect those of the analytical determinations. Furthermore, the chemical 

equilibrium models can be viewed as a conservative estimate of the Cu2+

free

concentrations. Even these conservative estimates of the Cu2+

free concentrations 

were substantially less than the 2 μg/L toxicity threshold. The fact that modeling 

copper speciation requires substantially less time and effort than analytically 

determining Cu2+

free concentrations makes this option appealing. With a few 

relatively simple analytical measurements (Cu2+

diss, dissolved organic carbon, 

alkalinity, hardness, and ionic strength), a reasonable estimate of the aqueous 

speciation of copper can be made using readily available models. The fact that 

these estimates (using chemical equilibrium models incorporating generic DOM 

models) are comparable to analytically determined values is a promising sign that 

the analytical determinations are correct. 

As stated above, the DOM models utilized in this work were derived using 

parameters specific to a single organic matter source (Suwannee River Fulvic 

Acid). It is highly likely that the organics in stormwater are different. More 

accurate predictions of Cu2+

free concentrations could be obtained through the 

development of DOM parameters reflective of stormwater DOM. This may 

include an increase in the number of copper-DOM sites, or in the competitive 

strength of copper for DOM sites. To determine an accurate fit to all free cation 

concentrations in stormwater a more in-depth investigation into the speciation of 

cations in stormwater would be necessary. The use of DOM models for 

stormwater will likely be limited by the fact that an ‘average’ DOM molecule 

may not exist that accurately represents the heterogeneous nature of highway 

stormwater runoff. Based on the CLE-ACSV analysis, the variation in ligand 

concentrations and stability constants at the four sites examined in this study is 

significant. In the meantime, one of the available humic binding models 

(preferably the NICA-Donnan or Stockholm Humic Model, as they more closely 

predicted measured values) could be used as a conservative estimate of Cu2+

free

concentrations in stormwater. More work is needed to determine if available 

models are appropriate for predicting copper speciation in stormwater and natural 

receiving waters. With regulatory agencies moving toward the use of Biotic 

Ligand models that account for humic binding, better characterization of the 

nature and characteristics of the organic matter present in stormwater and 

receiving waters will be necessary. 

100 
5.0 CONCLUSIONS 

The objectives of this study were 1) to identify the effects of site location, storm 

hydrology, and water quality parameters on the concentration of Cu2+

diss in Oregon 

highway runoff; 2) to establish an analytical technique suitable for the determination of 

copper speciation in highway stormwater runoff; 3) to compare analytically determined 

Cu2+

free concentrations in highway stormwater runoff with modeled concentrations; and 

4) to develop a qualitative understanding of where and when copper toxicity has the most 

potential to be problematic for receiving waters. The following conclusions were reached 

during the completion of these objectives: 

 In this study, stormwater runoff from an urban site characterized by high annual 

average daily traffic (AADT) (the I-5 site) had consistently higher event mean 

concentrations (EMCs) of measured Cu2+

diss and Cu2+free than three non-urban 

sites with lower AADT. The separate effects of urban site location and AADT 

could not be extracted in this study. In terms of practical implications, the 

distinction between the effects is often immaterial – urban sites are characterized 

by high traffic densities and vice versa. High AADT/urban highways show the 

most potential for producing runoff that would exert copper toxicity 

 There was little evidence to suggest significant differences in Cu2+

diss in 

stormwater measured at the other three sites, which varied in terms of eco-region 

and AADT. 

 First flush samples displayed consistently higher concentrations of both Cu2+

diss

and Cu2+

free. This agrees with many previous works. The first flush of a storm 

shows more potential for copper toxicity than runoff further along in the storm’s 

progression. 

 There was insufficient evidence to support any effects of antecedent dry period 

(ADP), total rainfall, rainfall duration, or average rainfall intensity on Cu2+

diss

concentrations in composite samples. There was also no significant effect of 

ADP on Cu2+

diss in first flush samples from the Dixon Outfall site. It is possible 

that if more samples were collected, significant correlations with these hydrologic 

parameters would be determined. 

 In a multiple linear regression (MLR) model, the water quality parameters which 

demonstrated the greatest ability to predict Cu2+

diss were dissolved organic carbon 

(DOC) and alkalinity, both having positive associations with Cu2+

diss. The model 

also included less significant terms accounting for negative correlations with pH 

and hardness. The primary value of this model is in determining important water 

quality predictors of copper, not in determining a quantitative equation, which 

would likely be inaccurate and highly location-specific. 

101 
 In an MLR model to predict Cu2+

tot concentrations in highway stormwater runoff, 

the most important parameters were determined to be DOC and total suspended 

solids (TSS), both having positive associations with Cu2+

tot. Again, developing a 

quantitative predictive model could give the false impression that copper 

concentrations could actually be calculated knowing other water quality 

parameters. 

 A modified CLE-ACSV technique utilizing salicylaldoxime (SA) as an added 

ligand can be used to determine the speciation of copper in highway stormwater 

runoff. Ambient stormwater pH in this study (6.80) lowered the sensitivity of the 

method; the addition of an electrolyte to compensate for this effect was necessary. 

Great care must be taken in the selection of SA concentrations to test stormwater, 

as the Cu-ligand stability constants can vary widely within and between sites. 

Furthermore, the method is difficult to implement in the dirtiest samples with high 

Cu2+

diss concentrations and surfactants or other compounds that may foul the 

mercury drop electrode. 

 Analytical results from composite stormwater samples suggest that dissolved 

copper in highway stormwater runoff is largely complexed by organic matter 

(typically > 99.9%) and that very little of the copper in stormwater is 

bioavailable; the concentrations of Cu2+

free were generally several orders of 

magnitudes below levels found to inhibit olfaction in ESA-listed fish species. 

 Elevated Cu2+

diss levels proved to be the greatest indicator of high Cu2+free

concentrations. Urban sites with AADT and first flush samples characterized by 

elevated concentrations of Cu2+

diss are of the greatest concern with respect to 

elevated free ionic copper concentrations. 

 Hardness, as a measure of major divalent cations, is also a good indicator of 

Cu2+

free concentrations. Calcium and magnesium competition with copper for 

ligand sites drives copper into its free ionic form. Hardness-related issues with 

Cu2+

free are of greatest concern after the application of road salts for anti-icing and 

de-icing of highways. This was revealed through examination of four of the 

Wemme composite samples, showing much higher concentrations of hardness 

compared with other Wemme composite samples. Consideration must be given to 

these effects to mitigate potential problems with Cu2+

free concentrations in 

receiving water bodies. 

 The overall effect of hardness on toxicity in stormwater is complex. Divalent 

cations compete with Cu2+

diss both for adsorption sites on DOM (increasing free 

Cu2+

free), but also compete with Cu2+free for biotic ligand sites on aquatic 

organisms (reducing toxicity). 

 Organic ligand and Cu2+

diss concentrations are positively correlated with DOC 

concentrations. Cu2+

free levels are also positively correlated with DOC 

concentrations. Likely there are co-variance issues, but overall, DOC is not a 

strong predictor of Cu2+

free concentrations. 

102 
 Cu-Ligand conditional stability constants vary widely within and across sampling 

sites. Highly urbanized sites, such as the I-5 site, generally had weaker ligands 

when compared with less-urbanized sites with mixed sources of organic material. 

Petroleum and chemical-based organics likely dominate urbanized sites and 

appear to have lower copper complexation capacities. 

 Available DOM models in Visual MINTEQ overestimate Cu2+

free concentrations 

when compared to analytically determined Cu2+

free concentrations. The 

Stockholm and NICA-Donnan models provide the best estimates for Cu2+

free

concentrations, but may exceed analytical concentrations by over an order of 

magnitude. This is likely due to the poor representation of highway stormwater 

runoff organic matter in these models. Despite these facts, the results from 

chemical equilibrium models followed similar trends as the analytical speciation 

results and are likely a reasonably conservative estimate of copper speciation in 

highway stormwater runoff. 

5.1 FUTURE WORK 

There are several key areas for future investigative work stemming from this study. 

Determining Cu2+

free concentrations in water bodies requires significant time, effort, skill, 

and analytical equipment. For this reason, routinely measuring Cu2+

free concentrations for 

regulatory purposes is not advisable. Although correlations were observed between bulk 

water quality parameters (dissolved copper, hardness, DOC, and alkalinity) and Cu2+

free

concentrations, these measurements in isolation do not lend themselves to robust 

predictive capabilities. It is suggested that copper toxicity levels be determined for 

species of interest (e.g., Coho salmon) through establishing Cu2+

diss concentrations at 

specific DOC and hardness levels that result in high risk for toxicity. In order to 

accomplish this, a replication of the Sandahl, et al. (2007) and McIntyre, et al. (2008) 

studies with the addition of copper speciation techniques and characterization of natural 

organic matter would more robustly quantify toxic Cu2+

diss concentrations for Coho 

salmon under different water quality conditions. This information could be incorporated 

into a Biotic Ligand Model or similar framework and used by regulators to determine the 

danger of copper levels to ESA-listed fish. 

The relationship between DOC concentrations, types of DOC, and Cu2+

free concentrations 

in highway stormwater runoff is unknown. Future work should attempt to more fully 

characterize DOC and copper speciation at a few selected sites in order to determine the 

effects of different types of DOC on Cu2+

free concentrations. Sites with a single dominant 

organic source (highly urbanized zones) could be contrasted with sites that have a variety 

of organic sources. This information could be used by regulators to establish ‘high risk’ 

locations to receive mitigation. Along these lines, characterizing DOM from streams 

inhabited by ESA-listed salmon species is of paramount importance. An effort to both 

characterize the DOM and measure Cu2+

free in a single receiving water may provide 

information on the role of specific functional groups in binding copper in natural waters. 

There is also potential work in analyzing the effects of highway runoff in receiving 

waters. Highway runoff comprises only a fraction of the total volume of a receiving 

103 
water. However, this fraction may be small or large, depending on a specific site’s 

surrounding paved area, location within a watershed, the time of the year, etc. A study 

investigating different dilutions of highway runoff with natural waters and/or the Cu2+

free

concentrations in receiving bodies as a function of distance from the stormwater source 

would be essential in determining whether or not stormwater is a potential threat to ESAlisted species. The extent to which copper bound with organics in stormwater will 

repartition once the stormwater mixes with natural waters needs investigation. 

Furthermore, the capacity of different natural waters to complex copper and the kinetics 

of those processes are not well understood. 

The very nature of stormwater studies makes strong conclusions difficult to reach. 

Highway sites have different traffic levels, are located in different eco-regions, are 

surrounded by a variety of land uses, and even experience storms of varying magnitude 

and nature at different points in time. Therefore, it is nearly impossible to isolate the 

effect of a single variable in a stormwater study. Future work involving stormwater 

should include more extensive sampling efforts focused on examining a single variable. 

For example, the separate effects of AADT and eco-region could be examined at a large 

number of different sites, all located within the same eco-region. Another possible study 

would involve isolating the individual effects of AADT and urban sites. This could be 

researched by selecting a number of sampling sites with various AADTs which are all 

located within the same urban area. Such a study would also eliminate some of the 

inherent variability in analyzing samples arising from different storms. Any future 

stormwater study should be long term – over the course of years. This would help 

determine if any observed trends are only a function of seasonality. 

Finally, an analytical model that accurately determines Cu2+

free concentrations in 

stormwater is currently unavailable. An investigation into common DOC characteristics 

and the speciation of cations in a stormwater would go a long way in aiding the 

establishment of an accurate copper speciation model. This model would be a powerful 

tool for regulators to determine the speciation of copper in stormwater. As regulations 

move towards the use of Biotic Ligand Models to predict toxicity of metals in surface 

waters, it will be important to insure that the humic binding models accurately represent 

the varied sources of organic matter present in stormwater and natural waters. The 

framework for these models exists, but continued characterization of additional organic 

matter types is necessary, to provide inputs to the existing models. 

5.2 SIGNIFICANCE 

In addressing the significance of this project for regulatory agencies, several important 

topics emerge. These include delineation of the appropriate conditions for the application 

of the analytical technique, use of the data from this study to identify (high risk) sites for 

future study, a discussion of the limitations of available chemical equilibrium models, the 

influence of de-icing salts, and a summary of suggestions for future monitoring of copper 

in stormwater. 

Routine measurement of Cu2+

free concentrations in waters of interest (stormwater samples 

or receiving bodies) by regulatory organizations is not the appropriate use of the CLEACSV technique. This technique requires specialized training, an extensive time 

104 
commitment to fully characterize a sample, and a high level knowledge of the meaning of 

the results. The modified CLE-ACSV technique has been shown to be instrumental in 

characterizing composite highway stormwater runoff samples. However, the method has 

shown difficulties in characterizing samples with extremely high Cu2+

diss concentrations 

(as high or higher than those reported from the I-5 site) or a high concentration of 

surfactants (potentially any stormwater sample, especially first flush samples). All these 

problematic issues cause CLE-ACSV to be cumbersome for regularly monitoring water 

quality. Utilization of CLE-ACSV should remain primarily a research tool. 

Potentially lost amidst the correlations and analyses developed in the preceding sections 

is a simple but important fact: salmonids don’t live in stormwater. Although examining 

copper speciation in stormwater is useful and does provide an idea of when and where 

toxicity in receiving waters might be a problem, failing to study the actual receiving 

waters would be a great oversight. However, in examining both the statistics on Cu2+

diss

and the speciation for Cu2+

free, it was shown that highway runoff arising from the first 

flush of a storm, as well as highway runoff from urban/high-traffic areas with little to no 

natural organic sources, has the most potential to adversely affect ESA-listed species. 

Additionally, the first flush effect is amplified in small catchments and highly impervious 

watersheds. It follows then, that treating the first flush of a storm in an urban area would 

have the greatest effect on reducing the potential toxicity to aquatic species. Therefore, 

the most effective best management practices (BMPs) for treating highway runoff should 

focus on those sites and conditions. It is important to recognize, however, that in this 

study it was found that the vast majority of Cu2+

diss in highway stormwater runoff 

samples was bound with organic matter and likely not bioavailable. 

Hardness was shown to have a strong positive correlation with free Cu2+. This is of 

particular interest because the very high concentrations of hardness, which significantly 

affect copper’s speciation, likely arise from adding de-icing salts to the road, specifically 

magnesium chloride. However, as noted in Chapter 2, calcium and magnesium have also 

shown a significant effect in reducing copper toxicity to fish. This effect is primarily due 

to their competition with Cu2+

free for biotic ligand sites on the fish. Therefore, the overall 

effect of hardness on toxicity in the presence of organic ligands is still in question. 

Properly characterizing this effect in receiving waters would be important prior to making 

a determination about how the application of magnesium chloride to Oregon highways 

affect copper toxicity in aquatic species. 

The comparison between modeled and analytically determined Cu2+

free concentrations is 

important. These results show that the models utilized in this study are not reflective of 

actual DOM in Oregon highway stormwater runoff. Model predictions would likely be 

improved with improved characterization of the organic matter present in highway 

stormwater runoff. However, existing models may still be reasonably accurate for 

regulatory purposes in Oregon highway stormwater runoff with low Cu2+

diss

concentrations (non-urbanized sites) and situations where the toxicity levels exceed 

predicted Cu2+

free concentrations by multiple orders of magnitude (as was the case in this 

study for non-I-5 sites/non-first flush samples). All equilibrium model results for Cu2+

free

concentrations appear to provide conservative estimates of Cu2+

free concentrations when 

applied to Oregon highway stormwater runoff. 

105 
The work reported here builds on an already substantial body of knowledge involving the 

presence of heavy metals in stormwater, aquatic speciation of heavy metals, and heavy 

metal toxicity to aquatic organisms. This study is the first to analytically determine 

copper speciation in stormwater. As such, the work provides unique insight into the risks 

associated with copper in highway stormwater runoff. Although the results reported here 

indicate that copper is largely bound with organics in stormwater, many questions remain 

regarding the nature of those organics, what happens to the copper once the stormwater is 

discharged to surface waters, how best to mitigate copper in stormwater, and how to best 

incorporate this information into a regulatory framework. Biotic Ligand Models that are 

currently being used to regulate heavy metals in surface waters would benefit from 

continued efforts to analytically verify model outputs and efforts to more fully 

characterize the organic ligands present in stormwater and natural waters. The methods 

developed as part of this study will be instrumental in accomplishing those objectives. 

106 
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116 
A-1 

APPENDICES 

A.1 QUALITY ASSURANCE/QUALITY CONTROL 

A variety of steps were taken in this study to check the quality of the data being obtained, 

including measuring replicate samples, testing field and method blanks, and analyzing 

spiked samples. The results for these measurements are shown in Table A.1 through 

Table A.4. Blanks were used to estimate contamination from field and lab practices and 

procedures. Most analytes for the field blanks (shown in Table A.1 and Table A.2) 

measured below detection – either below the detection limit for the instrument or below 

the least concentrated standard used to generate a calibration curve. Unusually high 

concentrations of Zn were measured in the Dixon 2/23/09 and 3/14/09 field blanks, while 

the Dixon 4/12/09 field blank showed a relatively high Na concentration. The high NO3 

concentration in the 10/13/09 field blank is likely due to residue from cleaning out the 

Teflon intake tubing with nitric acid, though the tube was rinsed with 2 L of DDI after the 

acid wash. This residue was likely further diminished by intaking volume calibration 

samples at the site, which occurred after taking the field blank. The measured NO3 found 

in the method blank was likely due to the filters being acidified in nitric acid prior to use, 

though each filter was rinsed with 250 mL of DDI prior to use. Low levels of copper 

were found in the 12/1/08 and 3/14/09 field blanks. 

Table A.1: Cation and DOC blank measurements 

Sample Ca Fe K Mg Na Cd Cu Ni Pb Zn DOC 

ppm ppm ppm ppm ppm ppb ppb ppb ppb ppb ppm 

Dixon 12/1/08 FB 0.16 bdl bdl bdl 0.72 .009 1.23 bdl bdl 2.31 0.23 

Dixon 2/23/09 FB bdl bdl bdl bdl bdl bdl bdl bdl 1.99 14.5 0.26 

Dixon 3/14/09 FB bdl bdl 0.13 bdl 0.51 bdl 1.43 1.62 bdl 13.6 0.34 

Dixon 4/12/09 FB 0.08 bdl bdl bdl 2.05 bdl bdl bdl bdl 5.57 0.20 

Dixon 10/13/09 FB bdl bdl bdl bdl 0.11 bdl bdl bdl bdl bdl bdl 

Dixon 10/13/09 MB bdl bdl bdl bdl 0.083 bdl bdl bdl bdl bdl 0.32 

Table A.2: Anion blank measurements 

Sample Cl NO2 NO3 PO4 SO4

ppm ppm ppm ppm ppm

Dixon 10/13/09 FB 0.23 bdl 1.85 bdl bdl 

Dixon 10/13/09 MB bdl bdl 0.53 bdl 0.083 

For some samples, a synthetic spike from our lab was added in a 4:1 sample:spike ratio. 

The lab results from analyzing matrix spikes in samples are shown in. This check was 

meant to analyze how effective a method is at quantifying an analyte, by comparing 

analytically measured concentrations against predicted concentrations. The percent 

differences displayed in the table were calculated according to Equation shown below. 

Measurements marked ‘N/A’ indicate that the sample measurement for that analyte was 

below detection, which would inherently lead to less accurate results. Relatively high 

variation between measured and expected results was found in some spiked samples for 
K, Na, Ni, and Pb. Expected and measured values for Cu were always in good agreement 

in the spiked samples. 

(. . % 100 .Predicted Conc Measured Conc Diff Measured Conc )   (A-1) 

Table A.3: Matrix spike percent differences 

Sample Ca Fe K Mg Na Cd Cu Ni Pb Zn 

Dixon 2/6/09 Comp +3.8 N/A N/A +4.1 +9.8 -1.5 -1.6 +2.0 +0.8 -1.8 

Dixon 3/14/09 Comp +5.5 N/A -3.2 +6.0 +2.0 N/A +0.1 N/A -30.4 -2.8 

Dixon 10/13/09 

Comp 

-9.6 N/A +18.2 +1.7 +36.

4 

N/A +2.1 N/A N/A -1.3 

Wemme 5/5/09 

Comp 

-12.8 N/A -32.8 +8.9 -31.8 N/A -0.7 +47.

0 

+13.

1 

-0.6 

Table A.4 below shows the results from examining sample replicates. Samples were 

measured in triplicate for most analyses in an attempt to quantify the consistency of both 

lab practices and the analytical method. The Coefficient of Variation (COV) for each 

sample was measured as the sample standard deviation divided by the sample mean and 

represents the relative variability within a sample. A high COV indicates poor 

consistency within a sample measurement. A COV of 0.20 (or 20% variability) was 

chosen as a cutoff point for samples exhibiting high variability. Most samples were well 

below this criterion, depending on the analyte. The data from these samples was 

recorded, but flagged. K, Ni, and Pb consistently showed high variation in sample 

replicates. This fact may also contribute to the poor spike results for those analytes 

discussed earlier. Ni, Pb, NO2 and PO4 frequently measured below detection limits. Fe 

in many samples was not accurately quantified due to it being below the detection limit 

for ICP-AES and unable to be quantified on ICP-MS. Of particular importance to this 

study is the low variation exhibited in measurements for Cu and DOC. 

Table A.4: Coefficient of variation analysis and sample measurement notes 

Analyte COV > 0.20 Other Notes 

pH 0/43 COV not meaningful on log scale 

Conductivity 4/43 

Alkalinity 1/43 

TSS 3/43 

DOC 1/71 

Ca 1/85 6 samples above calibration range 

Fe 2/78 21 samples not accurately measured 

K 13/90 1 sample below detection 

Mg 1/83 8 samples above calibration range 

Na 5/91 

Cu 0/114 

Cd 0/40 74 samples below detection 

Ni 16/97 

Pb 22/82 32 samples below detection 

Zn 1/95 19 samples above calibration range 

Cl 2/48 

NO2 1/9 37 samples below detection 

NO3 1/48 

PO4 3/6 40 samples below detection 

SO4 1/48 

A-2 
A.2 DIXON OUTFALL STORM DATA 

This appendix is a collection of the summarized data for Storms collected at Dixon 

Outfall. The data includes measured values data recorded by the autosampler. The data 

is presented as hyetographs and their corresponding stormwater hydrograph. Also 

displayed on the graphs are the points in the storm where samples were taken. Samples 

taken throughout the storm but only used in creating a composite for the storm are 

referred to on the graphs as “Taken Samples”; while samples that were measured for 

DOC and trace metals, at a minimum, are referred to as “Measured Samples”. 

A-3 
Figure A.1: Dixon Outfall 11/8/08 storm data 

A-4 
Figure A.2: Dixon Outfall 11/20/08 storm data 

A-5 
Figure A.3: Dixon Outfall 12/1/08 storm data 

A-6 
Figure A.4: Dixon Outfall 2/6/09 storm data 

A-7 
Figure A.5: Dixon Outfall 2/23/09 storm data 

A-8 
Figure A.6: Dixon Outfall 3/14/09 storm data 

A-9 
Figure A.7: Dixon Outfall 4/12/09 storm data 

A-10 
Figure A.8: Dixon Outfall 5/13/09 storm data 

A-11 
Figure A.9: Dixon Outfall 10/13-10/14/09 storm data 

A-12 
A.3 COMPILED STORMWATER DATA 

The measured concentrations from the stormwater samples are presented in this 

appendix. For easier presentation of the data, the original spreadsheet has been broken 

down into three categories: Herrera samples, Dixon Outfall composite and first flush 

samples, and Dixon Outfall grab and flow-weighted samples. Furthermore, each of these 

subsets is broken into figures: measurement of general water quality parameters and 

cations, and measurements of anions and hydrologic parameters. Gray rows indicate total 

samples. Lab measurements from the study by Herrera Environmental Consultants are 

presented in parentheses where applicable. 

A-13 
Figure A.10: Data from Herrera sites 

A-14 
Figure A.11: Data from Herrera sites, continued 

A-15 
Figure A.12: Dixon Outfall composite and first flush data 

A-16 
Figure A.13: Dixon Outfall composite and first flush data, continued 

A-17 
Figure A.14: Dixon Outfall grab and flow-weighted sample data 

A-18 
Figure A.15: Dixon Outfall grab and flow-weighted sample data, continued 

A-19 
A.4 MINTEQ SPECIATION DATA 

The concentration and distribution of 4 copper species, Cu2+

free (referred to as “CuFree”), 

Cu-DOM, CuOH+

, and CuCO3

2- determined from the visual MINTEQ modeling using a 

Gaussian unimodal DOM model to account for species binding to organics are presented 

in Figure A.16. Again, these species accounted for over 99% of all copper species in all 

but one sample, as shown by the column marked “Percent Check”. Concentration data 

presented in the table is in terms of nM. 

A-20 
Figure A.16: Visual MINTEQ raw copper speciation data 

A-21 
The following tables are complete summary of the cation-complex concentrations and 

copper complexation determined in each of the three DOM models used to model the 

composite samples that were compared with analytical speciation results. 

Table A.5: Summary of cation-DOM complexes determined for each model and sample (1 of 2) 

Calcium Magnesium Hydrogen Zinc Copper Other

μM μM μM μM μM μM

Gaussian 3.469 1.070 0.288 0.077 0.067 0.002

NICA‐Donnan 0.409 1.625 15.560 0.003 0.082 0.007

SHM 0.988 2.384 12.110 0.122 0.082 13.448

Gaussian 4.087 2.406 0.217 0.131 0.107 0.010

NICA‐Donnan 0.216 1.441 15.814 0.002 0.140 2.930

SHM 1.144 5.347 13.321 0.185 0.141 12.345

Gaussian 8.700 0.274 0.207 0.088 0.265 0.002

NICA‐Donnan 1.541 0.967 20.913 0.005 0.356 0.010

SHM 4.166 1.041 16.151 0.161 0.357 17.536

Gaussian 11.669 0.383 0.290 0.160 0.225 0.004

NICA‐Donnan 2.072 1.331 28.608 0.009 0.280 0.014

SHM 5.541 1.429 22.050 0.310 0.280 23.948

Gaussian 6.199 0.198 0.232 0.107 0.156 0.002

NICA‐Donnan 1.064 0.687 17.910 0.006 0.201 0.007

SHM 2.596 0.655 13.716 0.191 0.201 15.856

Gaussian 8.018 0.222 0.229 0.175 0.203 0.005

NICA‐Donnan 1.435 0.818 20.839 0.009 0.271 0.015

SHM 3.658 0.801 15.960 0.308 0.272 17.891

Gaussian 3.462 0.136 0.219 0.064 0.058 0.349

NICA‐Donnan 0.454 0.357 12.264 0.003 0.075 1.077

SHM 1.202 0.375 9.728 0.078 0.075 12.441

Gaussian 1.895 0.073 0.177 0.062 0.044 0.002

NICA‐Donnan 0.208 0.166 7.797 0.002 0.060 0.553

SHM 0.565 0.174 6.192 0.095 0.060 7.773

Gaussian 1.281 0.038 0.174 0.035 0.029 0.006

NICA‐Donnan 0.126 0.083 6.188 0.001 0.039 0.517

SHM 0.333 0.078 4.948 0.052 0.039 6.366

Gaussian 3.855 0.108 0.290 0.052 0.058 0.021

NICA‐Donnan 0.607 0.365 14.896 0.003 0.070 0.037

SHM 1.268 0.278 11.331 0.093 0.070 14.051

Gaussian 3.419 0.102 0.332 0.100 0.104 0.002

NICA‐Donnan 0.376 0.241 14.239 0.003 0.124 1.055

SHM 1.028 0.238 11.331 0.145 0.124 14.311

Sample Model

Dixon

4/12/09

I‐5 10/06/08

I‐5 11/02/08

I‐5 11/03/08

Dixon

2/06/09

Dixon

2/23/09

Dixon

3/14/09

Bend

12/29/08

I‐5 11/20/08

Dixon

11/20/08

Bend

3/16/09

A-22 
Table A.6: Summary of cation-DOM concentrations calculated in each model and sample (2 of 2) 

Calcium Magnesium Hydrogen Zinc Copper Other

μM μM μM μM μM μM

Gaussian 6.155 0.183 0.520 0.193 0.185 4.112

NICA‐Donnan 1.194 0.731 29.047 0.011 0.208 4.414

SHM 2.164 0.487 22.563 0.164 0.207 34.241

Gaussian 4.010 0.092 0.293 0.112 0.042 0.000

NICA‐Donnan 0.564 0.290 14.998 0.005 0.052 0.471

SHM 1.344 0.241 11.600 0.198 0.052 14.362

Gaussian 1.281 0.034 0.133 0.014 0.019 0.000

NICA‐Donnan 0.190 0.114 5.699 0.001 0.028 0.000

SHM 0.370 0.078 4.319 0.028 0.028 5.467

Gaussian 1.115 0.132 0.098 0.012 0.022 0.000

NICA‐Donnan 0.149 0.286 4.735 0.001 0.036 0.001

SHM 0.327 0.312 3.644 0.022 0.036 4.385

Gaussian 6.268 4.974 0.075 0.049 0.030 0.007

NICA‐Donnan 0.510 4.238 16.433 0.002 0.060 0.123

SHM 1.778 11.475 13.440 0.109 0.060 6.386

Gaussian 2.812 0.112 0.241 0.040 0.040 0.001

NICA‐Donnan 0.426 0.341 11.540 0.002 0.050 0.004

SHM 0.880 0.276 8.778 0.075 0.050 10.890

Gaussian 2.799 1.132 0.054 0.013 0.037 0.000

NICA‐Donnan 0.281 1.370 7.244 0.001 0.081 0.001

SHM 0.932 3.076 5.825 0.026 0.082 4.335

Gaussian 3.731 0.916 0.025 0.034 0.014 0.002

NICA‐Donnan 0.168 0.579 5.639 0.001 0.030 0.163

SHM 1.663 3.454 4.611 0.073 0.030 0.021

Gaussian 2.197 0.864 0.045 0.023 0.019 0.001

NICA‐Donnan 0.204 0.975 5.748 0.001 0.047 0.163

SHM 0.732 2.351 4.648 0.044 0.047 3.556

Gaussian 1.155 0.156 0.154 0.029 0.035 0.001

NICA‐Donnan 0.147 0.318 6.076 0.001 0.049 0.004

SHM 0.294 0.316 4.643 0.042 0.049 5.772

Wemme

3/16/09

Wemme

3/24/09

Wemme

5/05/09

Sample Model

Dixon

5/13/09

Dixon

10/13/09

Dixon

12/01/08

Wemme

12/01/08

Wemme

1/06/09

Wemme

3/11/09

Wemme

11/20/08

A-23 
Table A.7: Summary of free ionic copper concentrations and copper complexation calculated for 

each model and sample 

Date Site Gaussian 

Cufree

Gaussian 

Cu-DOM 

Complex

NICA-Donnan 

Cufree

NICA-Donnan 

Cu-DOM 

Complex

SHM Cufree SHM Cu-DOM 

Complex

M% M % M %

12/29/2008 Bend ‐7.93 81.41 ‐10.10 99.87 ‐10.41 99.94

3/16/2009 Bend ‐7.59 76.13 ‐9.16 99.52 ‐9.77 99.87

10/6/2008 I5 ‐7.18 74.29 ‐8.96 99.57 ‐9.40 99.84

11/2/2008 I5 ‐7.40 80.34 ‐9.50 99.84 ‐9.84 99.93

11/3/2008 I5 ‐7.47 77.29 ‐9.41 99.74 ‐9.77 99.89

11/20/2008 I5 ‐7.34 74.73 ‐9.24 99.68 ‐9.57 99.85

11/20/2008 Dixon ‐7.99 83.01 ‐10.31 99.92 ‐10.58 99.96

12/1/2008 Dixon ‐8.14 67.62 ‐10.29 99.77 ‐10.64 99.90

2/6/2009 Dixon ‐7.87 77.37 ‐9.96 99.41 ‐9.83 99.76

2/23/2009 Dixon ‐7.90 74.02 ‐9.74 99.62 ‐10.17 99.86

3/14/2009 Dixon ‐8.07 74.06 ‐9.93 99.64 ‐10.39 99.88

4/12/2009 Dixon ‐7.80 84.23 ‐9.62 99.76 ‐10.11 99.92

5/13/2009 Dixon ‐7.74 89.12 ‐9.96 99.93 ‐9.13 99.55

10/13/2009 Dixon ‐8.13 82.12 ‐10.57 99.93 ‐10.70 99.95

11/20/2008 Wemme ‐8.08 79.31 ‐10.38 99.90 ‐10.67 99.95

12/1/2008 Wemme ‐7.94 60.96 ‐9.81 99.46 ‐10.24 99.80

1/6/2009 Wemme ‐7.45 44.62 ‐9.15 98.89 ‐9.60 99.61

3/11/2009 Wemme ‐7.64 48.85 ‐10.20 99.86 ‐10.22 99.87

3/24/2009 Wemme ‐7.66 39.83 ‐9.45 99.03 ‐9.83 99.59

5/5/2009 Wemme ‐7.94 71.47 ‐9.78 99.59 ‐10.18 99.83

A-24 
A.5 S-PLUS MATRIX PLOTS 

This appendix shows matrix plots obtained from S-Plus when modeling Cu2+

diss and 

Cu2+

free. The matrix plots are a useful tool because they provide simple visual 

correlations between an array of variables. The variable above/below a specific plot is 

represented on the x-axis, while the variable to the left/right of a specific plot is 

represented on the y-axis. 

LogCu

6.10

6.35

6.60

6.85

7.10

7.35

0.5

0.7

0.9

1.1

1.3

1.5

0.5

1.0

1.5

2.0

2.5

-0.1 0.4 0.9 1.4

6.106.356.606.857.107.35

pH

LogCond

0.20.71.21.72.22.7

0.50.70.91.11.31.5

LogAlk

LogHardness

0.5 1.0 1.5 2.0 2.5

0.5 1.0 1.5 2.0 2.5

LogTSS

-0.1

0.4

0.9

1.4

0.2

0.7

1.2

1.7

2.2

2.7

0.5

1.0

1.5

2.0

2.5

LogDOC

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5

Matrix plot for Dissolved Cu and Water Quality Parameters (all samples)

Figure A.17: Dissolved copper – water quality parameters matrix plot 

A-25 
LogCu

0.6

1.1

1.6

2.1

0.0

0.5

1.0

1.5

0.1 0.6 1.1 1.6

0.6 1.1 1.6 2.1

LogHardness

TSS

0 50 100 150 200 250

0.1

0.6

1.1

1.6

0.0 0.5 1.0 1.5

0

50

100

150

200

250

LogDOC

Matrix Plot for Tot Cu with Water Quality Parameters

Figure A.18: Total Copper – water quality parameters matrix plot 

LogCu

1.0

1.5

2.0

2.5

0.6

0.8

1.0

1.2

1.4

1.6

0.10 0.35 0.60 0.85 1.10 1.35

1.0 1.5 2.0 2.5

LogADP

Log.Rainfall

-1.10-0.85-0.60-0.35-0.100.150.40

0.6 0.8 1.0 1.2 1.4 1.6

Log.Duration

0.10

0.35

0.60

0.85

1.10

1.35

-1.10

-0.85

-0.60

-0.35

-0.10

0.15

0.40

Log.Intensity

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-2.0-1.8-1.6-1.4-1.2-1.0-0.8

Matrix plot for Diss Cu with Hydrological Parameters (composite samples)

Figure A.19: Dissolved copper – hydrologic variables matrix plot 

A-26 
LogCuFree

-0.1

0.4

0.9

1.4

0.5

0.7

0.9

1.1

1.3

1.5

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5

-0.1 0.4 0.9 1.4

LogCu

pH

6.106.356.606.857.107.35

0.5 0.7 0.9 1.1 1.3 1.5

LogAlk

LogHardness

0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5

6.10

6.35

6.60

6.85

7.10

7.35

0.5

1.0

1.5

2.0

2.5

LogDOC

Matrix Plot for Modeled Cu2+ With WQ parameters

Figure A.20: Modeled Free ionic copper – water quality parameters matrix plot 

A-27 
PerCuFree

-0.1

0.4

0.9

1.4

0.5

0.7

0.9

1.1

1.3

1.5

0.0

0.5

1.0

1.5

0.0 0.1 0.2 0.3

-0.1 0.4 0.9 1.4

LogCu

pH

6.106.356.606.857.107.35

0.5 0.7 0.9 1.1 1.3 1.5

LogAlk

LogHardness

0.5 1.0 1.5 2.0 2.5

0.0

0.1

0.2

0.3

0.0 0.5 1.0 1.5

6.10

6.35

6.60

6.85

7.10

7.35

0.5

1.0

1.5

2.0

2.5

LogDOC

Matrix Plot for Fraction Cu2+ with WQ Parameters

Figure A.21: Modeled Percent Free ionic copper – water quality parameters matrix plotx contains the 

relevant S-Plus output from the statistics analysis. Annotations denoting the nature of the analysis and 

any conclusions drawn accompany the output. 

A-28 
Below is the Fisher’s LSD simultaneous comparison of LogCu in composite samples at 

all sites:

*** Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogCu ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 1.674504 0.718946 

Deg. of Freedom 3 18 

Residual standard error: 0.1998536 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 1.674504 0.5581680 13.97465 0.00005991871 

Residuals 18 0.718946 0.0399414 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009 

response variable: LogCu 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.198 0.158 -0.13400 0.5300 

 Bend-I-5 -0.380 0.167 -0.73200 -0.0289 ** ** 

 Bend-Wemme 0.346 0.160 0.00968 0.6830   

 Dixon-I-5 -0.578 0.114 -0.81800 -0.3390 ** ** 

Dixon-Wemme 0.148 0.103 -0.06910 0.3660 

 I-5-Wemme 0.727 0.117 0.48100 0.9720   

The results show the following significant differences in LogCu Levels:

I-5 > Bend: 2.40 times more Cu (1.07-5.40, 95% CI)

Bend > Wemme: 2.22 times more Cu (1.02-4.82, 95% CI)

I-5 > Dixon: 3.78 times more Cu (2.18-6.58, 95% CI)

I-5 > Wemme: 5.33 times more Cu (3.03-9.38, 95% CI)

A-29 
Below is the Fisher’s LSD simultaneous comparison of LogDOC in composite samples at 

all sites:

** * Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogDOC ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 0.6699371 0.7085800 

Deg. of Freedom 3 18 

Residual standard error: 0.1984075 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 0.6699371 0.2233124 5.672786 0.006465129 

Residuals 18 0.7085800 0.0393656 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009 

response variable: LogDOC 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.150 0.157 -0.1790 0.4800 

 Bend-I-5 -0.148 0.166 -0.4970 0.2010 

 Bend-Wemme 0.318 0.159 -0.0160 0.6520 

 Dixon-I-5 -0.298 0.113 -0.5360 -0.0604 ** ** 

Dixon-Wemme 0.168 0.103 -0.0476 0.3840 

 I-5-Wemme 0.466 0.116 0.2220 0.7100   

The results show the following significant differences in LogCu Levels:

I-5 > Dixon: 1.99 times more DOC (1.15-3.44, 95% CI)

I-5 > Wemme: 2.92 times more DOC (1.67-5.13, 95% CI)

A-30 
Below is the Fisher’s LSD simultaneous comparison of LogTotCu in composite samples 

at all sites:

** * Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogCu ~ Site, data = STATS091028...CompTot, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 1.932551 1.211631 

Deg. of Freedom 3 18 

Residual standard error: 0.2594472 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 1.932551 0.6441835 9.569995 0.0005335319 

Residuals 18 1.211631 0.0673128 

Estimated Coefficients: 

 (Intercept) SiteDixon SiteI-5 SiteWemme 

 1.374617 -0.386052 0.278731 -0.4503735 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009 

response variable: LogCu 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.3860 0.205 -0.0449 0.817 

 Bend-I-5 -0.2790 0.217 -0.7350 0.177 

 Bend-Wemme 0.4500 0.208 0.0133 0.887 ** ** 

 Dixon-I-5 -0.6650 0.148 -0.9760 -0.354   

Dixon-Wemme 0.0643 0.134 -0.2180 0.346 

 I-5-Wemme 0.7290 0.152 0.4100 1.050 ** ** 

The results show the following significant differences in LogTotCu Levels

I-5 > Dixon: 4.62 times more TotCu (2.26-9.46, 95% CI)

I-5 > Wemme: 5.36 times more TotCu (2.57-11.22, 95% CI)

Bend > Wemme: 2.82 times more TotCu (1.03-7.71, 95% CI)

A-31 
Below is the regression analysis that examines the effect of AADT. This examines all of 

the composite samples, including I-5 samples. The 2nd model shows Cu correlated to I5 

site association and AADT.

*** Linear Model *** 

Call: lm(formula = LogCu ~ AADT, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3284 -0.1105 -0.006719 0.09367 0.5417 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.5369 0.0569 9.4421 0.0000 

 AADT 0.0000 0.0000 5.8032 0.0000 

Residual standard error: 0.2112 on 20 degrees of freedom 

Multiple R-Squared: 0.6274 

F-statistic: 33.68 on 1 and 20 degrees of freedom, the p-value is 0.00001118 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 AADT 1 1.501657 1.501657 33.67727 0.00001118255 

Residuals 20 0.891793 0.044590 

 

*** Linear Model *** 

Call: lm(formula = LogCu ~ I5.ind + AADT, data = STATS091028...CompDiss, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3248 -0.111 -0.0178 0.1058 0.5602 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.4740 0.1328 3.5689 0.0020 

 I5.ind -0.6635 1.2598 -0.5267 0.6045 

 AADT 0.0000 0.0000 1.0195 0.3208 

Residual standard error: 0.2151 on 19 degrees of freedom 

Multiple R-Squared: 0.6328 

F-statistic: 16.37 on 2 and 19 degrees of freedom, the p-value is 0.00007362 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 I5.ind 1 1.466401 1.466401 31.69836 0.0000199 

 AADT 1 0.048087 0.048087 1.03947 0.3207511 

Residuals 19 0.878961 0.046261 

Based on both of the outputs above, AADT does have a significant direct relationship w/ 

Cu in composite samples; but only one of the variables – AADT or the I-5 indicator –

accounts for this variability

A-32 
Below is the regression analysis that examines the effect of AADT in non-urban samples. 

This examines all of the composite samples NOT from I-5.

*** Linear Model *** 

Call: lm(formula = LogCu ~ AADT, data = STATS091028...CompDiss.NOI5, na.action 

= na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3248 -0.1236 -0.01925 0.1124 0.5602 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.4740 0.1458 3.2503 0.0054 

 AADT 0.0000 0.0000 0.9285 0.3678 

Residual standard error: 0.2362 on 15 degrees of freedom 

Multiple R-Squared: 0.05436 

F-statistic: 0.8622 on 1 and 15 degrees of freedom, the p-value is 0.3678 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 AADT 1 0.0480873 0.04808725 0.8621999 0.3678257 

Residuals 15 0.8365911 0.05577274 

 

Based on the above output, there is no significant correlation b/w Cu levels at non-urban 

sites w/ varying AADTs. This doesn’t necessarily mean there isn’t a relationship, but we 

certainly don’t have a large enough dataset to find one.

A-33 
Below is the regression analysis that examines the difference b/w Cu and DOC in FF 

samples versus composite samples at the Dixon site. 

*** Linear Model *** 

Call: lm(formula = LogCu ~ FF.ind, data = STATS091028...DixCompFFDiss, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3858 -0.1597 -0.01027 0.1216 0.4843 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.6362 0.0860 7.3987 0.0000 

 FF.ind 0.6034 0.1216 4.9614 0.0002 

Residual standard error: 0.2432 on 14 degrees of freedom 

Multiple R-Squared: 0.6374 

F-statistic: 24.62 on 1 and 14 degrees of freedom, the p-value is 0.000209 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 FF.ind 1 1.456205 1.456205 24.61531 0.0002089761 

Residuals 14 0.828219 0.059158 

*** Linear Model *** 

Call: lm(formula = LogDOC ~ FF.ind, data = STATS091028...DixCompFFDiss, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3302 -0.1261 -0.0294 0.09492 0.4447 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.5879 0.0740 7.9447 0.0000 

 FF.ind 0.6768 0.1046 6.4678 0.0000 

Residual standard error: 0.2093 on 14 degrees of freedom 

Multiple R-Squared: 0.7493 

F-statistic: 41.83 on 1 and 14 degrees of freedom, the p-value is 0.00001478 

Analysis of Variance Table 

Response: LogDOC 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 FF.ind 1 1.832457 1.832457 41.83288 0.0000147835 

Residuals 14 0.613259 0.043804 

So both higher Cu and higher DOC concentrations are significantly associated w/ a firstflush phenomenon. 

A-34 
Below is the model-building results for predicting Cu concentrations in composite 

samples as a function of LogADP (Antecedent Dry period), Log.Rainfall (Total Rainfall), 

Log.Intensity (Average Rainfall Intensity), AFTER accounting for site associations. 

Log.Duration (rainfall duration) was screened out as a variable due to covarying with 

Log.Rainfall.

 *** Stepwise Regression *** 

 *** Stepwise Model Comparisons *** 

Start: AIC= 1.2383 

 LogCu ~ I5.ind + Bend.ind + Wemme.ind + LogADP + Log.Rainfall + Log.Intensity 

Single term deletions 

Model: 

LogCu ~ I5.ind + Bend.ind + Wemme.ind + LogADP + Log.Rainfall + Log.Intensity 

scale: 0.0458637 

 Df Sum of Sq RSS Cp 

 <none> 0.5962280 1.238320 

 LogADP 1 0.00000841 0.5962365 1.146601 

 Log.Rainfall 1 0.09246753 0.6886956 1.239060 

Log.Intensity 1 0.01391247 0.6101405 1.160505 

Step: AIC= 1.1466 

 LogCu ~ I5.ind + Bend.ind + Wemme.ind + Log.Rainfall + Log.Intensity 

Single term deletions 

Model: 

LogCu ~ I5.ind + Bend.ind + Wemme.ind + Log.Rainfall + Log.Intensity 

scale: 0.0458637 

 Df Sum of Sq RSS Cp 

 <none> 0.5962365 1.146601 

 Log.Rainfall 1 0.09480012 0.6910366 1.149674 

Log.Intensity 1 0.01538608 0.6116225 1.070259 

Step: AIC= 1.0703 

 LogCu ~ I5.ind + Bend.ind + Wemme.ind + Log.Rainfall 

Single term deletions 

Model: 

LogCu ~ I5.ind + Bend.ind + Wemme.ind + Log.Rainfall 

scale: 0.0458637 

 Df Sum of Sq RSS Cp 

 <none> 0.6116225 1.070259 

Log.Rainfall 1 0.07942346 0.6910460 1.057956 

Step: AIC= 1.058 

 LogCu ~ I5.ind + Bend.ind + Wemme.ind 

 *** Linear Model *** 

Call: lm(formula = LogCu ~ I5.ind + Bend.ind + Wemme.ind, data = 

 STATS091028...CompDiss, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

A-35 
 -0.3858 -0.1106 0.002999 0.05849 0.4843 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.6362 0.0735 8.6591 0.0000 

 I5.ind 0.5783 0.1185 4.8814 0.0002 

 Bend.ind 0.0806 0.2204 0.3656 0.7194 

 Wemme.ind -0.1509 0.1122 -1.3442 0.1976 

Residual standard error: 0.2078 on 16 degrees of freedom 

Multiple R-Squared: 0.6986 

F-statistic: 12.36 on 3 and 16 degrees of freedom, the p-value is 0.0001945 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 I5.ind 1 1.504047 1.504047 34.82366 0.0000223 

 Bend.ind 1 0.019694 0.019694 0.45598 0.5091526 

Wemme.ind 1 0.078044 0.078044 1.80698 0.1976211 

Residuals 16 0.691046 0.043190 

This output shows no significant effect of any of the hydrologic variables on composite 

samples after accounting for their location.

A-36 
Below is the model-building results for a predictive DISSOLVED Cu model using water 

quality parameters.

*** Stepwise Regression *** 

 *** Stepwise Model Comparisons *** 

Start: AIC= 0.7773 

 LogCu ~ pH + LogCond + LogAlk + LogHardness + LogTSS + LogDOC 

Single term deletions 

Model: 

LogCu ~ pH + LogCond + LogAlk + LogHardness + LogTSS + LogDOC 

scale: 0.01653735 

 Df Sum of Sq RSS Cp 

 <none> 0.545733 0.777256 

 pH 1 0.085500 0.631232 0.829680 

 LogCond 1 0.000007 0.545739 0.744188 

 LogAlk 1 0.408351 0.954084 1.152532 

LogHardness 1 0.042475 0.588208 0.786656 

 LogTSS 1 0.038384 0.584116 0.782565 

 LogDOC 1 1.506971 2.052703 2.251151 

Step: AIC= 0.7442 

 LogCu ~ pH + LogAlk + LogHardness + LogTSS + LogDOC 

Single term deletions 

Model: 

LogCu ~ pH + LogAlk + LogHardness + LogTSS + LogDOC 

scale: 0.01653735 

 Df Sum of Sq RSS Cp 

 <none> 0.545739 0.744188 

 pH 1 0.089825 0.635564 0.800938 

 LogAlk 1 0.461036 1.006776 1.172149 

LogHardness 1 0.080184 0.625923 0.791297 

 LogTSS 1 0.041571 0.587310 0.752684 

 LogDOC 1 1.595148 2.140887 2.306261 

 *** Linear Model *** 

Call: lm(formula = LogCu ~ pH + LogAlk + LogHardness + LogTSS + LogDOC, data = 

 STATS091028...Diss, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.2424 -0.05989 -0.0008963 0.07676 0.3026 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.9282 0.5114 1.8152 0.0783 

 pH -0.1837 0.0777 -2.3656 0.0238 

 LogAlk 0.8125 0.1516 5.3594 0.0000 

LogHardness -0.1986 0.0889 -2.2351 0.0321 

 LogTSS 0.0916 0.0569 1.6093 0.1168 

 LogDOC 0.6544 0.0656 9.9689 0.0000 

Residual standard error: 0.1267 on 34 degrees of freedom 

Multiple R-Squared: 0.8859 

F-statistic: 52.77 on 5 and 34 degrees of freedom, the p-value is 4.663e-015 

Analysis of Variance Table 

A-37 
Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 pH 1 0.017844 0.017844 1.1117 0.2991534 

 LogAlk 1 2.506984 2.506984 156.1871 0.0000000 

LogHardness 1 0.083412 0.083412 5.1966 0.0290260 

 LogTSS 1 0.032061 0.032061 1.9974 0.1666574 

 LogDOC 1 1.595148 1.595148 99.3790 0.0000000 

 Residuals 34 0.545739 0.016051 

Important correlations are as follows:

Definitely Significant: DOC (+) > Alkalinity (+)

Significant: pH (-) > Hardness (-)

Questionable significance: TSS (+) –> p =0.117 > 0.05

Below is the Correlation matrix for WQ parameters with Cu in all measured dissolved 

samples.

*** Correlations for data in: STATS091028...Diss *** 

 LogCu LogDOC LogTSS LogHardness LogAlk LogCond 

 LogCu 1.00000000 0.87235734 0.23405853 0.4357839 0.6749536 0.3689499 

 LogDOC 0.87235734 1.00000000 0.11813486 0.3403690 0.4715702 0.2384475 

 LogTSS 0.23405853 0.11813486 1.00000000 0.4359164 0.2238464 0.2228422 

LogHardness 0.43578390 0.34036904 0.43591642 1.0000000 0.7064009 0.8324036 

 LogAlk 0.67495362 0.47157017 0.22384641 0.7064009 1.0000000 0.6828413 

 LogCond 0.36894992 0.23844752 0.22284219 0.8324036 0.6828413 1.0000000 

 pH 0.06109058 0.02895802 -0.08825452 0.1522432 0.4473318 0.1366475 

 pH 

 LogCu 0.06109058 

 LogDOC 0.02895802 

 LogTSS -0.08825452 

LogHardness 0.15224322 

 LogAlk 0.44733181 

 LogCond 0.13664750 

 pH 1.00000000 

Therefore, the strongest correlations with dissolved Cu are DOC (0.872) and Alkalinity 

(0.675).

A-38 
Below is the results for analyzing whether DOC or Alkalinity still has an effect on Cu in 

dissolved samples after the effect of Total Cu has been accounted for.

*** Linear Model *** 

Call: lm(formula = LogCu ~ LogTotCu + LogAlk + LogDOC, data = 

STATS091028...CuStats2, 

 na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.358 -0.04666 0.01221 0.065 0.242 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -0.2360 0.0750 -3.1468 0.0032 

 LogTotCu 0.4270 0.0803 5.3176 0.0000 

 LogAlk 0.1812 0.0997 1.8185 0.0769 

 LogDOC 0.5125 0.0653 7.8481 0.0000 

Residual standard error: 0.1135 on 38 degrees of freedom 

Multiple R-Squared: 0.8998 

F-statistic: 113.8 on 3 and 38 degrees of freedom, the p-value is 0 

28 observations deleted due to missing values 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LogTotCu 1 3.546153 3.546153 275.3741 0.00000000 

 LogAlk 1 0.057410 0.057410 4.4581 0.04136938 

 LogDOC 1 0.793175 0.793175 61.5934 0.00000000 

Residuals 38 0.489348 0.012878 

*** Linear Model *** 

Call: lm(formula = LogCu ~ LogTotCu + LogDOC + LogAlk, data = 

STATS091028...CuStats2, 

 na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.358 -0.04666 0.01221 0.065 0.242 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -0.2360 0.0750 -3.1468 0.0032 

 LogTotCu 0.4270 0.0803 5.3176 0.0000 

 LogDOC 0.5125 0.0653 7.8481 0.0000 

 LogAlk 0.1812 0.0997 1.8185 0.0769 

Residual standard error: 0.1135 on 38 degrees of freedom 

Multiple R-Squared: 0.8998 

F-statistic: 113.8 on 3 and 38 degrees of freedom, the p-value is 0 

28 observations deleted due to missing values 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LogTotCu 1 3.546153 3.546153 275.3741 0.00000000 

 LogDOC 1 0.808000 0.808000 62.7447 0.00000000 

 LogAlk 1 0.042584 0.042584 3.3068 0.07688124 

Residuals 38 0.489348 0.012878 

A-39 
*** Linear Model *** 

Call: lm(formula = LogCu ~ LogTotCu + LogAlk, data = STATS091028...CuStats2, 

 na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.4427 -0.1047 0.04144 0.1278 0.2612 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -0.2367 0.1199 -1.9746 0.0554 

 LogTotCu 0.7625 0.1086 7.0200 0.0000 

 LogAlk 0.2103 0.1591 1.3213 0.1941 

Residual standard error: 0.1813 on 39 degrees of freedom 

Multiple R-Squared: 0.7375 

F-statistic: 54.79 on 2 and 39 degrees of freedom, the p-value is 4.705e-012 

28 observations deleted due to missing values 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LogTotCu 1 3.546153 3.546153 107.8343 0.0000000 

 LogAlk 1 0.057410 0.057410 1.7458 0.1941104 

Residuals 39 1.282523 0.032885 

Yes, DOC still has a significant effect on the presence of Cu in dissolved samples. There 

is no significant effect of alkalinity (p=0.19) on dissolved Cu after total Cu has been 

accounted for.

A-40 
Below is the model-building results for a predictive TOTAL Cu model using water 

quality parameters (Hardness, TSS, and DOC).

*** Stepwise Regression *** 

 *** Stepwise Model Comparisons *** 

Start: AIC= 1.8908 

 LogCu ~ LogHardness + LogTSS + LogDOC 

Single term deletions 

Model: 

LogCu ~ LogHardness + LogTSS + LogDOC 

scale: 0.04297283 

 Df Sum of Sq RSS Cp 

 <none> 1.547022 1.890805 

LogHardness 1 0.002005 1.549027 1.806864 

 LogTSS 1 0.848402 2.395423 2.653260 

 LogDOC 1 1.495364 3.042386 3.300223 

Step: AIC= 1.8069 

 LogCu ~ LogTSS + LogDOC 

Single term deletions 

Model: 

LogCu ~ LogTSS + LogDOC 

scale: 0.04297283 

 Df Sum of Sq RSS Cp 

<none> 1.549027 1.806864 

LogTSS 1 1.180281 2.729308 2.901199 

LogDOC 1 1.687014 3.236041 3.407933 

Single term additions 

Model: 

LogCu ~ LogTSS + LogDOC 

scale: 0.04297283 

 Df Sum of Sq RSS Cp 

 <none> 1.549027 1.806864 

LogHardness 1 0.002005168 1.547022 1.890805 

 *** Linear Model *** 

Call: lm(formula = LogCu ~ LogTSS + LogDOC, data = STATS091028...Tot, na.action 

= 

 na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.5018 -0.1028 -0.002281 0.09106 0.5244 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.0036 0.1476 0.0246 0.9805 

 LogTSS 0.4349 0.0819 5.3096 0.0000 

 LogDOC 0.5807 0.0915 6.3479 0.0000 

Residual standard error: 0.2046 on 37 degrees of freedom 

Multiple R-Squared: 0.677 

F-statistic: 38.77 on 2 and 37 degrees of freedom, the p-value is 8.346e-010 

A-41 
Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LogTSS 1 1.558977 1.558977 37.23767 4.565006e-007 

 LogDOC 1 1.687014 1.687014 40.29596 2.125562e-007 

Residuals 37 1.549027 0.041866 

Important correlations are as follows:

Definitely Significant: DOC (+) > TSS (+)

Below is the Correlation matrix for WQ parameters with Cu in all measured dissolved 

samples.

*** Correlations for data in: STATS091028...Tot *** 

 LogCu LogDOC LogTSS LogHardness 

 LogCu 1.0000000 0.6563562 0.5701968 0.4611405 

 LogDOC 0.6563562 1.0000000 0.1181349 0.3219472 

 LogTSS 0.5701968 0.1181349 1.0000000 0.5044135 

LogHardness 0.4611405 0.3219472 0.5044135 1.0000000 

Therefore, the strongest correlations with dissolved Cu are DOC (0.656) and TSS 

(0.570).

A-42 
Below is the analysis of Cu and DOC throughout the course of a storm – looking at the 

flow-weighted samples from some of the Dixon Outfall storms. First Cu vs V/Vtot is 

examined, then DOC vs V/Vtot.

*** Linear Model *** 

Call: lm(formula = LogCu ~ V.Vtot, data = STATS091028...Flow, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.52 -0.1598 -0.08293 0.1366 0.7177 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.9108 0.1009 9.0273 0.0000 

 V.Vtot -0.6008 0.1713 -3.5064 0.0014 

Residual standard error: 0.2984 on 31 degrees of freedom 

Multiple R-Squared: 0.284 

F-statistic: 12.29 on 1 and 31 degrees of freedom, the p-value is 0.001409 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 V.Vtot 1 1.094571 1.094571 12.29457 0.001408807 

Residuals 31 2.759894 0.089029 

*** Linear Model *** 

Call: lm(formula = LogDOC ~ V.Vtot, data = STATS091028...Flow, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.4354 -0.2109 -0.02955 0.1996 0.5806 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.8502 0.0888 9.5758 0.0000 

 V.Vtot -0.5816 0.1508 -3.8570 0.0005 

Residual standard error: 0.2626 on 31 degrees of freedom 

Multiple R-Squared: 0.3243 

F-statistic: 14.88 on 1 and 31 degrees of freedom, the p-value is 0.0005427 

Analysis of Variance Table 

Response: LogDOC 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 V.Vtot 1 1.025829 1.025829 14.87638 0.0005427125 

Residuals 31 2.137665 0.068957 

*** Linear Model *** 

Call: lm(formula = LogCu ~ LogDOC + V.Vtot, data = STATS091028...Flow, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.495 -0.09022 0.04409 0.1303 0.3676 

A-43 
Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.2067 0.1397 1.4796 0.1494 

 LogDOC 0.8280 0.1421 5.8288 0.0000 

 V.Vtot -0.1192 0.1451 -0.8214 0.4179 

Residual standard error: 0.2077 on 30 degrees of freedom 

Multiple R-Squared: 0.6642 

F-statistic: 29.67 on 2 and 30 degrees of freedom, the p-value is 7.773e-008 

Analysis of Variance Table 

Response: LogCu 

Terms added sequentially (first to last) 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LogDOC 1 2.531151 2.531151 58.67275 0.0000000 

 V.Vtot 1 0.029109 0.029109 0.67475 0.4178792 

Residuals 30 1.294205 0.043140 

The above sets of output suggest that, both Cu and DOC values decrease over the course 

of a storm

A-44 
A.6 COMPOSITE STATISTICS 

Analysis of Variance Models** Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogCu ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 1.674504 0.718946 

Deg. of Freedom 3 18 

Residual standard error: 0.1998536 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 1.674504 0.5581680 13.97465 0.00005991871 

Residuals 18 0.718946 0.0399414 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009000000000002 

response variable: LogCu 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.198 0.158 -0.13400 0.5300 

 Bend-I-5 -0.380 0.167 -0.73200 -0.0289 ** ** 

 Bend-Wemme 0.346 0.160 0.00968 0.6830   

 Dixon-I-5 -0.578 0.114 -0.81800 -0.3390 ** ** 

Dixon-Wemme 0.148 0.103 -0.06910 0.3660 

 I-5-Wemme 0.727 0.117 0.48100 0.9720   

Appears to be significant differences in Cutot concentrations between I5 and 

all other sites, and between Bend and Wemme. 

A-45 
Lgands 

log LIGANDS 

** * Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = logL ~ Site, data = X10uMStats, na.action = na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 0.2088314 0.5679475 

Deg. of Freedom 3 15 

Residual standard error: 0.1945846 

2 observations deleted due to missing values 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 0.2088314 0.06961048 1.838475 0.1834906 

Residuals 15 0.5679475 0.03786317 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1314 

response variable: logL 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.1060 0.154 -0.2220 0.4330 

 Bend-I5 -0.0969 0.169 -0.4560 0.2620 

 Bend-Wemme 0.1950 0.163 -0.1520 0.5420 

 Dixon-I5 -0.2030 0.119 -0.4560 0.0515 

Dixon-Wemme 0.0893 0.111 -0.1470 0.3260 

 I5-Wemme 0.2920 0.131 0.0136 0.5700 ** ** 

Appears to be significant differences in Ligand concentrations between I5 and 

Wemme. 

A-46 
lgK 

*** Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = logK ~ Site, data = X10uMStats, na.action = na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 3.448158 6.215737 

Deg. of Freedom 3 14 

Residual standard error: 0.6663191 

3 observations deleted due to missing values 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 3.448158 1.149386 2.588817 0.09432543 

Residuals 14 6.215737 0.443981 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1448 

response variable: logK 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon -0.917 0.527 -2.050 0.212 

 Bend-I5 0.177 0.608 -1.130 1.480 

 Bend-Wemme -0.296 0.557 -1.490 0.900 

 Dixon-I5 1.090 0.451 0.127 2.060 ** ** 

Dixon-Wemme 0.622 0.380 -0.193 1.440 

 I5-Wemme -0.473 0.487 -1.520 0.571 

Appears to be significant differences in K-CuL between I5 and Dixon. 

A-47 
Cfree 

*** Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = logCufree ~ Site, data = Model.vs.Exp, na.action = na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 48.59906 17.85262 

Deg. of Freedom 3 16 

Residual standard error: 1.056309 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 48.59906 16.19969 14.5186 0.00007903105 

Residuals 16 17.85262 1.11579 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1199 

response variable: logCufree 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.925 0.835 -0.845 2.700 

 Bend-I5 -3.190 0.915 -5.130 -1.250 ** ** 

 Bend-Wemme 0.488 0.862 -1.340 2.320 

 Dixon-I5 -4.110 0.647 -5.480 -2.740   

Dixon-Wemme -0.437 0.570 -1.650 0.773 

 I5-Wemme 3.670 0.682 2.230 5.120 ** ** 

Appears to be significant differences in Cufree concentrations between I5 and 

all other sites. 

A-48 
Akalinity 

*** Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogAlk ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 0.8183919 0.5338832 

Deg. of Freedom 3 18 

Residual standard error: 0.1722213 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 0.8183919 0.2727973 9.197427 0.0006591279 

Residuals 18 0.5338832 0.0296602 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009 

response variable: LogAlk 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.2150 0.1360 -0.0713 0.5010 

 Bend-I-5 -0.2730 0.1440 -0.5760 0.0297 

 Bend-Wemme 0.1570 0.1380 -0.1330 0.4470 

 Dixon-I-5 -0.4880 0.0982 -0.6940 -0.2810 ** ** 

Dixon-Wemme -0.0577 0.0891 -0.2450 0.1300 

 I-5-Wemme 0.4300 0.1010 0.2180 0.6420   

Appears to be significant differences in alkalinity between I5 and Wemme and I5 and Dixon. 

A-49 
Hrdness 

** * Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogHardness ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 1.615319 0.254814 

Deg. of Freedom 3 14 

Residual standard error: 0.1349111 

4 observations deleted due to missing values 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 1.615319 0.5384398 29.58299 2.570929e-006 

Residuals 14 0.254814 0.0182010 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1448 

response variable: LogHardness 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.750 0.1070 0.5210 0.9790 ** ** 

 Bend-I-5 0.151 0.1130 -0.0907 0.3930 

 Bend-Wemme 0.584 0.1230 0.3200 0.8480   

 Dixon-I-5 -0.599 0.0769 -0.7640 -0.4340 ** ** 

Dixon-Wemme -0.166 0.0913 -0.3620 0.0301 

 I-5-Wemme 0.433 0.0985 0.2210 0.6440   

Appears to be significant differences in hardness between I5 and Wemme,I-5 and 

Dixon, Bend and Dixon, and Bend and Wemme. 

A-50 
DC 

** * Analysis of Variance Model *** 

Short Output: 

Call: 

 aov(formula = LogDOC ~ Site, data = STATS091028...CompDiss, na.action = 

 na.exclude) 

Terms: 

 Site Residuals 

 Sum of Squares 0.6699371 0.7085800 

Deg. of Freedom 3 18 

Residual standard error: 0.1984075 

Estimated effects may be unbalanced 

 Df Sum of Sq Mean Sq F Value Pr(F) 

 Site 3 0.6699371 0.2233124 5.672786 0.006465129 

Residuals 18 0.7085800 0.0393656 

95 % non-simultaneous confidence intervals for specified 

linear combinations, by the Fisher LSD method 

critical point: 2.1009 

response variable: LogDOC 

intervals excluding 0 are flagged by ' ' 

 Estimate Std.Error Lower Bound Upper Bound 

 Bend-Dixon 0.150 0.157 -0.1790 0.4800 

 Bend-I-5 -0.148 0.166 -0.4970 0.2010 

 Bend-Wemme 0.318 0.159 -0.0160 0.6520 

 Dixon-I-5 -0.298 0.113 -0.5360 -0.0604 ** ** 

Dixon-Wemme 0.168 0.103 -0.0476 0.3840 

 I-5-Wemme 0.466 0.116 0.2220 0.7100   

Appears to be significant differences in DOC between I5 and Wemme, and I-5 and 

Dixon. 

A.6.1 Paired t-Tests 

A.6.1.1 10μM SA vs 2μM SA Tests 

L10 vs L2 

Paired t-Test 

data: x: L10 in X10uM.vs.2uM , and y: L2 in X10uM.vs.2uM 

t = -3.7639, df = 4, p-value = 0.0197 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 -325.21382 -49.10218 

sample estimates: 

 mean of x - y 

 -187.158 

Mean ligand concentration determined at 10uM SA is 187.2 nM less than the 

ligand concentration determined at 2uM (49.1 to 325.2nM, 95%CI). 

K10 vs K2 

Paired t-Test 

A-51 
data: x: logK10 in X10uM.vs.2uM , and y: logK2 in X10uM.vs.2uM 

t = 6.2261, df = 3, p-value = 0.0084 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 0.5585153 1.7264847 

sample estimates: 

 mean of x - y 

 1.1425 

Mean conditional stability constants determined at 10uM SA are 1.1 log units 

stronger than constants determined at 2uM (0.6 to 1.7 log units, 95%CI). 

A.6.1.2 Modeled Cufree Concentrations vs Experimental 

SHM 

Paired t-Test 

data: x: SHM.Cufree in SHM , and y: logCufree in SHM 

t = 2.9903, df = 19, p-value = 0.0075 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 0.3291906 1.8649502 

sample estimates: 

 mean of x - y 

 1.09707 

Mean free copper concentration calculated by SHM is 1.1 log units higher than 

actual concentrations (0.33 to 1.86 log units, 95%CI). 

Gaussian 

Paired t-Test 

data: x: G.Cufree in Gaussian , and y: logCufree in Gaussian 

t = 9.247, df = 19, p-value = 0 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 2.619679 4.152543 

sample estimates: 

 mean of x - y 

 3.386111 

Mean free copper concentration calculated by Gaussian DOM model is 3.39 log 

units higher than actual concentrations (2.62 to 4.15 log units, 95%CI). 

NICA 

Paired t-Test 

data: x: ND.Cufree in NICA , and y: logCufree in NICA 

t = 3.7459, df = 19, p-value = 0.0014 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 0.6061475 2.1413117 

sample estimates: 

 mean of x - y 

 1.37373 

Mean free copper concentration calculated by the NICA-Donnan DOM model is 1.37 

log units higher than actual concentrations (0.61 to 2.14 log units, 95%CI). 

A-52 
A.6.2 Other t-Tests 

Bend (3) vs Wemme (4) CuTot 

Welch Modified Two-Sample t-Test 

data: x: LogCu with Site = Bend , and y: LogCu with Site = Wemme 

t = 2.6806, df = 1.4516814649241221, p-value = 0.1602 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -0.4683204 1.1609882 

sample estimates: 

 mean of x mean of y 

 2.031283 1.684949 

(INSIGNIFICANT) 

I5 (1) vs Bend (3) (Cutot) 

Welch Modified Two-Sample t-Test 

data: x: LogCu with Site = Bend , and y: LogCu with Site = I-5 

t = -3.0133, df = 1.3226930265858172, p-value = 0.1526 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -1.2997432 0.5392642 

sample estimates: 

 mean of x mean of y 

 2.031283 2.411523 

(INSIGNIFICANT) 

I5 (1) vs Wemme (4) CuTot 

Welch Modified Two-Sample t-Test 

data: x: LogCu with Site = I-5 , and y: LogCu with Site = Wemme 

t = 10.2681, df = 9.9778049885117035, p-value = 0 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 0.5688617 0.8842851 

sample estimates: 

 mean of x mean of y 

 2.411523 1.684949 

Median dissolved copper concentration at I-5 is 5.3 times higher than at Wemme 

(3.7 to 7.7 fold, 95%CI). 

I5 (1) vs Wemme (4) Ligands 

Welch Modified Two-Sample t-Test 

data: x: logL with SiteNum = 1 , and y: logL with SiteNum = 4 

t = 1.8834, df = 6.976473980885312, p-value = 0.1018 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -0.07480899 0.65844744 

sample estimates: 

 mean of x mean of y 

 2.245437 1.953618 

(INSIGNIFICANT)

I5 (1) vs Dixon (2) Cutot 

A-53 
Welch Modified Two-Sample t-Test 

data: x: LogCu with Site = Dixon , and y: LogCu with Site = I-5 

t = -5.3834, df = 9.6480853724751867, p-value = 0.0003 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -0.8188854 -0.3377784 

sample estimates: 

 mean of x mean of y 

 1.833191 2.411523 

Median dissolved copper concentration at I-5 is 3.8 times higher than at Dixon 

Outfall (2.2 to 6.6 fold, 95%CI). 

I5 vs Dixon (CuFREE) 

Welch Modified Two-Sample t-Test 

data: x: logCufree with Site = Dixon , and y: logCufree with Site = I5 

t = -4.0519, df = 3.28971163034866, p-value = 0.0227 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -7.185665 -1.037332 

sample estimates: 

 mean of x mean of y 

 -12.195 -8.083501 

Mean free copper concentration at I-5 is 4.1 log units higher than at Dixon 

Outfall (1.0 to 7.2 log units, 95%CI). 

I5 (1) vs Wemme (4) Cufree 

Welch Modified Two-Sample t-Test 

data: x: logCufree with SiteNum = 1 , and y: logCufree with SiteNum = 3 

t = 3.3444, df = 3.5527422923508607, p-value = 0.0344 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 0.4756851 7.0193149 

sample estimates: 

 mean of x mean of y 

 -8.1775 -11.925 

Mean free copper concentration at I-5 is 3.7 log units higher than at Wemme 

(0.5 to 7.0 log units, 95%CI). 

I5 (1) vs Bend (3) Cufree 

Welch Modified Two-Sample t-Test 

data: x: logCufree with SiteNum = 1 , and y: logCufree with SiteNum = 3 

t = 2.9597, df = 3.8057746730213009, p-value = 0.0442 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 0.1361209 6.2368764 

sample estimates: 

 mean of x mean of y 

 -8.083501 -11.27 

Mean free copper concentration at I-5 is 3.2 log units higher than at Bend (0.1 

to 6.2 log units, 95%CI). 

I5 (1) vs Dixon logK 

Welch Modified Two-Sample t-Test 

A-54 
data: x: logK with SiteNum = 1 , and y: logK with SiteNum = 2 

t = -3.7131, df = 3.2656098628248924, p-value = 0.0294 

alternative hypothesis: difference in means is not equal to 0 

95 percent confidence interval: 

 -1.9523665 -0.1943002 

sample estimates: 

 mean of x mean of y 

 11.59333 12.66667 

Two of the Dixon Stability constants used in this comparison may not be 

accurate, so the significance between I-5 and Dixon stability constants is in 

question. 

A.6.3 Linear Regression Models 

logCutot vs logDOC 

** * Linear Model *** 

Call: lm(formula = LogCu ~ LogDOC, data = STATS091028...CompDiss, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3348 -0.1079 -0.008873 0.1055 0.3595 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.2411 0.1013 12.2463 0.0000 

 LogDOC 1.1275 0.1525 7.3954 0.0000 

Residual standard error: 0.179 on 20 degrees of freedom 

Multiple R-Squared: 0.7322 

F-statistic: 54.69 on 1 and 20 degrees of freedom, the p-value is 3.84e-007 

There is a correlation with dissolved copper and dissolved organic carbon 

concentration. 

logCutot vs logHardness 

*** Linear Model *** 

Call: lm(formula = LogCu ~ LogHardness, data = STATS091028...CompDiss, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.4383 -0.1698 -0.03054 0.1993 0.495 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.1515 0.2347 4.9065 0.0002 

LogHardness 0.7086 0.1924 3.6833 0.0020 

Residual standard error: 0.2631 on 16 degrees of freedom 

Multiple R-Squared: 0.4589 

F-statistic: 13.57 on 1 and 16 degrees of freedom, the p-value is 0.002012 

4 observations deleted due to missing values 

There is a correlation with dissolved copper concentration and hardness. 

logCutot vs logAlkalinity 

*** Linear Model *** 

Call: lm(formula = LogCu ~ LogAlk, data = STATS091028...CompDiss, na.action = 

A-55 
na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.4758 -0.1247 -0.06523 0.1724 0.5282 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.0735 0.2050 5.2356 0.0000 

 LogAlk 0.9278 0.2132 4.3513 0.0003 

Residual standard error: 0.2479 on 20 degrees of freedom 

Multiple R-Squared: 0.4863 

F-statistic: 18.93 on 1 and 20 degrees of freedom, the p-value is 0.0003095 

There is a correlation with dissolved copper and alkalinity. 

logL10 vs logDOC 

*** Linear Model *** 

Call: lm(formula = logL ~ logDOC, data = X10uMStats, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.2662 -0.1287 0.02001 0.1254 0.3113 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.7495 0.1078 16.2299 0.0000 

 logDOC 0.5193 0.1615 3.2167 0.0051 

Residual standard error: 0.1685 on 17 degrees of freedom 

Multiple R-Squared: 0.3784 

F-statistic: 10.35 on 1 and 17 degrees of freedom, the p-value is 0.005063 

2 observations deleted due to missing values 

There is a correlation with ligand concentration and DOC. 

logL10 vs logHardness 

 *** Linear Model *** 

Call: lm(formula = logL ~ logHardness, data = X10uMStats, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.2869 -0.1499 0.03471 0.1039 0.3568 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.8420 0.1800 10.2318 0.0000 

logHardness 0.2021 0.1538 1.3140 0.2086 

Residual standard error: 0.1916 on 15 degrees of freedom 

Multiple R-Squared: 0.1032 

F-statistic: 1.726 on 1 and 15 degrees of freedom, the p-value is 0.2086 

4 observations deleted due to missing values 

(INSIGNIFICANT) 

logL10 vs Alkalinity 

*** Linear Model *** 

Call: lm(formula = logL ~ logAlkalinity, data = X10uMStats, na.action = 

na.exclude) 

Residuals: 

A-56 
 Min 1Q Median 3Q Max 

 -0.3003 -0.1393 0.04074 0.09637 0.3073 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

 (Intercept) 1.6685 0.1668 10.0042 0.0000 

logAlkalinity 0.4518 0.1802 2.5069 0.0226 

Residual standard error: 0.1826 on 17 degrees of freedom 

Multiple R-Squared: 0.2699 

F-statistic: 6.285 on 1 and 17 degrees of freedom, the p-value is 0.02262 

2 observations deleted due to missing values 

There is a correlation with ligand concentration and alkalinity. 

logCufree vs logDOC 

*** Linear Model *** 

Call: lm(formula = logCufree ~ logDOC, data = Model.vs.Exp, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.7435 -0.4752 -0.1158 0.4342 1.144 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -12.9557 0.3847 -33.6746 0.0000 

 logDOC 1.9386 0.6343 3.0562 0.0080 

Residual standard error: 0.5876 on 15 degrees of freedom 

Multiple R-Squared: 0.3837 

F-statistic: 9.34 on 1 and 15 degrees of freedom, the p-value is 0.008002 

3 observations deleted due to missing values 

There is a correlation between free copper and dissolved organic carbon 

concentration. 

*** Linear Model *** 

Call: lm(formula = logCufree ~ logHardness, data = Model.vs.Exp, na.action = 

 na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.5845 -0.3191 -0.125 0.1629 1.116 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -13.8002 0.4869 -28.3446 0.0000 

logHardness 1.6627 0.4427 3.7555 0.0027 

Residual standard error: 0.4775 on 12 degrees of freedom 

Multiple R-Squared: 0.5403 

F-statistic: 14.1 on 1 and 12 degrees of freedom, the p-value is 0.002744 

6 observations deleted due to missing values 

There is a correlation between free copper concentration and hardness.

logCufree vs logCuTot 

*** Linear Model *** 

Call: lm(formula = logCufree ~ logCuTot, data = Model.vs.Exp, na.action = 

na.exclude 

 ) 

Residuals: 

A-57 
 Min 1Q Median 3Q Max 

 -0.9401 -0.3349 -0.1056 0.3378 1.121 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) -15.2932 1.0673 -14.3294 0.0000 

 logCuTot 1.8597 0.5738 3.2412 0.0055 

Residual standard error: 0.574 on 15 degrees of freedom 

Multiple R-Squared: 0.4119 

F-statistic: 10.51 on 1 and 15 degrees of freedom, the p-value is 0.005481 

3 observations deleted due to missing values 

There is a correlation between free copper and dissolved copper.

logCufree vs Alkalinity 

*** Linear Model *** 

Call: lm(formula = logCufree ~ logAlkalinity, data = Model.vs.Exp, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -1.425 -0.8586 -0.2291 0.7071 2.158 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

 (Intercept) -16.9884 1.0286 -16.5167 0.0000 

logAlkalinity 6.4813 1.1069 5.8556 0.0000 

Residual standard error: 1.127 on 18 degrees of freedom 

Multiple R-Squared: 0.6558 

F-statistic: 34.29 on 1 and 18 degrees of freedom, the p-value is 0.00001517 

There is a correlation between free copper and alkalinity.

logK10 vs log DOC 

*** Linear Model *** 

Call: lm(formula = logK ~ logDOC, data = X10uMStats, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -1.655 -0.3541 -0.0576 0.4123 1.337 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 12.8436 0.4863 26.4086 0.0000 

 logDOC -1.0157 0.7528 -1.3493 0.1960 

Residual standard error: 0.7364 on 16 degrees of freedom 

Multiple R-Squared: 0.1022 

F-statistic: 1.821 on 1 and 16 degrees of freedom, the p-value is 0.196 

3 observations deleted due to missing values 

(INSIGNIFICANT) 

LogK10 vs logAlkalinity 

*** Linear Model *** 

Call: lm(formula = logK ~ logAlkalinity, data = X10uMStats, na.action = 

na.exclude) 

A-58 
Residuals: 

 Min 1Q Median 3Q Max 

 -1.171 -0.2656 -0.0798 0.2153 1.137 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

 (Intercept) 14.3309 0.5201 27.5526 0.0000 

logAlkalinity -2.4021 0.5769 -4.1640 0.0007 

Residual standard error: 0.5384 on 16 degrees of freedom 

Multiple R-Squared: 0.5201 

F-statistic: 17.34 on 1 and 16 degrees of freedom, the p-value is 0.0007316 

3 observations deleted due to missing values 

There is a correlation between the calculated conditional stability constant 

and alkalinity.

Log10 vs logHardness 

*** Linear Model *** 

Call: lm(formula = logK ~ logHardness, data = X10uMStats, na.action = 

na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.7087 -0.2939 -0.0731 0.3704 0.785 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 13.9269 0.4370 31.8705 0.0000 

logHardness -1.3954 0.3805 -3.6672 0.0025 

Residual standard error: 0.4539 on 14 degrees of freedom 

Multiple R-Squared: 0.4899 

F-statistic: 13.45 on 1 and 14 degrees of freedom, the p-value is 0.002537 

5 observations deleted due to missing values 

There is a correlation between the calculated conditional stability constant 

and hardness.

Alkalinity vs Hardness 

 

*** Linear Model *** 

Call: lm(formula = LogAlk ~ LogHardness, data = STATS091028...CompDiss, 

na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.3443 -0.09597 0.05272 0.122 0.271 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.1551 0.1609 0.9640 0.3494 

LogHardness 0.6408 0.1319 4.8577 0.0002 

Residual standard error: 0.1804 on 16 degrees of freedom 

Multiple R-Squared: 0.5959 

F-statistic: 23.6 on 1 and 16 degrees of freedom, the p-value is 0.0001746 

4 observations deleted due to missing values 

There is a correlation between alkalinity and hardness.

A-59 
A.6.4 Intra Storm Samples 

Below is the summary of the one-sample t-test comparing Ligand concentrations between FF and 

composite samples on a particular day. For this reason, it is a one-sample t-test; I assume there is 

dependence between the FF and composite samples for a particular storm. What’s quantified here is the 

difference b/w the 2 values.

One-sample t-Test 

data: D.L in FF.Comp.Speciation.Summary 

t = 2.9108, df = 7, p-value = 0.0226 

alternative hypothesis: mean is not equal to 0 

95 percent confidence interval: 

 25.12649 242.67351 

sample estimates: 

 mean of x 

 133.9 

 One-sample t-Test 

data: D.LogL in FF.Comp.Speciation.Summary 

t = 2.7489, df = 7, p-value = 0.0286 

alternative hypothesis: mean is not equal to 0 

95 percent confidence interval: 

 0.03564474 0.47436309 

sample estimates: 

 mean of x 

 0.2550039 

Therefore, with no transformations, FF samples have [L] that’s 133.9 nM higher than their composite 

samples (25.1 – 243, 95% CI). This is significant (p = 0.0226).

Considering the log-transformation, FF samples average [L] 1.8 times higher than their composite samples 

(1.09 – 2.98, 95% CI). This is significant (p = 0.0286).

Paired t-Test 

data: x: Lff in STATS.FF.Composite.Comparison , and y: Lcomp in 

STATS.FF.Composite.Comparison 

t = 2.934, df = 7, p-value = 0.0219 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 27.59075 256.77425 

sample estimates: 

 mean of x - y 

 142.1825 

 Paired t-Test 

data: x: logLff in STATS.FF.Composite.Comparison , and y: logLcomp in 

STATS.FF.Composite.Comparison 

t = 2.8078, df = 7, p-value = 0.0262 

alternative hypothesis: mean of differences is not equal to 0 

95 percent confidence interval: 

 0.04152703 0.48469452 

sample estimates: 

 mean of x - y 

 0.2631108 

Below is the summary of the [L] vs DOC association

A-60 
*** Linear Model *** 

Call: lm(formula = .L. ~ DOC, data = 

Speciation.Summary...Dixon.Outfall...STATS, 

 na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -194.5 -37.25 -2.837 48.53 144.1 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 14.6830 23.7205 0.6190 0.5420 

 DOC 13.7094 1.6788 8.1662 0.0000 

Residual standard error: 75.39 on 23 degrees of freedom 

Multiple R-Squared: 0.7436 

F-statistic: 66.69 on 1 and 23 degrees of freedom, the p-value is 3.009e-008 

*** Linear Model *** 

Call: lm(formula = log.L. ~ LogDOC, data = 

 Speciation.Summary...Dixon.Outfall...STATS, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.6969 -0.08464 0.08434 0.2046 0.4301 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.4381 0.1664 8.6414 0.0000 

 LogDOC 0.6871 0.1730 3.9713 0.0006 

Residual standard error: 0.3236 on 23 degrees of freedom 

Multiple R-Squared: 0.4068 

F-statistic: 15.77 on 1 and 23 degrees of freedom, the p-value is 0.0006041 

Therefore, with no transformations, [L] is significantly (p<0.0001) positively associated with DOC. On 

average, a unit (1 mg/L) increase in DOC is associated with an increase in [L] of 13.7 nM (10.2 – 17.2, 

95% CI).

Considering the log-transformation, [L] is significantly (p=0.0006) positively associated with DOC. On 

average, a doubling in DOC is associated with a 1.61-fold increase in [L] (1.26 – 2.06, 95% CI).

A-61 
Below is the summary of the [L] vs Hardness association. We didn’t examine the untransformed [L]-

Hardness association because it looks to violate the equal variance assumption.

*** Linear Model *** 

Call: lm(formula = log.L. ~ LogHard, data = 

 Speciation.Summary...Dixon.Outfall...STATS, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.6865 -0.1634 0.05888 0.2686 0.4934 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.2614 0.2507 5.0322 0.0000 

 LogHard 0.6783 0.2079 3.2620 0.0034 

Residual standard error: 0.3474 on 23 degrees of freedom 

Multiple R-Squared: 0.3163 

F-statistic: 10.64 on 1 and 23 degrees of freedom, the p-value is 0.003429 

Considering the log-transformation, [L] is significantly (p=0.0034) positively associated with Hardness. 

On average, a doubling in Hardness is associated with a 1.60-fold increase in [L] (1.19 – 2.16, 95% CI).

A-62 
A-63 

Below is the summary of the [L] vs Alkalinity association. I didn’t examine the 

untransformed [L]-Alkalinity association because it looks to violate the equal variance 

assumption.

*** Linear Model *** 

Call: lm(formula = log.L. ~ LogAlk, data = 

 Speciation.Summary...Dixon.Outfall...STATS, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -0.9082 -0.1732 0.1009 0.1809 0.6115 

Coefficients: 

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.4856 0.3624 4.0996 0.0004 

 LogAlk 0.6256 0.3939 1.5881 0.1259 

Residual standard error: 0.3988 on 23 degrees of freedom 

Multiple R-Squared: 0.09882 

F-statistic: 2.522 on 1 and 23 degrees of freedom, the p-value is 0.1259 

Considering the log-transformation, [L] is NOT significantly associated with Alkalinity 

(p>0.05).