## COPPER SPECIATION IN HIGHWAY STORMWATER RUNOFF AS RELATED TO BIOAVAILABILIT Y 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 2011Technical 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 Stor mwater 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 Organiza tion 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 (Cu 2+ diss ) in Oregon highway runoff; 2) establish an analytical technique suitable for the determination of copper speciation in highway stormw ater runoff; 3) compare analy tically determined free ionic copper (Cu 2+ free ) concentrations in highway stormwater runoff with modeled concentrations; and 4) develop a qualitative understanding of where and when coppe r 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 Cu 2+ diss and Cu 2+ free than the non-urban sites with lower AADT. First flush samples displayed consistently higher concentrations of both Cu 2+ diss and Cu 2+ 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 high way stormwater runoff. Analytical resu lts 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 stormwat er is bioavailable; the concentrations of Cu 2+ free were generally several orders of magnitudes below levels found to inhibit olfaction in Endangered Species Act listed fish species. Elevated Cu 2+ diss levels proved to be the greatest indicator of high Cu 2+ free concentrations.

Urban sites with AADT and first flush samples ch aracterized by elevated concentrations of Cu 2+ diss are of the greatest concern with respect to elevated free ionic copper concentrations. Available dissolved organic matter models in Visual MINTEQ overestimate Cu 2+ free concentrations when compared to analytically determined Cu 2+ 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

iSI* (MODERN METRIC) CONVERSION FACTORSAPPROXIMATE CONVERSIONS TO SI UNITSAPPROXIMATE CONVERSIONS FROM SI UNITSSymbolWhen You KnowMultiply ByTo FindSymbolSymbolWhen You KnowMultiply ByTo FindSymbolLENGTHLENGTHininches25.4millimetersmmmmmillimeters0.039inchesinftfeet0.305metersmmmeters3.28feetftydyards0.914metersmmmeters1.09yardsydmimiles1.61kilometerskmkmkilometers0.621milesmiAREAAREAin2square inches645.2millimeters squaredmm2mm2millimeters squared0.0016square inchesin2ft2square feet0.093meters squaredm2m2meters squared10.764square feetft2yd2square yards0.836meters squaredm2m2meters squared1.196square yardsyd2acacres0.405hectareshahahectares2.47acresacmi2square miles2.59kilometers squaredkm2km2kilometers squared0.386square milesmi2VOLUMEVOLUMEfl ozfluid ounces29.57millilitersmlmlmilliliters0.034fluid ouncesfl ozgalgallons3.785litersLLliters0.264gallonsgalft3cubic feet0.028meters cubedm3m3meters cubed35.315cubic feetft3yd3cubic yards0.765meters cubedm3m3meters cubed1.308cubic yardsyd3NOTE: Volumes greater than 1000 L shall be shown in m3.MASSMASSozounces28.35gramsgggrams0.035ouncesozlbpounds0.454kilogramskgkgkilograms2.205poundslbTshort tons (2000 lb)0.907megagramsMgMgmegagrams1.102short tons (2000 lb)TTEMPERATURE (exact)TEMPERATURE (exact)°FFahrenheit(F-32)/1.8Celsius°C°CCelsius1.8C+32Fahrenheit°F*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. Th e financial support of this project allowed the education and traini ng 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 than k Kathy Motter from the OSU Institute for Water and Watersheds Collaboratory for her help w ith dissolved organic carbon measurements, Andy Ungerer from the W.M Keck Collaboratory for Pl asma Spectrometry at OSU for assistance with trace metals analysis, and Mohammad Azizian for assistance with genera l 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 Environm ental 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 Committ ee (Miguel Estrada, William Fletcher, Devin Simmons, William VanPeeters, Chris Woods, Je ffrey 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 St ates Government assume no liability of its contents or use thereof.

The contents of this report refl ect the view of the au thors who are solely responsible for the facts and accuracy of the material presented. The conten ts do not necessarily reflect the official views of the Oregon Department of Tran sportation 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 th e object of this document.

This report does not constitute a stan dard, specification, or regulation.

iiiiv

### COPPER SPECIATION IN HIGHW AY STORMWATER RUNOFF AS RELATED TO BIOAVAILABILITY AN D 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 SPECIAT ION 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 Qu antifying 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 .......................................................................................................3 1

v3.1.3 Sa mpling 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 .........................................................................................................4 9 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

x CuSA 

###  ............................................................................................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 Li gand 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

vi4.2.4.4 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..............................................................................................................1 04

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 aeria l view............................................................................................. ...................................28 Figure 3.2: Portland/ I-5 site aer ial view ...................................................................................... ................................29 Figure 3.3: Bend site aeria l view .............................................................................................. ...................................30 Figure 3.4: Wemme site aerial view ............................................................................................. ...............................31 Figure 3.5: Portland/I-5 site pipe in stalla tion ................................................................................ ..............................32 Figure 3.6: Wemme site pipe inst allation ....................................................................................... .............................32 Figure 3.7: Bend site pipe installation ........................................................................................ .................................33 Figure 3.8: Dixon Outfall site pipe in stalla tion ............................................................................... ............................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 tota l 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 fo ur sampling sites ...................................................................................... .........................54 Figure 4.5: Relationship between dissolved copper concentrations in composite samples and annual average daily traffic for the th ree 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 sa mpling s ites ..................................................................................................... ...............................58 Figure 4.10: Relationship between dissolved copper concentrations in composite samples and rainfall duration at all four sa mpling s ites ..................................................................................................... ...............................59 Figure 4.11: Relationship between dissolved copper concentrations in composite samples and rainfall intensity at all four sa mpling s ites ..................................................................................................... ...............................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 or ganic carbon in all samples ............................62 Figure 4.14: Relationship be tween dissolved copper and al kalinity in a ll 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 concentra tions and dissolved organic carbon in all samples ............64 Figure 4.17: Relationship between dissolved and total copper c oncentrations 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 Outf all s ite......................................................................................... ........................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 norma lized 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 normal ized 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 adde d copper ............................................................................................................. ..............................70 Figure 4.23: Copper titration of Wemme composite sample from 3/24/09 containi ng 2 μM SA................................71 Figure 4.24: Non-linear least squares fit of the titra tion 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

viiiFigure 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 contai ning 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 containi ng 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 detec tion wi ndow ............................................................................. .........77 Figure 4.29: Relationship between total ligand concentration and dissolved organic carbon content of composite samples collected at a ll for sampling sites .................................................................................... .....................77 Figure 4.30: Determined stability constants for th e 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 [Cu 2+ diss ] > [ L T ]; a “worst case” [Cu 2+ free ] was estimated as [Cu 2+ diss ]

− [ L T ]...........82 Figure 4.34: Effects of dissolved copper on free ionic copp er concen trations ................................................... .........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 samp les at Dixo n Outfall ...................................... 89 Figure 4.38: Comparison between [L T ] values for first flush and composite samples ................................................90 Figure 4.39: Relationship between [L T ] and DOC for Dixon Outfall samples............................................................91 Figure 4.40: Relationship between [L T ] and hardness for Dixo n 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 Cu 2+ in composite samples.............................95 Figure 4.43: The first flush effect on free ionic co pper ...............................................................................................96 Figure 4.44: The first flush effect on Percent fre e ionic copper ............................................................... ...................97 Figure 4.45: Intra-storm fr ee ionic copper and Cu-DOM.......................................................................... ..................98 Figure A.1: Dixon Outf all 11/8/08 storm data................................................................................... ........................A-4 Figure A.2: Dixon Outfa ll 11/20/08 storm data.................................................................................. .......................A-5 Figure A.3: Dixon Outf all 12/1/08 storm data................................................................................... ........................A-6 Figure A.4: Dixon Outf all 2/6/09 st orm data.................................................................................... .........................A-7 Figure A.5: Dixon Outf all 2/23/09 storm data................................................................................... ........................A-8 Figure A.6: Dixon Outf all 3/14/09 storm data................................................................................... ........................A-9 Figure A.7: Dixon Outf all 4/12/09 storm data................................................................................... ......................A-10 Figure A.8: Dixon Outf all 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 fr om Herrera sites ........................................................................................... ...........................A-14 Figure A.11: Data from He rrera sites, continued................................................................................ .....................A-15 Figure A.12: Dixon Outfall co mposite 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 sp eciation data .......................................................................... ...........A-21 Figure A.17: Dissolved copper – wa ter quality paramete rs matrix plot ........................................................... .......A-25 Figure A.18: Total Copper – water quality parameters matrix plot............................................................... ..........A-26 Figure A.19: Dissolved copper – hy drologic variables matrix plot............................................................... ..........A-26 Figure A.20: Modeled Free ionic copper – water quality parame ters 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. Annota tions denoting the nature of the analysis and any conclusions drawn acco mpany the output......................................................................................... .............A-28

ixx 

### LIST OF TABLES

Table 2.1: Water quality parameter s influencing copper toxicity................................................................ ...........4 Table 2.2: Freshwater copper co ncentrations a nd speciat ion..................................................................................6 Table 2.3: Marine water copper concentrations and speciation................................................................... ...........6 Table 2.4: Total copper in sto rmwater from prev ious studies.................................................................... .............8 Table 2.5: Dissolved copper in sto rmwater from prev ious studies................................................................ ..........8 Table 2.6: Summary of analytical techni ques for quantifying free ionic copper .................................................1 3 Table 2.7: Advantages and disadvantages fo r copper speciation anal ytical techniques......................................14 Table 2.8: Literature alpha values for Cu-SA complex........................................................................... ...............18 Table 2.9: A list of copper speciation data determi ned in seawater in previous studies through CLEACSV and SA .................................................................................................................... ...............................18 Table 2.10: A list of copper speciation data determi ned in freshwater in previous studies through CLE-ACSV and SA................................................................................................................ ..........................19 Table 3.1: Concentrations used for cation calib ration curves ................................................................... ............39 Table 3.2: VA 797 In strument 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 conc entrations 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 (Cu 2+ diss ) ............................................................61 Table 4.4: MLR variable selectio n results for total copper (Cu 2+ 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 co llected at all sites ................................................................................... .................75 Table 4.7: Speciation results for first flush, intras torm, and composite samples from Dixon Outfall...............88 Table 4.8: Comparison of experimentally determine d 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 sp ike percent di fferences .................................................................................... .....................A-2 Table A.4: Coefficient of variation an alysis 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 ca lculated 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

Cu 2+ 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 consis ting of a metal and nonmetal (ligand) molecule Cu 2+ 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 Plas ma Atomic Emission Spectrometry ICP-MS Inductively Coupled Plasma Mass Spectrometry ICP-OES Inductively Coupled Plas ma 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 L T Organic ligand concentration NICA-Donnan Humic model based on th e combination of the Non-Ideal Competitive Adsorption a nd 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

xixii SHM Stockholm Humic Model TDS Total Dissolved Solids TSS Total Suspended Solids UV Ultraviolet

## 1.0 INTRODUCTION

Recent research has shown that very low concen trations of dissolved copper can inhibit the olfactory system of salmon listed as threaten ed and endangered under the U.S. Endangered Species Act (ESA). Highway stormwater runoff is a source of copper to su rface waters inhabited by ESA-listed species. Informed by this rese arch, the National Marine Fisheries Service (NMFS) is more likely to determine in their Biol ogical Opinions (permits issued as part of the consultations performed under Sectio n 7 of the ESA) that transpor tation projects are “Likely to Adversely Affect” the ESA-listed fish. The deliver y of transportation projects is tied to these consultations and Biological Opin ions; 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 complexe d species) is bioavailable and toxic to aquatic species. At present, it is unclear how the total dissolved copper is part itioned between ionic and complexed forms in highway stormwater runo ff and how that influences copper toxicity.

The overall objective of this study was to deve lop a fundamental framework for estimating the likely impact of copper in highway stormwater runo ff that discharges to surface receiving waters inhabited by ESA-listed fish species in the Stat e of Oregon. This guidance will allow ODOT to predict when, where, and to what extent copper t oxicity 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 concentrat ions of other constituents that influence copper toxicity are keys to this analysis and theref ore 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, st orm 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 i onic copper concentr ations in highway

stormwater runoff with m odeled concentrations; and

4) Develop a qualitative understa nding of where and when copper toxicity has the most potential to be problematic.

12 

### 1.2 APPROACH

The foundation of this work was an extensiv e 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 se lected flow-weighted samples were analyzed for trace metals and dissolved organic carbon (DOC), at a minimu m. Storm data was collected throughout each sampled storm.

Objective 1 was achieved through st atistical analyses of highway runoff samples gathered during the sampling effort. These analyses were aimed at identifying significan t differences in copper concentrations between different sites and sample types, as well as determining important variables controlling or correlating with coppe r concentrations. Objective 2 was accomplished through the development of a competitive li gand exchange–adsorptiv e cathodic stripping voltammetry technique suitable for use in stormwat ers. The method was then used to determine the free ionic copper concentrati on 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 su bsequently compari ng the modeled and experimental results. For Ob jective 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 pr ocedures used to analyze the results;

 Chapter 4 describes the copper speciation results, correlati on 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 implica tions of the results of this study and recommendations for future work.

## 2.0 BACKGROUND

2.1 COPPER TOXICITY

Though other copper oxidation states exist, Cu 2+ is the most stable and abundant oxidation state in aqueous environments ( USEPA 1980; 2007 ). The total concentra tion of copper (henceforth Cu 2+ tot ) in aqueous environments consists of the pa rticulate, dissolved, and colloidal forms. Particulate copper is less prev alent in aquatic systems than dissolved copper (henceforth Cu 2+ diss ) or colloidal copper, and can be a source or sink for Cu 2+ diss , depending on the conditions present ( Sigg and Behra 2005; USEPA 200 7; Wells, et al. 1998 ). In this study, Cu 2+ diss is operationally defined as the fraction of copper that passes thro ugh a 0.45 μm pore size filter. In natural aquatic systems a substantial fraction of copper thus defi ned 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 Cu 2+ free ) or complexed with organic or inorganic ligands (compounds and elements that form coordination complexes with the metal). The toxi city of copper is directly dependent on its bioavailability to organisms; in gene ral, bioavailability is limited to Cu 2+ free and weakly complexed copper ( Brooks, et al. 2007; Luider, et al. 2004; Sigg and Behra 2005; USEPA 2007 ).

In the past, both Cu 2+ tot and Cu 2+ diss have been utilized as primary indicators of copper toxicity. Although elevated Cu 2+ diss and Cu 2+ tot concentrations can suggest elevated toxicity, the complex aquatic chemistry of Cu 2+ free prevents any straightforward correlations between Cu 2+ tot or Cu 2+ diss and Cu 2+ free . A number of other water quality paramete rs affect copper toxicity: temperature, the presence of natural ligands (inorganic and orga nic), 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) th at often outcom pete inorganic ligands for Cu 2+ free in aquatic systems and form strong complexes ( Buck and Bruland 2005 ). The concentration and binding strength of organi c ligands are critical factors in determining copper toxicity ( Bruland, et al. 2000; Linton, et al. 2007; Lu ider, et al. 2004; Plöger, et al. 2005; Sigg and Behra 2005; USEPA 2007 ).

3Table 2.1: Water qua lity parameters influencing copper toxicity

CONSTITUENT EFFECT OF INCREASED CONSTITUENT CONCENTRATION ON

COPPER TOXICITY MECHANISM

Alkalinity (HCO 3 − , CO 3 2

− ) Decrease Increased complexation of Cu 2+ free DOC Decrease Increased complexation of Cu 2+ free

Hardness (Ca 2+ , Mg 2+ , Fe 2+

, etc.) Decrease Competition with Cu 2+ free for adsorption sites on the organism Indicator of incr eased inorganic ligand concentrations ( i.e.
, increased complexation of Cu 2+ free ) Competition with Cu 2+ free for DOM sites

H + (decreasing pH) Increase Competition with Cu 2+ free for ligands Increased solubility (Cu 2+ diss ) Dissolved copper Increase Increased Cu 2+ free Anions (NO 3 − , SO 4 2

− , PO 4 3

−
, S 2

− , Cl − , etc.) Decrease Increased complexation of Cu 2+ free Temperature Increase Increased solubility (Cu 2+ 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 Cu 2+ diss ) can be used exclusively to determine copper toxicity; rather, toxi city is determined through direct measurement of Cu 2+ 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

4Ligand Models (BLMs) ( Bryan, et al. 2002; Luider, et al. 2004; USEPA 2 007 ). Though the equilibrium models are useful, further refinement is necessary to describe the complexation of copper with natural organic matte r (NOM) and various biotic lig ands, 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 rese arch by Sandahl, et al. ( 2007 ) has shown that low concentrations (2-5 μ g/L) of Cu 2+ diss can impair the olfactory system of juvenile Coho salmon, one of several ESA-listed fish species. Damage to the chemos ensory system reduces the ability of fish to navigate and avoid predators, li kely increasing morta lity. Much of the re search 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 re port an 8-57% reduction in predator avoidance behavior at Cu 2+ 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 minut es 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. Th e authors found that in creasing both water hardness (0.2-1.6 mM Ca) and alkalinity (0.2-3.2 mM HCO 3 - ) only slightly decreased the inhibitory effects of copper. Increasing DOC (0-6 mg/L) showed a mu ch greater capacity for protecting salmon from copper toxic ity. Specifically, 19% of all surface waters collected for the study had enough DOC to reduce sub-lethal copper toxi city 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, al kalinity, and DOC were similar in a recent study examining copper’s toxicity on the mech anosensory system of zebrafish ( Linbo, et al. 2009 ). Though highway runoff is well recognized as a si gnificant contributor to copper in natural waters, no attempts have yet been made to char acterize copper speciation as a function of these water quality parameters.



### 2.2 CONCENTRATION AND SPECIATI ON IN NATURAL WATERS

The U.S. Environmental Protection Agency (E PA) has established hardness dependent water quality criteria for acute (1 hr) and chronic (96 hr) exposure to Cu 2+ diss . With an assumed hardness of 100 mg/L as CaCO 3 , the acute criteria is 13 μg/L, a nd chronic criteria is 9 μg/L for freshwater ( USEPA 2008 ). In marine water the acute criter ia is 4.8 μg/L and the chronic criteria is 3.1 μg/L. Currently, there are no regulations for Cu 2+ free in either fresh or marine waters. Oregon freshwater and marine exposure lim its are identical to federal standards.

Bowen ( 1985 ) reported typical Cu 2+ diss concentrations for freshw ater between 0.20-30 μg/L and for marine water between 0.03 and 0.23 μg/L. Ther e have been a number of studies quantifying both Cu 2+ diss and Cu 2+ free . In all cases where both parameters were reported, Cu 2+ free was only a small fraction of the Cu 2+ 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).

5Table 2.2: Freshwater copper concentrations and speciation

STUDY SITE Cu 2 + diss (μg/L) (a) Cu 2 + 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 Cu 2+ tot

Table 2.3: Marine water copper concentrations and speciation

STUDY SITE TYPE Cu 2 + diss (μg/L) (a) Cu 2 + 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 Cu 2+ tot

Oregon freshwater and marine exposure limits are id entical to federal standa rds. A collection of Oregon data collected from July 1977 to October 2006 showed Cu 2+ diss concentrations ranging from <0. 3 μg/L to 40.2 mg/L with a median value of <10 μg/L, and Cu 2+ 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 Ore gon surface water sites are currently listed as impaired with respect to Cu 2+ diss (ODEQ 2008). In a study focusing on trace elements in the Willamette River Basin, Cu 2+ diss concentration ranged from <0.5 to 4.6 μg/L (14 sites sampled)

6and the Cu 2+ 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 quantif ied data reported values ranging from <1 to 20 μg/L.



### 2.3 COPPER IN STORMWATER

Urban stormwater runoff is an important non-po int source of many contaminants present in aquatic environmental systems. In 1983, the EPA’s National Urban R unoff Program (NURP) examined the effects of urban stormwat er runoff on receiving water quality ( Athayde, et al. 1983 ). Among the main conclusions of this projec t was that heavy metals are the most prevalent priority pollutant found in urban stormwater, and that the effect st ormwater runoff had on receiving waters was highly site-specific. Th e results of the NURP sh owed 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 de tected 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 exce eded 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 st andards 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 aquati c species were exposed directly to roadway stormwater runoff ( Kayhanian, et al. 2007 ). These results show that copper is not only commonly found in high way runoff, but that it also of ten exceeds existing standards for acute toxicity in aquatic species. Howeve r, most existing standa rds are conservative estimates based on Cu 2+ 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 stormwat er 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 lin ings released approxima tely 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 in clude: engine oil, combustion of lubricating oils, roof/gutter runoff, building siding corrosion, fertilizer, pe sticides, industrial rel eases, 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 Stormw ater

Typical total copper concentrations in stormwater range from belo w 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 Cu 2+ 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; St recker, et al. 1997; USEPA 1983; WERF, et al. 2007 ). The Kayhanian, et al. ( 2003 ) study focused specifically on r unoff generated from highways and showed a median for Cu 2+ tot of 20.2 μg/L (ranging from 1- 9500 μg/L) and a median for Cu 2+ 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 sto rmwater from previous studies

STUDY LOCATION NUMBER OF SITES RANGE OF Cu 2+ tot (μg/L) MEDIAN Cu 2+ 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 Cu 2+ dis (μg/L) MEDIAN Cu 2+ 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 bot h the particulate and di ssolved fractions of stormwater runoff ( Dean, et al. 2005; Grant, et al . 2003; Prestes, et al. 2006 ). The dissolved

8fraction is most comm only 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 snowm elt events ( Breault and Granato 2000 ). The dissolved fraction is of immediate concern to aquatic sp ecies, though the particulate-bound fr action 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 dissolv ed 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 CuCO 3 species were found to be the most prevalent, but Cu 2+ free was a significant species in their models (about 8-40%). Fact ors affecting the specia tion included: rainfall intensity, rainfall pH, concentr ation of copper, concentratio n of ligands, and alkalinity.



### 2.4 FACTORS AFFECTING COPPER IN RUNOFF

2.4.1 First Flush

Heavy metals, total suspended soli ds (TSS), and other stormwater pol lutants 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 coll ected 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 po llutant 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 indi cates an increasingly pronounced fi rst flush. Noting a first flush can be helpful in determining what best mana gement 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 Cu 2+ 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 toward s 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 po llutant delivery that is roughly

9proportional to the storm hydrograph ( Dean, et al. 2005; Sansalone and Buchberger 1997 ). As such, flow-limited events often do not exhibit fi rst flush behavior. Sansalone and Buchberger ( 1997 ) showed a first flush for Cu 2+ diss in flow-limited and mass-limited events; evidence of a first flush for particulate-bound sp ecies 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 polluta nts, over 40% of the toxicity t ypically 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 expos ed to composite samples, even when a storm exhibited strong first flush behavior.

Snowmelt runoff events can also affect the amou nt 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 r unoff 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 du e to the large volume of runoff.



### 2.4.2 Hydrologic Effects

Total event rainfall and runoff volume have been hypothesized to correlat e 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. Th e EMC is defined as the flow-weighted average concentration of the pollutant ove r 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 su mmation form for discrete data points. Although these values are useful, EMCs give no informati on 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 polluta nt EMCs typically found weak negative correlations, indi cating 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 Cu 2+ tot and correlated negatively with Cu 2+ diss EMCs. This result indicates that rainfall intensit y is closely tied to total event rainfall – meaning

10that the ef fects of dilution were more importa nt 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 re sults suggest that although the EM C from an intense storm may be lower than that of a calmer storm, the coppe r concentration early in the intense storm may be higher than it would be at a ny point during a calmer storm.



### 2.4.3 Traffic Effects

The average daily traffic (ADT) has been show n 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 Cu 2+ tot while non-urban highways showed a me dian of 22 μg/L in stormwater runoff ( Driscoll, et al. 1990 ). There was little correlation f ound 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 di fference 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 signifi cant difference in Cu 2+ tot EMC. Other traffic-related variables, such as traffic during storms and braking intensity, have rarely been st udied, but may also alter copper concentration in highway runoff.



### 2.4.4 Other Effects

Antecedent Dry Period (ADP), the period of time be tween the end of one storm and the start of the next one, has been correlated to pollutant EM Cs 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 ap proximately 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 significan tly different dissol ved pollutant loads than storms with 1-2 week ADPs. Some studi es 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 surr ounding highways has also been thought to affect pollutant concentrations. The NURP did not find any statis tically significant variations in EMCs between land use categories, outside of the differen ces between urban and non-urban categories ( Athayde, et al. 1983 ). The report concluded that if land use cat egory does have an effect, it seems to be eclipsed by storm-to-storm vari ability. Kayhanian, et al. ( 2003 ) determined that land use category (with the exception of industrial and mixed- use) did not have a si gnificant correlation to Cu 2+ tot EMCs, especially when other variables (eve nt rainfall, ADP, cumulative precipitation, and ADT) were taken into account; higher than av erage copper concentrations were found in industrial and mixed land use areas.

11The NURP also did not find any consistent re lationship betw een copper EMCs and location ( Athayde, et al. 1983 ). Additionally, significant differences in copper concentrations found in geographically distinct locations ar e 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 disso lved. This can be summarized simply as: 

#              2 2 2 2 diss col part tot CuCuCuCu (2-1)
where [Cu 2+ tot ] refers to the total concentration of copper within the system consisting of particulate [Cu 2+ part ], colloidal [Cu 2+ col ], and dissolved [Cu 2+ diss ] copper.

Within each environmental compartment, copper can exist in many different forms or “species.” Cu 2+ 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, Cu 2+ free forms complexes with many inorganic and organic ligands. When present, copper preferentia lly binds to organic ligands ( Buck and Bruland 2005 ). The total concentration of Cu 2+ 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 i CuX free iCuL free diss

H Cu CuX CuL CuCu

n n 2

2 2 2 2

' '' ''

   (2-2)

where 

#  is the total dissolved copper concentration includi ng free ionic copper ( 

#   )
and copper bound with organics, 

##  

#   2 diss Cu  2 free Cu 

## 
i L  and inorganics, 

##  
i X 

. CuL   and CuX 

 represent the conditional stability constants fo r each of the organic and inorga nic 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 wate r (such as Inductively Coupled Plasma Mass Spectrometry) are not capable of measuring Cu 2+ free without a separation step. With the rising concern of c opper toxicity in natural aqua tic environments, a number of analytical techniques have been developed fo r measurement of metal speciation in natural environments. These technique s 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 e ither mode. Dynamic techniques recognize and account for the kinetics of copper binding reactions . As such, these techniques are characterized by measuring labile Cu 2+ rather than Cu 2+ free . A complex is considered labile if it is prone to change, or likely to undergo one. Lability is an operationally de fined characteristic that depends on the technique’s response and accumulation time ( Sigg, et al. 2006 ). In general, labile Cu 2+ includes Cu 2+ free and Cu 2+ weakly complexed with orga nics and inorganics.

Equilibrium techniques typically determine Cu 2+ free after allowing the solution to equilibrate with, or without, an added ligand. Equilibri um techniques are commonl y 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 kine tic dependence of the reactions ( Scally, et al. 2003 ). Table 2.6 and Table 2.7 below summarize the important characteristics and advantages and d isadvantages for each of these techniques.

Table 2.6: Summary of analytical tec hniques 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 report ed detection limit of Cu(SA) x of 0.1 nM and the competition strength of the binding ligand ( Campos and van den Berg 1994 ).

13Table 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 Cu 2+ , not Cu 2+ free

DMT (b)  Simultaneous metal determination  Requires long equilibration times (1-4 days) in equilibrium mode to measure low metal concentrations

 HCO 3 - , CO 3 2- , and OH - can complex with Cu 2+ 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 Cu 2+ free as opposed to molar concentration  Membrane fouling by organics

 Cross contamination

 Slow response time at low ion concentrations

 Declining performance ov er the lifespan of the electrode

ASV (e)  Simultaneous metal determination

 Can be combined with competitiveligand techniques to characterize copperorganic complex strengths  Electrode fouling by organics

 Measures labile Cu 2+ , not Cu 2+ 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 Cu 2+ , not Cu 2+ 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 )

14In general, equilib rium techniques have lowe r detection limits than dynamic techniques. Furthermore, many techniques requ ire long equilibration or prec oncentration/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 dete ction limits more than an order of magnitude lower than the closest competitor. Detection limits reported in Table 2.6 are conservative estim ates based on low concentrations of the co mpeting ligand and a depo sition time of 1 min. CLE-ACSV outperformed several ot her 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-AC SV is that it can be used to determine the relative strength of the naturally occurring Cu-lig and complexes. Because copper speciation in stormwater has not been analytically examined, this added capabili ty is quite valuable. Determination of binding strengths between copper and organi c matter present in stormwater will aid in verifying results generated using equilibrium mode ls. 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 prep aration and the ability to account for the kinetic aspects of copper complexation. That being said, the technique s that can be used in situ often require deployment/accumula tion 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 ol factory 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 eff ects. Furthermore, many of the alternative techniques have high detection limits (relative to CLE-ACSV), are pr one to electrode fou ling by colloids and NOM, and are unable to measure Cu 2+ free alone. On these merits, it was determined that CLE-ACSV was the most appropriate tec hnique for use in this study.

2.5.1.1 CLE-ACSV

CLE-ACSV has been used to quantify both fr ee metal ion concentrations and the relative binding strengths of several transiti on metals with natural ligands ( Rue and Bruland 1995; Miller and Bruland 1997; Buck and Brul and 2005; Sigg, Black, et al. 2006 ). CLEACSV consists of three primary st eps. First, a competing ligand,

a L , is added to a natural water sample and allowed to equilibrate; during equilibration, some of the dissolved Cu 2+ complexes with the added ligand (see Equation (2-3) ). The variables and are the stoich iometric coefficients and molecular charge, respectively. 

### m n



#      

#   mn

m a K
n

a free LCu LmCu CuLa


 

      

  2

......... 2 (2-3)

Second, the complex is adsorbed onto a hanging m ercury drop electrode; it is this pre-concentration step that results in extremely low detection limits ( Wang 1985 ). Finally, Cu 2+ is reduced to Cu 0 by cathodically scanning the potential (towards more negative values). The induced curren t is proportional to the amount of Cu 2+ adsorbed onto the mercury drop. By titrating the original sample (and the added competitive ligand) with copper, the Cu 2+ free concentration and the relative binding strengths of a CuL

15copper with the naturally pres ent ligands can be determ ined . 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
instrum ent 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 (sensitiv ity) of the method. p i 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 plasm a spectroscopy can be used to determine the Cu 2+ diss concentration; ion chromatography and alka linity 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 f or the Cu 2+ free concentration.

As described above, CLE-ACSV requires the additi on of an added ligand that binds with copper to form an electroactive complex. There are mu ltiple 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 CuL i CuX i diss i i

n
CuOH CuSAx Cu Cu L Cu X Cu

Cu H Cu  

   

        

  

             

##  
 2  (2-5)

This competition between SA, inorganic ligands, i X , and organic ligands, , can be described in an equilibrium equation also: i L 

16

##    
i i 

### CuL L    ' 

### '  

#       

##  

x i i free XCu X Cu CuX

    

  ' 2 ' 


##     

##  

x SACU SA CuSAx

    '  (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 X CuSA CuSA CuSA 

### K SA SA   ' 

###          



###   (2-7)

Where is the concentration of unbound SA in solution and ' 

### SA   CuSA K  and 2 CuSA 

 are conditional stability consta nts for the complex CuSA + and Cu(SA) 2 , respectively. In actuality CLE-ACSV is thought to m easure 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 speciati on studies in rain wate r, seawater, and fresh water ( Buck and Bruland 2005; Campos and van de n 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 great er sensitivity to copper (3-4 fold greater) compared with previous added li gands (tropolone, 8-quino linol, and catechol) and its detection window that is centered in between the detec tion limits of tropolone and 8- quinolionol.

Alpha values for CuSA were calculated in sea water conditions w ith varying salinity (representing estuaries) and in glacier wa ter with varying calcium concentrations. Table 2.8 shows a lis t of determined CuSAx  values. Table 2.9 and Table 2.10 include a list of copper specia tion results obtained with SA; all but Monticelli and coworkers use alpha values determined by Campos and van den Berg. L igand concentrations and binding strengths are represented by 

##  

x L and respectively, where x is a demarcation for weak and strong ligand classes in the Buck and Bruland study. Cu 2+ diss concentrations range from 3.1 to 49.6 nM for seawater studies; 2.9 to 6.3 nM for glacier supplied streams; a nd 1.4 to 80.1 nM in rainwater studies. Cu 2+ free concentrations were gene rally 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). CuLx K

17Table 2.8: Literature alpha values for Cu-SA complex SA CONCENTRATION (μM) log α CuSAx STANDARD DEVIATION CONDITIONS

2 4.71 a 0.03 10 5.87 a 0.09 25 6.60 a 0.07 Seawater with Salinity of 1 psu, pH 8.35

2 3.85 a 0.20

25 5.83 a 0.20 Seawater with Salinity of 35 psu, pH 8.35

2 3.94 b 0.02 10 4.81 b 0.01 0.4 ppm Calcium, pH 7.68

25 5.19 b 0.01 0.4 ppm Ca, pH 7.68 25 5.16 b 0.01 4 ppm Ca, pH 7.68 25 4.75 b 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 [Cu 2+ diss ] (nM) Log [Cu 2+ free ] (M) Log K CuL1 (K CuL2 ) [L 1 ] ([L 2 ]) (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 Mediterranean b 3.1 nr 13.3 10.8 Tested at 2 μM SA Atlantic b 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

18Table 2.10: A list of copper speciation data determi ned in freshwater in previous studies through CLE-ACSV and SA SITE [Cu 2+ diss ] (nM) Log [Cu 2+ free ] (M) Log K CuL1 (K CuL2 ) [L 1 ] ([L 2 ]) (nM) CONDITIONS

Rutor 1 a 4.3 nr 12.5 10.2 Distance from source: 0 km Rutor 2 a 6.3 nr 12.9 9.1 0.50 km Rutor 3 a 6.9 nr 12.7 11.6 0.96 km Rutor 4 a 5.2 nr 12.4 16.0 1.64 km Rutor 5 a 5.6 nr 12.9 11.9 2.56 km Rutor 6 a 2.9 nr 12.5 9.6 4.04 km Rutor 7 a 4.8 nr 12.5 13.4 7.67 km Rutor 8 a 3.6 nr 12.5 15.7 10.06 km Rutor 9 a 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 #446 c 11.1 nr 15.5 10.5 Mixed storm Event #478 c 22.0 -13.8 14.0 35.2 Mixed storm Event #488 c 80.1 -12.2 13.8 82.2 Continental storm Event #493 c 23.5 -14.1 14.5 32.4 Continental storm Event #494 c 10.3 nr nr nr Marine storm Event #496 c 23.6 -15.3 15.8 31.1 Marine storm Event #534 c 78.5 nr 14.0 64.5 Mixed storm Event #540 c 11.4 -13.2 14.0 13.1 Mixed storm Event #553 c 10.2 nr 14.0 6.5 Marine storm Event #554 c 55.1 nr 13.9 30.4 Mixed storm Event #559 c 2.1 -15.2 14.7 8.3 Continental storm Event #562 c 49.8 nr 14.0 12.5 Mixed storm Event #563 c 21.0 -14.5 15.5 23.1 Mixed storm Event #564 c 26.0 -13.3 13.9 32.9 Mixed storm Event #567 c 37.8 nr 14.7 34.6 Mixed storm Event #568 c 23.0 nr nr nr Mixed storm Event #570 c 1.4 -15.1 15.1 2.9 Mixed storm Event #572 c 12.9 nr 15.0 9.2 Mixed storm Event #575 c 7.0 nr nr nr Continental storm Event #577 c 10.0 nr 16.1 6.2 Mixed storm Event #578 c 4.4 nr 14.5 3.8 Marine storm Event #580 c 3.4 -14.7 14.2 13.3 Mixed storm Event #582 c 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 interp reting 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 tr eatment of groups in a study. The present work is an example of an observati onal study – we have no control over how much rain/traffic/etc. is applied to the stormwater sample s. This distinction is important in regards to the scope of inference. It is impossible to dr aw causal conclusions in an observational study from statistical analysis alone. There is always a possibility of confound ing variables – variables that are associated to both group assignment a nd the outcome – affecting the measured outcome of a study. This study may only determine di fferences in groups and associations with explanatory variables. Signi ficant 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 meas urements from different groups must be equal.

3. The observations within a sample group ar e 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 departur es 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 i ndependence. 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 outc ome and not solely on ex planatory 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 gr oup becomes the explanatory variable, while the observation means become the response variable. As their name suggests, the explanatory variables aim to quantify the res ponse variable. Simple linear re gression is defined by a single explanatory variable, while multiple linear regression (MLR) is defined by two or more

20explanatory variab les 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 th e explanatory variable is a straight line.

2. The deviation of responses at different values of the explanatory va riable is equal across all levels of the explanatory variable.

3. The subsets of responses at diffe rent explanatory variable valu es 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 respon se variable should be co nstant 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 qua ntified – in this case a difference between the mean values of each sa mple group. Hypothesis testing then addresses the question of the level of signi ficance in the difference of the mean of the groups. A typical hypothesis test might be concer ned with proving or disprovi ng 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 un certainty 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, 2 

### Y and 1 

### Y represent the sample means for groups 2 and 1, respectively;

2 

###  and 1 

###  represent the true means for grou ps 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 distribut ion 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 obse rved test-statistic (i .e., the difference of means) yields a p-value. Since the “true m ean” of any sample measurement can never be

21quantified, and the actual m easur ements are only representative of that true mean, there can never be 100% certainty that there is a difference in the mean s 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), wh ile a one-sided t-test is only concerned with one of those possibilities.

Confidence intervals expressing th e difference in means are constr ucted 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 research er should control for individual (pair-wise) confiden ce levels. If comparisons are unplanned, the researcher should control for an overall (family-wise) confidence le vel. 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 compar isons (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 th e 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 th e 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 

### { | , , ... , }

... n n 

### YXX X X X X

n 

###        

22In this equation, the param eters 1 

###  thru

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 s quared 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 qua ntify statistical parameters ( 1 

### X

n 

### X

 -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 analys is 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 pr oduced by a model. Large residual sum of squares measurements imply a high degree of vari ation 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 numbe r of observations. Notations for 

### re and 

### d f ull 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 ot her parameters have been accounted for.

Sometimes many different explanatory variab les 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 th erefore 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 relati onship between each other. When two predictors have a linear relationship with each other, they are not simulta neously significant in determining the response variable.

There are various statistics that can be calculated to compare models. R 2 and adjusted R 2 can be used in model selection, but both of these st atistics favor models with too many variables ( Ramsey and Schafer 2002 ). More commonly used variable selection statistics include the Cp

23statistic, Ak aike’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 variable s 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 mode l cannot be improved. Two-way stepwise regression selectively adds or subtracts explanat ory variables from a sp ecified 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-f itting 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 variable s are correlated, and inte rpreting their regression coefficients can be difficult. The central of di fficulty of interpreting regression coefficients in large models is that a single explanatory vari able typically doesn’t change while all other explanatory variables stay cons tant – one predictor usually won’t change in isolation ( Ramsey and Schafer 2002 ). Variable selection techniques ar e useful in situations where many explanatory variables exist, and quantifying the eff ect 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 Visu al MINTEQ or MINEQL+, have been developed to rapidly solve aquatic equilibrium problems. Users supply informati on 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 betw een 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 genera l 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 disc rete 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 disc rete ligand models are those developed by Tao ( 1992 ), Tipping and Hurley ( 1992 ), Westall ( 1995 ), and Gustafsson ( 2001 ).

24Continuous distribution models express the heterogeneity of binding sites by correlating site abundance and binding affinity. The most comm only used continuous distribution m odels 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 distribut ed with respect to their log K

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 descri bed by the following equation:

2 

### Log( ) exp 0.5 Log( ) 2 i M M L i M 

### K C C d 

    

    

        K

In this equation, is the concentra tion of binding site i , is the total concentration of ligands, i 

### C L 

### C

i M 

### K is the stability constant for a specific metal M binding to ligand site i , M 

###  is the mean of a Log(

i ll

M 

### K ) v alues, 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 num ber of total binding sites ( ), is equal to (8 σ )/i max ( Susetyo, et al. 1991 ).

ma 

### i

x 

### Lo d g( ) M 

### K

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

25The NICA-Donnan m odel 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 monodent ate (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 bindi ng, but the magnitude of the effect may be due more to differing types of DOM (fulvic acid from Suwannee Rive r, 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 co rrect for electrostatic effects. Both SHM and NICA-Donnan model show ed little effect of ionic strength on copper speciation and an underestimation of Cu 2+ /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 co mposite samples collected and sent to OSU by Herrera Environmental Consultants. While th e present study focuses on copper speciation in highway runoff, Herrera was working on anothe r ODOT-funded study to characterize highway runoff water quality. Grab, first flush, and fl ow-weighted samples were collected at the Dixon Outfall site in Corvallis, Oregon from Oct ober 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 hencefor th referred to as the “Herrera sites”. All aerial views of the sampli ng 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 ju st 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 ft 2 or 0.11 acres). Precipitation measuremen ts 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 imm ediate vicinity of the site is re sidential, though the Corvallis Wastewater Treatment Plant is located nearby.

27Figure 3.1: Dixon site aerial view

3.1.1.2 I-5 Convention Center

This site is located in Portland in a manhol e in the Oregon Convention Center Exhibitor Parking area just west of NE 1 st 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 em phasi zes the urban nature of this site in comparison to the other sampling sites.

28Figure 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 vi a 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. Preci pitation measurements were obtained from a rain gauge owned by the City of Bend located ac ross the street from the site. Figure 3.3 displays an aerial view of the area surrounding the Bend sampli ng site. As can be seen in this figure, the Bend site is not rural in the strictest sense. The highway AADT and t he proximity to large areas of pervious, natura l surfaces are both consistent with it being classified as non-urban.

29Figure 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 AA DT of this site is approximately 12,000. The outfall is a 24” diameter, tar-lined corr ugated steel pipe that drains to the Sandy River through a grass field. Th e drainage area is approximate ly 8.7 hectares (21.5 acres) and covers 1.36 miles of Highway 26. Preci pitation measurements were taken from the Welches rain gauge (CW 6318), part of the Ci tizen Weather Observer Program (CWOP). Figure 3.4 displays an aerial view of th e area surrounding the We mme site. The immediate vicinity is forested a nd distant from any urban centers.

30Figure 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 He rrera 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 le ss 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 webs ite (www.weather.gov). Antecedent dry periods were calculated from data at the MAR754 w eather station provided by APRSWXNET and found at the Weather Underground webs ite (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 collect ed. 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 porta ble autosampler (Sigma 800 at I-5 and Bend,

31Sigm a SD900 at Wemme) to take 100 ml sub-sa mples 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, W emme, and Bend sampling sites, resp ectively (pictures courtesy of Herrera).

Figure 3.5: Portland/I-5 site pipe installation

Figure 3.6: Wemme site pipe installation

32Figure 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 le vels 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 pi ece of tygon tubing equipped with a Teflon-coated strainer on the end to collect samples. A 12” Th el-mar V-notch weir was placed at the end of the pipe to create submerged conditions. These c onditions allowed for accura te level measurement and created a sufficient stormwater volume from wh ich to collect samples. This installation is shown in Figure 3.8

.

33Figure 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 manufactur er-programmed V-notch weir equation. Data for level, precipitation, and sampling events was typical ly 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 Micr osoft Excel.



### 3.1.4 Field Sampling Procedures

3.1.4.1 Dixon Outfall

Prior to sample collection at the Dixon Outfa ll site, the Teflon intake tubing was rinsed successively with acid and distilled and de ionized (DDI) water (Barnstead NANOpure II system). 2 L of 10% HCl were drained through the tube followed by 2 L of 10% HNO 3
. 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” sam ples from the storms. Th e 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 hand s 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 m onitoring site after a sufficient amount of data was collected. Afte r each targeted storm event, field personnel returned to the sampling site to verify th e proper function of the sampling equipment and upload the sample collection data from the au tomated samplers to a laptop computer or a proprietary data transfer de vice. Carboys were then removed from the autosampler, shaken to homogenize the sample, a nd divided into laboratory bottles.



### 3.2 LABORATORY METHODS

The goals for this project included quantifying copper speciation and exam ining correlations for copper and copper speciation with other water quality parameters, st orm characteristics, and site characteristics. In order to accomplish these obje ctives, a variety of analyses were performed on each sample. The flowchart shown in Figure 3.9 displays the parameters measured and the aliquo t 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, conduc tivity, alkalinity, cati ons (both major cations and metals),

35anions, to tal dissolved solids (TDS), di ssolved organic carbon (DOC), and Cu 2+ 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 th e Dixon Outfall site in Corvallis and flow-weighted composite samples collected and sent to the OSU lab by Herrera Environmental Consultants. These composite sa mples 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 Portla nd and W emme sites were separated from the bulk sample at Herrera’s Portland office. Herrera engineers fi rst obtained enough of the

36storm water samples to analyze for their stormwat er characterization study prior to pouring off an aliquot for our analysis. This separation typically occurred with in 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 He rrera 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 Ou tfall 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 ea ch step in sample analysis. Labware cleaning procedures are detailed in Section 3.2.2 . Composite samples from Dixon Outfall were formed f rom discrete flow-weighted samples in the la b. Individual flow-w eighted samples were thoroughly mixed and an aliquot wa s drawn out and pipetted into a new, clean container for the composite sample. The final volume of the co mposite sample was 800-1000 mL. The volume of the aliquots varied based on the number of flow-w eighted samples taken for a specific storm. Snub-nosed volumetric pipets (to av oid 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 an alyzed 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 ref ers to the concentration of cations released afte r an acid addition of 1% (v/v) ultrapure HNO 3 (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 ava ilable 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 HNO 3 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 HNO 3 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 samp le, the remainder was stored in a freezer (-20o C) awaiting speciation testing. Aliquots for to tal cations were transferred directly from the vacuum flasks to approp riately sized containers.



### 3.2.2 Cleaning Procedures

3.2.2.1 General Containers/Labware

Majority of labware was cleaned in successi ve acid baths and DDI water. Labware cleaned in this process includes glasswar e, HDPE containers (including autosampler bottles), and Teflon bottles (used for copper speciation measurements). This cleaning procedure is detailed below:

371) 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 HC l bath and put into a 10% HNO 3 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 HNO 3 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 we re 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 thoroughl y with DI water and allowed to dry. Finally, the bottles were ashed at 550oC in a muffle furnace for at least two hours and subs equently stored in a closed container.

3.2.2.3 Organic Carbon Septum Caps

After use, the organic carbon bottle septum caps were rinsed twi ce with DDI water and stored in a re-sealable plasti c zipper bag filled with DDI wate r 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 HNO 3 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 m easure initial pH, conductiv ity 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 buffe rs 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 so lution. 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 th is 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 ca tion 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 st ored in HDPE or glass c ontainers. 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 fo r 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 st ored and capped in single-use BD Falcon polypropylene conical test tubes.

Standards were made from single element ICP St andards (BDH; Aristar). For the ICP-MS, an internal standard was added to each sample (2 ppb Indium). DDI blanks with a 1% ultrapure HNO 3 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 plot s over the entire range of concentrations measured, high- and low-range calibration curves were used for each element.

39Low-range calibrations m easured 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 se t of standards was remeasured at the end of a sample run. Hardne ss 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 n itrate, nitrite, sulfate, phosphate, and chloride (the major anions in stormwater) using a Di onex Ion Chromatograph (DX500). Each sample was measured as a 10x dilution and at full stre ngth 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 al ongside the samples. The standards ranged from 50 ppb to 20 ppm. Due to the large range of con centrations, the standard curves were separated into two sets – a high set used to determine con centrations 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 (a nion and cation) was reduced to a molar concentration and then summed. Equation (3-1) below shows the general formula used. Here, C

i and A i represent individual cation and an ion 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) determina tions were made on a Shimadzu TOC-V CSH total organic carbon analyzer at Ore gon 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 d ilution 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 Co llaboratory 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 de scribed in Campos and van den Berg ( 1994 ) was used for determination of CuSAx  . A modified version of CLE-AC SV method from Buck and Bruland ( 2005 ) was utilized for copper spec iation. Important differences in the CLE-ACSV methodology developed for seawater and the methodology deve loped in this study for stormwater are the selection of pH and ionic strength.

For both CuSAx  determinations and copper speciation expe riments 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 Cu 2+ 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 valu e 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 ra nging from 0.003 to 0.016 M. A second result of increasing ionic strength is an e nhancement to the sensitivity of the method. This adjustment boosted the sensitivity in stormwater grab sa mples 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 NH 4 OH (BDH Aristar Ultra) and HNO 3 (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 we re made from Na 2 EDTA (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 titra tion with a known liga nd. Identical to Campos and van den Berg ( 1994 ), ethylenediaminetetraacetic acid (EDTA) was selected as the known ligand of choice. A bulk so lution was made containing DDI, 100 nM of copper, 5 mM PIPES, varying calcium con centrations (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 fo r 30 minutes. After conditioning, a second 10 mL aliquot of the solution was placed in each conditioned bottle with a variable addition

41of EDTA (0 to 6 μM) and allowed to equi librate overnight. SA was added 15 m inutes 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 u Cu  

C EDTA Cu CuSAx X
X      

  

   (3-2)

Where,

' Cu  is the alpha coefficient for inorga nic complexes, described in full by Equation (3-3) ; CuEDT A  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 p i (3-5) ( Campos and van den Berg 1994 ).



##   

#   

#                

 

 



i n free CuOH free n i CuX X

n ' Cu H Cu Cu

n 2 2

' ' '1    (3-3)



##   CuEDTA TA K EDT     A (3-4) CuED

W here is the conditional stability constant for the copper-EDT A complex for a specific pH and ionic strength; CuEDTA K  

##   E

DTA  represents the concentration of EDTA that is not bound by copper. Cu EDTA  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 

##    
T EDTA EDT A 

 , but this was not a reasonable assumption
because the 

##  
T A EDT used in these titrations was on th e 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)    

W here
, 

##  
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 signa l from background noise at a low peak ratio (high EDTA concentration). Applying this criterion to all determinations provided consistency as well.

423.2.10.3 Total Dissolved Copper

Dissolved copper concentrations, 

#    2 diss Cu , for copper speciation were measured with ICPOES/MS. Previous studies have used UV di gested water and CLE-ACSV to determine ambient copper concentrations. Preliminary tests in this study using CLE-ACSV on UV digested stormwater resulte d in under estimations of Cu 2+ 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 a liquots. 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 Tefl on 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 instru ment 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 stir ring 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 CuSA 2 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 Cu 2+ (to Cu 0 ) 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: 

#          

#     

x i free diss CuSA CuX CuL CuCu 2 2 (2-5) 

#   2 diss Cu 

#   represents the entirety copper in th e system, including added copper in the
titration. 

#  represents the free ionic copper in th e titration test (distinctly different
from free ionic copper in the sample at normal conditions).  2 free Cu 

##   CuL is the concentration of copper bound to organic ligands in the system. 

##   i CuX represents the en tirety of copper bound to inorganic ligands in the system. 

##  

x CuSA represents the amount of copper

bound to SA. 

##  

x CuSA was determined with Equation (3-6) ; where is the instrument p i

43response (current) and is the sample sensitivity. The se nsitiv ity 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 free Cu 

#  was calculated with Equation (3-7)

.


#    

Cu CuSAx x free CuSA (3-7)

    2 Cu



##   i CuX was calculated with Equation (3-8)

.


##     

#     2 1 free Cu i Cu  (3-8) CuX


##  

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 Comput race 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

443.2.10.6 Speciation Calculations for Stormwater

Ligand concentrations, 

##   T L , and conditional stability constants, , were determined through non-linear least s quares fits of Equation CuL K ' (3-9) . This equation is a version of the Langm uir equation ( Gerringa, et al. 1995 ).



##    

#      

  2 2

'1 '

free CuL free CuL
T CuK CuK LCuL (3-9)

For 

##   

#     2
tot T CuL , free ionic copper in stormwater samples was calculated iteratively with Equation (3-10) . This equation was deri ved through solving Equation (3-11) for 

#   free  2 Cu

.


#       

#    


  

2 ' 2 2

'1 '

free CuL TCuL Cu diss free

CuK LK Cu Cu

 (3-10)



##   

#     ' '
2 L Cu CuL K

free CuL   (3-11)



### 3.3 STATISTICS

The S-Plus® 8.0 statistical softwa re was used for all statistical analyses. A 95% confidence level was used for all analyses. Therefore, for significa nce 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 inte r-site differences in speciation results. These comparisons were pl anned, so individual confidence levels are acceptable. Differences between sites were qua ntified by Fisher’s Least Significant Difference (LSD) multiple comparison procedure. This proce dure uses an F-test to first determine if any differences between any two sites ar e 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 bina ry term that indicates the group association of a particular measurement. Indicator variables are 1 when th eir specific association is met, and 0 in all other cases. In this case, the indicator was one for fi rst flush samples and zero otherwise. Site indicator variables were also us ed in the MLR analysis examining the effects of

45hydrologic param eters on Cu 2+ 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. S ite 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 m ean square of the model being considered, and is the residual mean square of the
f ull model. p n

full 2 

2 



### 3.4 VISUAL MINTEQ MO DEL 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 Visu al MINTEQ were used in modeling cation-DOM interactions. The default μ values for the vari ous DOM interactions with other species were taken from research by Susetyo, et al. ( 1991 ), which examined metal-humic interactions using Lanthanide Ione Probe Spect roscopy on Suwanee River DOM.

All measured dissolved concentrat ions chemical constituents were input into the program by the user. DOM was accounted for by entering in the m easured concentration of DOC. The pH was set constant at the value analyt ically measured in the sample. Solids were not allowed to precipitate. None of th e thermodynamic parameters in Visual MINTEQ’s database were altered from their default values. The ionic strength of the solution was not se t 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 conc entration of the models were adjusted to reflect experimental test conditions (pH 6.80, ionic st rength of 0.05M). Default mode l parameters were used in all models; a concise list is shown in Ta ble 3.3, Table 3.4, and Table 3.5.

46Table 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

4748

## 4.0 RESULTS AND DISCUSSION

4.1 TOTAL AND DISS OLVED COPPER

This study focuses on the speciation of copper in highway runoff and factor s that may affect the presence and form of copper from roadways dr aining into surface waters . From the literature review, some of the factors likel y to influence the presence of copper in runoff samples include AADT, Urban site classification, ADP, the Firs t 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 para meters, 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; St ead-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 N OM) have been examined in some of the statistical analyses. For the purposes of data analysis, the concentr ations of most constituents and hydrologic variables have been log-transformed. These tr ansformations 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 bett er statistical distributions for most measurements in this study. For clarity, dissolved copper will be referred to as Cu 2+ diss, and total copper will be referred to as Cu 2+ 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

sam ples) and Table 4.2 (total samples). In ge neral, 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 unusua lly 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 th e above listed parameters due to their artificially high values of conductivity and hardness. On ly two samples were received from the Bend site, which limits the inferences that can be made for that site.

49Table 4.1: Summary of dissolved constituent co ncentrations 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 CaCO 3 ) 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 CaCO 3) 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

50Table 4.2: Summary of total constituent con centrations 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 proce dure was used to analyze the differing copper concentrations in the composite samples at individual 95% confidence intervals. Figure 4.1 displays Cu 2+ diss concentrations as a function of site. The I-5 samples consistently contain more Cu 2+ diss than composite samples from other sites. Indeed, all differences in Cu 2+ diss concentrations between I-5 and ot her sites were significant (p<0. 05). The median concentration of Cu 2+ 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 Cu 2+ diss concentrations at I-5 were also 5.33 times higher than Wemme compos ites (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 Cu 2+ 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 term s 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% C I). 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 inab ility to determine any significant differences in DOC concentration with other sites.

51Bend Dixon I-5 Wemme Site 0.1 0.5 0.9 1.3LogCu (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 1.0LogDOC (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

52Figure 4.3 shows the results for Cu 2+ tot across the four sites. Similar trends were seen with Cu 2+ tot as were witnessed with both Cu 2+ diss and DOC. The median concentration of Cu 2+ tot at I-5 was 4.62 times higher than Dixon Outfall composite s (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 Cu 2+ tot concentration 2.82 times higher than Wemme co mposites (1.03-7.71, 95% CI). All other comparisons were not significant

Bend Dixon I-5 Wemme Site 0.0 0.5 1.0 1.5LogCu (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 an d dissolved) at the I-5 site compared to the others could be due to a variet y of factors. Possibly most important among these factors is AADT (since the primary source of copper on highways is brake pa ds) and urban site association. Using the roadway cl assification of Kayhanian, et al. ( 2003 ) based on AADT levels, only the I-5 site would be classi fied as urban (specifically medi um-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 Cu 2+ 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 simultane ously 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 Cu 2+ 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 i nsufficient evidence to establ ish an association between AADT and Cu 2+ diss (p>0.05, two

53sided t-test). From this study’s dataset, it is only possible to establish that the I-5 site shows higher copper concentrations. Th e individual effects of AADT a nd 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 re searchers. Previous research by Kayhanian, et al. ( 2003 ) showed that oil & grease was the only runo ff 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 R 2 of 0.139. Though significan t differences in pollutant concentrations have been demonstrated betw een 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 mo re 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 1.3LogCu (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

549000 14000 19000 24000 AADT (vehicles/day) 0.1 0.3 0.5 0.7 0.9 1.1LogCu (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 Cu 2+ diss and DOC (respectively) at Dixon Outfall. Both Cu 2+ diss and DOC showed significantly highe r 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 Cu 2+ 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% C I). The similar correlations of Cu 2+ diss and DOC with the first flush effect suggest a possibility that the Cu 2+ 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 un surprising, 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 Cu 2+ tot , which potentially imp lies a strong correlation between the two parameters. The observation of a first flush for copper suggests that best management practices which contro l early-storm runoff are likely to decrease the overall effect of copper in receiving waters.

55Composite First Flush Sample Type 0.0 0.5 1.0 1.5LogCu (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 1.5LogDOC (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 Hy drologic Parameters

The possible effect of hydrologic parameters on c opper concentrations coul d potentially assist in determining what kinds of storms are likely to cause high copper con centrations in receiving waters. To examine the effect of hydrologic variables on Cu 2+ diss in composite samples, a backwards stepwise regression procedure was used . The reduced model contained only the site indicator variables, while the full model containe d site indicator variables as well as variables accounting for ADP, total rainfall, and rainfall in tensity. 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 thes e figures. T he MLR analysis confirms the lack of any significant relationship between Cu 2+ diss and these hydrologic para meters after the effect of different sites is accounted for – none of the hydrologic paramete rs 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 Cu 2+ diss only as a function of ADP (since all first flush samples are only from Dixon Outfall). No si gnificant statistical association between Cu 2+ diss and ADP was found in first flush sample s (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 st udies that revealed pollutant correlations to hydrologic parameters ( Driscoll, et al. 1990; Kay hanian, 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 rela tively small sample size. This fact may also have contributed to a lack of significant correla tion between copper and ADP in both composite and first flush samples. Many factors outside of ADP affect po llutant transport on roadways, so to quantify a direct relationship between concentration a nd ADP with high confid ence would require many samples. Longer ADPs have been shown to have a positive correlation w ith pollutant EMCs in other studies, though that relations hip 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 summe r storms (i.e., long ADPs) would have provided data over a much greater range of ADPs. Howe ver, their unpredictability (and infrequency) makes sampling these summer storms diffi cult in terms of practicality.

570 50 100 150 200 250 300 350 ADP (hrs) 0.1 0.5 0.9 1.3LogCu (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 1.3LogCu (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

580

. 7 0

. 9 1

. 1 1

. 3 1

. 5 1

. 7 Log(Rainfall Duration) (Rainfall Duration in hrs) 0.1 0.5 0.9 1.3LogCu (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 1.3LogCu (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

59100 150 200 250 300 350 ADP (hrs) 0.9 1.1 1.3 1.5LogCu (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 w ith Water Quality Parameters

To examine the effect of water quality parameters on the concentration of Cu 2+ diss in stormwater samples, backwards stepwise regression was employe d. This variable se lection process differs from the analysis in section 4.1.3 as this process is con cerned with using m easured concentrations of constituents in the water samples to predict th e concentration of copper. The previous analysis looked at parameters which might affect th e concentrations of many different constituents, like site association and total rain fall. 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 reach ed in the analysis ke pt all of the water quality predictor vari ables with th e exception of conductivity. Table 4.3 summarizes the results of

this analysis.

60Table 4.3: MLR variable selection results for dissolved copper (Cu 2+ diss ) Parameter MLR Relationship w/ Cu 2+ 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 Cu 2+ diss were DOC and alkalinity, both having pvalues <0.0001 (two-way t-test). Looking at a ma trix plot of all the predictor variables (see Appendix A.5), hardness and alkalin ity were well-correlated to each other. Therefore, only one of these terms is of great significance to predict Cu 2+ 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 variatio n not captured by alkalinity. The weak negative association with pH is reasonabl e, as acidic runoff would be mo re likely to bring particulate copper into solution. The relativ ely low p-value may be indicative of the fact that the pH of stormwater samples is approximately neutral an d 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 Cu 2+ diss in solution through complexation. Looking at Figure 4.13 , an order of magnitude increase of Cu 2+ diss concentrations at the ppb leve l are correlated with an or der of magnitude increase of DOC at the ppm level. This suggests that whil e NOM does bind copper, th e binding sites are not common moieties on the NOM mol ecules. Hoffman, et al. ( 2007 ) suggested that copper is bound to uncommon binding sites or sites with speci al conformation within the macromolecule. The Hoffman research concluded that bulk NOM characteristics, such as aromaticity and elemental ratios, are not very important in dete rmining the ability of NOM to bind trace metals. Though relatively few sites are likely able to bi nd copper within a NOM molecule, NOM is often present in great excess compared to copper.

Graphs showing the DOC- Cu 2+ diss and alkalinity- Cu 2+ diss correlations can be seen in Figure 4.13 and Figure 4.14 , respectively. The associ ation of these two param eters with increased copper levels does not necessarily imply ca usation. The association of Cu 2+ diss with DOC or alkalinity may only demonstrate the presence of many pollutants, including c opper, in the runoff samples. However, the simultaneous significance of these two parameters in predicting Cu 2+ 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 concentrati ons in stormwater (including copper), they would not both have been found to be so si gnificant in the final model. DOC and alkalinity are also more easily quantif ied than trace metals but may provide some information on the amount of Cu 2+ diss during a runoff event.

610.05 0.30 0.55 0.80 1.05 1.30 1.55 LogDOC (DOC in mg/L) -0.1 0.4 0.9 1.4LogCu (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 CaCO 3 ) -0.1 0.4 0.9 1.4LogCu (Cu in ppb) Dissolved Cu vs Alkalinity (all samples)

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

62D etermining a model for Cu 2+ tot in samples was done in a similar manner to the model-building process for Cu 2+ diss . The predictor variables that were ex amined in the full model were hardness, DOC, and TSS, and the analysis was done on the same 39 samples examined in the Cu 2+ diss model. Table 4.4 below summarizes the results of this analysis.

Table 4.4: MLR variable selectio n results for total copper (Cu 2+ tot ) Parameter MLR Relationship w/ Cu 2+ tot p-value

Hardness not significant not reported TSS positive <0.0001 DOC positive <0.0001

The final model showed significant positive associations of Cu 2+ tot with both TSS and DOC (p<0.0001). The direct Cu 2+ tot -TSS and Cu 2+ tot -DOC relationships are shown in Figure 4.15 and Figure 4.16 , respectively. The matrix plot for th is MLR m odel showed covariation between hardness and TSS. So again, only one of th ese 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 correla tion of TSS with Cu 2+ tot is unsurprising, the direct relationship between Cu 2+ tot and TSS does not appear particularly strong. The absence of TSS as an important variable in the Cu 2+ diss model may indicate that other water quality parameters, primarily DOC and alkalinity, may faci litate the dissoluti on of copper.

As with Cu 2+ diss , DOC is still an important variable in predicting Cu 2+ tot concentrations, likely due to copper’s affinity for NOM. The simulta neous 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 frac tion of the samples; and b) TSS becomes an important predictor, as it acc ounts for any copper which remained adsorbed to solids in the sample. These two fractions are summed in the measurement of the total samples.

Since Cu 2+ diss predictably correlates with Cu 2+ tot (see Figure 4.17 ), another MLR model was constructed to see if DOC or alkalinity had any effect on Cu 2+ diss concentrations after the concentration of Cu 2+ tot present was accounted for. This mo del revealed that , after accounting for the effect of Cu 2+ tot , DOC was still a signifi cant predictor of Cu 2+ diss (p<0.0001, F-test). Alkalinity showed an insignificant association with Cu 2+ diss after Cu 2+ 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 Cu 2+ diss , only the relationship with DOC coul d potentially be causative. The inclusion of alkalinity in the Cu 2+ diss model may only be indicativ e of the presence of Cu 2+ tot . This is a reasonable conclusion – DOM has a str ong affinity for copper a nd is likely to cause particulate copper (part of the Cu 2+ tot measurement) to partition into the dissolved phase.

630.9 1.4 1.9 2.4 LogTSS (TSS in mg/L) 0.0 0.5 1.0 1.5LogCu (Cu in ppb) Total Copper vs TSS (all samples)

Figure 4.15: Relationship between total copper concen trations 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 1.5LogCu (Cu in ppb) Total Copper vs DOC (all samples)

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

640.00 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 1.4Log(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-weight ed 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 show s higher concentrations of Cu 2+ 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 con centrations of both copper and DOC after the first flush is precipitous at first, and more gr adual as the storm continues. Th is trend was consistent in other storms as well. This figure also suggests a w eak flush occurring subsequent to a pause in the storm. For example, the concentrations of Cu 2+ diss and DOC slightly increased from the 2 nd to the 3 rd samples from this storm; between these two samples there was a 4-hour break in the storm. This short dry period may have allowe d a small amount of pollutant accumulation on the roadway, which resulted in the jump in Cu 2+ diss and DOC concentrations present in the 3 rd 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 Cu 2+ diss
. The intra-storm data was analyzed with simple linear regressions, modeling Cu 2+ diss or DOC concentration as a function of V i /V tot , or ‘normalized cumulative volume.’ V i /V tot 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 ca n be applied to all storms and is used here to show the overall progress of a storm. Both Cu 2+ diss and DOC concentrations were found to

65significan tly decrease over the cour se 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 sim ple analysis, V i /V tot is by no means an accurate predictor of Cu 2+ diss . The primary weakness of this parameter is that it does not reveal the magnitude of a storm. A sample collected at a given V i /V tot within a large storm would be expected to have more dilute concentrations of pollutants than another sample with the same V i /V tot from a small storm. Additionally, there may not be a linear relationship between V i /V tot and Cu 2+ diss . The most important result of this analys is 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

660.1 0.3 0.5 0.7 0.9 V i /V tot 0.0 0.4 0.8 1.2LogDOC (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 stor m is represented as the no rmalized cumulative volume.

0.1 0.3 0.5 0.7 0.9 V i /V tot -0.1 0.3 0.7 1.1 1.5LogCu (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 applic ation 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 co mpetition strength of the added ligand (SA) was determined; this consisted of determining the value of

x CuSA  as described previously.

x CuSA  values determined in this study are presented and contrasted with previous ly reported values for natural waters. Second, the analytical proced ures and data analysis procedures used for determining copper speciation are discussed usin g 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 copp er concentrations determined in this study are compared to those predicted using thr ee different Visual MINTEQ DOM models.



### 4.2.1 Determination of

x CuSA 

### 

As described in Chapter 3, the binding strength of the added ligand is characterized through the side-reaction constant

x CuSA  ; values of the constant determin ed 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 w ith 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 CaCO 3 1 2.5

-- b
10 25 4.22±0.06 2 50 125 3.66±0.10 1 2.5 5.81±0.11 10 25 4.37±0.09 10 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 valu es 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. Ioni c strength increases lead to incr eases in instrument response and stability constants, resulting in greater alpha values. The inst rument 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

x CuSA 

.

68Differences 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 performe d 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/n M 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 in formation was recovered up to 3mg Triton X/L (but not at 6 mg/L). Therefore, speciation result s 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 co llected 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 fo r all stormwater samples. Figure 4.22 is an example of a voltamm agram (stripping potential vs. instrument re sponse) obtained from one of the many 10 mL aliquots analyzed from th e Wemme site sample. Curves like these were produced as Cu 2+ was reduced to Cu 0 at the mercury electrode after adsorbing onto the drop as CuSA 2 . This specific figure represents a 100 nM Cu additi on during a titration in which the added copper concentration was varied from 0 to 200 nM. C opper peaks in this study were typically centered

69at

− 0.14 volts (

− 0.12 to

− 0.18 volts). This is in contrast to the work of Cam pos 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 th e W emme sample with 2 μM of SA. As copper is added to the samp le, the concentration of th e electroactive complex CuSA 2 increases, leading to a rise in measured peak current ( ). Even tually, with continuing copper additions, the sites on the natural organi cs in the sample become saturated with copper. At this point, the ti tration 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 [ p i

S

x CuSA ] using Equation (3-6) . As described in Chapter 3, using th is in ternal slope can correct for signal suppression due to surfactants ( Kogut and Voelker 2001 ). Figure 4.23 illustrates the sensitivity d etermination for the Wemme sample.

70Figure 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 m ethod described in Section 3.2.10.6 . Figure 4.24 illustrates how the ligand concentration and stability constant infor ma tion is obtained from this procedure. The total concentration of ligands (i.e., sites), [ ], calcu lated for the Wemme 3/24/09 sample was 161.5 nM; the conditional stability constant for the Cu-Ligand complex, , was 10 10.92

. T L

CuL K 

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

714.2.3.3 Detection Windows

The competitive strength of the added ligand( ) CuSAx  can be adjusted through the variation of the added SA concentration. Liga nds of different strengt hs 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 obtai n ‘snap shots’ of different natural ligand populat ions and their respective stab ility constants. Bruland, et al. ( 2000 ) have termed these anal yses ‘detection windows.’ Ligand information within the same window can be compared readily, wh ereas it is impossibl e 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 det ection windows for the Di xon Outfall 2/06/09

com posite sample.

Figure 4.25: Copper titrations of the Dixon 2/06/09 compos ite 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 separa te ligand concentrations and stability constants. The lower (2 μM SA) detection window reveals a liga nd concentration of 335 nM and a stability constant of 10 11.26 . The higher (10 μM SA) window reveals a lower ligand concentration, 68.4 nM, a nd a higher stability constant, 10 12.80 . Comparison between each detection window fo r the five samples tested with both SA levels reveals that ligand concentrations decrease and stabil ity 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 th e Dixon Outfall 2/06/09 com posite sample to Equation (3-9)

.

72Figure 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. Th is behavior is due to the high Cu 2+ diss concentrations relative to the number of available natural ligand sites in some stormwater samples. The ligand

73sites in sam ples with high Cu 2+ 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 La ngmuir curve is truncated and lit tle information about stability constants is provided at low concentrations (see  2 free Cu Figure 4.27 for an example). Other (erron eous) fitted data sets had decreasing trends (negative slopes) with increasing concentration and/or negative CuL v alues for the entire range of concentrations. These
results ar e discussed below in the Wemme site overview.  2 free Cu

 2 free Cu



### 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 (e xcluding pH and tem perature) known to affect either free ioni c copper concentrations or copp er toxicity are supplied alongside the speciation results. represents the total dissol ved copper concentration; [ ]

repres ents the natural orga nic ligand concentrations; 

#   2 diss Cu T L

CuL K  is the stability constant associated with those ligands; represents uncomplexed, or free ionic copper; and ‘Cu

Com plexation’ is the percentage of Cu 2+ diss that was complexed by organic ligands in the sample. Cu 2+ free concentrations were calculated using Equation 

#    2 free



## 
T L

  Cu

diss Cu 2 (3-10) , but where samples had then a ‘worst case’ scen ario was assumed and Cu 2+ free concentrations were approximated as 

#   . Stormwater samples that fell into this category were re-tested at 2 μM SA when sample volume allowed. Lowe ring the detection window usually resulted in a more representative picture of the organic ligand s. 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 diss T Cu L 

#  

Differences between ligand concentrations and st ability cons tants were significant for the two detection windows at 2 μM and 10 μM SA. The m ean 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 de tection windows is co rroborated by Buck and Bruland ( 2005 ). It is important to rec ognize that this analytical proc edure 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 Cu 2+ free concentrations are independent of th e detection window; thus, calculated Cu 2+ free concentrations determined at the 2 μM and 10 μM SA detection windows can be compared.Table 4.6: Ligand characteristics, freeionic copper concentrations and related water quality parameters for composite samplescollected at all sitesSiteStormSA[Cu2+diss]DOCHardnessAlkalinity[LT]logK′CuLlog [Cu2+free]Cu ComplexationμMμg/LnMmg/Lmg/L as CaCO3mg/L as CaCO3nMM%12-29-08105.2182.005.1627.309.68173.5811.65−11.6999.9903-16-09108.95140.865.8072.84a10.1114.1511.89−7.57e81.04

Bend03-16-0928.95140.865.8072.84a10.1432.7310.54−10.8599.9910-06-081022.70357.267.0030.8518.41316.0111.14−7.38e88.4511-02-081017.80280.149.5030.1519.0797.96c−6.74e34.9711-03-081012.80201.455.8819.5813.78190.5412.02−7.96e94.5811-03-08212.80201.455.8819.5813.78382.9911.08−11.0399.9911-20-081017.30272.276.9024.9515.15162.5611.62−6.96e59.71

I-511-20-08217.30272.276.9024.9515.15206.19c−7.18e75.7311-20-08104.4369.724.778.374.4598.2012.59−12.2099.9912-01-08101.7828.011.815.754.19111.0512.28−12.7599.9902-06-09104.8075.544.2412.1810.0268.3012.80−8.14e90.4202-06-0924.8075.544.2412.1810.02334.7811.26−11.8099.9902-23-09103.8159.962.628.136.91128.6412.56−12.6299.9903-14-09102.5039.352.085.244.30105.2112.75−12.9799.9904-12-09107.88124.024.797.516.66132.6913.49−12.3499.9905-13-091013.20207.7410.788.855.91211.0412.97−11.1199.99

Dixon Outfall10-13-09103.2851.624.909.428.0178.1112.06−11.7799.9911-20-08103.1950.23.699.054.3699.5712.61−12.6099.9912-01-08102.3036.201.5413.603.9166.7712.58−12.5099.9901-06-09105.2081.842.56148.41a9.5964.8211.68−7.77e79.2001-06-0925.2081.842.56148.41a9.59179.4710.94−11.0299.9903-11-09103.8460.435.97476.59a,b14.09239.8910.40−10.8699.9803-16-09101.9029.902.0495.948.84d03-24-09103.0247.532.50136.34a9.98d03-24-0923.0247.532.50136.34a9.98161.4710.92−11.3099.99

Wemme05-05-09103.1048.791.969.613.6256.7113.06−12.2799.99Notes:(a) Mg outside upper limit of ICP-AES calibration(b) Ca outside upper limit of ICP-AES calibration(c) KCuLwas 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]75W hat follows is an examination of the ligand characteristics and Cu 2+ free concentrations determined using the CLE-ACSV method. In addition to summarizi ng the differences in ligand properties and Cu 2+ free concentrations in composite sa mples at different sites, the influence of water quality parame ters on ligand properties and Cu 2+ 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 com pared 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 poten tially a seasonal outlier. However, these sites are plotte d with a different symbol ( open circle) to indicate where they fall in the trends. Cu 2+ 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

sam ples collected at each site. The medi an 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 stormw ater is a highly he terogeneous 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 an thropogenic derived material (industrial chemicals, petroleum products, partia lly combusted hydrocarbons, pesticides, etc.). Wemme, Dixon Outfall, and Bend are all sites that would receive both types of organic material, wh ereas the urban isolation of the I-5 site may result in petroleum products, combustion byproducts and industrial chemicals dominating the mixture.

76Figure 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 correlate d with DOC (p = 0.005). As expected, ligand increases as the mass of organics in the system increases. Corroborating the relationship between Cu 2+ diss and DOC in Section 4.1.4 , large differences between the m agnitude of DOC and liga nd concentrations indicate that cation binding sites consist of a small portio n of organic molecules present in
stormwater. Figure 4.29 shows the relationship between DOC and ligand concentratio n.

Figure 4.29: Relationship between total ligand concentration and dissolved organic carbon content of composite samples collected at all for sampling sites

774.2.4.2 Conditional Stability Constants

Figure 4.30 compares the conditional stabil ity constants for the Cu-Ligand

com plexes determined in the composite samples collected at each site. There are significant differences in ligand site bi nding strengths between Dixon and the I-5 site. The median value determined at Dixon Outf all 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

com parisons revealed statistically significant differences. Within samples collected at each site, the conditional stab ility constants were widely variable. This variation can be attributed to the likely difference in t ype of organic matter present in each sample. For example, one difference could be the relative concentrations of ligands from anthropogeni c sources and biogenic sources. This difference is most likely the cause for the significance betw een the Dixon (varied ligand sources) and I-5 (primarily an thropogenic 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. CuL K 

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 Cu 2+ diss concentration, leading to a decrease in the conditional stability constant. CuL K 

78Figure 4.31: Effect of hardness on conditional st ability constants (empty circles are high hardness Wemme samples)

4.2.4.3 Free ionic copper Concentrations

The Cu 2+ free concentation can be calculated from the ambient Cu 2+ diss concentration, the to tal 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 Cu 2+ 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 Cu 2+ free concentrations determined in com posite samples at each site using a 10 μM SA detection window. Cu 2+ 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 tota l 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 la rge concentrations of Cu 2+ 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 st art of the ti trations and added copper immediately began to comple x 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 Cu 2+ diss concentration exceeded the total ligand con centration, and a “worst-case” estimate of Cu 2+ diss had to be made.

79Figure 4.32: Summary of free ionic copper concentra tions calculated in composite samples collected at the four sampling sites

As outlined in Chapter 2, the presence of Cu 2+ diss (more specifically, Cu 2+ 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 Cu 2+ diss (added as CuCl 2 , so virtually all of the Cu 2+ diss was in the free ionic form). The background Cu 2+ 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 Cu 2+ 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 Cu 2+ diss above background concentrations (defined as ≤ 3μg/L) to have a signifi cant effect on the olfaction of unexposed juvenile salmon. In addition, McIntyre, et al. ( 2008 ) have shown that the presence of alkalinity, hardne ss and dissolved organic carbon acts to reduce the neurotoxic effects of Cu 2+ diss . Due to the uncertainty in the background concentration of copper used in the Hecht, et al. st udy, we have adopted the nominal 2 μg/L (10 − 7.5 M) concentration of Cu 2+ free as a benchmark for potential toxicity in the following discussion.

Cu 2+ diss and Cu 2+ free concentrations for each composite sample are compared to the juvenile Coho salmon toxicity level in Figure 4.33 . The Cu 2+ 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., 

#    
T diss L Cu   2 ), copper was > 99.9% complexed by organics in the stormwater. As a result, calculated concentrations of Cu 2+ free ranged from 10 − 5 to 10 − 3 μg/L, well below the effect threshold of

802 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 Cu 2+ 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 Cu 2+ free concentrations at the 10 μM SA detection window for the 3/16/2009 Bend samp le, the 2/6/2009 Dixon sample, the 11/3/2008 I-5 sample and the 1/6/2009 We mme sample were below the toxicity threshold. In all but one of these sample s, the use of a lower detection window (2 μM SA) revealed large concentrations of weaker ligands and allowed a better estimate of Cu 2+ free
. Clearly, these results indicate that the vast majority of Cu 2+ diss is complexed with organics present in highway stormwater runoff. Only samples collected at the urban I-5 site had Cu 2+ free concentrations that exceeded 2 μg/L, and those estimates were worst-case scenarios base d on incomplete characterization of the natural ligands. The comple xation 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 me tals 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 re sults, indicating that high-traffic, urban areas are at the greatest risk for copper toxicity.

81Figure 4.33: Concentrations of total dissolved, and free ionic copper in composite stormwater samples * indicates samples where [Cu 2+ diss ] > [ L T ]; a “worst case” [Cu 2+ free ] was estimated as [Cu 2+ diss ]

− [ L T ]

The three samples that exceeded the toxicity threshold we re all collected from the I-5 site, suggesting that copper toxicity is more likely to be a problem at urban sites where Cu 2+ diss concentrations are higher. Th e three exceeding samples had at least 70 nM more of Cu 2+ diss , higher hardness, and higher DOC than the one I-5 sample that did not exceed the toxicity le vel. However, as mentioned previously, these three I-5 samples may not have been fully characterize d and therefore the Cu 2+ free concentrations are likely lower than th e worst-case scenario reported here.

Although no other samples at any of the site s exceeded the toxicity level, there was a pattern within the sites between high Cu 2+ free concentrations and other water quality parameters. The 12/29/08 Bend sample with the highest Cu 2+ free concentration had higher hardness and Cu 2+ diss concentrations than the only other Bend sample. The 3/11/09 Wemme sample with the highest Cu 2+ free concentrations had the highest hardne ss, DOC concentration, and highest Cu 2+ diss concentrations (except for the 3/24/09 sample) when compared to other Wemme samples. Examining these trends further shows that Cu 2+ free concentrations are positively correlated with Cu 2+ 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.

82Figure 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)

83Figure 4.36: Effects of DOC on free ionic copper concentrations

The relationship between Cu 2+ diss and Cu 2+ free is expected – as Cu 2+ 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 c opper to bind and increase the amount of free ions in solution. The effect s of alkalinity were expected to be negative due to the increased complexation of copper by carbonate species, but the co-var iation of alkalinity with hardness likely masks those effects.

If Cu 2+ free concentrations were the only indicator for the toxicity of copper, then these results suggest that waters containing high hardness and Cu 2+ diss concentrations are more likely to have toxic levels of Cu 2+ 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 co mpete with copper for these biotic sites, just as they do for sites on the natural organi c ligands. Therefore, the effects of hardness on copper toxicity are likely more complicated than pictured here. Regardless, elevated Cu 2+ diss concentrations in stormwater are a ca use for concern, and may be the best single indicator for Cu 2+ free and toxicity levels.

The positive relationship between Cu 2+ free and DOC was not expected. An increase in DOC corresponds with an increase in ligand sites, and therefore Cu 2+ free concentrations should decrease due to increased binding with organics. The positive relationship between DOC and Cu 2+ diss (i.e., the fact that more highl y polluted waters contain more copper and more DOC) likely overwhelms th e relationship between DOC and ligand concentration. There is a weak negative correlation between DOC and CuL K  that may
contribute to an overall decrease in the stre ngth of CuL complexes as DOC increases. The co-variation of dissolv ed organic carbon with Cu 2+ 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 Cu 2+ free concentrations difficult. Analytical speciation, like that performed in this

84study, and chem ical equilibrium modeling (d iscussed in the next section) are the preferred methods for characterizing th e effects of dissolved organic carbon on Cu 2+ free concentrations.

4.2.4.4 Dixon Outfall Speciation Results

A 10 μM SA detection window was appropria te for characterizat ion of the ligands in all but one sample (2/06/09) from Dixon Outfall. Cu 2+ diss was 99.99% complexed with organic ligands in all Dixon Outfall samples. Cu 2+ 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 prov ided stronger coppe r complexation. CuL K 

4.2.4.5 I-5 Speciation Results

High Cu 2+ diss concentrations in the I-5 samples led to a truncation of the titration curves. As such, I-5 sites were char acterized by low ligand concentrations relative to ambient Cu 2+ diss concentrations within the 10 μM SA detection window. These low ligand c oncentrations led to Cu 2+ 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 suffi cient volume to reanalyze at a 2 μM SA detection window. Analysis at the lower detection window rev ealed 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 Cu 2+ diss concentration. High Cu 2+ 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 anth ropogenic DOC at the I-5 site, such as petroleum and industrial chemicals, form s only weak(er) complexes with copper. 34.97% or more of the Cu 2+ 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 bu t one of the worst-case estimates for Cu 2+ free exceeded 2 μg/L. Hardness concentr ations 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 Cu 2+ free concentrations exceeded all other sites based on the worst-case estimates. Thes e results suggest th at the elevated Cu 2+ diss concentration at the I-5 site were th e leading cause of differences in Cu 2+ free concentrations.

4.2.4.6 Bend Speciation Results

Through analysis at 10 μM of SA, ligand c oncentration and stability constants at Bend were not significantly di fferent from the other sites. One sample (3/16/09) contained more Cu 2+ diss than ligand sites. Further i nvestigation with the 2 μM SA

85window showed that weaker ligands were present. Overall, at least 99.99% of Cu 2+ 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

CuL K  values at Wemme varied widely, ranging from 10 10.40 to 10 13.06 , 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 (MgCl 2 ) 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. Cu 2+ free concentrations were higher than in Di xon Outfall samples, but otherwise were low, not surpassing 2 μg/L in any sample s. Extremely high hardness (476.6 mg/L as CaCO 3 ) corresponded to the elevated Cu 2+ free concentration in the 3/11/09
storm.

CuL K  and [ could not be quantified for two samples (3/24/09 and 3/16/09) at the 10 μM SA window due to misleading result s 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 negativ e slope for the Langmuir isotherm. This negative slope was caused by the sensitivity decreasing as the titration progressed. Approach of electrode saturation was the like ly 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 [CuSA x ] can lead to negative CuL concentrations if [CuSA x ] exceeds [Cu 2+ diss ]. Overestimation of [CuSA x ] can occur in the presence of sufficient surf actants; as titration sensitivity decreases with increasing surfactant concentration, leading to an overestimation of [CuSA x ] (see Equation (3-6) ). Using an internal slope (sensitivity) can correct for this suppression ( Kogut and Voelker 2001 ), but tests on W illamette 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 Cu 2+ diss concentrations in stormwater sa mples were typically greater than those measured in previous fresh/seawater studies; in some cases, Cu 2+ 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 surfac e freshwater and rainwater. Mean Cu 2+ free concentrations were also generally great er than those measured in previous freshwater/seawater studies. No free copper concentrati ons in the fresh/seawater studies exceeded 2 μg/L. Differences in pH and ionic st rength prevent any comparison of and ligand concentrations between the studies. CuL K 

8687 Buck and Bruland ( 2005 ), noted that the copper comp lexation capacity of one of their sites at Dumbarton Br idge was near 108 nM of Cu 2+ diss – over double the measured concentration of Cu 2+ diss . The implication of these results is the possibility that fresh/seawater sites ma y have substantial capacity to complex copper spikes and/or point source pollution from stormwater. Therefore, it is important to examine the characteristic s of the receiving waters (e.g, organic matter type and concentration) to fully ev aluate the potential for copper toxicity. Elevevated dissolved and Cu 2+ 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 w ith 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 cap acity, 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 para meters are also incl uded. In general, speciation an alysis of the first-flush and grab samples was hampered by the high Cu 2+ diss concentrations and elevated levels of other pollutants (e.g., surfactants, etc.) in th ese samples, which created problems for the analytical method. The majority of first flush and intra-storm samples did not yield reliable information for conditi onal stability constants or Cu 2+ 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 stor m. These samples had low hardness, Cu 2+ diss , and DOC concentrations. Conversely, th e first flush samples that did not yield copper speciation information had high Cu 2+ 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 w ith sufficient volume were also tested at 2 μM SA, but analyses did not yield additional information due to negative CuL concentrations or negatively sl oped 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 OutfallStormTypeSA[Cu2+diss]DOCHardnessAlkalinity[LT]log K′CuL2logfreeCuCu ComplexationμMμg/LnMmg/Lmg/L as CaCO3mg/L as CaCO3nMM%11/20/2008Composite104.4369.724.778.374.4598.212.59−12.2099.9911/20/2008First Flush109.02141.9612.5219.17.0915812.45−11.4999.9911/20/2008#3+4102.5540.132.596.133.1843.413.08−11.9999.9912/1/2008Composite101.7828.011.815.754.19111.0512.28−12.7599.9912/1/2008First Flush1016.43258.5814.241.0413.5621612.91−7.37e83.502/6/2009Composite104.875.544.2412.1810.0268.312.80−8.14e90.422/6/2009Composite24.875.544.2412.1810.02334.7811.26−11.8099.992/6/2009First Flush1012.2192.0115.5763.1120.58137c−7.26e71.382/23/2009Composite103.8159.962.628.136.91128.6412.56−12.6299.992/23/2009First Flush1020.9328.9318.4547.0618.55285c−7.36e86.733/14/2009Composite102.539.352.085.244.3105.2112.75−12.9799.993/14/2009First Flush1017.5275.4216.0252.23b9.7522411.91c−7.29e81.384/12/2009Composite107.88124.024.797.516.66132.6913.49−12.3499.994/12/2009First Flush1035.6560.2827.4427.2410.2153513.10c−7.60e95.525/13/2009Composite1013.2207.7410.788.855.91211.0412.97−11.1199.995/13/2009First Flush1030.5480.013734.2712.447912.92c−9.00e99.795/13/2009#41024.5385.5826.0820.879.2741312.88−11.7099.999/29/2009First Flush1010.3162.1016.7528.296.4118.6912.56c−7.36e73.079/29/2009#2107.28114.579.7911.154.3540.612.98c−7.13e35.4110/13/2009Composite103.2851.624.99.428.0178.1112.06−11.7799.9910/13/2009First Flush1011.5180.9915.7928.111.236.7c−6.84e20.1410/13/2009#4104.5872.085.268.669.993.112.13−11.5499.9910/13/2009#5105.281.848.5816.4513.737.312.29c−7.35e45.4210/13/2009#11102.3737.304.257.537.5835.712.59−8.81e95.8510/13/2009#17100.9715.272.815.546.341212.65−8.49e78.8010/13/2009#20101.3621.403.67.555.7361.212.01−12.2899.99Notes:(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+freevalues 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+freeconcentration is best estimated by [Cu2+diss]−[LT]884.2.5.1 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 liga nd 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 con centration within each storm and the overall differences between first flush and composite samples, respectively. The higher organic ligand concentrations in the first flush samp les 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

89First Flush Composite Sample Type 1.5 1.9 2.3 2.7log([L]) ([L] in nM) [L]: The First Flush Effect

Figure 4.38: Comparison between [L T ] values for first flush and composite samples

As was the case with composite samples, [L T ] was positively associated with DOC (p = 0.0006). On average, a doubli ng in DOC was associated with a 1.61- fold increase in [L T ] (1.26 – 2.06, 95% CI). This relationship between [L T ] 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 stor m may have a greater number of copper binding sites available.

900.0 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 2.5Log([L]) ([L] in nM) [L] vs DOC

Figure 4.39: Relationship between [L T ] and DOC for Dixon Outfall samples

Figure 4.40 shows the relationship between [L T ] and hardness for all the Dixon Outfall samples. Considering the log-transformation, [L T ] was positively associated with hardness (p = 0.0034). On average, a doubling in hardness was associated with a 1.60-fold increase in [L T ] (1.19 – 2.16, 95% CI). Hardness should not be directly related to [L T ]; therefore the positive association is likely due to major cations (Ca, Mg, etc) and organic matter (DOC) being flushed into the stormwater together.

910.7 0.9 1.1 1.3 1.5 1.7 1.9 LogHardness (Hardness in mg/L as CaCO 3 ) 1.0 1.5 2.0 2.5Log([L]) ([L] in nM) [L] vs Hardness

Figure 4.40: Relationship between [L T ] and hardness for Dixon Outfall samples

4.2.5.2 Free Ionic Copper Concentrations in First Flu sh and Discrete Samples

Calculated Cu 2+ free concentrations in 6 of the 9 first flush samples exceeded 2 μg/L. In each case, the Cu 2+ free concentration was a worst case estimate, due to the fact that the ligands were saturate d 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 su rfactants or other constituents in the samples. Only 2 of the 8 discrete sa mples collected duri ng individual storms exceeded the toxicity threshold. Again, these estimates were worst case estimates obtained at the 10 μM SA de tection window. In every sample where the ligands were fully characterized at the 10 μM SA detection window, the Cu 2+ free concentrations were well belo w the toxicity threshold.

As with the composite samples, trends in the Cu 2+ free concentrations tracked well with the relative concentrations of Cu 2+ diss present in the samples. First flush samples had significantly more Cu 2+ diss than the composite samples and discrete samples collected over the course of individual storms. These elevated Cu 2+ diss concentrations were likely the reason that the ligands in these samples could not be fully characterized. Based on these re sults it does appear that the risk of copper toxicity from the release of hi ghway stormwater at the beginning of a storm poses a greater risk than event m ean 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 heterogene ous nature of the

92dirtier first-flush sam ples prevented accura te characterization of the stormwater organics in this study and limits the abil ity to make firm statements about the likely toxicity of first flush samples.



### 4.2.6 Chemical Equilibrium Modeling

Speciation modeling using Vi sual 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 mode led 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 fr om Dixon Outfall. Subsequently, more sophisticated DOM models (NICA-Donnan a nd SHM) were utilized to examine the speciation in composite samples. Results from the chemical equilibrium models were compared with analytical determinations of Cu 2+ free . This analysis was limited to the composite samples because of the larger unc ertainty 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 an alysis using the Gaussian DOM model predicted that the concentrations of the cupric ion (Cu 2+ ), the modeled Cu-DOM complex, CuOH + and CuCO 3 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 Cu 2+ 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). Cu 2+ free accounted for a range of 2.4- 35% of the Cu 2+ diss in the samples, with a median of 8.4%. Samples with elevated concentrations of Cu 2+ free , and higher percent Cu 2+ free coincided with high hardness concentr ations, especially at the Bend and Wemme sites. As mentioned previously, higher hardness at these sites coincided with the application of MgCl 2 road salts. The eff ect of hardness on copper speciation is more thoroughly examined in subsequent sections. Complete results from the copper speciation modeling e ffort are provided in Appendix A.4.

Overall, the results of the Gaussi an DOM modeling suggested that Cu 2+ free concentrations were quite low and that the majority of Cu 2+ diss was bound with organic matter in highway stormwater runoff. Comparing the modeled Cu 2+ free concentrations with the 2 μg/L toxicity criterion, only one of the 48 samples showed an exceeding Cu 2+ concentration (the I-5 10/6/08 composite).

4.2.6.2 Site Comparisons

Figure 4.41 presents the modeled Cu 2+ free concentrations at the four sites. For reference, the 2 μg/L toxicity threshold equates to a log[Cu 2+ free ] value of 1.5. The trends in modeled Cu 2+ free are similar to the trends in measured Cu 2+ diss at the four sites. As with Cu 2+ diss , the I-5 site showed consis tently higher concentrations of Cu 2+ free than the other three sites. The si milarity in these plots underscores the direct relationship between Cu 2+ diss and Cu 2+ free . Furthermore, results suggest that

93the elevated DOC conce ntrations in th e I-5 samples were not high enough to increase in the copper complexation capacity and keep the Cu 2+ free concentrations low. The most obvious difference between Figure 4.1 (Cu 2+ diss concentrations) and Figure 4.41 (modeled Cu 2+ free ) is the elevated and highly varied concentrations of Cu 2+ free in the Wemme samples. The peak Cu 2+ free of the Wemme samples approaches I-5 levels, while the lowest concentration of Cu 2+ 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 1.5LogCu2+(Cu2+in nM) Modeled Cu 2+ By Site (Composite Samples)

Figure 4.41: Modeled free ionic copper concentrations in composite samples

Although not as important as th e overall concentration of Cu 2+ free , examining the percentage of Cu 2+ free provides insight into instan ces where copper toxicity might become a problem. Figure 4.42 displays the percentage of Cu 2+ 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 Cu 2+ diss in the free ionic form, while the Wemme subset disp lays a wide range of Cu 2+ free percentages with a much higher median. From these data, it is evident that the Wemme samples exhibited a much larger and wi der distribution of percent Cu 2+ free . Further analysis shows that four of the Wemme composite samples contained high levels of hardness, and this signif icantly affected the copper sp eciation of these samples. On average, these four samples containe d six times the concentration of calcium and over 20 times the amount of magnesium compared to the other three Wemme composite samples. The increased cal cium and magnesium concentrations resulted in hardness that was 10 or mo re times greater than the other Wemme

94com posites and caused an increase in the percent of Cu 2+ 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 sample s 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 sa mples to 66-68% in the high hardness samples, thereby reducing the potential for DOM to complex Cu 2+ free
.

Bend Dixon I-5 Wemme Site 0 10 20 30Percent Cu2+ Percent Cu 2+ (Composite Samples)

Figure 4.42: Modeled percentage of dissolved copper present as free Cu 2+ in composite samples

4.2.6.3 The First Flush Effect

The first flush effect on modeled Cu 2+ free is similar to the first flush effect witnessed on Cu 2+ diss concentrations in Section 4.1.2

. Figure 4.43 compares Cu 2+ 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 Cu 2+ free than their respective composite samples. The percentage of Cu 2+ diss present as Cu 2+ free in the two sample types is shown in Figure 4.44 . Interestingly, a lower percentage of Cu 2+ 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

95are cha racterized by high concentrations of most runoff constituents, including hardness (Ca and Mg), DOC, and Cu 2+ diss . The fact that lo wer percentages of Cu 2+ free are present in first flush samples rev eals that, in the model, the increased concentration of DOM overwhelms the e ffects of increased concentrations of divalent cations, which compete with Cu 2+ free for binding sites on organics. Therefore, it is likely that the increased Cu 2+ free seen in Figure 4.43 is attributable to higher Cu 2+ diss concentrations, and not co mpetition 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 1.3LogCu2+(Cu2+in nM) First.Flush Composite Cu 2+ : The First Flush Effect

Figure 4.43: The first flush effect on free ionic copper

96Nov 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 15Percent Cu2+ First.Flush Composite Percent Cu 2+ : 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 speciati on using the Gaussian DOM model. The results of that analysis are summarized in Figure 4.45 . The storm hydrograph is shown in the prim ary vertical axis, while concentrations of Cu 2+ free , Cu-DOM, and the baseline salmonid Cu 2+ free toxicity levels reported from Sandahl ( 2007 ) (2 μg/L, 31.5 nM) are on the secondary vert ical 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 5 th and 6 th samples) to 6.50 nM (in the 3 rd sample) while the concentration of CuCO 3 ranged from 0.4 nM (in the 5 th sample) to 3.3 nM (in the 1 st 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 Cu 2+ 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, Cu 2+ diss , and Cu 2+ free . The highest concentration of Cu 2+ 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 appear ed to result in small intra-storm flushes of Cu 2+ diss also produced slight increases in Cu 2+ free , notable in the 3 rd and 6 th samples collected in this storm. The decrease in Cu 2+ free over the course of this storm supports the idea that highway runo ff early in a storm is potentially more

97toxic to aqu atic life than runoff later on in the storm. However, in this case, even in the first flush, Cu 2+ 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 distributi on of binding site number to binding site strength (as outlined in Section 2.7 ) may or may not accurately p redict copper-DOM binding in these samples. For these reasons, analytically determining copper specia tion in stormwater is important. Comparisons of analytically determined and modeled Cu 2+ free concentrations are discussed in the following section.

984.2.6.5 Comparison Chemical Equilibrium Modeling with CLE-ACSV Results

The Gaussian DOM model utilized in the a bove analysis is a relatively simple organic binding model. For the purposes of comparing model results with analytically determined speciation, tw o more sophisticated DOM models (the NICA-Donnan model and the Stockholm Humic Model) we re 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 Cu 2+ free concentrations with analytical measurements.

Table 4.8: Comparison of experi mentally determined and modeled free ionic copper concentrations Sample Measured log [ Cu 2+ free ]

(M) Gaussian log [ Cu 2+ free ]

(M) NICADonnan log [ Cu 2+ free ]

(M) SHM log [ Cu 2+ free ]

(M)

Bend Site 12-29-09

− 11.69

− 7.93

− 10.10

− 10.41 03-16-09 a

− 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 11-03-08 a

− 11.08

− 7.47

− 9.41

− 9.77 11-20-08 a

− 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 02-06-09 a

− 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 01-06-09 a

− 11.02

− 7.45

− 9.15

− 9.60 03-11-09

− 10.86

− 7.64

− 10.20

− 10.22 03-24-09 a

− 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 Cu 2+ free concentrations cannot be made due to the unknown error associated with the models. However, all three DOM models overestimate the Cu 2+ free concentration determined by CLE-ASCV by one to three orders of magnitude. The Gaussian DOM model consistently predicted the highest Cu 2+ free concentrations, while the Stockholm Hu mic Model predicted the smallest

99concentratio ns of Cu 2+ free . In general, results from the NICA-Donnan and Stockholm Humic Model were similar. Fo r the majority of samples the primary copper species were: 

##   

#     

#      CuOH CuCO Cu DOMCu free 3 2 (4-1)

As discussed above, the Wemme sample co llected on 3/24/09 was unique in that Cu 2+ free was the most abundant species in th e Gaussian model due to Ca and Mg competing with copper for DOM. Over all, the trend in modeled species concentrations in Equation (4-1) agrees with experimental results.

D espite the fact that modeled Cu 2+ free concentrations overestimate the concentrations determined by CLE-ACSV, general trends in the modeled results reflect those of the analytical determ inations. Furthermore, the chemical equilibrium models can be viewed as a conservative estimate of the Cu 2+ free concentrations. Even these conservative estimates of the Cu 2+ free concentrations were substantially less than the 2 μg/L toxicity threshold. The fact that modeling copper speciation requires s ubstantially less time and e ffort than analytically determining Cu 2+ free concentrations makes this opt ion appealing. With a few relatively simple analytical measurements (Cu 2+ 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 an alytically 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 stor mwater are different. More accurate predictions of Cu 2+ free concentrations could be obtained through the development of DOM parameters reflective of stormwater DOM. This may include an increase in the number of c opper-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-d epth investigation into the speciation of cations in stormwater would be nece ssary. 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-AC SV 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 Cu 2+ free concentrations in stormwater. More work is needed to determine if available models are appropriate for predicting c opper speciation in stormwater and natural receiving waters. With regulatory agen cies moving toward the use of Biotic Ligand models that account for humic binding, better characterization of the nature and characteristics of the orga nic matter present in stormwater and receiving waters will be necessary.

100

## 5.0 CONCLUSIONS

The objectives of this study were 1) to id entify the effects of site location, storm hydrology, and water quality parame ters on the concentration of Cu 2+ diss in Oregon highway runoff; 2) to establish an analytical technique suitable fo r the determination of copper speciation in highway st ormwater runoff; 3) to comp are analytically determined Cu 2+ free concentrations in highway stormwater runoff with modeled concentrations; and 4) to develop a qualitative understanding of wh ere and when copper toxicity has the most potential to be problematic for receiving wate rs. 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 s ite) had consistently higher event mean concentrations (EMCs) of measured Cu 2+ diss and Cu 2+ free than three non-urban sites with lower AADT. The separate e ffects 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 im material – urban sites are characterized by high traffic densities and vice versa. High AADT/urban highways show the most potential for producing runoff th at would exert copper toxicity

 There was little evidence to sugge st significant differences in Cu 2+ diss in stormwater measured at the other three si tes, which varied in terms of eco-region and AADT.

 First flush samples displayed consiste ntly higher concentrations of both Cu 2+ diss and Cu 2+ 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 suppor t any effects of antecedent dry period (ADP), total rainfall, rainfall duration, or average rainfall intensity on Cu 2+ diss concentrations in composite samples. There was also no significant effect of ADP on Cu 2+ diss in first flush samples from the Dixon Outfall site. It is possible that if more samples were collected, si gnificant correlations w ith these hydrologic parameters would be determined.

 In a multiple linear regression (MLR) model, the water quality parameters which demonstrated the greatest ability to predict Cu 2+ diss were dissolved organic carbon (DOC) and alkalinity, both having positive associations with Cu 2+ diss . The model also included less significant terms accounti ng for negative correlations with pH and hardness. The primary value of this model is in determining important water quality predictors of copper, not in de termining a quantitative equation, which would likely be inaccurate and highly location-specific.

101 In an MLR model to predict Cu 2+ 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 Cu 2+ tot . Again, developing a quantitative predictive model could gi ve the false impression that copper concentrations could actually be calculated knowing other water quality parameters.

 A modified CLE-ACSV tec hnique utilizing salicylal doxime (SA) as an added ligand can be used to determine the speci ation 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 co mpensate 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 impl ement in the dirtiest samples with high Cu 2+ diss concentrations and su rfactants or other compounds that may foul the mercury drop electrode.

 Analytical results from composite stor mwater 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 Cu 2+ free were generally several orders of magnitudes below levels found to inhibit ol faction in ESA-listed fish species.

 Elevated Cu 2+ diss levels proved to be the gr eatest indicator of high Cu 2+ free concentrations. Urban sites with AADT and first flush samples characterized by elevated concentrations of Cu 2+ diss are of the greatest c oncern with respect to elevated free ionic copp er concentrations.

 Hardness, as a measure of major divalent cations, is also a good indicator of Cu 2+ free concentrations. Calcium and ma gnesium competition with copper for ligand sites drives copper into its free ionic form. Hardness-related issues with Cu 2+ free are of greatest concern af ter 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 samp les. Consideration must be given to these effects to mitigate potential problems with Cu 2+ free concentrations in receiving water bodies.

 The overall effect of hardness on toxicity in stormwater is complex. Divalent cations compete with Cu 2+ diss both for adsorption site s on DOM (increasing free Cu 2+ free ), but also compete with Cu 2+ free for biotic ligand sites on aquatic organisms (reducing toxicity).

 Organic ligand and Cu 2+ diss concentrations are positively correlated with DOC concentrations. Cu 2+ free levels are also positively correlated with DOC concentrations. Likely there are co-var iance issues, but overall, DOC is not a strong predictor of Cu 2+ 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 Cu 2+ free concentrations when compared to analytically determined Cu 2+ free concentrations. The Stockholm and NICA-Donnan models pr ovide the best estimates for Cu 2+ 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 sim ilar trends as the analytical speciation results and are likely a reasonably conser vative estimate of copper speciation in highway stormwater runoff.



### 5.1 FUTURE WORK

There are several key areas for future inve stigative work stemming from this study.

Determining Cu 2+ free concentrations in water bodies requires significant time, effort, skill, and analytical equipment. For this reason, routinely measuring Cu 2+ free concentrations for regulatory purposes is not advisable. A lthough correlations were observed between bulk water quality parameters (dissolved coppe r, hardness, DOC, and alkalinity) and Cu 2+ free concentrations, these measurements in is olation do not lend themselves to robust predictive capabilities. It is suggested that copper toxici ty levels be determined for species of interest (e.g., C oho salmon) through establishing Cu 2+ diss concentrations at specific DOC and hardness levels that result in high risk fo r 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 Cu 2+ diss concentrations for Coho salmon under different water quality conditions. This information could be incorporated into a Biotic Ligand Model or similar framew ork and used by regulators to determine the danger of copper levels to ESA-listed fish.

The relationship between DOC concen trations, types of DOC, and Cu 2+ free concentrations in highway stormwater runoff is unknown. Fu ture work should attempt to more fully characterize DOC and copper spec iation at a few selected site s in order to determine the effects of differen t types of DOC on Cu 2+ free concentrations. Sites with a single dominant organic source (highly urbanized zones) could be contrasted with site s 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 Cu 2+ free in a single receiving water may provide information on the role of specific functional groups in binding coppe r in natural waters.

There is also potential work in analyzing the effects of highway runoff in receiving waters. Highway runoff comprises only a fr action of the total vol ume of a receiving

103water. However, this fraction m ay be small or large, depending on a specific site’s surrounding paved area, location within a watershe d, the time of the year, etc. A study investigating different diluti ons of highway runoff with na tural waters and/or the Cu 2+ free concentrations in receiving bodies as a f unction 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 coppe r 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 ma kes 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 effort s focused on examining a single variable. For example, the separate effects of AADT a nd 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 indi vidual effects of AADT and ur ban sites. This could be researched by selecting a number of samp ling sites with various AADTs which are all located within the same urba n area. Such a study would also eliminate some of the inherent variability in analyzing samples ar ising from different storms. Any future stormwater study should be long term – over th e course of years. This would help determine if any observed trends are only a function of seasonality.

Finally, an analytical model that accurately determines Cu 2+ free concentrations in stormwater is currently unavailable. An investigation into common DOC characteristics and the speciation of cations in a stormw ater 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 determ ine 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 presen t 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 proj ect 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 fr om 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 Cu 2+ free concentrations in waters of interest (stormwater samples or receiving bodies) by regulator y organizations is not the appropriate use of the CLEACSV technique. This technique requires specialized training, an extensive time

104comm itment to fully characterize a sample, a nd 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 Cu 2+ 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 shoul d 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. Howeve r, in examining both the statistics on Cu 2+ diss and the speciation for Cu 2+ free , it was shown that highway r unoff arising from the first flush of a storm, as well as highway runoff from ur ban/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 re ducing the potential toxicity to aquatic species. Therefore, the most effective best management practi ces (BMPs) for treati ng highway runoff should focus on those sites and conditions . It is important to rec ognize, however, that in this study it was found that the vast majority of Cu 2+ 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 Cu 2+ . This is of particular interest because the very high c oncentrations of hardness, which significantly affect copper’s speciation, likel y arise from adding de-icing sa lts to the road, specifically magnesium chloride. However, as noted in Chapter 2, calcium and magnesium have also shown a significant effect in reducing copper toxi city to fish. This effect is primarily due to their competition with Cu 2+ 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 receivi ng 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 Cu 2+ free concentrations is important. These results show that the models utilized in this study are not reflective of actual DOM in Oregon highway stormwater r unoff. 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 highw ay stormwater runoff with low Cu 2+ diss concentrations (non-urbanized sites) and situations where the toxicity levels exceed predicted Cu 2+ free concentrations by multiple orders of magnitude (as was the case in this study for non-I-5 sites/non-first flush sample s). All equilibrium model results for Cu 2+ free concentrations appear to provide conservative estimates of Cu 2+ free concentrations when applied to Oregon highway stormwater runoff.

105The work reported here builds on an already substantial bod y of knowledge involving the presence of heavy m etals in stormwater, aq uatic speciation of heavy metals, and heavy metal toxicity to aquatic organisms. This study is the first to an alytically determine copper speciation in stormwater. As such, th e work provides unique in sight into the risks associated with copper in highway stormwater runoff. Although the results reported here indicate that copper is largely bound with organics in st ormwater, 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 m itigate copper in stormwater, and how to best incorporate this information into a regulatory framework. Biotic Li gand Models that are currently being used to regulate heavy meta ls in surface waters would benefit from continued efforts to analytically verify model outputs and efforts to more fully characterize the organic ligands present in st ormwater and natural waters. The methods developed as part of this study will be in strumental in accomplishing those objectives.

106

## 6.0 REFERENCES

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115116A-1 

## APPENDICES

A.1 QUALITY ASSURANCE/QUALITY CONTROL

A variety of steps were taken in this study to check the qualit y of the data being obtained, including measuring replicate samples, tes ting 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 a nd lab practices and procedures. Most analytes fo r the field blanks (shown in Table A.1 and Table A.2 )

m easured below detection – either below the detection limit for the instrument or below the least concentrated standard used to ge nerate 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 relativ ely high Na concentration. The high NO 3 concentration in the 10/13/09 fi eld 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 furthe r diminished by intaking volume calibration samples at the site, which occurred after taking the field blank. The measured NO 3 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 us e. 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 Ca Fe K Mg Na Cd Cu Ni Pb Zn DOC Sample
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 Cl NO 2 NO 3 PO 4 SO 4 Sample
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 an alyte, by comparing analytically measured concen trations against predicted concentrations. The percent differences displayed in the table were calc ulated according to Equation shown below. Measurements marked ‘N/A’ indicate that th e sample measurement for that analyte was below detection, which would i nherently lead to less accurate results. Relatively high variation between measured and expected re sults was found in some spiked samples forK, Na, Ni, and Pb. Expected and m easured 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

m easured 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 rela tive variability within a samp le. 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 exhibiti ng high variability. Mo st samples were well below this criterion, depending on the anal yte. The data from these samples was recorded, but flagged. K, Ni, and Pb cons istently showed high variation in sample replicates. This fact may also contribute to the poor spike results for those analytes discussed earlier. Ni, Pb, NO 2 and PO 4 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 IC P-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 NO 2 1/9 37 samples below detection NO 3 1/48 PO 4 3/6 40 samples below detection SO 4 1/48

A-2

### A.2 DIXON OUTFALL STORM DATA

This appendix is a collecti on 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 stor mwater 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-3Figure A.1: Dixon Outfall 11/8/08 storm data

A-4Figure A.2: Dixon Outfall 11/20/08 storm data

A-5Figure A.3: Dixon Outfall 12/1/08 storm data

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

A-7Figure A.5: Dixon Outfall 2/23/09 storm data

A-8Figure A.6: Dixon Outfall 3/14/09 storm data

A-9Figure A.7: Dixon Outfall 4/12/09 storm data

A-10Figure A.8: Dixon Outfall 5/13/09 storm data

A-11Figure 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 sample s, Dixon Outfall composite and first flush samples, and Dixon Outfall grab and flow-weighted samples. Furthermore, each of these subsets is broken into figures: measuremen t of general water qua lity parameters and cations, and measurements of an ions and hydrologic parameters. Gray rows indicate total samples. Lab measurements from the st udy by Herrera Environmental Consultants are presented in parentheses where applicable.

A-13Figure A.10: Data from Herrera sites

A-14Figure A.11: Data from Herrera sites, continued

A-15Figure A.12: Dixon Outfall composite and first flush data

A-16Figure A.13: Dixon Outfall composite and first flush data, continued

A-17Figure A.14: Dixon Outfall grab and flow-weighted sample data

A-18Figure 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, Cu 2+ free (referred to as “CuFree”), Cu-DOM, CuOH + , and CuCO 3 2- determined from the visu al MINTEQ modeling using a Gaussian unimodal DOM model to account for sp ecies binding to organics are presented in Figure A.16 . Again, these species accounted for over 99% of all copper species in all but one sam ple, as shown by the column mark ed “Percent Check”. Concentration data presented in the table is in terms of nM.

A-20Figure A.16: Visual MINTEQ raw copper speciation data

A-21The f ollowing 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 w ith 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-22Table A.6: Summary of cation-DOM concentrations calculated in ea ch 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-23Table A.7: Summary of free ioni c 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

‐ 10.5 8 99.96 12/1/2008 Dixon

‐ 8.14 67.62

‐ 10.29 99.77

‐ 10.6 4 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

‐ 10.7 0 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

‐ 10.2 4 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

‐ 10.1 8 99.83

A-24

### A.5 S-PLUS MATRIX PLOTS

This appendix shows matrix plots ob tained from S-Plus when modeling Cu 2+ diss and Cu 2+ 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 6.106.35 6.606.857.10 7.35

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-25LogCu

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 -0.35-0.10 0.150.40 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-26LogCuFree

-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 Cu 2+ With WQ parameters

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

A-27PerCuFree

-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 Cu 2+ with WQ Parameters

Figure A.21: Modeled Percent Free ionic copper – wa ter 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-28Below 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-29Below 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-30Below is the Fisher’s LSD simultaneous comp arison of LogTotCu in composite sam ples 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 signifi cant d ifferences 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-31Below is the regression analysis that examines the effect of AADT. This exam ines all of the composite samples, including I-5 samples. The 2 nd 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 ha ve a significant dire ct relationship w/ Cu in composite samples; but only one of the variables – AADT or the I-5 indicator – accounts for this variability

A-32Below 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 signi ficant correlation b/w Cu levels at non-urban sites w/ varying AADTs. This doesn’t necessari ly mean there isn’t a relationship, but we certainly don’t have a larg e enough dataset to find one.

A-33Below is the regression analysis that exam ines 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 concentratio ns are significantly associated w/ a firstflush phenomenon.

A-34Below 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 Rain fall Intensity), AFTER accounti ng 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 a ny of the hydrologic variables on composite samples after accounting for their location.

A-36Below is the model-building results for a pr edictive DISSOLVED Cumodel 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-37Response: 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-38Below 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 afte r total Cu has been accounted for.

A-40Below 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-41Analysis 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-42Below is the analysis of Cu and DOC thr oughout 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-43Coefficients: 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-45Lgands 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-46lgK

*** 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-47Cfree

*** 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-48Akalinity

*** 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-49Hrdness

** * 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-50DC

** * 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-51data: 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-53Welch 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-54data: 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-55na.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-56Min 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-57Min 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-58Residuals: 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 re ason, 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 averag e [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 significan tly (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-61Below 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 significan tly (p=0.0034) positively associated with Hardness. On average, a doubling in Hardness is associated w ith a 1.60-fold increase in [L] (1.19 – 2.16, 95% CI).

A-62A-63 Below is the summary of the [L] vs Alkali nity association. I didn’t examine the untransformed [L]-Alkalinity a ssociation 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 NO T significantly associated with Alkalinity (p>0.05).