Office of Water

EPA 820 -R- 15

- 106

March 2016



# Draft Technical Support Document:

Recommended Estimat es for Missing Water Quality Parameters for Application in EPA’s Biotic Ligand Model

### i 

# Discl aimer Page

This technical support document (herein referred to as the “Missing Parameters TSD”) summarizes data analysis approaches EPA used to develop recommendations for default values for water quality parameters used in the copper BLM when data are lacking. When published in final form, this document will provide information to states, tribes

, and the regulated community interested in us ing the Biotic Ligand Model to protect aquatic life from toxic effects of copper. Unde r the CWA, states and tribes are to establish water quality criteria to protect designated uses. State and tribal decision makers retain the discretion to adopt approaches on a case

- by

- case basis when appropriate. This

document does not substitute for the CWA or EPA’s regulations; nor is it a regulation itself. Thus, it cannot impose legally binding requirements on EPA, states, tribes, or the regulated community, and might not apply to a particular situation based upon the specific circumstances. EPA may ch ange this document in the future. This document has been approved for publication by the Office of Science and Technology, Office of Water, U.S. Environmental Protection Agency.

Mention of trade names or commercial products does not constitute endorsement or recommendation

for use. This document can be downloaded from: http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/copper/2007_index.cfm

Cover photo: Credit USEPA

### ii 

# Acknowledgements

Luis A. Cruz ( D ocument C oordinator and C ontributor)

U nited States Environmental Protection Agency Health and Ecological Criteria Division Washington, D.C. 20460

Reviewers

Eli z abeth Southerland, Elizabeth Behl, Kathryn Gallagher United States Environmental Protection Agency Office of Science and Technology Washington, D.C. 20460

### iii 

# Acronyms or Abbreviation s

Acronym /Abbreviation Definition

ACR Acute to Chronic Ratio

ASE Average Standard Error

BLM Biotic Ligand Model

BOD Biochemical Oxygen Demand

CCC Criterion Continuous Concentration

CMC Criterion Maximum Concentration

CWA Clean Water Act

df Degre e s of Freedom

DOC Dissolved Organic Carbon

E coregion A reas i n which ecosystems and the type, quality, and quantity of environmental

resources are generally similar

. In this document is represented by EPA Level III
Ecoregion s

EMAP Environmental Monitoring and Assessment Program

EPA United States Environmental Protection Agency

FSC Fixed Site Criteria

GI Geochemical Ions; ion parameters for the Biotic Ligand Model (Ca, Mg, Cl, Na, K, SO 4 , alkalinity)

GIS Geographic Information System

GLEC Great Lakes Environmental Center

HUC H ydrologic U nit C ode (a two to eight digit code that identifies each hydrologic unit)

IQR Interquartile range

IWQC Instantaneous Water Quality Criteria

LCL Lower Confidence Limit

LDC Legacy Data Center (EPA historical STORET database

, recently renamed)

LR Linear Regression
mg/L Milligrams per liter

NHD National Hydrography Dataset

NHD

- Plus National Hydrography Dataset Plus

NOCD Na tional Organic Carbon Database (combined organic carbon data from two databases: USGS WATSTORE and EPA STORET)

NRC National Research Council

NRSA National River and Stream Assessment

NWIS National Waters Information System

PCS Permit Compliance System

RPDs Relative Percent Differences

PCA Principal Components Analysis

pH Negative logarithm of the hydrogen ion concentration ( in mol es per liter ); scale range

from 0 to 14

POC Particulate Organic Carbon

POTW s P ublicly O wned T reatment W orks

RMSE Root Mean Square Error

### iv Acronym /Abbreviation Definition

RSS Residual Sum of Squares

SPLOM Scatter Plot Matrices

SO Strahler Stream Order

STORET EPA STOrage and RETrieval Data Warehouse ( recently renamed Legacy Data Center

, LDC )

TDS Total Dissolved Solids

TSD Technical support document

TSS Total Suspended Solids

TOC Total Organic Carbon

UCL Upper Confidence Limit

U.S. United States

USGS United States Geological Survey

WATSTORE USGS National WATer Data STOrage and RE trieval System (predecessor of NWIS)

WSA Wadeable Stream Assessment

WQC Water Quality Criteria
μS/cm Microsiemens per centimeter
μg/L Micrograms per liter

### v 

# Executive Summary

The United States (U.S.) Environmental Protection Agency (EPA) developed revised freshwater aquatic life criteria for copper using the Biotic Ligand Model (BLM) in 2007. The 2007 Freshwater Copper BLM predicts acute copper toxicity based on site

- specific water quality parameters

, and calculates aquatic

life criteria based on the predicted copper toxicity

. The current freshwater copper BLM requires 10 input parameters to calculate copper criteria : temperature, pH, dissolved organic carbon (DOC), alkalinity

, calcium, magnesium, sodium, potassium, sulfate, and chloride

, t he last s even of which are also referred to as geochemical ions (GI)

. Previously available hardness

- based copper criteria incorporated consideration only of the effects of hardness on bioavailability , while t he BLM
incorporates consideration of all of the water chemistry parameters that have a major influence on metal bioavailability . This allows the BLM

- based criteria to be customized to the particular water body

under consideration

. However, g iven the broad geographical range over which the BLM is likely to be applied

, and the limited a vailability of data for input parameters in many areas, a practical method to estimate missing water quality parameters wa s needed to successfully run the BLM. This technical support document (herein referred to as the “Missing Parameters TSD”) summarizes data analysis approaches EPA used to develop recommendations for default values for water quality parameters

used in the Freshwater Copper BLM when data are lacking

. These default values could also be used to fill in missing water quality input parameters in the application of other metal BLM models as well, when data are lacking. EPA used three approaches t o develop these default value recommendations :

• Conducted g eostatistics and c onductivity analyses to p redict GI p arameters

• Applied s tream o rder to r efine p redict ion of GI p arameters

• Mined the National Organic Carbon Database (NOCD) to estimate DOC

In brief, EPA found that an approach that used c orrelation (with conductivity and discharge as explanatory variables )

, combined with geostatistical techniques ( k riging )

, and a consideration of

stream order produced the best estimates for BLM GI parameters. Tables 8

, 9 , and 1 0 present estimated inputs for each GI and water hardness in each ecoregion categorized by stream order for low, medium, and high order stream s, respectively

. Recommended GI values are based on the 10 th



### percentile of ecoregional L evel III values for the appropriate stream order (size) and are expected to yield appropriately protective criteria values when applied in the BLM model. In Table 20 of Section 4

, EPA provide s estimates for DOC by ecoregion based on an analysis of a compilation of national organic

carbon databases

. The 10 th 

### percentile of ecoregional L evel III values are recommended for DOC. There

was insufficient data to refine the DOC e st imates by stream order. EPA recommends measurement of
pH and temperature directly to use as an input in the BLM. Temperature is a commonly measured parameter, and should be easily obtainable for use in the BLM. The following paragraphs summarize

the cont ents of each section in this report.

Section 1 provides an introduction to this study, including backgroun d on the BLM. In developing the approaches outlined in this study, EPA relied upon several previous studies that attempted to estimate

values for BLM input water quality parameters; these studies are outlined briefly in Section 1 and are described in detail in Appendices A through D. This earlier work demonstrat e s that protective water

### vi quality criteria (WQC) for copper generally corresponded to a low pe rcentile of the distribution of instantaneous water quality criteria (IWQC) predicted by the BLM.

Section 2 provides a discussion of the approach taken by EPA to estimat e BLM GI parameter values
using geostatistics, which are a suite of statistical methodologies that use spatial coordinates to formulate models used in estimation and prediction. Section 2 also describes how EPA supplemented

the geostatistical approach with conductivity as an explanatory variable

, because conductivity data are abundant and correlate well to the BLM GI parameters.

Section 3 provides an analysis and discussion of the EPA approach to estimation of BLM GI parameters incorporating stream order as a variable, with a goal of providing BLM users with tab les of

reco mmended GI parameter estimates based upon both ecoregion and stream order. For each Level III ecoregion, we recalculated t he 10 th 

### percentiles of the distributions of all daily water quality parameters

measured at all NWIS stations taking into account strea m orders or ranges (groups) of stream orders

within each eco region

. V alues of the BLM GI parameter s generally increased with stream order. Based upon this trend, we grouped the estimates for each parameter by stream order: 1 through 3 (headwater streams), 4 through 6 (mid

- reaches)

, and 7 through 9 (rivers).

Section 4 discusses the estimation of DOC based on the NOCD and two other databases . Th e N OC D was compiled from a number of sources, including EPA’s Storage and Retrieval Data Warehouse ( STORET )

and the United States Geological Survey’s National Water Data Storage and Retrieval System ( WATSTORE ) (the predecessor of the National Waters Information S ystem (NWIS))

. The t wo other

database s , the Wadeable Stream Assessment (WSA) and the National River and S tream Assessment (NRSA)

, were used to supplement and update the DOC analysis

. Secti on 4 summarizes the data sources, analysis, and uncertainty associated with ecoregional statistics for the NOCD and outlines how

tests for bias in the data influence selecti on of 10 th 

### percentile DOC concentrations from either the

NOCD or the WSA or NRSA data base s

. The importance of field sampling for DOC is highlighted in

Section 4 b ecause of limitations of the NOCD and the importance of DOC in criteria calculation.

Section 5 provides a summary of the three approaches used to develop EPA’s recommendations. Taken together, the approaches presented in this TSD describe EPA’s recommendations for default input parameters in the BLM to derive protective freshwater aquatic life cri teria when data are lacking

. However, it should be noted that site

- specific data are always preferable for developing criteria based

on the BLM and should be used when possible

. Users of the BLM are encouraged to sample their water body of interest, and to analyze the samples for the constituent (parameter) concentrations as a basis for determining BLM inputs where possible.

### vii 

# Table of C ontents

Disclaimer Page

................................ ................................ ................................ ................................ ................................ .... i

Acknowledgements

................................ ................................ ................................ ................................ ............................. ii

Acronyms or Abbreviations

................................ ................................ ................................ ................................ ................ iii

Executive Summary

................................ ................................ ................................ ................................ ............................. v

Table of Con tents

................................ ................................ ................................ ................................ .............................. vii

List of Figures

................................ ................................ ................................ ................................ ................................ ....... x

List of Tables

................................ ................................ ................................ ................................ ................................ ..... xiii

1 INTRODUCTION

................................ ................................ ................................ ................................ .......................... 1

1.1 Background and Objective

................................ ................................ ................................ ................................ ............. 1

1.2 Input Data and the BLM

................................ ................................ ................................ ................................ ................ 1

1. 3 Previous Studies

................................ ................................ ................................ ................................ ............................. 2

1.3.1 An Examination of Spatial Trends in Surface Water Chemistry in the Continental United States: Implications for the Use of Default Values as Inputs to the BLM for Prediction of Acute Metal Toxicity to Aquatic Organisms (Carlton, 2006)

................................ ................................ ................................ ................................ .... 2

1.3.2 Approaches for Estimating Missing BLM Input Parameters: Correlation Approaches to Estimate BLM Input Parameters Using Conductivity and Discharge as Explanatory Variables (USEPA, 2007)

................................ ..... 2

1.3.3 Copper Biotic Ligand Model (BLM) Software and Supporting Documents Preparation: Development of Tools to Estimate BLM Parameters (USEPA, 2008)

................................ ................................ ................................ ........ 3

1.3.4 Approaches for Estimating Missing BLM Input Parameters: Projections of Total Organic Carbon as a Function of Biochemical Oxygen Demand (USEPA, 2006a)

................................ ................................ ................................ . 4

1.4 Approaches to Estimate Water Quality Parameters for the BLM

................................ ................................ .................. 4

2 USING GEOSTATISTICS AND CONDUCTIVITY TO PREDICT GI PARAMETERS

................................ ................................ . 5

2.1 Data Source and Processing

................................ ................................ ................................ ................................ .......... 5

2.2 Geostatistical Analysis of National Data for Geochemical I ons

................................ ................................ ..................... 8

2.2.1 Kriging of Conductivity Data

................................ ................................ ................................ ................................ 9

2.2.2 Co

- kriging of GI Data

................................ ................................ ................................ ................................ .......... 10

2.2.3 Projection of Geostatistical Predictions onto Level III Ecoregions

................................ ................................ ..... 12

2.2.3.2 Averaging Methods

................................ ................................ ................................ ................................ .. 17

2.2.3.3 Tabulations of Ecoregional Estimated BLM Water Quality Parameters

................................ ................... 17

2.2.3.4 Confirmation of Results

................................ ................................ ................................ ............................ 21

2.2.3.5 Conclusions for Selection of Water Quality Parameters

................................ ................................ .......... 33

2.2.3.6 Guidance Regarding Selection of Water Quality Parame ters: pH and DOC

................................ ............. 33

3 USING STREAM ORDER TO REFINE PREDICTION OF GI PARAMETERS

................................ ................................ ....... 34

3.1 Determining SO of NWIS Surface Water Sampling Locations

................................ ................................ ...................... 34

3.2 Estimating BLM Parameters for Ecoregions and SO

................................ ................................ ................................ .... 35

3.3 Results

................................ ................................ ................................ ................................ ................................ ......... 35

### viii 3.3.1 Dependence of Ecoregional Parameter Estimates on SO

................................ ................................ .................. 35

3.3.2 SO

- Based Parameter Estimates

................................ ................................ ................................ ......................... 40

3.3.3 Comparison of Parameter Estimates to Results of Probab ility

- Based Surface Water Sampling

....................... 50

3.4 Summary

................................ ................................ ................................ ................................ ................................ ...... 56

4 DOC ESTIMATION USING THE NATIONAL ORGANIC CARBON DATABASE

................................ ................................ .. 58

4.1 Description of the NOCD

................................ ................................ ................................ ................................ .............. 58

4.2 Recalculation of Ecoregional DOC Percentiles for Rivers and St reams

................................ ................................ ........ 59

4.3 Testing for Bias in the NOCD

................................ ................................ ................................ ................................ ....... 62

4.3.1 Previous Efforts Using EMAP Data

................................ ................................ ................................ ..................... 62

4.3.2 Testing for Bias Using Data from the WSA

................................ ................................ ................................ ......... 63

4.3.2.1 Selection of Statistical Test to Assess Potential Bias in DOC Data

................................ ............................ 64

4.3.2.2 Rank

- Sum Test Comparing WSA DOC Data to NOCD

................................ ................................ ............... 64

4.3.3 Results and Implications of Bias Testing

................................ ................................ ................................ ............ 73

4.4 Comparing NOCD t o WSA/NRSA DOC Data

................................ ................................ ................................ ................. 75

4.5 Conclusions

................................ ................................ ................................ ................................ ................................ .. 81

5 SUMMARY AND RECOMMENDATIONS

................................ ................................ ................................ ..................... 82

5.1 Recommendations for BLM inputs for geochemical ions where site

- specific data are not available

.......................... 82

5.2 Recommendations for BLM inputs for DOC where site

- specific data are not available

................................ .............. 83

5.3 Recommendations for BLM inputs for pH where site

- specific da ta are not available

................................ ................. 83

5.4 Conclusions

................................ ................................ ................................ ................................ ................................ .. 83

REFERENCES

................................ ................................ ................................ ................................ ................................ ...... 84

Appendix A: An Examination of Spatial Trends in Surface Water Chemistry in the Continental United States: Implications for the Use of Default Values as Inputs to the Biotic Ligand Model for Prediction of Acute Metal Toxicity to Aquatic Organisms

................................ ................................ ................................ ......... 87

A.1 Abstract

................................ ................................ ................................ ................................ ................................ .. 87

A.2 Background

................................ ................................ ................................ ................................ ............................. 87

A.3 Description of Data

................................ ................................ ................................ ................................ ................. 88

A.4 Data Analysis

................................ ................................ ................................ ................................ .......................... 89

A.5 Developing Regional Defaults

................................ ................................ ................................ ................................ . 97

A.6 Discussion

................................ ................................ ................................ ................................ ............................... 99

A.7 Conclusions

................................ ................................ ................................ ................................ ............................. 99

A.8 References

................................ ................................ ................................ ................................ ............................... 99

Appendix B: Approaches for Estimating Missing Biotic Ligand Model Input Parameters. Correlation approaches to estimate Biotic Ligand Model input parameters us ing conductivity and discharge as explanatory variables

................................ ................................ ................................ ................................ ................................ 100

B.1 Introduction

................................ ................................ ................................ ................................ .......................... 100

B.2 Data

................................ ................................ ................................ ................................ ................................ ...... 103

### ix B.3 Results

................................ ................................ ................................ ................................ ................................ ... 103

B.4 Discussion

................................ ................................ ................................ ................................ ............................. 111

B.5 References

................................ ................................ ................................ ................................ ............................. 112

Appendix C: Development of Tools to Estimate Biotic Ligand Model Parameters

................................ ........................ 114

C.1 Introduction

................................ ................................ ................................ ................................ ............................... 114

C.2 Regression Analysis

................................ ................................ ................................ ................................ ................... 114

C.2.1 pH

................................ ................................ ................................ ................................ ................................ ..... 114

C.2.2 DOC

................................ ................................ ................................ ................................ ................................ .. 115

C.2.3 Alkalinity

................................ ................................ ................................ ................................ .......................... 115

C.2.4 Calcium

................................ ................................ ................................ ................................ ............................. 115

C.2.5 Magnesium

................................ ................................ ................................ ................................ ...................... 115

C.2.6 Sodium

................................ ................................ ................................ ................................ ............................. 116

C.2.7 Potassium

................................ ................................ ................................ ................................ ......................... 116

C.2.8 Sulfat e

................................ ................................ ................................ ................................ .............................. 116

C.2.9 Chloride

................................ ................................ ................................ ................................ ............................ 116

C.3 Application of Conductivity Regressions

................................ ................................ ................................ .................... 116

C.3.1 Naugatuck River, Connecticut

................................ ................................ ................................ .......................... 117

C.3.2 San Joaquin River, California

................................ ................................ ................................ ............................ 119

C.3.3 South Platte River, Colorado

................................ ................................ ................................ ............................ 121

C.3.4 Halfmoon Creek, Colorado

................................ ................................ ................................ ............................... 122

C.3.5 Summary of Site

- Specific Test Results

................................ ................................ ................................ ............. 124

C.4 Combining GI

- Conductivity Regressions with Geostatistical Techniques

................................ ................................ .. 127

C.5 References

................................ ................................ ................................ ................................ ................................ . 131

Appendix D: Approaches for Estimating Missing Biotic Ligand Model Input Parameters: Projections of Total Organic Carbon as a Function of Biochemical Oxygen De mand

................................ ................................ ............. 132

D.1 Introduction

................................ ................................ ................................ ................................ .......................... 132

D.2 Data

................................ ................................ ................................ ................................ ................................ ...... 132

D.3 Results

................................ ................................ ................................ ................................ ................................ ... 133

D.3.1 TOC and BOD at All Monitoring Locations

................................ ................................ ................................ ....... 133

D.3.2 TOC and BOD at Eff luent Monitoring Locations

................................ ................................ .............................. 135

D.3.3 TOC and DOC at CARP Effluent Monitoring Locations

................................ ................................ ..................... 140

D.4 Discussion

................................ ................................ ................................ ................................ ............................. 142

D.5 Referenc es

................................ ................................ ................................ ................................ ............................. 145

### x 

# List of Figures

Figure 1. NWIS sample collection locations in the continental U.S. (Carleton, 2006)

................................ ................................ . 6

Figure 2. Kriged prediction surface for 10 th percentile of conductivity in the contine ntal U.S. (sample locations in
blue)

................................ ................................ ................................ ................................ ................................ .................... 10

Figure 3. Co

- kriged prediction surface for 10 th percentile of calcium in the continental U .S.

................................ ................... 11

Figure 4. Co

- kriged prediction surface for 10 th percentile of alkalinity in the continental U.S.

................................ ................ 12

Figure 5. Map of Level III ecoregions in the U.S.

................................ ................................ ................................ ........................ 16

Figure 6. Scatter plot matrix of ecoregional average 10 th percentiles of data for conductivity (COND_SAM) and GI
parameters (calcium=CA_SAM, magnesium=MG_SAM, sodium=NA_SAM, potassium=K_SAM,
alkalinity= ALK_SAM, chloride=CL_SAM, sulfate=SO4_SAM)

................................ ................................ .............................. 22

Figure 7. Scatter plot matrix of ecoregional average 10 th percentiles of geostatisti cal predictions of conductivity
(COND_KR) and BLM GI parameters (calcium=CA_CO, magnesium=MG_SCO, sodium=NA_CO,
potassium=K_CO, alkalinity=ALK_CO, chloride=CL_CO, sulfate=SO4_CO)

................................ ................................ ......... 23

Figure 8. Ecoregional averages of kriged 10 th percentiles of conductivity versus data

................................ ............................. 25

Figure 9. Ecoregional averages of cokriged 10 th percentiles of alkalinity versus data

................................ .............................. 26

Figure 10. Ecoregional averages of cokriged 10 th percentiles of calcium versus data

................................ ............................... 27

Figure 11. Ecoregional averages of cokriged 10 th per centiles of magnesium versus data

................................ ........................ 28

Figure 12. Ecoregional averages of cokriged 10 th percentiles of sodium versus data

................................ ............................... 29

Figure 13. Ecoregional averages of cokriged 10 th percentiles of potassium versus data

................................ ........................... 30

Figure 14. Ecoregional averages of cokriged 10 th percentiles of sulfate versus data

................................ ................................ 31

Figure 15. Ecoregional averages of cokriged 10 th percentiles of chloride versus data

................................ .............................. 32

Figure 16. Distribution of NWIS surface water sampling locations by SO

................................ ................................ ................. 35

Figure 17. Box plot of estimated ecoregional conductivities as a function of SO

................................ ................................ ..... 36

Figure 18. Box plot of estimated ecoregional a lkalinity concentrations as a function of SO

................................ .................... 37

Figure 19. Box plot of estimated ecoregional calcium concentrations as a function of SO

................................ ...................... 37

Figure 20. Box plot of estimated ecoregional magnesium concentrations as a function of SO

................................ ................ 38

Figure 21. Box plot of estimated ecoregional sodium concentrations as a function of SO

................................ ....................... 38

Figure 22. Box plot of estimated ecoregional potassium concentrations as a function of SO

................................ .................. 39

Figure 23. Box plot of estimated ecoregional sulfate concentrations as a function of SO

................................ ........................ 39

Figure 24. Box plot of estimated ecoregional chloride concentrations as a function of SO

................................ ...................... 40

Figure 25. BLM parameter estimates (10 th percentile values) for each SO group in Ecoregion 46 (Northern Glaciated
Plains)

................................ ................................ ................................ ................................ ................................ ................. 48

Figure 26. BLM parameter estimates for each SO group in Ecoregion 83 (Eastern Great Lakes Lowland)

............................... 49

### xi Figure 27. BLM parameter estimates for each SO group in Ecoregion 54 (Central Corn B elt Plains)

................................ ........ 50

Figure 28. Scatter plot of ecoregional 25 th percentile conductivity for NWIS Data (SO Class 1

- 3) versus ecoregional

25 th percentile conductivity for Griffith data (mostly SO 1

- 4)

................................ ................................ ............................ 51

Figure 29. Scatter plot of ecoregional 25 th percentile calcium concentration for NWIS data (SO class 1

- 3) versus
ecoregional 25 th percentile calcium concentration for Griffith data (mostly SO 1

- 4)

................................ ........................ 52

Figure 30. BLM predictions of copper criteria made with GI water quality parameters based on ecoregional 25 th

percentile from NWIS data (SO class 1

- 3) versus ecoregional 25 th p ercentile calcium Concentration for Griffith
data (mostly SO 1

- 4).

................................ ................................ ................................ ................................ .......................... 56

Figure 31. Comparison of probability distributions of DOC concentrations in Ecoregion 23

................................ .................... 74

Figure 32. Comparison of probability distributions of DOC concentrations in Ecoregion 77

................................ .................... 74

Figure A

- 1. NWIS sample collection locations in the continental U.S.

................................ ................................ ....................... 88

Figure A

- 2. Intensity of sampling (number of separate sampling dates) at each NWIS site

................................ ...................... 89

Figure A

- 3. Median measured alkalinity (mg/L as CaCO3) at NWIS locations

................................ ................................ ........... 90

Figure A

- 4. HUC

- averaged mea n median observed alkalinity in the continental U.S.

................................ ............................... 90

Figure A

- 5. Kriging prediction map of median alkalinity

................................ ................................ ................................ ............ 92

Figure A

- 6. Kriging map of alkalinity, projected into vertical dimension

................................ ................................ ................... 92

Figure A

- 7. Kriging

- based alkalinity predictions, averaged over 8

- digit HUC polygons

................................ ............................. 93

Figure A

- 8. Kriging

- predicted vs. calculated HUC

- averaged alkalinity; r 2 =0.537

................................ ................................ ....... 93

Figure A

- 9. Scatter plot matrix of median concentration kriged predictions, averaged over 8

- digit HUCs regions
covering the continental U.S.

................................ ................................ ................................ ................................ ............. 94

Figure A

- 10. Scatter plot matrix of median concentrations from 772 monitoring locations in the continental U.S.

................ 95

Figure A

- 11. Variance plot from PCA of HUC

- average kriging

- predicted concentrations

................................ ......................... 96

Figure A

- 12. Variance plot from PCA of site

- median measured concentrations

................................ ................................ ....... 96

Figure A

- 13. Kriging 25th percentile map of median alkalinity

................................ ................................ ................................ . 98

Figure A

- 14. Comparison of observed site

- minimum alkalinities with HUC

- mean 25th percentile kriging

- predicted

values

................................ ................................ ................................ ................................ ................................ .................. 98

Figure B

- 1. Relation of conductivity to chloride, hardness and sulfate concentrations in the Gila River at Bylas,

Arizona

................................ ................................ ................................ ................................ ................................ .............. 102

Figure B

- 2. Scatter plot matrix for first quartile of site

- specific data for discharge (LNDISCH), conductivity (COND),
and BLM water quality parameters

................................ ................................ ................................ ................................ .. 108

Figure B

- 3. Scatter plot matrix for fifth quantile of site

- specific data for discharge (LNDISCH), conductivity (COND),

and BLM water qua lity parameters

................................ ................................ ................................ ................................ .. 109

Figure B

- 4. Scatter plot matrix of BLM water quality parameter data from NWIS Station 384551107591901
(Sunflower Drain at Highway 92, near Read, Delta County, Colorado)

................................ ................................ ............ 110

Figure B

- 5. Time series plot of conductivity (diamond symb ols) and discharge (open circles connected by dashed
line) at Station 384551107591901 (Sunflower Drain at Highway 92, near Read, Delta County, Colorado)

.................... 111

### xii Figure C

- 1. Instantaneous Criteria (IC) predicted with the BLM using site

- specific data and IC predicted using
measured pH and organic carbon and projected values of the GI BLM input parameters

................................ .............. 125

Figure C

- 2. 10 th percentile of the IC distributions using data and projected (predicted) values of the GI BLM

parameters

................................ ................................ ................................ ................................ ................................ ....... 126

Figure C

- 3. Percentile of the IC corresponding to the FSC for each site as a function of the correlation coefficient
between the copper concentrations and the IC wh en the FSC is calculated with Copper correlation and when
FSC is calculated without Copper correlation

................................ ................................ ................................ ................... 127

Figure C

- 4. Kriged sur face of the 10th percentile of conductivity at all stations in Colorado, Utah and Wyoming

................ 129

Figure C

- 5. Kriged surface of the 10th percentile of hardness at all stations in Colorado

................................ ...................... 129

Figure C

- 6. Comparison of the 10 th percentile of hardness at all stations in Colorado with estimates based on (a)
direct kriging of hardness data and (b) kriging of conductivity to station locations and projecting conductivity
to hardness via regression (“kriging/regression”)

................................ ................................ ................................ ............ 130

Figure D

- 1. Scatter Plot of Average Monthly Data (all Monitoring Locations)

................................ ................................ ........ 134

Figure D

- 2. Scatter Plot of Maximum Monthly Data (all Monitoring Locations)

................................ ................................ ..... 135

Figure D

- 3. Scatter Plot of Average Monthly Data (Effluent Monitoring Locations)

................................ ............................... 137

Figure D

- 4. Scatter Plot o f Maximum Monthly Data (Effluent Monitoring Locations)

................................ ............................ 138

Figure D

- 5. Residuals of the linear model, TOCavg = a + b BODavg

................................ ................................ ........................ 139

Figure D

- 6. Scatter plot of TOC versus DOC in CARP effluent monitoring data

................................ ................................ ....... 141

Figure D

- 7. Scatter plot of TOC versus DOC in CARP effluent monitoring data (TOC <50 mg/L)

................................ ............. 142

### xiii 

# List of Tables

Table 1. Summary of water quality data retrieved from NWIS

................................ ................................ ................................ ... 7

Table 2. Model selection and cross val idation statistics for geostatistical fitting of 10 th percentiles of BLM GI

parameters

................................ ................................ ................................ ................................ ................................ ........... 9

Table 3. Level III ecoregions of the U.S . organized according to broader Level I ecoregions

................................ .................... 13

Table 4. Predicted 10 th percentile concentrations for conductivity (μS/cm) , BLM GI water quality parameters (mg/L)

and hardness in each Level III ecoregion in the continental U.S.

................................ ................................ ....................... 18

Table 5. Spearman ra nk correlation matrix for unbiased log means of 10 th percentile concentrations measured in
Level III ecoregions

................................ ................................ ................................ ................................ ............................. 24

Table 6. Spearman rank correlation matrix for unbiased log means of 10 th percentile predicted (kriged/cokriged)

concentrations in Level III ecoregions

................................ ................................ ................................ ................................ 24

Table 7. Correlation coefficients and linear regression (LR) statistics between ecoregional average 10 th percentiles
of data and geostatistical predictions

................................ ................................ ................................ ................................ 32

Table 8. Recommended 10 th percentile conductivity, GIs, and hardness estimates for SO Group 1 through 3
(number of stations shown in parentheses if n<10)

................................ ................................ ................................ ........... 41

Table 9. Recommended 10 th percentile conductivity, GIs, and hardness estimates for SO group 4 through 6
(number of stations shown in parentheses if n<10)

................................ ................................ ................................ ........... 43

Table 10. Recommended 10 th percentile conductivity, GIs, and hardness estimates for SO group 7 through 9
(number of stations shown in parentheses if n<10)

................................ ................................ ................................ ........... 45

Table 11. Characteristics of the conductivity data for Ecoregion 19 in the low SO group.

................................ ....................... 53

Table 12. Characteristics of the conductivity data for Ecoregion 37 in the low SO group.

................................ ....................... 53

Table 13. Characteristics of the conductivity data for Ecoregion 38 in the low SO group

................................ ........................ 54

Table 14. Characteristics of the calcium data for Ecoregion 75 in the low SO group.

................................ ............................... 54

Table 15. Characteri stics of the conductivity data for Ecoregion 78 in the low SO group.

................................ ....................... 54

Table 16. Lower percentile values of DOC in U.S. streams and rivers by ecoregion, including 95% confidence limits
for percentile concentrations if n>20.

................................ ................................ ................................ ................................ 60

Table 17. Results of rank

- sum test comparing Level III ecoregional DOC data from WSA and NOCD

................................ ...... 66

Table 18. DOC concentrations (mg/L) in each Level III ecoregio n based upon data from the NOCD and the
combined WSA/NRSA data: number of data (n); 10th percentiles; and results of the Wilcoxon 2

- sample test

............... 76

Table 19. DOC concentrations (mg/L) in 24 ecoregions where no significant difference in DOC concentrations was
found between national organic carbon database (NOCD) and the WSA/NRSA datasets: number of data (n);

10 th percentiles from comb ined NOCD & WSA/NRSA data

................................ ................................ ................................ 78

Table 20. Recommended ecoregional DOC concentrations (mg/L) based upon combined data from the NOCD a nd
the WSA/NRSA data in 83 Level III ecoregions: number of observations (n); 10 th percentiles; and source of
data for each ecoregion

................................ ................................ ................................ ................................ ...................... 79

Table A

- 1. Matrices of correlation coefficients between constituent concentrations

................................ .............................. 95

### xiv Table A

- 2. Loadings onto original va riables from PCA on HUC

- averaged predictions and site

- median concentrations

........... 97

Table B

- 1. Number of observations and sites reported in NWIS for streams and rivers in Colorado

................................ ..... 104

Table B

- 2. Results of Spearman rank tests for correlation ( ρ ) between median values of variables at each site.

.................. 105

Table B

- 3. Results of Spearman rank tests for correlation ( ρ ) between the first quartile of values at each site.

................... 106

Table B

- 4. Results of Spearman rank tests for correlation ( ρ ) between the fifth quantile of values at each site.

.................. 106

Table C

- 1. Copper Fixed Site Criterion predictions for the Naugatuck River, Connecticut using various calculation

methods

................................ ................................ ................................ ................................ ................................ ............ 118

Table C

- 2. Copper Fixed Site Criterion predictions for the San Joaquín River, California using various calculation

methods

................................ ................................ ................................ ................................ ................................ ............ 120

Table C

- 3. Copper Fixed Site Criterion predictions for the South Platte River, Colorado using various calculation

methods

................................ ................................ ................................ ................................ ................................ ............ 121

Table C

- 4. Copper Fixed Site Criterion predictions for the Halfmoon Creek, Colorado using various calculation

methods

................................ ................................ ................................ ................................ ................................ ............ 123

Table D

- 1. Least squares regression of average monthly TOC and BOD data for all monitoring locations

............................. 133

Table D

- 2. Least squares regression of maximum monthly TOC and BOD data for all monitoring locations

......................... 134

Table D

- 3. Least squares regress ion of average monthly TOC and BOD data for effluent monitoring locations

.................... 136

Table D

- 4. Least squares regression of maximum monthly TOC and BOD data for effluent monitoring locations

................ 138

Table D

- 5. CARP organic carbon and total suspended solids (TSS) mo nitoring data for New Jersey discharger

.................... 140

Table D

- 6. Summary statistics for POTW average monthly effluent TOC concentrations, ca tegorized according to
average monthly effluent BOD concentration

................................ ................................ ................................ ................. 144

Table D

- 7. Summary statistics for POTW maximum monthly efflu ent TOC concentrations, categorized according to
maximum monthly effluent BOD concentration

................................ ................................ ................................ .............. 144

### 1 

## 1 INTRODUCTION

1.1 Background and Objective

The United States (U.S.) Environmental Protection Agency (EPA) has a congressional mandate to develop and publish criteria for water quality that reflects the effects of pollutants on aquatic life and human health under 304(a)(1) o f the Clean Water Act (CWA)

. The CWA was intended to protect the chemical, physical, and biological integrity of the Nation’s waters. Section 304(a)(1 ) of the CWA , 33 U.S.C. § 1314(a)(1), directs the Administrator of EPA to publish water quality criteria t hat accurately reflect the latest scientific knowledge on the kind and extent of all identifiable effects on health and welfare that might be expected from the presence of pollutants in any body of water, including ground water. Under this authority, EPA d eveloped revised aquatic life criteria for copper that are based on the Biotic Ligand Model (BLM) in 2007. The BLM predicts metal toxicity based on site

- specific water quality parameters, and derives acute and chronic criteria from the predicted toxicity. Derivation of water quality criteria using the BLM requires 10 input parameters (temperature, pH, dissolved organic carbon (DOC), alkalinity, calcium, magnesium, sodium, potassium, sulfate, and chloride ) . Data regarding these parameters may not be availabl e for many receiving waters. Given that the BLM is likely to be applied over a broad geographical range, and that limited data are available for many areas, a practical method to estimate missing water quality parameters was needed to facilitate full use o f the BLM in water quality standards across the U

. S. This technical support document (herein referred to as the “Missing Parameters TSD”) summarizes data analysis approaches EPA used to develop recommendations for default values for water quality parameter s that may be used in the BLM when data are lacking. The section of the CWA related to the development of the information presented in this technical support document is CWA Section 304(a)(2). CWA Section 304(a)(2) generally requires EPA to develop and pub lish information on the factors necessary to restore and maintain the chemical, physical, and biological integrity of navigable waters. Section 304(a)(2) also allows EPA to provide information on the conditions necessary for the protection and propagation of shellfish, fish, and wildlife in receiving waters and for allowing recreational activities in and on the water.

The objective of this report is to summarize recommendations that B LM users can apply to estimate

values for missing input water quality parameters.

1.2 Input Data and t he BLM

The BLM calculates metal toxicity to aquatic organisms as a function of the concentrations of certain chemical constituents of water, including

, for example

, ions that can complex with metals and limi t biological availability, and ions that compete with metals for binding sites at the ion exchange tissues of aquatic organisms (e. g., at the fish gill). The BLM predicts the metal criteria concentration s , such as copper in freshwater, which will vary acco rding to changes in the associated water quality parameters.

An appropriately protective acute and chronic copper (or other metals) criteria must reflect the variability of water quality parameters at the site. In previous analyses, EPA found that protective water quality criteria for copper generally correspond to approximately the 2.5 th 

### percentile of the

### 2 distribution of instantaneous water quality criteria (IWQC) predicted by the BLM 1 

### (USEPA, 2002) . Thus, predictions made for a site us ing the corresponding low percentile of the water quality parameter

distributions are appropriately protective. Copper BLM predictions are most sensitive to the following five important parameters: DOC, pH , and calcium, magnesium

, and sodium concentration s ( taken together ). Estimates are most sensitive to DOC, and vary in direct proportion to a change in value (i.e., they are 100% sensitive to DOC ). Estimates are 50% sensitive to a change in pH, and 20% sensitive to the combined concentrations of calcium, magnesium, and sodium.

1.3 Previous Studies

EPA has conducted previous studies to develop tools to estimate BLM water quality parameters for sites where there may be few (or no) water quality data available. Brief summaries of these previous studies are prov ided below, and more detailed descriptions are provided in Appendices A through D

.

1.3.1 An Examination of Spatial Trends in Surface Water Chemistry in the Continental United States: Implications for the Use of Default Values as Inputs to the BLM for Prediction of Acute Metal Toxicity to Aquatic Organisms (Carlton, 2006)
A large database of surface water chemistry monitoring data was examined to look for spatial trends in five chemical constituents that are key inputs to a model for predicting m etal toxicity to aquatic
organisms : pH, dissolved organic carbon, alkalinity, calciu m , and sodium

. Continuous prediction maps of concentrations were generated using various kriging techniques to interpolate between site

- median

values measured at several th ousand separate locations throughout the continental U.S . Continuous concentration surfaces were then averaged over 8

- digit Hydrologic Unit (HUC) polygons to produce

block

- averaged mean estimates of site

- median concentrations. Pairwise comparisons indicate d distinct

trends between various HUC

- averaged predicted constituents. The same analyses performed on data from 772 locations where all five constituents had been measured revealed similar relationships between monitored constituents. Principal components analyses (PCA) performed on these data sets showed that 80 to 90 percent of the variance in both cases could be explained by a single component with loadings on three of the five constituents. The use of kriging to produce appropriate quantile maps for blo ck

- averaging is suggested as a possible approach for developing regional values to use as default model inputs, when site

- specific monitoring data are lacking. Refer to Appendix A for more

information.

1.3.2 Approaches for Estimating Missing BLM Input Parameters : Correlation A pproaches to

E stimate BLM I nput P arameters U sing C onductivity and D ischarge as E xplanatory V ariables (USEPA, 2007)

In this 2007 report

, EPA developed regression models to project BLM water quality parameters from conductivity data. EPA assessed supplementing the geostatistical approach with classical estimation methods, such as regression and correlation by assessing the degree of correlation between conductivity and each of the BLM water quality parameters using National Water Infor mation System

1 This was the median for 17 sites; the range was 1 to 36%.

### 3 (NWIS) data from three states (Colorado, Utah

, and Wyoming). These states were selected because of the large spatial and temporal variability observed.

EPA concluded that c onductivity is significantly correlated with BLM water quality parame ters between sites, especially for the low

- end distribution statistics of interest for parameter estimation. Since conductivity data are abundant and correlate well with BLM water quality parameters, EPA determined it is reasonable to incorporate conductiv ity in spatial projections of BLM parameters. C orrelation coefficients were lower for pH and DOC than for the geochemical ions ( GIs ) and alkalinity, but were

also significant. Refer to Appendix B for more information.

1.3.3 Copper Biotic Ligand Model (BLM) Softw are and Supporting Documents Preparation: Development of Tools to Estimate BLM Parameters (USEPA, 2008)

In order to predict parameters based on geographic location, t his 2008 report investigated how to project BLM water quality parameters for a given site based on other site data using geostatistical

methods . There are a number of ways in which the conductivity regressions can be used to project BLM water quality inputs. The regressions allow estimates of the BLM water quality inputs from either: (1) a limited number of conductivity measurements, or (2) a low

- end conductivity value estimated by geostatistical methods.

The first approach, projecting BLM water quality inputs from conductivity measurements, was

demonstrated for a limited number of test si tes. Regression models were developed to project 10 th



### percentiles of BLM water quality input parameters from the 10 th 

### percentile of measured conductivity distributions at sites in Colorado, Utah

, and Wyoming. T he 10 th 

### percentile is the value below which 10 percent of the observations may be found. The regression models were tested using data and copper BLM predictions for four sites, and produc ed highly consistent results. The regression models for pH and DOC, the most sensitive of the BLM water quality par ameters, were not sufficiently accurate to

make reliable BLM parameter predictions. However, regression models for the GI parameters (calcium, magnesium, sodium, potassium, chloride, sulfate

, and alkalinity) were reasonably accurate, as judged by comparing model predictions made using projected values of the these BLM input parameters to model predictions made using measured input data. No estimate for site

- specific pH was superior to the observed weak conductivity regression. To improve upon this estimate, it was necessary to use

actual site

- specific pH data. For DOC, the Level III e coregion (referred to herein as simply “ecoregion”) and water body type

- specific DOC concentration percentiles tabulated by EPA for the National

Bioaccumulation Factors Technica l Support Document (USEPA, 2003) appear to be far better estimates

of lower

- percentile DOC concentrations than the estimate s made using the conductivity regression.

EPA also provided a proof of concept for the second approach, which was to see whether comb ining the kriged conductivities with the conductivity

- hardness regression would project the 10 th 

### percentiles of hardness better than direct kriging of the hardness data. EPA found that b oth approaches produce estimates of hardness that correlate significan tly with the measured data (correlation coefficient r=0.80 for direct kriging of hardness; r=0.95 for conductivity kriging + regression). However, the kriging

+ regression approach fits the hardness data substantially better than direct kriging. Refer to Appendix

C for more information.

### 4 1.3.4 Approaches for Estimating Missing BLM Input Parameters: Projections of Total Organic Carbon as a Function of Biochemical Oxygen Demand (USEPA, 2006a)

DOC concentrations downstream of an effluent discharge are nece ssary inputs for the BLM to predict toxicity associated with a wastewater discharge. Effluent DOC is monitored by very few publicly

- owned treatment works (POTWs) according to data retrieved from EPA’s Permit Compliance System (PCS). Biochemical oxygen dema nd (BO D) is monitored by most POTWs. EPA developed regressions to project total organic carbon (TOC) concentrations from BOD values using effluent samples at all POTWs reporting data for both parameters in EPA’s PCS. EPA concluded that this regression give s reasonable estimates of TOC in POTWs effluents and are likely the best available estimates of effluent TOC to

determine D OC concentrations for the BLM. Refer to Appendix D for more information.

1.4 Approaches to Estimate Water Quality Parameters for the BLM

Building upon the studies described above , this report uses three approaches to develop default

estimates for parameters needed for the BLM when empirical data are lacking

. The three approaches

are listed below and are detailed in the following sections :

 

### Section 2 : Using Geostatistics and Conductivity to Predict GI Parameters

 

### Section 3: Using Stream Order to Refine Prediction of GI Parameters

 

### Section 4 : Using the N ational Organic Carbon Database to estimate DOC

EPA recommends that temperature and pH be mea sured directly in the field.

### 5 

## 2 USING GEOSTATISTICS AND CONDUCTIVITY TO PREDICT GI PARAMETERS

T he following section describe s studies that demonstrate how geostatistical techniques, coupled with conductivity correlations, can be used to predict BLM input pa rameters for GI s wh en site

- specific data

are unavailable

. In a previous study (USEPA, 2008) EPA demonstrated that combining kriging with
regressions to estimate inputs based on conductivity improve s the accuracy of GI estimates. In this

section EPA has expanded on this approach and develop ed national estimates at the Level III e coregion

in the continental U.S.

The current freshwater copper BLM requires 10 input parameters that reflect water chemistry in order to calculate copper criteria: temperature

, p H, DOC, alkalinity

, calcium, magnesium, sodium, potassium, sulfate, and chloride , the last seven of which are GI s . The concentrations of GIs vary in surface waters due to dissolution, weathering, ground water

- surface water interactions, and other geologic processes in the watershed, in addition to dilution by snowmelt and precipitation. Consequently, the concentrations of GI parameters tend to vary according to the regional geology. For example, alkalinity has noticeable geographic trends. Areas dominated b y carbonate rocks, such as limestone as in the prairie states, tend toward high alkalinity. Areas dominated by igneous rocks, such as granite, such as parts of the northeast, tend toward low hardness and alkalinity.

In this section we expand on the EPA 20 0 8 proof of concept (in Appendix C ) u sing geostatis t ics to
dev e lop default missing GI parameter values based on geography

. Geostatistics are statistical methodologies that use spatial coordinates to help formulate models used in estimation and prediction (ESRI, 2003). Geostatistical techniques are attractive because they explain parameter variation arising from spatial correlations, which are not used in conventional statistics. We have supplemented the geostatistical approach by adding conductivity as an additional explanatory variable. Conductivity is one of the most widely monitored water quality indicators in the U

. S

. Because conductivi ty data are abundant and correlate well to the BLM GI parameters, we incorporated conductivity in spatial projections of BLM parameters. Based on the proof of concept described above (and in Appendix C ), we expected that this approach, which can be impleme nted by co

- kriging ( i.e., an interpolation technique that allows for better estimates by the incorporation of well

- sampled

, correlated secondary data) in geostatistical software, would allow more robust spatial projections of BLM water quality

parameters.

2.1 Data Source and Processing

Water quality data for conductivity and BLM GI water quality parameters were retrieved from the United States Geological Survey ( USGS ) National Water Information System ( NWIS ) . NWIS contains data from millions of sampling events at tens of thousands of individual sampling locations (stations) in the continental U.S. (Figure 1). Not all water quality parameters of relevance to the BLM were monitored at each location. The numbers of sampling events at individual locations also rang e widely, with a mean of 15, and a mode of one (i.e.

, most sites were only sampled once). Examination of the spatial distribution of numbers of sampling events per site reveals that the Midwestern and W estern states tended to be sampled most intensively (C arleton, 2006). Because environmental sampling data tend to be lognormally distributed, disparities in numbers of samples may tend to produce higher

mean and median values at locations that have been sampled more frequently . As spatial distributions of rep resentative (e.g.

, median) concentrations are examined, it should be kept in mind that apparent

### 6 geographic trends in concentration may be in part simply the result of uneven sampling intensity (Carleton, 2006).

Figure 1. NWIS sample collection location s in the continental U.S. (Carleton, 200 6 )

We focused our e fforts on data collected from rivers and streams between 1984 and 2009. Data collected prior to 1984 were excluded because a number of the analytical methods used by USGS prior

to that date have been replaced by methods with improved precision and lower detection limits

. Furthermore, only sites with 40 or more samples were included in the analysis. With support from USGS staff, we obtained a complete download of national water quality data from NW IS , which totaled 4,714,165 measurements from 959,946 samples, collected at 5,901 sites. These data included

measurements for BLM water quality input parameters required to calculate copper criteria using the

BLM : pH, DOC, alkalinity, calcium, magnesium, s odium, potassium , sulfate, and chloride

. Data were

also collected on filtered (dissolved) copper , and the spatial coordinates (latitude and longitude) of each samplin g station were also retrieved. No data were collected on temperature . O nly the GI data

wer e included in the geostatistical analysis. A summary of the water quality data retrieved from NWIS is provided in Table 1

.

### 7 Table 1. Summary of water quality data retrieved from NWIS

BLM Water Quality

Parameter NWIS Parameter

Code Parameter Description Number of

Observations

Conductivity 1 00094 Specific conductance, water, unfiltered, field, microsiemens per centimeter at 25 degrees Celsius 553,700

00095 Specific conductance, water, unfiltered, laboratory,

microsiemens per centimeter at 25 degrees Celsius 799

pH 00400 pH, water, unfiltered, field,

standard units 352,336

00403 pH, water, unfiltered, laboratory,

standard units 151,161

Dissolver Organic

Carbon 00681 Organic carbon, water, filtered, laboratory, milligrams per liter 30,008

Alkalinity 00410 Acid neutralizing capacity, water, unfiltered, fixed endpoint (pH 4.5) titration, field, milligrams per liter

as calcium carbonate 35,232

00417 Acid neutralizing capacity, water, unfiltered, fixed endpoint (pH 4.5) titration, laboratory, milligrams per

liter as calcium carbonate 15,264

00419 Acid neutralizing capacity, water, unfiltered, incremental titration, field, milligrams per liter as calcium

carbonate 10,198

00418 Alkalinity, water, filtered, fixed endpoint (pH 4.5) titration, field, milligrams per liter as calcium

carbonate 2,686

Calcium 00915 Calcium, water, filtered, laboratory, milligrams per liter 146,608

Magnesium 00925 Magnesium, water, filtered, laboratory, milligrams per liter 145,938

Sodium 00930 Sodium, water, filtered, laboratory, milligrams per liter 136,310

Potassium 00935 Potassium, water, filtered, laboratory, milligrams per liter 132,659

Sulfate 00945 Sulfate, water, filtered, laboratory, milligrams per liter 147,824

Chloride 00940 Chloride, water, filtered, laboratory, milligrams per liter 146,601

1 Conductivity is not a BLM parameter, but was used as an explanatory variable for the other water quality parameters.



### The data were screened using established quality as surance procedures. All data were checked to confirm that they contained numerical values without null (missing) records and remark codes were

### 8 identified and reviewed. Minimum and maximum values for each parameter were confirmed to be within expected range s and frequency distributions were plotted and examined for each of the parameters to identify outliers. We also confirmed that the spatial coordinate data placed each sampling location within the continental U.S

. Additional data processing included the f ollowing steps:

 

### For the data at each station, the observations for each variable were averaged on a daily basis. This was done to reduce the influence of high frequency sampling at a few stations.

 

### Data were edited by censoring parameter(s) with fewer than 10 to 20 daily values at a station.

The 10 th 

### percentile for that parameter at that station was censored to improve the reliability of

the lower

- tail (i.e., 10 th 

### ) percentile statistics.

 

### Tenth (rank order/nonparametric) percentiles of the distributions of all water quality parameters measured at each station were calculated.

It should be emphasized that all of the statistical and geostatistical analyses and predictions presented

in this r eport are based on the 10 th 

### percentiles of the concentration distribution measured for each parameter at every station. The estimates of water quality parameter values for “missing” data are

therefore also 10 th 

### percentile concentrations. We selected the 10 th 

### percentile of the site parameter distributions as a statistic that is a practical compromise between a lower

- bound concentration and a percentile that can be reliably determined from small sample sizes. Initial testing with the BLM suggested that protec tive water quality criteria (WQC) for copper generally corresponded to approximately the 2.5 th 

### percentile of the distribution of instantaneous water quality criteria (IWQC) predicted by the BLM. Thus, BLM predictions made for a site using the corresponding low percentiles of the water quality parameter distributions should (logically) also be a conservative approximation of
a protective criterion. As a more reliably determined statistic , the 10 th 

### percentile of water quality parameters will also derive reaso nably protective criteria , especially for small sample sizes where there may be greater uncertainty at lower percentile estimates . The 10 th 

### percentile estimates presented in this document were initially developed to implement the copper BLM published by EP A in 2007 and will apply to other metals as well

.

2.2 Geostatistical Analysis of National Data for Geochemical Ions

The ESRI ArcGIS Geostatistical Analyst tool was used to create statistically valid two

- dimensional surface models for conductivity and for each of the BLM GI parameters. Using the 10 th 

### percentile daily

average concentrations at each sampling location from the NWIS data, Geostatistical Analyst was used to create predictions for unmeasured locations throughout the continental U

. S. For each parameter

, the surface models were fit by minimizing the statistical error of the predicted surface. Surface fitting involved three steps: exploratory spatial data analysis, structural analysis (modeling the semivariogram to analyze surface properties of data from nearby locations), and surface prediction and assessment of the results. The semivariogram represents autocorrelation of measured data points spatially.

Modeling of the semivariogram was based on cross

- validation, which calculates error statistics that

ser ve as diagnostics to indicate whether the model is reasonable for map production. Cross

- validation was used to select the models that provided the most accurate predictions. The following criteria w ere used to evaluate goodness of fit for the semivariogram model:

 

### Mean Standardized Error: close to 0;

 

### Root Mean Square Error (RMSE): as small as possible;

### 9  

### Root

- Mean

- Square Standardized Error: close to 1; and,

 

### RMSE close to Average Standard Error ASE)

. The difference between the prediction and the measured data v alue is the prediction error. For a model that provides accurate predictions, the mean prediction error should be close to 0 if the predictions are unbiased. The root

- mean

- square standardized prediction error should be close to 1 if

the standard errors are accurate, and RMSE should be small if the predictions are close to the measured values (ESRI, 2003).

A tabulation of the geostatistical model selected for each water quality parameter, the number of data points interpolated, and the resulting error statistics are presented in Table 2. We used the optimal parameters for a spherical semivariogram as calculated by the Geostatistical Analyst. No transformations were applied to the data. Anisotropy (directional influence on the semivariogram) was
not incorpora ted in the semivariogram models.

Table 2. Model s election and c ross v alidation s tatistics for g eostatistical f itting of 10 th



### p ercentiles of BLM GI p arameters

Parameter Geostatistical

m odel Number of samples Mean

standardized

error Root mean

s quare error RMS

s tandardized

error Average

s tandard

error

Conductivity Universal kriging 4833 - 0.01038 1361 1.081 1259

Alkalinity Universal cokriging with conductivity 1372 - 0.001115 36.62 1.09 33.23

Calcium Universal cokriging with conductivity 2590 0.0001694 26.81 1.186 22.02

Magnesium Universal cokriging with conductivity 2578 - 0.002258 15.92 1.16 13.58

Sodium Universal cokriging with conductivity 2439 - 0.002929 156.3 1.583 95.78

Potassium Universal cokriging with conductivity 2379 - 0.001184 3.488 1.429 2.381

Sulfate Universal cokriging with conductivity 2650 - 0.0000225 114.5 1.29 87.04

Chloride Universal cokriging with conductivity 2792 0.001653 375.2 1.51 247



### 2.2.1 Kriging of Conductivity Data

Universal kriging with a constant trend was used to map the surface of 10 th 

### percentile conductivity values. Kriging weights the surrounding measured values to derive a prediction for each location. The weights are based on the distances between the measured points and the prediction location, as well

as the overall spatial arrangement among the measured points. The kriged prediction surface of 10 th



### percentiles of conductivity is mapped in Figure 2. As the kriging results show, conductivities are highest

in the south

- central and southwestern regions, as well as along the Gulf and southern Atlantic coasts. Regions of lower conductivity are found in a number of parts of the country.

### 10 Figure 2. Kriged prediction surface for 10 th 

### percentile of conductivity in the continental U.S. (sample locations in blue)

2.2.2 Co

- kriging of GI Data

Co

- kriging was used to improve surface predictions of the BLM GI parameters by taking into account secondary variables, in this case conductivity. As demonstrated above, conductivity is significantly

correlated to all of the BLM GIs. Universal co

- kriging with conductivity, assuming a constant trend, was used to map the surface of 10 th 

### percentile BLM GI s concentrations. For each of these parameters, co

kriging produced cross

- validation errors that were superior in terms of the goodness

- of

- fit criteria to errors produced by universal kriging. Prediction surfaces for calcium and alkalinity are mapped in Figures 3 and 4. The spatial distribution of calcium (Figure 3) shares a number of similarities with the mapping of conductivity (Figure 2). The co

- kriged alkalinity surface (Figure 4) is rather different, with high alkalinity values reflecting geographic features (such as the carbonate geology of the prairie states) and low alkalinity values that reflect the granitic geology of the northea st. Prediction surfaces for the other BLM GI’s are generally similar to those for conductivity and calcium.

### 11 Figure 3. Co

- kr iged prediction surface for 10 th 

### percentile of calcium in the continental U.S.

### 12 Figure 4. Co

- kri ged prediction surface for 10 th 

### percentile of alkalinity in the continental U.S.

2.2.3 Projection of Geostatistical Predictions onto Level III Ecoregions

Although maps of the geostatistical predictions are informative, a tabulation of the results is preferable for the purpose of providing gu idance to BLM users. We chose to spatially average the geostatistical predictions of BLM water quality parameters according to the Level III ecoregions of the continental

U

. S

. (Table 3) , as these ecoregions provide a sound basis for spatial averaging of th e water quality predictions. Ecoregions are designed to serve as a spatial framework for environmental resource management and denote areas within which ecosystems (and the type, quality, and quantity of environmental resources) are generally similar. They typically provide a logical and useful spatial (geographical) framework for organizing the results of environmental measurements (Omernik and Griffith, 2014 ). Ecoregions can be distinguished by landscape

- level characteristics that cause ecosystem

componen ts to reflect different patterns in different regions (Omernik, 1987). “ Level III Ecoregions of

the Continental U.S. ” map layer shows ecoregion delineation based on common patterns of geology, physiography, vegetation, climate, soils, land use, wildlife, w ater quality, and hydrology. Th e map layer
in Figure 5 was compiled by EPA (USEPA, 2013a) ( http://www.epa.gov/wed/pages/ecoregions/level_iii _iv .htm ).

### 13 Table 3. Level III e coregions of the U.S. o rganized a ccording to b roader Level I e coregions

Level I E cological R egions

Level III E coregion Name of E coregion

Marine West Coast Forest

1 Coast Range

2 Puget Lowland

3 Willamette Valley

111 Ahklun and Kilbuck Mountains

113 Alaska Peninsula Mountains

115 Cook Inlet

119 Pacific Coastal Mountains

120 Coastal Western Hemlock

- Sitka Spruce Forests

Northwestern Forested Mountains

4 Cascades

5 Sierra Nevada

9 Eastern Cascades Slopes and Foothills

11 Blue Mountains

15 Northern Rockies

16 Idaho Batholith

17 Middle Rockies

19 Wasatch and Uinta Mountains

21 Southern Rockies

41 Canadian Rockies

77 North Cascades

78 Klamath Mountains

105 Interior Highlands

116 Alaska Range

117 Copper Plateau

118 Wrangell Mountains

Mediterranean California

6 Southern and Central California Chaparral and Oak Woodlands

7 Central California Valley

8 Southern California Mountains

North American Deserts

10 Columbia Plateau

12 Snake River Plain

13 Central Basin and Range

14 Mojave Basin and Range

18 Wyoming Basin

20 Colorado Plateaus

22 Arizona/New Mexico Plateau

### 14 Table 3. Level III e coregions of the U.S. o rganized a ccording to b roader Level I e coregions

Level I E cological R egions

Level III E coregion Name of E coregion

24 Chihuahuan Deserts

80 Northern Basin and Range

81 Sonoran Basin and Range

Temperate Sierras

23 Arizona/New Mexico Mountains

Great Plains

25 Western High Plains

26 Southwestern Tablelands

27 Central Great Plains

28 Flint Hills

29 Central Oklahoma/Texas Plains

30 Edwards Plateau

31 Southern Texas Plains

34 Western Gulf Coastal Plain

40 Central Irregular Plains

42 Northwestern Glaciated Plains

43 Northwestern Great Plains

44 Nebraska Sand Hills

45 Piedmont

46 Northern Glaciated Plains

47 Western Corn Belt Plains

48 Lake Agassiz Plain

Eastern Temperate Forest

32 Texas Blackland Prairies

33 East Central Texas Plains

35 South Central Plains

36 Ouachita Mountains

37 Arkansas Valley

38 Boston Mountains

39 Ozark Highlands

51 North Central Hardwood Forests

52 Driftless Area

53 Southeastern Wisconsin Till Plains

54 Central Corn Belt Plains

55 Eastern Corn Belt Plains

56 Southern Michigan/Northern Indiana Drift Plains

57 Huron/Erie Lake Plains

59 Northeastern Coastal Zone

60 Northern Appalachian Plateau and Uplands

### 15 Table 3. Level III e coregions of the U.S. o rganized a ccording to b roader Level I e coregions

Level I E cological R egions

Level III E coregion Name of E coregion

61 Erie Drift Plain

63 Middle Atlantic Coastal Plain

64 Northern Piedmont

65 Southeastern Plains

66 Blue Ridge

67 Ridge and Valley

68 Southwestern Appalachians

69 Central Appalachians

70 Western Allegheny Plateau

71 Interior Plateau

72 Interior River Valleys and Hills

73 Mississippi Alluvial Plain

74 Mississippi Valley Loess Plains

75 Southern Coastal Plain

82 Laurentian Plains and Hills

83 Eastern Great Lakes and Hudson Lowlands

84 Atlantic Coastal Pine Barrens

Northern Forests

49 Northern Minnesota Wetlands

50 Northern Lakes and Forests

58 Northeastern Highlands

62 North Central Appalachians

Tropical Wet Forests

76 Southern Florida Coastal Plain

Southern Semi

- Arid Highlands

79 Madrean Archipelago

Taiga

101 Arctic Coastal Plain

102 Arctic Foothills

103 Brooks Range

104 Interior Forested Lowlands and Uplands

106 Interior Bottomlands

107 Yukon Flats

108 Ogilvie Mountains

Tundra

109 Subarctic Coastal Plains

110 Seward Peninsula

112 Bristol Bay

- Nushagak Lowlands

114 Aleutian Islands

### 16 Figure 5. Map of Level III e coregions in the U.S.
(Image taken from ftp://ftp.epa.gov/wed/ecoregions/us/Eco_Level_III_US.pdf )

### 17 Using the differences in land and water interactions, regional variations in attainable water quality, distinct biogeographical patterns (MacArthur, 1972), and similarities and diff erences in ecosystems to delineate ecoregions makes the application of ecoregions in environmental analyses a powerful tool with which to organize environmental information. The approach can take into account regional factors related to attainable water qu ality, and thus can be used to designate lakes for protection and

to establish lake

- restoration goals that are appropriate for each ecoregion (National Research Council (NRC), 1992). The NRC of the National Academy of Sciences has similarly endorsed the us e of the concept in restoring and managing streams, rivers, and wetlands (NRC, 1992).

The theory of ecoregion delineation states that natural water quality characteristics of lakes and streams within a single ecoregion will be more similar than the charact eristics between ecoregions (Perry and Vanderklein, 1996). Water quality characteristics exist in a landscape framework; neither normal nor impacted conditions of water resources can be separated from controlling influences of the surrounding landscape. Th e ecoregion concept has been applied and tested rather extensively in streams, rivers, and lakes. Testing and validation has been conducted in many diverse areas of the U.S.

, including several streams in Arkansas, Colorado, Kansas, Minnesota, Ohio, and Ore gon, and in lakes of Michigan, Minnesota, Ohio

, and Wisconsin (NRC, 1992).

Carleton (2006) used HUCs instead of ecoregions as the basis for averaging geostatistical results. HUCs are spatial delineations used for river basin management. Although a river ba sin may offer a logical framework for water supply management, for water quality management river basins are less applicable. The assumption that basins share similar properties is not always borne out, because river basins are often linked only by the wat er that flows through them. As Carleton (2006) noted, the use of HUCs for spatial averaging of surface water concentrations presents other conceptual difficulties. Only about 45% of HUCs are actual watersheds (Omernik, 2003); the rest receive drainage from additional upgradient areas. Concentrations measured in flowing waters reflect the soil, vegetation, and land use properties of the aggregate upstream drainage areas rather than of the sampling locations themselves (Smith et al., 1997).

2.2.3.1 Averaging Methods

To average the geostatistical predictions, a uniform grid was laid over the predicted surfaces and the predictions were sampled at the grid points falling within the polygons representing each ecoregion. The grid spacing was sized so that at least 30 poin ts were sampled within each ecoregion. Unbiased log means were then calculated from the sampled concentration predictions in each ecoregion. The logarithmic transformation was applied because this normalized the concentration distributions in

almost all of the ecoregions.

2.2.3.2 Tabulations of Ecoregional Estimated BLM Water Quality Parameters

The average predicted 10 th 

### percentile concentrations for conductivity and the GI parameters for each

of the L evel III ecoregion s in the continental U

. S

. are presented in Tab le 4. For each of these parameters, there is considerable variation between the ecoregional averages nationally. Chloride concentrations exhibit the greatest variation, with ecoregional 10 th 

### percentile averages that range from

0.7 to 573 milligrams per lit er ( mg/L ) . Alkalinity was the least variable, but the ecoregional 10 th


### percentile average concentrations still ranged from 12 to 163 mg/L.

### 18 Table 4. Predicted 10 th 

### percentile concentrations for conductivity (μS /cm), BLM GI water quality parameters (mg/L) and h ardness in each Level III ecoregion in the continental U.S.

Unbiased log

mean of 10 th

percentile

concentrations

Level III Ecoregion Ecoregion

Name Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness 1

1 Coast Range 102 8.4 3.2 4.1 0.64 33 3.2 4.8 34.12

2 Puget Lowland 80 7.1 1.9 2.8 0.64 22 2.3 5.6 25.54

3 Willamette Valley 91 8.2 2.9 4.4 0.90 30 4.7 3.8 32.39

4 Cascades 107 6.6 2.9 3.5 0.74 35 2.2 3.2 28.39

5 Sierra Nevada 195 8.3 4.7 8.8 1.3 58 5.8 11 40.02

6 Southern and

Central California Chaparral and Oak

Woodlands 600 42 24 48 2.5 124 56 136 203.4

7 Central California Valley 378 21 16 25 1.7 91 21 58 118.1

8 Southern California

Mountains 772 63 25 63 3.8 150 54 171 260

9 Eastern Cascades Slopes and

Foothills 212 8.2 3.8 6.0 1.0 44 3.2 5.0 36.08

10 Columbia Plateau 166 15 5.2 9.3 1.8 40 3.3 10 58.82

11 Blue Mountains 142 11 3.9 7.7 1.4 49 3.3 7.1 43.49

12 Snake River Plain 273 33 10 13 2.3 109 10 22 123.5

13 Central Basin and
Range 426 43 16 45 3.6 120 45 83 173.1

14 Mojave Basin and
Range 976 69 27 81 6.3 138 85 258 283.2

15 Northern Rockies 90 11 3.1 2.3 0.67 44 0.72 4.4 40.21

16 Idaho Batholith 91 13 3.8 3.6 0.88 62 1.9 5.9 48.08

17 Middle Rockies 300 30 10 14 1.9 105 7.6 55 116

18 Wyoming Basin 446 35 13 33 1.7 96 7.2 104 140.8

19 Wasatch and Uinta

Mountains 426 61 27 61 3.3 155 55 155 263.2

20 Colorado Plateaus 639 65 26 57 2.6 117 28 197 269.1

21 Southern Rockies 259 26 8.0 12 1.4 55 3.8 56 97.8

22 Arizona/New

Mexico Plateau 697 50 15 65 3.0 96 65 143 186.5

23 Arizona/New

Mexico Mountains 879 66 18 85 3.4 102 100 189 238.8

24 Chihuahuan

Deserts 2712 176 50 379 8.6 106 573 608 645

25 High Plains 1770 104 35 191 6.0 112 281 353 403.5

26 Southwestern 2147 114 34 316 4.9 94 512 374 424.4

### 19 Table 4. Predicted 10 th 

### percentile concentrations for conductivity (μS /cm), BLM GI water quality parameters (mg/L) and h ardness in each Level III ecoregion in the continental U.S.

Unbiased log

mean of 10 th

percentile

concentrations

Level III Ecoregion Ecoregion

Name Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness 1

Tablelands

27 Central Great

Plains 1228 84 24 176 6.9 121 245 204 308.4

28 Flint Hills 406 42 8.5 30 4.3 121 42 45 139.85

29 Central Oklahoma/Texas

Plains 925 60 16 107 4.1 95 164 108 215.6

30 Edwards Plateau 596 48 14 38 2.7 98 62 52 177.4

31 Southern Texas

Plains 798 56 14 58 3.8 129 73 91 197.4

32 Texas Blackland

Prairies 364 39 5.8 21 3.2 92 26 33 121.28

33 East Central Texas

Plains 367 36 6.3 23 3.8 98 29 29 115.83

34 Western Gulf

Coastal Plain 565 43 10 62 3.9 87 86 78 148.5

35 South Central

Plains 160 12 2.8 11 2.3 34 15 15 41.48

36 Ouachita

Mountains 116 7.9 2.8 8.4 1.3 34 10 11 31.23

37 Arkansas Valley 192 16 4.7 15 1.8 51 20 16 59.27

38 Boston Mountains 152 18 3.3 4.3 1.3 53 6.7 8.2 58.53

39 Ozark Highlands 258 31 10 4.5 1.6 96 6.0 20 118.5

40 Central Irregular

Plains 310 39 8.5 11 3.0 100 13 50 132.35

41 Canadian Rockies 164 22 8.7 15 0.57 80 1.7 38 90.67

42 Northwestern

Glaciated Plains 545 37 20 61 5.9 163 8.1 147 174.5

43 Northwestern

Great Plains 828 49 24 84 5.3 151 10 247 220.9

44 Nebraska Sand Hills 486 47 13 35 6.9 151 10 96 170.8

45 Piedmont 75 5.8 1.9 4.0 1.5 19 3.9 4.1 22.29

46 Northern Glaciated

Plains 524 40 20 38 9.1 163 13 106 182

47 Western Corn Belt

Plains 464 48 16 16 3.4 136 16 45 185.6

48 Lake Agassiz Plain 441 42 18 16 5.1 140 8.6 62 178.8

49 Northern

Minnesota 229 24 10 3.2 1.4 95 2.6 8.4 101

### 20 Table 4. Predicted 10 th 

### percentile concentrations for conductivity (μS /cm), BLM GI water quality parameters (mg/L) and h ardness in each Level III ecoregion in the continental U.S.

Unbiased log

mean of 10 th

percentile

concentrations

Level III Ecoregion Ecoregion

Name Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness 1

Wetlands

50 Northern Lakes and

Forests 166 19 6.5 2.5 0.78 83 3.4 6.1 74.15

51 North Central

Hardwood Forests 295 31 12 5.6 1.7 115 8.9 15 126.7

52 Driftless Area 348 34 15 5.1 1.6 107 10 17 146.5

53 Southeastern

Wisconsin Till

Plains 510 39 20 16 2.1 112 31 25 179.5

54 Central Corn Belt

Plains 546 53 24 14 1.7 124 30 46 230.9

55 Eastern Corn Belt

Plains 463 49 15 11 2.0 116 23 32 184

56 Southern Michigan/Northern

Indiana Drift Plains 463 52 15 14 1.9 134 28 29 191.5

57 Huron/Erie Lake

Plains 467 52 15 13 2.2 125 27 32 191.5

58 Northeastern Highlands 97 11 1.9 5.7 0.69 24 10 7.4 35.29

59 Northeastern

Coastal Zone 176 8.3 2.0 14 1.3 15 22 8.4 28.95

60 Northern Appalachian

Plateau and Uplands 271 33 7.3 37 1.3 53 64 22 112.43

61 Erie Drift Plain 364 31 8.1 19 2.3 64 29 38 110.71

62 North Central Appalachians 184 13 3.9 7.1 1.0 41 11 15 48.49

63 Middle Atlantic

Coastal Plain 793 6.7 2.3 6.8 1.8 15 83 22 26.18

64 Northern Piedmont 208 21 5.8 10 1.9 39 17 15 76.28

65 Southeastern

Plains 121 7.4 2.2 5.5 1.5 19 15 7.2 27.52

66 Blue Ridge 121 11 3.2 3.0 1.3 23 3.4 6.0 40.62

67 Ridge and Valley 163 17 4.5 4.6 1.4 33 6.3 15 60.95

68 Southwestern Appalachians 151 13 3.2 2.5 1.3 42 3.2 11 45.62

69 Central Appalachians 193 16 5.6 4.6 1.3 34 4.5 33 62.96

### 21 Table 4. Predicted 10 th 

### percentile concentrations for conductivity (μS /cm), BLM GI water quality parameters (mg/L) and h ardness in each Level III ecoregion in the continental U.S.

Unbiased log

mean of 10 th

percentile

concentrations

Level III Ecoregion Ecoregion

Name Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness 1

70 Western Allegheny

Plateau 276 23 7.3 10 1.7 46 14 47 87.43

71 Interior Plateau 237 27 5.9 4.4 1.5 65 7.0 20 91.69

72 Interior River Valleys and Hills 326 34 12 11 2.4 87 17 46 134.2

73 Mississippi Alluvial

Plain 255 14 5.5 17 2.6 44 22 13 57.55

74 Mississippi Valley

Loess Plains 102 10 2.9 3.9 1.6 38 4.5 8.4 36.89

75 Southern Coastal

Plain 726 18 5.0 20 2.0 41 390 38 65.5

76 Southern Florida

Coastal Plain 682 49 6.6 25 2.4 103 43 15 149.56

77 North Cascades 93 6.5 2.3 2.3 0.64 27 1.2 4.9 25.68

78 Klamath Mountains 156 8.7 4.6 4.0 0.66 44 2.1 3.5 40.61

79 Madrean Archipelago 625 42 11 45 2.8 92 39 78 150.1

80 Northern Basin and
Range 298 26 8.2 20 2.7 89 15 24 98.62

81 Sonoran Basin and
Range 991 64 24 115 4.4 121 131 192 258.4

82 Laurentian Plains

and Hills 104 4.8 0.78 2.5 0.48 12 2.7 4.4 15.198

83 Eastern Great

Lakes and Hudson

Lowlands 294 34 6.8 21 1.3 61 40 26 112.88

84 Atlantic Coastal

Pine Barrens 261 7.4 2.7 10 1.7 23 16 11 29.57

1 2+ 2+

Water Hardness calculated as equivalents CaCO3 = 2.5 (Ca ) + 4.1 (Mg )



### 2.2.3.3 Confirmation of Results

To confirm the results of the geostatistical predictions, a number of comparisons were made between the ecoregional average predictions and averages based directly on the data. For each GI parameter, we compared the ecoregional average predictions against the corresponding averages calculated from the data for each ecoregion.

Scatter plot matrices provide a visual presentation of the correlations between different parameters.

S catter plot matrices were developed for the ecoregional averages of conductivity and GI parameters. Figure 6 is the scatter plot matrix for ecoregional averages based on the data, and Figure 7 is the

scatter plot matrix for ecoregional averages based on the geostatistical predictions. Comparison of

### 22 these figur es shows that the predicted averages capture the same trends in terms of distributions and parameter correlations as those that are found for the ecoregional data. The similarities in the distributions and correlation structures between the ecoregional ave rages in Figures 6 and 7

demonstrate that the geostatistical ecoregion predictions are reasonable.

Figure 6. Scatter plot ma trix of ecoregional average 10 th 

### percentiles of data for conductivity (COND_SAM) and GI parameters (calcium=CA_SAM, magnesium=MG_S AM, sodium=NA_SAM, potassium=K_SAM, alkalinity=ALK_SAM, chloride=CL_SAM, sulfate=SO4_SAM)

COND_SAM COND_SAM CA_SAM MG_SAM NA_SAM K_SAM ALK_SAM CL_SAM SO4_SAM COND_SAM

CA_SAM CA_SAM
MG_SAM MG_SAM

NA_SAM NA_SAM

K_SAM K_SAM ALK_SAM ALK_SAM

CL_SAM CL_SAM

COND_SAM SO4_SAM

CA_SAM MG_SAM NA_SAM K_SAM ALK_SAM CL_SAM SO4_SAM SO4_SAM

### 23 Figure 7. Scatter plot mat rix of ecoregional average 10 th 

### percentiles of geostatistical predictions of conductivity (COND_KR) and BLM GI parameters (calcium=CA_CO, magnesium=MG_SCO, sodium=NA_CO, potassium=K_CO, alkalinity=ALK_CO, chloride=CL_CO, sulfate=SO4_CO)

In addition to scatter plots, correlation coefficient matrices between the parameters in each of the two data sets were generated. The Spearman (rank order) correlation coefficients for data

- based ecoregional av erages are presented in Table 5 ; correlation coefficients for ecoregional average geostatistical predictions are presented in Table 6. Although not identical, the correlation coefficients

are similar b etween the two datasets, again demonstrating that the geostatistical predictions are

reasonable. COND_KR COND_KR CA_CO MG_CO NA_CO K_CO ALK_CO CL_CO SO4_CO COND_KR

CA_CO CA_CO

MG_CO MG_CO

NA_CO NA_CO

K_CO K_CO
ALK_CO ALK_CO

CL_CO CL_CO

COND_KR SO4_CO

CA_CO MG_CO NA_CO K_CO ALK_CO CL_CO SO4_CO SO4_CO

### 24 Table 5. Spearman rank correlation matrix for unbiased log means of 10 th 

### percentile concentrations measured in Level III ecoregions

Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate

Conductivity 1

Calcium 0.895 1

Magnesium 0.877 0.927 1

Sodium 0.865 0.819 0.813 1

Potassium 0.823 0.758 0.73 0.859 1

Alkalinity 0.769 0.881 0.898 0.698 0.679 1

Chloride 0.815 0.774 0.702 0.855 0.788 0.595 1

Sulfate 0.894 0.864 0.85 0.883 0.786 0.725 0.744 1



### Table 6. Spearman rank correlation matrix for unbiased log means of 10 th 

### percentile predicted (kriged/cokriged) concentrations in Level III ecoregions

Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate

Conductivity 1

Calcium 0.872 1

Magnesium 0.843 0.924 1

Sodium 0.87 0.82 0.778 1

Potassium 0.842 0.803 0.753 0.836 1

Alkalinity 0.747 0.868 0.901 0.672 0.745 1

Chloride 0.829 0.715 0.592 0.826 0.725 0.461 1

Sulfate 0.889 0.873 0.875 0.893 0.831 0.751 0.715 1



### As a final test of the accuracy of the geostatistical predictions, we regressed the ecoregional averages based on the geostatistical predictions against the ecoregional averages based on the data. Scatter plots and fitted regression lines are shown for each of the parameters: conductivity (Figure 8), alkalinity (Figure 9), calcium (Figure 10), magne sium (Figure 11), sodium (Figure 12), potassium (Figure 13), sulfate (Figure 14), and chloride (Figure 15). Statistics for the linear regressions are provided in Table 7. For each of the parameters, the predicted and data

- based ecoregional averages are significantly correlated. In each case, the linear regression coefficient is nearly 1.0, with a highly significant P value. As with the previous comparisons, the linear regression results demonstrate that the accuracy of the geostatistical pr edictions is high.

### 25 Figure 8. Ecor egional averages of kriged 10 th 

### percentiles of conductivity versus data 

# 100 1000 conductivity (uS/cm) 500 1000 1500 2000 2500 3000 kriged conductivity (uS/cm)

### 26 Figure 9. Ecoregional averages of cokriged 10 th 

### percentiles of alkalinity versus data 50 100 150 200 250



# alkalinity (mg CaCO3/L) 40 80 120 160 cokriged alkalinity (mg CaCO3/L)

### 27 Figure 10 . Ecoreg ional averages of cokriged 10 th 

### percen tiles of calcium versus data 

# 1 10 100 calcium (mg/L) 40 80 120 160 200 cokriged calcium (mg/L)

### 28 Figure 1 1 . Ecoreg ional averages of cokriged 10 th 

### percentiles of magnesium versus data 10 20 30 40 50

60

70 

# magnesium (mg/L) 10 20 30 40 50 60 cokriged magnesium (mg/L)

### 29 Figure 1 2 . Ecoreg ional averages of cokriged 10 th 

### percentiles of sodium versus data 

# 1.0 10.0 100.0 sodium (mg/L) 10 100 cokriged sodium (mg/L)

### 30 Figure 13 . Ecoreg ional averages of cokriged 10 th 

### percentiles of potassium versus data 2 4 6 8 10

12 

# potassium (mg/L) 2 4 6 8 10 cokriged potassium (mg/L)

### 31 Figure 14 . Ecoreg ional averages of cokriged 10 th 

### percentiles of sulfate versus data 

# 1.0 10.0 100.0 sulfate (mg/L) 10 100 cokriged sulfate (mg/L)

### 32 Figure 1 5. Ecoregional averages of cokriged 10 th 

### percentiles of chloride versus data

Table 7. Correlation coefficients and linear regression (LR) statistics between ecoregional average 10 th 

### percentiles of data and geostatistical predictions

Parameter C orrelation coefficient, r LR coefficient P (2 Tail) LR

constant P (2 Tail)

Conductivity 0.908 0.98 <0.001 - 20.992 0.495

Alkalinity 0.915 1.209 <0.001 - 12.161 0.028

Calcium 0.922 1.061 <0.001 - 1.994 0.365

Magnesium 0.885 1.097 <0.001 - 1.169 0.22

Sodium 0.93 1.101 <0.001 - 4.78 0.169

Potassium 0.906 1.073 <0.001 - 0.184 0.297

Sulfate 0.865 1.264 <0.001 - 13.599 0.041

Chloride 0.839 1.039 <0.001 - 7.271 0.386 

# 0.1 1.0 10.0 100.0 1000.0 chloride (mg/L) 1.0 10.0 100.0 cokriged chloride (mg/L)

### 33 2.2.3.4 Conclusions for Selection of Water Quality Parameters

In this section we used geostatistics to estimate an intermediate step in generating missing GI parameter values based on geography . We supplemented the geostatistical approach by adding conductivity as an additional explanatory variable to generate a more robust spatial estimate of the GI water quality inputs for the BLM because conductivity is one of the most widel y monitored water quality indicators in the U. S

. and correlates well with GIs. In section 3, these estimates are further refined by stream order. We present here t he average predicted 10 th 

### percentile concentrations for the BLM GI water quality parameters, as presented in Table 4 by ecoregion s

. Because they are based on

the 10 th 

### percentiles of the daily average data from each USGS monitoring station

, they are expected to yield copper criteria that are reasonably protective of aquatic life when applied as missing data for parameters in the BLM model. These data could also be used to fill in missing water chemistry

parameters in the application of other metal BLM models. The most appropriate par ameter selection

however would include consideration of stream order in GIs estimates

. Section 3 presents further refinements of estimates of the GI parameters by stream order and EPA’s recommendations for

de fault GI parameters for the BLM when data are la cking.

As with any estimate or prediction, it is appropriate to seek alternative estimates for the purpose of comparison or confirmation. If conductivity data are available for the site, either site

- specific measurement data or data of opportunity from a database such as the NWIS, the regressions in EPA (2008; Appendix C ) can be used to make independent estimate s of the missing BLM water quality parameters. If the regression projections differ from the geostatistical average predictions, the lower (more co nservative) estimate is recommended for application to ensure protection of aquatic life. As always, u sers of the BLM should be also encouraged to sample the water body of interest and to analyze for the constituent (parameter) concentrations as a basis fo r determining reliable BLM inputs.

2.2.3.5 Guidance Regarding Selection of Water Quality Parameters: pH and DOC

Although the geostatistical and regression

- based approaches can be used to reliably estimate GI parameters used as BLM inputs, the same approaches do not produce accurate site

- specific estimates for the two most important BLM inputs: pH and DOC. The BLM is less sensitive to the GI parameters

than to pH and DOC predicting site

- specific criteria for copper . Since our analysis indicates that there is

littl e or no trend in relationships between conductivity and pH, and direct kriging produced similarly ambiguous predictions, site

- specific data for pH must be used for BLM application at a site.

For DOC, analysis of NWIS data indicated a weak relationship wit h conductivity, so the regression approach is not appropriate for this parameter. In 2008, EPA recommended use of the ecoregional DOC concentration percentiles tabulated by EPA for the Development of National Bioaccumulation Factors Technical Support Docum ent (USEPA, 2003) because they appeared to offe r reasonable

estimates of lower percentile DOC concentrations, and were based on substantially more DOC data

than were available in the NWIS. In S ection 4 of this report we further tested the se ecoregional DOC

concentration s for use in the BLM whe re site

- specific data are not available.

### 34 

## 3 USING STREAM ORDER TO REFINE PREDICT ION OF GI PARAMETERS

The following section discusses how stream order was used to address anthropogenic impacts. The goal is to provide BLM users with tables of appropriately protective estimates of GI parameters, building on the ecoregion al work described in Section 2

.

E stimation s of values for the GI parameters ( alkalinity, calcium, magnesium, sodium, potassium,

sul fate, and chloride ) tend to vary regionally

. As demonstrated in Section 2, the spatial variation of these factors is generally known or at least predictable, and therefore spatial or geographic analysis of

data can be used to estimate GI input parameter va lues. However, these values also vary due to anthropogenic impact. In the case of conductivity and GI parameters, a positive correlation between ion concentrations and measures of human activity, such as population density, urban and agricultural land use, road density, point and nonpoint pollutant sources, among other activities

, is expected and may confound the pattern of geographic variability both within and between ecoregions.

One way to account for surface water quality variability within ecoregions is to distinguish water bodies according to the Strahler stream order (SO). The SO is used to define stream size based on a hierarchy of tributaries (Strahler

, 1952 ; 1957) and may range from 1 st 

### order (a stream with no tributaries) to 12 th 

### order (the Amazon, at its mouth). First through 3 rd 

### order streams are called headwater streams (source waters of a stream). Over 80% of Earth's waterways are headwater streams (Strahler

, 1957) . A stream that is 7 th 

### order or larger constitutes a river. For examp le, t he Ohio River is

8 th 

### order and the Mississippi River is 10 th 

### order. According to the River Continuum Concept, changes in water quality are commonly observed between the upper, middle

, and lower reaches of a stream ( FISRWG

, 1998 ; Ward

, 1992 ; USEPA

, 201 5).

In this section we consider variability in GIs by determining the SO of each surface water sampling

location in the USGS NWIS 2 

### database, and explore methods of incorporating SO variation in the
parameter estimates. Tables are provide d in this section showing tabulations of parameter estimates based upon both ecoregion and SO to maximize the accuracy of estimated input paramet er s

.

3.1 Determining SO of NWIS Surface Water Sampling Locations

GIS was used to determine the SO of each NWIS surface water sampling location. Flowlines and

catchments with SO were obtained from the NHD

- Plus V2 geospatial hydrologic framework (McKay et

al.

, 2012)

. 3 

### The point locations corresponding to the latitude

- longitude coordinates of the NWIS sampling stations were snappe d to the NHD

- Plus flowlines using ArcGIS. A spatial join was then performed between these shapefiles and the NHD

- Plus flowlines to link stream order to the sampling

locations. Some of the NHD

- Plus flowlines did not have SO data associated with the record. When a sampling location occurred on a flowline that didn’t have a SO , the SO from the catchment was used.

When the catchment also did not have a SO , the SO of the nearest stream was applied. SO was added

as an attribute to the information for each station in the database.

2 http://waterdata.usgs.gov/nwis

3 http://www.horizon

- systems.com/NHDPlus/NHDPlusV2_home.php

### 35 3.2 Estimating BLM Parameters for Ecoregions and SO
Estimated (10 th 

### percentile) BLM water quality parameters were presented in S ection 2 for 84 Level III ecoregions of the continental U

. S. In the work presented here, the parameter estimates were recalculated for individual SOs or ranges (groups) of SOs within each eco region

.

3.3 Results
The distribution of NWIS sampling locations by SO is presented in Figure 1 6 . The largest proportion of sampled locations (78%) was found to be in SO 1 through 4.

Figure 1 6 . Distribution of NWIS s urface w ater s ampling l ocations by SO

3.3.1 Dependence of Ecoregional Parameter Estimates on SO

Box plots were constructed to examine how the GIs estimates varied with SO . Box plots of conductivity (Figure 1 7 )

, alkalinity (Figure 18 ), calcium (Figure 19 ), magnesium (Figure 20 ), sodium (Figure 21 ), potassium (Figure 22 ), sulfate (Figure 23 ) and chloride (Figure 24 ) all show a general increase in the magnitude of the estimate with SO . This trend was most apparent and consistent when comparing medium stream orders (SO 4

- 6) to higher stream orders (SO ≥7). In addition, the upper quartile parameter estimates were generally higher in SOs 4 through 6 than in lower order streams (SO ≤3). Based upon these trends, we grouped the estimates for each parameter by SO : 1 through 3 (headwater 0% 5% 10% 15% 20% 25%

1 2 3 4 5 6 7 8 9Proportion of sites

Strahler stream order

### 36 streams), 4 through 6 (mid

- reaches) and 7 through 9 (river s ). There w ere no data for rivers with SO>9. Grouping simplified the presentation of results a nd improved the robustness of the parameter estimates, without losing significance of the SO trends. Parameter estimates for these three SO groups are included in the box plots in Figures 18 through 24 , labeled as “13

, ” “46

, ” and “79

. ” The classes depicted as 13, 46, and 79 reflect groupings according to SO (i.e., 1 through 3, 4 through 6, and 7 through 9).

Figure 17 . Box p lot of e stimated e coregional c onductivities as a f unction of SO

Note: Classifications depicted as 13, 46, and 79 reflect groupings according to stream order (i.e., 1 through 3, 4 through 6, and 7 through 9) as described in the text. For box plots, the bottom and top of each “box” displays the 25 th 

### and 75 th 

### percentile concentrations defined as the interquartile range (IQR) (i.e., the b ox contains 50% of the data values), respectively. The median is displayed as the horizontal line within the box. The “whiskers” show the relative distribution of data points outside of the IQR and represent 1.5 times the IQR. Data not included between the whiskers are plotted as outliers with a star/asterisk. 

### 1 13 2 3 4 46 5 6 7 79 8 9 

## Strahler stream order 10 100 1000 10000 Conductivity (uS/cm)

### 37 Figure 18 . Box p lot of e stimated e coregional a lkalinity c oncentrations as a f unction of SO
(Refer to note in Figure 1 7

. )

Figure 19 . Box p lot of e stimated e coregional c alcium c oncentration s as a f unction of SO
(Refer to note in Figure 1 7

. ) 

## 1 13 2 3 4 46 5 6 7 79 8 9 

# Strahler stream order 1 10 100 Alkalinity (mg/L as CaCO3)



### 1 13 2 3 4 46 5 6 7 79 8 9 

## Strahler stream order 0.1 1.0 10.0 100.0 Calcium concentration (mg/L)

### 38 Figure 2 0 . Box p lot of e stimated e coregional m agnesium c oncentration s as a f unction of SO
(Refer to note in Figure 1 7

. )

Figure 2 1 . Box p lot of e stimated e coregional s odium c oncentrations as a f unction of SO
(Refer to note in Figure 1 7

. ) 

## 1 13 2 3 4 46 5 6 7 79 8 9 

## Strahler stream order 0.10 1.00 10.00 100.00 Magnesium concentration (mg/L)



## 1 13 2 3 4 46 5 6 7 79 8 9 

## Strahler stream order 1.0 10.0 100.0 1000.0 Sodium concentration (mg/L)

### 39 Figure 22 . Box p lot of e stimated e coregional p otassium c oncentrations as a f unction of SO
(Refer to note in Figure 1 7

. )

Figure 2 3 . Box p lot of e stimated e coregional s ulfate c oncentrations as a f unction of SO
(Refer to note in Figure 1 7

. ) 

## 1 13 2 3 4 46 5 6 7 79 8 9 

# Strahler stream order 0.1 1.0 10.0 Potassium concentration (mg/L)



## 1 13 2 3 4 46 5 6 7 79 8 9 

# Strahler stream order 0.10 1.00 10.00 100.00 1000.00 Sulfate concentration (mg/L)

### 40 Figure 2 4 . Box p lot of e stimated e coregional c hloride c oncentrations as a f unction of SO

(Refer to note in Figure 1 7

. )

The range of values across SOs overlap greatly due to the inclusion of data across ecoregions. Tenth percentile estimates of conductivity increase with SO group in 58% of ecoregions when comparing low

to medium SO groups, and 84% of ecoregions when comparing medium to high SO groups. The same

trend was evident for the GIs . For example, 10 th 

### percenti les of calcium increased with SO group in 68% of ecoregions for low versu s medium SO and 83% of ecoregions for medium versu s high SO . In general, parameter estimates (10 th 

### percentiles of conductivity and ion concentrations) increased with SO, and the increase was most apparent and consistent for higher SOs (SO ≥7).

3.3.2 SO

- Based Parameter Estimates

Tenth percentile parameter estimates for conductivity

, GIs and hardness are group ed by SO and Level

III e coregions in Tables 8 through 10

. Tenth percentile parameter estimates for SOs 1 through 3 are presented in Table 8 , for SOs 4 through 6 are presented in Table 9 , and SOs 7 through 9 are presented

in Table 10 . The tables include the sample size for instances in which parameter estimates are highly uncertain due to limited data, i.e., cases where sample size is <10. Water quality data were limited in

four ecoregions (11, 16, 49

, and 78) for SO group 1 through 3, in Ecoregion 76 for SO group 4 through 6, and in 28 ecoregions for SO group 7 through 9. With the exception of the specific ecoregions and SO classes where data are limited, the parameter estimates in Tables 8 through 10 are recommended as improved default values for use in the BLM when data are not available for a location in a specific L evel III ecoregion and SO group. 

## 1 13 2 3 4 46 5 6 7 79 8 9 

# Strahler stream order 0.10 1.00 10.00 100.00 1000.00 10000.00 Chloride concentration (mg/L)

### 41 Table 8. Recommended 10 th 

### p ercentile c onductivity, GIs

, and h ardness e stimates for SO Group 1 through 3 ( n umber of s tations s hown in p aren theses if n<10)

Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

1 58 6.0 0.8 1.3 0.1 44 0.6 1.1 18.28

2 74 8.8 2.8 3.9 0.5 2.8 3.3 33.48

3 68 9.9 3.8 5.6 1.5 2.3 1.5 40.33

4 16 1.0 0.2 1.8 0.2 0.5 0.2 3.32

5 28 0.6 0.1 0.3 0.1 38 0.1 0.1 1.91

6 279 3.6 8.2 8.4 0.9 73 8.9 7.2 42.62

7 164 19 6.0 14 1.8 120 8.6 6.6 72.1

8 157 29 4.3 10 1.5 70 (7) 2.6 0.4 90.13

9 55 4.4 (8) 0.9 (8) 2.3 (9) 0.4 (9) 35 (2) 0.2 0.2 14.69

10 137 24.0 9.4 10.2 1.4 127 4.6 11 98.54

11 88 8.6 (2) 3.2 (2) 169 (2) 34.62

12 133 13 2.0 6.1 0.8 35 1.4 3.7 40.7

13 109 9.4 1.6 2.7 0.6 45 0.5 3.7 30.06

14 967 15 2.8 6.0 3.0 90 (7) 2.7 6.3 48.98

15 24 3.1 0.8 0.9 0.4 9.0 0.2 1.3 11.03

16 21

17 93 6.9 1.6 1.5 0.5 31 0.3 3.0 23.81

18 92 22 6.3 4.7 0.9 3.3 7.3 80.83

19 76 59 11 5.1 0.6 96 2.5 44 192.6

20 189 59 12 19 1.4 157 6.7 129 196.7

21 37 3.5 0.7 0.8 0.3 18 0.2 1.9 11.62

22 115 13 1.1 2.3 0.8 55 1.2 7.2 37.01

23 62 6.3 1.8 3.7 0.7 20 0.8 4.3 23.13

24 453 43 7.9 35 3.4 32 20 74 139.89

25 194 43 11 31 3.7 228 7.3 35 152.6

26 199 18 3.0 63 3.4 53 3.6 11 57.3

27 293 21 5.0 9.9 1.5 122 5.1 13 73

28 346 50 8.2 4.4 0.8 125 1.5 22 158.62

29 217 30 4.0 17 2.9 74 8.2 9.9 91.4

30 189 25 1.8 1.6 0.9 99 2.6 4.9 69.88

31 639 48 5.5 47 2.9 71 51 142.55

32 183 26 1.7 5.9 1.9 52 5.1 15 71.97

33 132 24 2.1 5.8 2.3 29 8.1 6.0 68.61

34 141 13 2.3 9.8 2.6 44 13 4.7 41.93

35 25 0.9 0.5 1.9 0.2 5.0 3.0 1.7 4.3

36 19 0.9 0.7 0.9 0.3 4.0 1.3 2.0 5.12

37 107 23 3.5 23 3.3 36 2.5 3.5 71.85

38 51 0.9 (7) 0.6 (7) 0.63 (7) 0.6 (7) 35 1.1 1.8 4.71

39 172 26 1.9 1.3 0.7 62 2.0 4.2 72.79

### 42 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

40 223 20 4.6 8.4 2.8 46 5.2 23 68.86

41 93 13 3.8 0.4 0.1 60 (9) 0.1 1.6 48.08

42 256 23 7.3 7.4 1.6 91 1.3 14 87.43

43 327 17 16 26 3.7 144 2.7 119 108.1

44 156 21 3.2 6.5 4.7 80 (2) 0.7 5.5 65.62

45 44 2.7 0.8 2.5 1.2 12 2.4 1.9 10.03

46 400 32 13 15 7.8 94 5.4 60 133.3

47 380 41 11 4.8 1.3 83 12 15 147.6

48 295 31 15 4.9 2.5 4.3 13 139

49 402 (4) 227 (1)

50 69 5.1 1.0 1.2 0.3 32 0.4 1.2 16.85

51 137 28 11 2.0 0.9 53 (4) 3.3 4.5 115.1

52 432 42 12 2.9 0.7 75 4.8 12 154.2

53 502 27 8.0 11 2.0 42 25 22 100.3

54 574 51 22 8.4 1.2 202 28 44 217.7

55 420 40 11 7.8 1.5 130 19 16 145.1

56 219 28 7.4 3.6 0.9 79 6.3 16 100.34

57 446 35 10 6.8 2.3 84 19 33 128.5

58 25 1.2 0.4 0.3 0.2 1.5 0.4 4.2 4.64

59 69 3.6 1.1 6.2 0.9 3.0 10 5.8 13.51

60 61 4.7 1.2 1.5 0.4 17 1.5 6.6 16.67

61 131 13.0 2.5 8.0 1.1 33 7.8 8.3 42.75

62 32 1.8 0.7 0.8 0.3 2.0 0.8 4.4 7.37

63 108 1.1 0.8 2.8 0.8 5.0 5.5 3.2 6.03

64 134 12.0 4.6 7.0 1.3 19 10 11 48.86

65 25 0.7 0.5 1.5 0.3 3.0 2.6 0.7 3.8

66 12 0.5 0.3 0.7 0.3 3.0 0.7 1.1 2.48

67 81 4.7 2.0 2.0 0.7 3.0 1.8 7.1 19.95

68 32 1.9 1.2 1.3 0.7 3.0 1.4 3.6 9.67

69 36 2.8 0.8 0.4 0.4 4.0 1.0 8.1 10.28

70 125 5.6 4.0 2.7 1.4 12 2.4 16 30.4

71 179 10 1.8 1.1 0.7 54 2.2 2.8 32.38

72 180 17 5.3 5.2 1.7 69 3.5 21 64.23

73 102 6.9 2.7 3.4 2.0 31 2.0 2.9 28.32

74 52 3.2 1.4 2.5 1.5 9.3 2.6 3.9 13.74

75 75 6.6 1.5 4.5 0.5 9.0 9.0 1.5 22.65

76 430 41 1.8 7.8 0.3 116 30 0.1 109.88

77 14 1.6 0.4 0.4 0.2 7.0 0.2 0.7 5.64

78 19 2.1

79 340 30 6.2 25 2.8 92 (2) 13 23 100.42

80 78 6.3 1.1 4.3 2.2 24 0.2 2.5 20.26

81 203 19 2.4 10 2.8 52 2.6 6.1 57.34

### 43 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

82 37 1.5 0.5 4.3 0.2 6.6 1.8 5.8

83 198 16 3.9 5.0 1.0 51 21 29 55.99

84 50 0.8 0.6 2.8 0.6 1.0 5.0 4.4 4.46



### Table 9

. Recommended 10 th 

### p ercentile c onductivity, GIs

, and h ardness e stimates for SO g roup 4 through 6 ( n umber of s tations s hown in p arentheses if n<10)

Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

1 52 3.6 1.0 2.0 0.2 15 1.6 2.2 13.1

2 49 5.3 1.2 1.3 0.1 16 0.8 1.8 18.17

3 62 7.1 2.5 4.3 0.8 29 4.6 2.8 28

4 35 3.5 1.0 2.8 0.4 16 0.8 0.8 12.85

5 18 0.9 0.1 0.6 0.2 5.0 0.4 0.4 2.66

6 316 9.1 4.8 5.4 1.0 32 2.3 4.1 42.43

7 67 6.5 2.5 2.9 0.9 33 1.7 3.2 26.5

8 93 9.0 1.5 8.4 1.0 17 3.2 6.0 28.65

9 52 5.5 0.8 2.4 0.5 22 0.9 2.2 17.03

10 83 8.6 3.2 4.0 0.9 33 1.4 3.1 34.62

11 52 3.7 0.8 1.6 0.7 16 0.3 0.7 12.53

12 95 13 2.5 4.9 1.2 40 2.2 3.8 42.75

13 124 12 3.4 9.6 1.8 68 3.9 8.1 43.94

14 688 58 13 86 7.9 225 55 86 198.3

15 34 3.5 1.1 0.9 0.4 16 0.2 1.2 13.26

16 22 2.4 0.4 1.3 0.4 10 0.2 0.6 7.64

17 123 10 2.5 1.4 0.6 44 0.5 2.2 35.25

18 145 15 4.0 7.1 0.9 57 1.4 16 53.9

19 135 34 9.1 6.7 1.3 7.3 9.5 122.31

20 260 38 9.6 16 1.5 107 4.0 55 134.36

21 74 6.5 1.3 1.9 0.6 18 0.4 3.2 21.58

22 215 25 4.3 9.5 1.3 62 2.4 18 80.13

23 289 31 9.5 5.6 1.1 101 3.5 2.4 116.45

24 240 24 5.1 18 1.5 80 7.0 21 80.91

25 220 14 3.4 8.7 1.6 84 4.1 29 48.94

26 367 39 9.1 29 2.8 79 11 56 134.81

27 351 39 7.1 11 5.1 79 5.6 16 126.61

28 298 68 14 9.6 2.1 150 7.4 39 227.4

29 351 39 6.8 20 2.7 80 20 19 125.38

30 377 44 12 6.5 0.8 140 10 13 159.2

31 447 54 9.4 10 1.8 142 12 29 173.54

32 311 35 2.8 12 2.8 94 12 22 98.98

33 334 33 5.0 16 2.1 45 23 28 103

34 125 10 3.5 13 2.7 40 12.2 3.1 39.35

### 44 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

35 67 4.2 1.1 6.5 1.5 10 3.5 5.0 15.01

36 24 1.0 0.8 1.1 0.5 4.0 1.5 2.0 5.78

37 55 3.8 2.3 5.2 1.3 9.6 2.0 2.0 18.93

38 41 8.4 1.0 1.2 0.8 13 1.5 3.2 25.1

39 160 19 1.8 1.6 0.9 73 2.3 3.6 54.88

40 258 33 5.7 6.8 2.7 64 6.7 20 105.87

41 133 19 4.5 0.7 0.2 0.1 1.9 65.95

42 285 22 9.2 12 2.3 141 2.3 34 92.72

43 342 28 8.5 13 2.7 145 2.2 45 104.85

44 232 27 3.8 9.4 7.0 184 (1) 2.5 2.7 83.08

45 44 3.0 1.2 3.0 1.3 13 2.9 2.9 12.42

46 480 32 17 30 8.6 153 9.1 89 149.7

47 390 43 10 7.0 1.7 109 8.5 19 148.5

48 422 40 18 8.5 4.1 170 7.0 32 173.8

49 75 7.9 2.4 2.3 0.7 25 2.3 4.5 29.59

50 78 7.9 2.4 1.2 0.4 37 0.9 2.7 29.59

51 125 19 6.5 2.3 0.9 102 3.0 3.6 74.15

52 221 15 6.3 4.2 1.5 50 6.4 10 63.33

53 389 36 19 9.1 2.2 138 18 20 167.9

54 520 49 22 7.4 1.0 148 22 37 212.7

55 413 43 12 5.6 1.9 162 19 22 156.7

56 389 44 14 10 1.5 133 18 21 167.4

57 489 56 15 10 2.6 108 22 31 201.5

58 38 4.9 0.9 2.9 0.5 5.0 4.3 6.1 15.94

59 81 5.1 1.4 7.8 1.1 10 11 7.3 18.49

60 101 12 2.5 5.3 1.0 20 3.9 8.4 40.25

61 178 20 4.7 8.4 1.8 47 61 11 69.27

62 50 3.9 0.9 4.0 0.6 5.0 2.6 6.1 13.44

63 65 3.6 0.9 3.9 1.2 8.0 6.6 3.2 12.69

64 175 13 4.7 9.9 1.2 48 15 13 51.77

65 43 2.8 0.9 2.2 0.8 6.0 3.4 1.7 10.69

66 14 1.0 0.3 0.8 0.5 4.0 0.4 1.1 3.73

67 89 7.9 2.0 2.9 1.0 14 3.4 8.8 27.95

68 42 4.0 0.9 1.0 0.8 16 1.4 4.4 13.69

69 115 6.8 1.5 1.7 0.6 9.0 1.8 8.8 23.15

70 108 11 3.1 4.2 1.2 11 4.5 22 40.21

71 145 18 2.6 1.4 1.0 53 2.8 3.7 55.66

72 251 25 7.7 8.2 2.1 61 10 30 94.07

73 99 6.4 2.4 3.9 2.2 34 3.0 5.0 25.84

74 46 2.3 1.0 2.2 1.1 11 2.0 1.0 9.85

75 57 2.2 1.0 3.8 0.5 1.0 6.1 1.7 9.6

76

### 45 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

77 44 4.7 1.5 1.1 0.2 34 (3) 0.1 0.9 17.9

78 92 7.9 3.2 4.0 0.6 36 2.1 2.4 32.87

79 371 33 7.1 25 2.0 89 6.5 18 111.61

80 204 15 5.7 4.1 0.8 54 2.0 9.3 60.87

81 146 30 5.7 11 2.0 54 4.5 25 98.37

82 29 3.5 0.7 2.1 0.4 9.2 2.3 5.4 11.62

83 97 11 1.9 3.2 0.7 54 22 25 35.29

84 41 0.8 0.5 2.4 0.7 1.0 4.4 4.5 4.05



### Table 10

. Recommended 10 th 

### p ercentile c onductivity, GIs

, and h ardness e stimates for SO g roup 7 through 9 ( n umber of s tations s hown in p arentheses if n<10)

Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

1 111 12 3.4 4.3 0.8 56 2.3 6.3 43.94

2

3 58 5.0 1.6 3.4 0.6 20 2.7 2.3 19.06

4 118 13 3.6 3.7 0.9 52 1.7 6.9 47.26

5

6 101 9.0 4.5 4.9 0.9 1.7 3.1 40.95

7 122 10 5.0 6.2 1.0 56 3.5 4.8 45.5

8

9

10 71 5.7 1.5 2.0 0.7 16 0.8 4.2 20.4

11 72 8.5 1.5 3.3 0.7 32 0.8 5.0 27.4

12 310 37 10 13 2.5 122 11 30 133.5

13 430 38 10 32 5.6 175 15 27 136

14 810 64 23 69 3.2 121 55 181 254.3

15 51 5.2 1.5 1.4 0.5 20 0.4 3.1 19.15

16

17 189 20 5.6 3.7 1.1 69 1.3 13 72.96

18 342 35 11 14 1.3 119 2.5 45 132.6

19 608 55 20 44 2.2 145 13.1 149 219.5

20 373 39 12 25 1.7 102 9.7 85 146.7

21

22 279 28 4.9 15 1.9 80 4.5 37 90.09

23

24 554 60 11 76 4.3 107 49 145 195.1

25 830 64 20 60 4.6 127 16 184 242

26 876 56 20 61 3.5 128 24 187 222

27 648 61 16 43 6.2 96 25 112 218.1

28 395 41 8.9 15 6.4 119 10 36 138.99

29 1194 71 19 132 4.7 89 210 130 255.4

### 46 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

30

31 817 59 16 76 4.1 102 88 141 213.1

32 438 45 6.2 28 3.9 107 37 38 137.92

33 428 46 5.3 31 4.7 128 42 28 136.73

34 477 47 6.9 36 4.3 103 49 35 145.79

35 85 6.9 1.6 5.8 1.4 46 5.0 8.0 23.81

36 179 2.4 0.7 (3) 1.4 (3) 1.7 (3) 20 21 8.87

37 355 28 7.0 29 2.9 73 30 28 98.7

38

39 215 28 7.7 2.8 1.1 96 3.4 6.0 101.57

40 310 34 5.2 6.4 3.0 96 7.3 24 106.32

41

42 338 49 18 26 3.4 144 8.7 87 196.3

43 394 36 12 24 2.4 122 6.2 74 139.2

44

45 53 4.1 1.7 6.0 1.5 13 5.0 7.3 17.22

46 642 52 25 49 12 176 22 149 232.5

47 570 48 12 15 3.7 159 11 44 169.2

48 425 44 19 14 5.3 188 9.9 61 187.9

49

50

51 353 44 16 7.2 2.1 217 10 13 175.6

52 115 12 4.4 2.9 1.1 40 4.4 5.0 48.04

53 544 53 33 7.9 1.8 19 22 267.8

54 388 41 18 9.7 2.1 131 16 25 176.3

55 502 48 18 20 3.0 182 (4) 32 33 193.8

56

57 405 43 12 9.5 2.8 104 20 30 156.7

58

59 65 3.9 0.7 8.5 0.8 6.0 13 6.0 12.62

60

61

62

63 80 3.6 1.4 5.1 2.0 8.5 6.5 7.4 14.74

64 148 14 3.5 4.6 1.3 28 8.0 20 49.35

65 69 4.7 1.2 3.7 1.2 15 4.1 5.7 16.67

66

67 96 15 3.4 4.7 1.2 28 5.7 12 51.44

68 138 17 3.5 4.1 1.1 57 (8) 5.7 12 56.85

69

70 225 21 5.4 9.8 1.4 29 10 44 74.64

71 183 23 4.3 3.2 1.4 56 3.8 13 75.13

### 47 Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness

72 310 34 10 8.3 2.3 88 12 23 126

73 146 14 3.7 3.4 1.7 54 3.5 6.0 50.17

74

75 70 4.8 1.2 3.2 1.0 15 4.6 3.9 16.92

76

77

78

79

80 71 8.9 (3) 2.4 (3) 7.7 (3) 2.1 (3) 2.1 (3) 5.1 32.09

81 898 64 23 80 3.8 123 69 160 254.3

82 38 4.0 0.8 1.9 0.4 8.1 2.4 4.5 13.28

83 174 18 3.2 6.1 0.8 41 10 12 58.12

84



### At the level of individual ecoregions, the trends in parameter estimates as a function of SO group often reflect the assessment presented in the previous section. In the majority of ecoregions, most of the parameter estimates increase with SO group, as illustrated in Figure 2 5 for Ecoregion 46, the Northern
Glaciated Plains. However, other trends were observed as well. In Ecoregion 83 (Eastern Great Lakes Lowland), conductivity and cation concentrations were approximately equal in the low and high SO groups and lower in the medium SO group, as shown in Figure 2 6 . Figure 2 7 illustrates the trends in Ecoregion 54, the Central Corn Belt Plains. In this ecoregion (and several others), most of the parameter estimates decreased with SO group. The explanation for different trends within ecoregions, which may reflect different causes, is beyond the scope of this effort.

### 48 Figure 25 . BLM p arameter e stimates (10 th 

### percentile values) for e ach SO g roup in Ecoregion 46 (Northern Glaciated Plains)

Key: Stream order: 1

- 3 are headwater streams, 4

- 6 are mid

- reaches, and 7

- 9 are rivers. 1 10 100 1000

Cond. Ca Mg Na K Alk. Cl SO4Tenth Percentile SO group 1

- 3

SO group 4

- 6

SO group 7

- 9

### 49 Figure 2 6 . BLM p arameter e stimates for each SO g roup in Ecoregion 8 3 (Eastern Great Lakes Lowland) 0.1

1 10 100 1000

Cond. Ca Mg Na K Alk. Cl SO4Tenth Percentile SO group 1

- 3

SO group 4

- 6

SO group 7

- 9

### 50 Figure 2 7 . BLM p arameter e stimates for each SO g roup in Ecoregion 54 (Central Corn Belt Plains)

3.3.3 Comparison of Parameter Estimates to Results of Probability

- Based Surface Water Samp ling

There have been relatively few efforts to estimate GI concentrations in surface water at the national scale. Carleton (2006) developed a prototype geostatistical approach to estimate BLM parameters averaged over 8

- digit HUC polygons. Carleton examined data from the NWIS and noted several limitations of this dataset in terms of uneven spatial sampling intensity. Carleton’s prototype did not
incorporate SO in the analysis, nor did it generate BLM parameter estimates.

Griffith (2014) compiled data from p robability

- based surface water quality sampling surveys conducted by EPA between 1985 and 2009, mostly from wadeable streams ( SO group 1 through 4). These surveys included the National Acid Precipitation Assessment Program surveys, EMAP and regional EMAP

s urveys, WSA, and NRSA. The probability

- based sample designs ensured that the results of these surveys represented the character of streams across the continental U.S. The water quality parameters included the same GIs as discussed above, and the results we re presented on the same Level III
ecoregion

- specific basis.

We compared current results to those of Griffith (2014) because the lack of a probability

- based sample design is a potential source of bias in the NWIS dataset. Parameter estimates based on the N WIS data

for SO group 1 through 3 were compared to the corresponding estimates calculated by Griffith (2014).

While Griffith did not tabulate 10 th 

### percentiles, he did tabulate first quartile (i.e., 25 th 

### percentile) 1 10 100 1000

Cond. Ca Mg Na K Alk. Cl SO4Tenth Percentile SO group 1

- 3

SO group 4

- 6

SO group 7

- 9

### 51 statistics for each ecoregion in supplemental material published with his article. Accordingly, we

calculated 25 th 

### percentiles of the ecoregional NWIS data in SO class 1 through 3 (in addition to the 10 th


### percentiles) to facilitate this comparison. The 25 th 

### percentiles from the two datase ts are compared for conductivity in Figure 28 and calcium in Figure 29 . The scatter plots reveal significant log

- linear relationships between 25 th 

### percentiles for the two datasets; the coefficient of determination (R 2 

### ) was 0.668 for conductivity and 0.551 for calcium. For conductivity, the 25 th 

### percentiles differed by more than a factor of 2 in 17% of the Level III ecoregions; for calcium, 26% of the ecoregional results differed by more than a factor of 2.

Figure 28

. Scatter plot of e coregional 25 th 

### p er centile c onductivity for NWIS Data (SO Class 1

- 3) v ersus

e coregional 25 th 

### p ercentile c onductivity for Griffith d ata ( m ostly SO 1

- 4)

Solid diagonal line represents 1:1 agreement. 1 10 100 1000

1 10 100 1000Ecoregional conductivity (Griffith data)

Ecoregional conductivity (NWIS data)

### 52 Figure 29

. Scatter plot of e coregional 25 th 

### p ercentile c alcium c oncentration for NWIS d ata (SO cl ass 1

- 3) v ersus e coregional 25 th 

### p ercentile c alcium c oncentration for Griffith d ata ( m ostly SO 1

- 4)

Solid diagonal line represents 1:1 agreement.

These results suggest reasonable overall consistency between datasets, as w ell as significant disparity in specific ecoregions. For example, agreement was especially poor in Ecoregions 19 (Wasatch and Uinta Mountains), 37 (Arkansas Valley), 38 (Boston Mountains), 39 (Ozark Highlands), 75 (Southern Coastal Plain)

, and 78 (Klamath Mountains). NWIS data were examined at the station

- specific level to understand why these ecoregional 25 th 

### percentiles of conductivity and calcium in the low SO group were so inconsistent with corresponding percentiles presented by Griffith. Table 1 1 prese nts salient characteristics of the conductivity data for Ecoregion 19, including the number of stations, samples per
station, 25 th 

### percentile conductivity, the lowest station

- specific 25 th 

### percentile conductivity in the NWIS data, and other remarks. In tha t ecoregion, conductivity data were reported for 62 stations in the NWIS

database ; the number of observations per station ranged from 1 to 189, with a median of 22 observations per station. In comparison, the EPA data analyzed by Griffith reported conducti vity data for 32 stations, with a single observation per station. The 25 th 

### percentile conductivity based on NWIS

data was 240 v ersus 22.9 microsiemens per centimeter ( μS/cm ) based on Griffith’s analysis of EPA

data. When recalculated for individual station s in Ecoregion 19 low SO group

, the 25 th 

### percentile conductivities varied widely, from 18.25 to 1590 μS/cm. Griffith reported a median conductivity of 213 0.1

1 10 100

0.1 1 10 100Ecoregional calcium concentration, mg/L (Griffith data)

Ecoregional calcium concentration, mg/L (NWIS data)

### 53 μS/cm ( nine times larger than the 25th percentile), indicating considerable variability in that data as

well.

Table 11. Characteristics of the conductivity data for Ecoregion 19 in the low SO group

.
NWIS data Griffith data

Number of stations 62 32

Samples per station 1 – 189 1

Median samples per station 22 1

25 th percentile conductivity (μS/cm) 240 22.9
Range of station

- specific 25 th
percentile conductivity (μS/cm) 18.25

- 1590

Other remarks 25 th percentiles of conductivity reported by Griffith were marginally higher than the

minimum station

- specific 25 th percentiles

calculated from the NWIS data Median conductivity = 213 μS/cm (19x the 25 th percentile value) indicates high variability



### The same information is tabulated for Ecoregions 37, 38, 75

, and 78 in Tables 12 through 15 . Although the details regarding the data vary in each of these ecoregions, a number of commonalities emerge

from these examinations:

 

### In four ecoregions, the number of NWIS stations in SO group 1 through 3 was small relative to other ecoregions (the median number of ecoregional stations in SO group 1 through 3 was 68).

 

### In three ecoregions, 10 th 

### and 25 th 

### percentiles of conductivity and/or calcium decreased with SO

group, contra ry to the general trend.

 

### In three ecoregions, 25 th 

### percentiles of conductivity reported by Griffith were marginally higher

than the minimum station

- specific 25 th 

### percentiles calculated from the NWIS data for the corresponding ecoregion and low SO group.

Table 12. Characteristics of the conductivity data for Ecoregion 37 in the low SO group

. NWIS data Griffith data

Number of stations 34 45
Samples per station 1 – 129 1

Median samples per station 2 1

25 th percentile conductivity (μS/cm) 350 32

Range of station

- specific 25 th
percentile conductivity (μS/cm) 29

- 846.5

Other remarks 10 th and 25 th conductivity percentiles decrease with SO

group; Small number of NWIS stations;

25 th percentiles of conductivity reported by Griffith were marginally higher than the minimum station

- specific 25 th

percentiles calculated from the NWIS data

### 54 Table 13. Characteristics of the conductivity data for Ecoregion 38 in the low SO group

NWIS data Griffith data

Number of stations 31 38

Samples per station 1 – 8 1

Median samples per station 3 1

25 th percentile conductivity (μS/cm) 137 22.9

Range of station

- specific 25 th
percentile conductivity (μS/cm) 22

- 384

Other remarks 10 th and 25 th conductivity percentiles decrease with SO

group; Small number of NWIS stations; 25 th percentiles of conductivity reported by Griffith were marginally higher than

the minimum station

- specific 25 th percentiles calculated

from the NWIS data



### Table 14. Characteristics of the calcium data for Ecoregion 75 in the low SO group

. NWIS data Griffith data

Number of stations 360 42

Samples per station 1 – 177 1
Median samples per station 17 1

25 th percentile calcium (mg/L) 13 1.41
Range of station

- specific 25 th
percentile calcium (mg/L) 0.02

- 91

Other remarks 10 th and 25 th calcium percentiles decrease with SO group



### Table 15. Characteristics of the conductivity data for Ecoregion 78 in the low SO group

.
NWIS data Griffith data

Number of stations 15 45

Samples per station 1 – 18 1
Median samples per station 8 1

25 th percentile conductivity (μS/cm) 26 98.4
Range of station

- specific 25 th
percentile conductivity (μS/cm) 19

- 326.5

Other remarks Small number of NWIS stations; 6 of 15 stations were Ashland Creek (OR) or tributaries



### It is possible that the disparities noted above arise in part from non

- representative sampling in the NWIS data. For example, representativeness of NWIS data is questionable in Ecoregion 78 because 40%

of the stations were sampl ed in a single water body. There was also a difference in the way data for repeated sampling at individual stations were processed, due to differences between the NWIS data and data compiled by Griffith. In the NWIS data, water quality was sampled repeatedly at a significant

### 55 number of stations, and we included daily averages of all of these measurements in the calculation of

10 th 

### percentile estimates. In the probabilistic EPA surveys analyzed by Griffith, individual stations were

usua lly sampled once. In the case of repeated sampling at a station, Griffith used data from only the first sample reported for the station in the statistics he calculated. This difference implies that our estimates of BLM parameters incorporate temporal as we ll as spatial variability in water quality, while Griffith’s results do not. Thus, it would be unrealistic to expect complete agreement between these results. It should also be reiterated that sampling bias in SO is probably not a factor in these disparities

because the estimates from the NWIS data were based on measurements from stream orders 1 through 3, which is generally consistent with the data compiled by Griffith (2014).

It is particularly of concern when p ercentiles based on NWIS data are higher than those calculated based on Griffith data because this suggests the parameter estimates may result in non

- conservative BLM predictions of copper , or others metals, criteria . To evaluate this concern, we ran the B LM (version 2.1.2) using the 25 th 

### percentile GI estimates of Griffith and those from the current analysis for
NWIS SO group 1 through 3 for each ecoregion in which parameters were available. If the 25 th



### percentile of a GI was not available, the value was p rojected from the 25 th 

### percentile of conductivity using regressions based on NWIS data. If the 25 th 

### percentile of conductivity was not available (this occurred in 24 ecoregions), no BLM prediction was made. The inputs for pH and DOC were ecoregional

values

. There were 60 ecoregions where BLM predictions of copper crite ri a using the 25 th 

### percentile GI estimates from NWIS and Griffith could be compared. The criteria estimated in these ecoregions using GI input parameters from the two sources agree very well, as shown in Figure 3 0 . The R 2 

### was 0.9897, and relative percent differences (RPDs) ranged from

- 21 to 39% with an average RPD of 3% and a

median of 0.1%.

### 56 Figure 3 0 . BLM predictions of copper criteria made with GI water quality parameters based on ecoregional 25 th 

### percentile from NWIS data (SO cl ass 1

- 3) versus ecoregional 25 th 

### percentile calcium Concentration for Griffith data (mostly SO 1

- 4)

.
R 2 

### = 0.99. Solid diagonal line represents 1:1 agreement

While it is possible that the GI estimates presented here as recommended input values for use in the

BLM for copper could be improved, it is not clear that such improvement would result in substantially better predictions of protective copper criteria based on the BLM

. This is because BLM predictions for

copper are much more sensitive to pH and DOC.

3.4 Summary

In this section we have incorporated SO variation in the GI water quality parameter estimates to refine the default parameters estimates for use in the BLM when data are not available

.
EPA found that v alues of the GI parameter estimates generally increased with SO . This trend was most apparent and consistent for higher order streams (SO≥7). Tenth percentile estimates of conductivity

increase with stream order group in 58% of ecoregions (comparing low to medium SO groups) and 84% of ecoregions (comparing medium to high SO groups). The same trend was evident for the GI s that are input parameters to the BLM. 0.1

1 10 100 1000

0.1 1 10 100 1000Copper IWQC (μg/L) calculated using Griffith 25thpercentile estimates

Copper IWQC (μ g/L) calculated using NWIS 25 th percentile estimates

### 57 We compared the parameter estimates for SO group 1 through 3 to those calculated by Griffith (2014). This comparison revealed significant log

- linear relationships between 25 th 

### percentiles for the two datasets; the coefficient of determination (R 2 

### ) was 0.668 for conductivity and 0.551 for calcium. For conductivity, the 25 th 

### percentiles differed by more than a factor of 2 in 17% of the Level III e coregions; for calcium, 26% of ecoregional results differed by more than a factor of 2. There is also considerable variability in the relationship between ecoregional statistics based on NWIS ver sus Griffith’s data. Possible causes of these disparities may be due to sampling bias in the NWIS database, limited numbers of samples in some ecoregions, and differences in the degree and treatment of repeated sampling at individual locations.

NWIS percen tiles higher than Griffith (2014) suggest that the recommended parameter estimates may

result in non

- conservative BLM predictions of copper criteria . The BLM was run to predict copper

criteria for 60 ecoregions using 25 th 

### percentile GI estimates as paramet er inputs from NWIS and

Griffith’s data. The criteria predicted using the two sets of GI parameter inputs agreed favorably. The

R 2 

### was 0.990, and RPDs ranged from

- 21 to 39% with an average RPD of 3% and a median of 0.1%.

These results demonstrated that the recommended default GI parameter estimates are reasonably

conservative.

EPA i ncorporat ed SO variation in the parameter estimates to refine recommended input values for use

in the BLM. EPA found the 10 th 

### percentile ecoregion, SO group specific estimates to be reasonably
protective inputs and recommends their use where site

- specific parameters are not readily available

.

### 58 

## 4 DOC ESTI MA TION USING THE NATIONAL ORGANI C CARBON DATABASE

The following section summarizes our investigation into whether ecoregion and water body

- type

specific DOC concentration percentiles tabulated by EPA for the Development of National

Bioaccumulation Factors Te chnical Support Document (hereafter referred to as the National Organic Carbon Database (NOCD)) (USEPA, 2003)) offer reasonable estimates of lower

- percentile DOC concentrations. A summary of the NOCD’s data sources, analysis, and uncertainty associated wit h ecoregional statistics derived from the NOCD is provided below. This section also discusses how we recalculated ecoregional DOC percentiles for rivers and streams, and the n tested for bias in the NOCD. Finally, we compared results based on the NOCD and data from the Wadeable Stream Assessment (WSA) (USEPA, 2006b)) and the National River and Stream Assessment (NRSA) (USEPA, 2013)).

4.1 Description of the NOCD

The NOCD is a compilation of pre

- 2003 organic ca rbon data derived from two sources: EPA’s Storage and Retrieval Data Warehouse (STORET), recently renamed the STORET Legacy Data Center (LDC), and USGS’s National Water Data Storage and Retrieval System (WATSTORE), the predecessor of NWIS. A complete back ground on the NO CD is available in USEPA 2003.

EPA’s LDC database contains water quality monitoring data collected by academia, volunteer group s , and tribes, as well as federal, state, and local agencies. Geograp hically, the LDC data represent all 50

state s and all U.S. territories and jurisdictions, along with portions of Canada and Mexico. The database queried for this investigation is often referred to as the “historical” or “old” STORET database because it contains water quality data dating back to the early part of the 20 th 

### century through the end of 1998. Data from 1999 to the present are stored in the “modernized” STORET Data Warehouse. 4 

### The LDC contains raw biological, chemical, and physical data for both surface water and groundwater. Each sampling result is accompanied by: information on sample collection location (latitude, longitude, state, county, HUC, and a brief site description), date the sample was gathered, the medium sampled, and the name of the organization that sponsored the monitoring.

We retrieved data from LDC and WATSTORE in January 2000

. A pproximately 800,000 records containing data on particulate organic carbon ( POC )

, dissolved organic carbon ( DOC ) , or total organic carbon ( TOC ) were obtained for the period beginning in 1970 throug h the latest year that data were available (1999 for WATSTORE and 1998 for LDC). This initial retrieval was limited to samples taken from ambient surface waters ( i.e., samples from wells, springs, effluents, and other non

- ambient sources were excluded). Ad ditionally, this retrieval included multiple types of organic carbon measurements to ensure that the data would be sufficiently comprehensive.

WATSTORE was established in 1972 to provide an effective and efficient means for processing and
maintaining wate r data collected through USGS activities and to facilitate release of that data to the public. The WATSTORE database resides on the central computer facilities of the USGS and contains results of approximately two million analyses of both surface and groun dwater that provide data on

4 Refer to http://www.epa.gov/storet/dbtop.html for more information on the STORET Data Warehouse and the LDC.

### 59 chemical, physical, biological, and radiological characteristics. EPA queried WATSTORE, the Water Quality File, to retrieve DOC data.

After retrieval, the data from LDC and WATSTORE were combined into a single database. The dat a were then processed and screened to ensure that only the most appropriate data would be retained. This screening process is outlined below:

 

### Values that were coded in such a way as to suggest uncertainty in the measurement were

deleted from the database

.

 

### The database was restricted to the following water body types: estuaries, lakes, reservoirs, and streams (including rivers).

 

### “Pseudo

- ecoregions” were added for the five Great Lakes.

 

### The time period for the data was restricted to 1980 through 1999.

 

### Some values for DOC were reported to be below analytical detection levels. In this situation, the value was assumed to be half of the reported detection level. Values with “high” detection levels (i.e., >1.0 mg/L for DOC) were deleted from the database bec ause of the greater uncertainty involved in estimating definitive values of DOC in these situations.

 

### A small fraction of the DOC and POC concentrations obtained from the LDC database exceeded concentrations considered to represent upper limits of DOC conce ntrations reported in U.S. water bodies (i.e., 0.2% exceeded 60 mg/L for DOC). These extreme values were based on a review of organic carbon data by Thurman (1985), who reported extreme values of DOC concentrations as high as 50 mg/L in dystrophic lakes an d 60 mg/L in tributaries draining wetland systems. Therefore, values for DOC above 60 mg/L were removed from the database.

T h e NOCD that resulted from processing and screening data retrieved from the LDC and WATSTORE

databases has some limitations , which are described below:

 

### The WATSTORE and LDC databases do not reflect a random sampling of U.S. surface waters

. They contain datasets with a diversity of sampling design and thus data may be biased towards locations and water bodies with known water quality i mpairments.

 

### These data also reflect spatial bias due to unequal sampling efforts in different areas. For example, about half of the DOC and POC values in the databases were from samples collected in Maryland, New York, Ohio, Florida, and Delaware. Therefo re some states are disproportionally represented, even when one considers the relative surface water area likely

to be contained within each state.

 

### WATSTORE and LDC generally contain more data from sampling sites in larger river and stream systems, and are as subjected to proportionately greater human influence compared with random statistical sampling.

4.2 Recalculation of Ecoregional DOC Percentiles for Rivers and Streams

Lower percentile (1 st 

### , 5 th 

### , 10 th



### , and 25 th 

### percentiles ) DOC concentrations were calculate d from all data for rivers and streams in each Level III ecoregion (Table 16 ). Nonparametric (i.e., rank) percentiles were calculated following the recommendations of Dierickx (2008) and Hyndman and Fan (1996). We also

calculated confidence limits for the percentiles using the method presented in Berthouex and Brown

### 60 (1994). Upper and lower 95% confidence limits (UCLs and LCLs) were calculated if 20 or more DOC concentrations were available for an ecoregion (Berthouex and Brown, 1994).

Table 16 . Lower p erce ntile v alues of DOC in U.S. s treams and r ivers by e coregion, i ncluding 95%

c onfidence l imits for p ercentile c oncentrations if n>20

.

Level III Ecoregion Ecoregion Name n (count) 1% 1% (LCL) 1% (UCL) 5% 5% (LCL) 5% (UCL) 10% 10% (LCL) 10% (UCL) 25% 25% (LCL) 25% (UCL)

1 Coast Range 91 ≤0.2 ≤0.2 0.6 0.9 ≤0.2 1.1 1.12 0.78 1.4 1.8 1.4 2

2 Puget Lowland 835 0.84 0.47 1 1.9 1.7 2 2.5 2.3 2.7 3.9 3.5 4.1

3 Willamette Valley 66 ≤0.5 ≤0.5 0.73 0.8 ≤0.5 1.08 1.07 0.68 1.2 1.48 1.2 2

4 Cascades 101 0 ≤0 0.2 0.3 ≤0 0.44 0.5 0.3 0.5 0.7 0.5 0.9

5 Sierra Nevada 32 ≤1.9 ≤1.9 1.9 1.9 ≤1.9 2.3 2.09 ≤1.9 2.3 2.55 2.13 3.11

6 Southern and Central California Chaparral and Oak Woodlands 480 1.1 ≤0 1.5 1.8 1.7 1.9 2.1 1.9 2.2 3 2.67 3.2

7 Central California Valley 180 1.21 ≤0.8 1.8 2.11 1.66 2.48 2.71 2.3 3.5 5.3 4.4 6.26

8 Southern California Mountains 6 ≤4.4 ≤4.4 ≤4.4 5.45

9 Eastern Cascades Slopes and

Foothills 13 ≤1.3 ≤1.3 1.42 1.75

10 Columbia Plateau 73 ≤0.7 ≤0.7 1.3 1.67 ≤0.7 2.03 2.04 1.29 2.34 2.9 2.3 3.2

11 Blue Mountains 26 ≤1 ≤1 1.05 1.07 ≤1 1.45 1.34 ≤1 1.81 1.9 1.28 2.61

12 Snake River Plain 50 ≤2 ≤2 2 2 ≤2 2.2 2.2 ≤2 2.43 3.08 2.27 3.68

13 Central Basin and Range 1553 0.8 0.69 0.9 1.2 1.2 1.3 1.5 1.4 1.6 2 2 2.1

14 Mojave Basin and Range 35 ≤2.5 ≤2.5 2.55 2.58 ≤2.5 3 2.84 ≤2.5 3.32 3.6 2.99 3.8

15 Northern Rockies 778 0.7 0.6 0.72 0.9 0.9 1 1 1 1.1 1.3 1.3 1.4

16 Idaho Batholith 29 ≤1.2 ≤1.2 1.2 1.2 ≤1.2 1.4 1.4 ≤1.2 1.8 1.9 1.39 2.31

17 Middle Rockies 87 ≤0 ≤0 0 0 ≤0 0.14 0.18 0 0.46 1.1 0.42 1.4

18 Wyoming Basin 150 2.05 ≤1.9 3.03 3.56 2.26 4.18 4.31 3.59 4.6 5.48 5.1 5.8

19 Wasatch and Uinta Mountains 46 ≤1.5 ≤1.5 1.5 1.57 ≤1.5 1.82 1.8 ≤1.5 2.51 2.88 1.9 3.6

20 Colorado Plateaus 798 1.5 0.36 1.6 2.4 2 2.6 3 2.7 3.25 4.3 4.1 4.6

21 Southern Rockies 1129 0.2 0.1 0.3 0.5 0.4 0.5 0.6 0.6 0.6 0.8 0.8 0.9

22 Arizona/New Mexico Plateau 281 1.65 ≤1.2 2.01 2.2 2.09 2.4 2.62 2.3 2.91 3.7 3.3 3.9

23 Arizona/New Mexico Mountains 37 ≤1.3 ≤1.3 1.41 1.48 ≤1.3 2.35 2.16 ≤1.3 2.64 2.8 2.33 3.57

24 Chihuahuan Deserts 116 0.5 ≤0.5 0.64 1 0.5 2.15 2.34 1 3 3.68 3 4.44

25 High Plains 439 0.3 ≤0.1 0.4 0.8 0.5 3 4.4 3.2 4.9 7 6.42 7.78

26 Southwestern Tablelands 167 1.94 ≤1.8 2.04 2.5 2 2.98 3.28 2.52 3.7 4.4 4 4.9

27 Central Great Plains 228 0.59 ≤0.4 2.01 3 1.8 3.2 3.8 3 4 5 4.8 5.71

28 Flint Hills 10 ≤4.9 ≤4.9 4.9 5.15

29 Central Oklahoma/Texas Plains 289 1.9 ≤1 2.09 3 2.5 3.38 3.8 3 4 4.8 4.21 5

30 Edwards Plateau 200 0.5 ≤0.5 0.5 0.5 0.5 0.82 1 0.5 1 1 1 1.6

31 Southern Texas Plains 58 ≤0.5 ≤0.5 0.5 0.5 ≤0.5 1 1 0.5 1.34 2 1 2

32 Texas Blackland Prairies 829 0.5 0.5 0.5 1 1 1 2 2 2 3 3 3.99

### 61 Level III Ecoregion Ecoregion Name n (count) 1% 1% (LCL) 1% (UCL) 5% 5% (LCL) 5% (UCL) 10% 10% (LCL) 10% (UCL) 25% 25% (LCL) 25% (UCL)

33 East Central Texas Plains 268 1 ≤1 1.95 2.6 2 3 3 3 3 3.83 3.1 4

34 Western Gulf Coastal Plain 399 2.8 ≤1.7 3 3.7 3 4 4 4 4 5 5 5.9

35 South Central Plains 523 1.45 ≤0.4 2.07 3.92 3.2 4 4.6 4.1 4.98 5.7 5.4 6

36 Ouachita Mountains 198 0 ≤0 0.47 0.8 0.4 1.1 1.1 0.86 1.6 2.38 2.08 2.8

37 Arkansas Valley 184 0.49 ≤0.4 0.65 0.8 0.55 1.4 1.9 0.85 2.39 3.73 3 4.4

38 Boston Mountains 21 ≤0.4 ≤0.4 0.4 0.4 ≤0.4 0.5 0.42 ≤0.4 0.5 0.5 0.4 0.74

39 Ozark Highlands 233 1.67 ≤1.5 2 2 2 2.12 2.3 2 2.5 3 2.7 3.15

40 Central Irregular Plains 434 2.71 ≤1.4 3 3.5 3.1 3.6 4 3.6 4 4.9 4.6 5

41 Canadian Rockies 36 ≤0.3 ≤0.3 0.35 0.39 ≤0.3 0.6 0.57 ≤0.3 0.9 0.93 0.6 1.03

42 Northwestern Glaciated Plains 36 ≤3 ≤3 3.05 3.09 ≤3 6.63 5.05 ≤3 11.23 13.25 6.12 16.34

43 Northwestern Great Plains 679 2.22 0.72 3.19 4.4 3.8 5.2 6.2 5.6 6.73 9.9 9.19 10

44 Nebraska Sand Hills 4 ≤1.4 ≤1.4 ≤1.4 1.4

45 Piedmont 309 0.4 ≤0 0.5 0.7 0.6 0.9 1 0.8 1.1 1.7 1.4 2.1

46 Northern Glaciated Plains 142 4.03 ≤3.3 8.01 9.13 5.11 9.82 9.9 9.16 11 12 11 12.86

47 Western Corn Belt Plains 193 0.44 ≤0.2 2.43 2.77 2.28 2.96 3.14 2.8 3.5 3.9 3.6 4.3

48 Lake Agassiz Plain 261 3.1 ≤0.8 4.52 6.41 4.72 7.1 7.6 6.87 7.97 9 8.7 9.22

49 Northern Minnesota Wetlands 44 ≤7.8 ≤7.8 8.25 9.05 ≤7.8 11 11 ≤7.8 12 13 11 14.88

50 Northern Lakes and Forests 403 2 ≤0.1 2.2 2.72 2.36 3.08 3.7 3.06 4.5 5.8 5.5 6.2

51 North Central Hardwood Forests 153 0.54 ≤0 2.19 2.67 1.42 2.9 3.1 2.7 3.77 4.95 3.9 5.6

52 Driftless Area 49 ≤1.7 ≤1.7 2.31 2.4 ≤1.7 3.25 3.1 ≤1.7 4.31 4.5 3.4 5.8

53 Southeastern Wisconsin Till

Plains 439 2 ≤0.25 2.1 3.4 2.7 4.3 5.3 4.3 5.6 6.8 6.5 7

54 Central Corn Belt Plains 202 1 ≤0.7 1.68 2.12 1.61 2.5 2.73 2.2 3 3.9 3.57 4.8

55 Eastern Corn Belt Plains 1398 0 0 0 0 0 0.2 3.8 3 4 5.2 5 5.3

56 S. Michigan/N. Indiana Drift

Plains 287 1.4 ≤1.4 1.92 2.7 2.05 3.43 3.8 3.17 4.2 5.4 4.9 5.7

57 Huron/Erie Lake Plains 3825 0 0 0 3.9 3.7 4.07 4.7 4.52 4.8 5.8 5.7 5.9

58 Northeastern Highlands 14044 0.25 0.25 0.25 0.25 0.25 0.5 0.64 0.62 0.65 0.95 0.94 0.97

59 Northeastern Coastal Zone 102 0.05 ≤0 1.6 2.02 ≤0 2.6 2.63 1.74 2.92 3.28 3 3.63

60 Northern Appalachian Plateau and Uplands 354 0.6 ≤0.5 0.8 1 0.87 1.1 1.3 1.1 1.4 2 1.9 2.3

61 Erie Drift Plains 919 0 0 0 4.8 4.6 4.9 5.1 5 5.1 5.5 5.4 5.6

62 North Central Appalachians 106 0.41 ≤0.4 0.5 0.5 ≤0.4 0.7 0.7 0.5 0.78 0.98 0.8 1.2

63 Middle Atlantic Coastal Plain 16730 1.1 1.01 1.2 1.8 1.8 1.8 2.2 2.2 2.2 2.7 2.7 2.7

64 Northern Piedmont 1525 0.43 0.3 0.5 1 0.99 1 1.3 1.2 1.4 2.2 2.1 2.4

65 Southeastern Plains 3813 1 0.51 1.1 2 1.9 2.03 2.4 2.38 2.5 3.3 3.2 3.3

66 Blue Ridge 699 0 0 0 0.4 0.3 0.4 0.5 0.4 0.5 0.6 0.6 0.7

67 Ridge and Valley 733 0.1 0.1 0.1 0.4 0.3 0.5 0.6 0.5 0.7 1.05 1 1.1

68 Southwestern Appalachians 47 ≤0.5 ≤0.5 0.5 0.58 ≤0.5 0.9 0.9 ≤0.5 1 1 0.9 1.38

69 Central Appalachians 864 0.3 0.15 0.4 0.6 0.5 0.6 0.7 0.7 0.8 1.1 1.1 1.2

70 Western Allegheny Plateau 1795 0 0 0 0.78 0.2 1 1.6 1.4 1.7 2.7 2.5 2.9

71 Interior Plateau 559 0.1 ≤0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.3

### 62 Level III Ecoregion Ecoregion Name n (count) 1% 1% (LCL) 1% (UCL) 5% 5% (LCL) 5% (UCL) 10% 10% (LCL) 10% (UCL) 25% 25% (LCL) 25% (UCL)

72 Interior River Valleys and Hills 328 1.32 ≤0.15 1.8 2.1 1.9 2.4 2.69 2.4 3.05 4.2 3.6 4.6

73 Mississippi Alluvial Plain 503 1.7 ≤1 2.14 2.82 2.4 3.1 3.4 3.2 3.6 4.3 4.1 4.5

74 Mississippi Valley Loess Plains 21 ≤1.4 ≤1.4 1.41 1.41 ≤1.4 2.61 1.72 ≤1.4 2.7 2.7 1.46 4.72

75 Southern Coastal Plain 4223 0.9 0.9 1.09 5.3 4.7 6 8 7.6 8.3 12.1 11.9 12.3

76 Southern Florida Coastal Plain

- - - - -

77 North Cascades 50 ≤0.2 ≤0.2 0.2 0.26 ≤0.2 0.4 0.4 ≤0.2 0.4 0.48 0.4 0.5

78 Klamath Mountains 8 ≤1.7 ≤1.7 ≤1.7 2.3

79 Madrean Archipelago 9 ≤2.6 ≤2.6 2.6 4.05

80 Northern Basin and Range 16 ≤1.6 ≤1.6 1.81 2.5

81 Sonoran Basin and Range 133 1.33 ≤1.3 1.7 1.8 1.38 2.1 2.2 1.8 2.6 3.85 2.94 4.4

82 Laurentian Plains and Hills 21 ≤4.6 ≤4.6 4.7 4.69 ≤4.6 5.68 5.52 ≤4.6 7.98 8.45 5.15 9.3

83 Eastern Great Lakes and Hudson

Lowlands 1430 0 0 0 0 0 0.2 1.9 1 2 5.1 5 5.5

84 Atlantic Coastal Pine Barrens 243 1 ≤0.9 1.1 1.22 1.1 1.5 1.6 1.32 2 2.6 2.4 3

Lake Erie 9 ≤1.1 ≤1.1 1.18 1.4

Lake Michigan 5 ≤2.6 ≤2.6 ≤2.6 2.6

Lake Ontario 14 ≤0.4 ≤0.4 0.4 2.2

Lake Superior 7 ≤1.2 ≤1.2 ≤1.2 1.4



### As was the case for the BLM GI input parameters, we consider low percentiles of the ecoregional DOC concentration distributions to be reasonably protective inputs to the BLM for sites where DOC

measurements are not available.

4.3 Testing for Bias in the NOCD

EPA conducted an evaluation of bias in the NOCD (USEPA, 2003) using data from EPA’s Environme ntal Monitoring and Assessment Program’s (EMAP) 1997 to 1998

, which sampled mid

- Atlantic streams and rivers (USEPA, 2006b). This effort is described below in Section 4 .3.1.

We also evaluated the bias in the NOCD using independent data from EPA’s Wadeable S treams Assessment (WSA), which included DOC measurements from a statistically based random sample of approximately 2,000 wadeable, perennial 1 st 

### through 5 th 

### order streams (USEPA, 2006c). In Section

4 .3.2, we compare the WSA data to the ecoregion

- specific D OC concentration percentiles calculated

from the NOCD.

4.3.1 Previous Efforts Using EMAP Data

Ideally, the data used to generate the distribution of national organic carbon concentration values should originate from a random sampling of U.S. surface waters, and should be appropriately stratified and weighted by spatial and temporal factors that would be expected to influence organic carbon concentrations in aquatic ecosystems (e.g., water body type, hydrologic and watershed characteristics, ecoregion, season). H owever, these data are not available on a national scale. The strength of this analysis is that the data from USGS’s WATSTORE and EPA’s LDC databases include a large number of

### 63 records (e.g., >110,000 DOC values), a representation of DOC values for all 50 s tates, and reasonably long period over which data were collected (1980 through 1999 for these analyses).

Data generated by EMAP are based on a stratified, random sampling strategy that was specifically designed to minimize the influence of sampling bias on the data and to enable statistically based extrapolations across geographic regions (Herlihy et al., 2000). At the time the NOCD was developed, the EMAP databases containing DOC measurements were limited to smaller geographic scales and specific water bod y types.

Previously, to address the question of sampling bias and its impact on the representativeness of the NOCD values, EPA made quantitative comparisons that involved contrasting geographically distinct subsets of the WATSTORE/LDC databases with geogr aphically similar subsets of data produced by

EMAP. DOC data from EMAP’s 1997 to 1998 sampling of mid

- Atlantic streams and rivers were compared with similar geographic subsets from the WATSTORE/LDC databases. The mid

- Atlantic EMAP

database was chosen becau se sufficient DOC data were available for rivers and streams to make meaningful comparisons at the state and ecoregion levels. Similar comparisons are made for four mid

Atlantic ecoregions (Piedmont, Ridge and Valley, Central Appalachians, Western Alleghen y Plateau)

which is well represented in the WATSTORE/LDC databases (USEPA

, 2003).

Based on both sets of comparisons, it is apparent that the agreement between the WATSTORE/LDC and EMAP data was best at the middle to lower tails of the distributions, and p oorest at the higher end of the distributions. At the lower tails of the distributions (e.g., 10 th 

### , 25 th 

### percentiles) the WATSTORE/LDC DOC data are generally within 30% of the EMAP data ( E coregion 70 being the only exception). The median DOC values of the WATSTORE/LDC data show a slightly higher bias compared with median values from the EMAP data, but are usually within a factor of 1.5 ( E coregions 47 and 70 are about a factor of 2 greater). This result is expected, given the greater focus of the WATSTORE/LD C sampling sites on larger river and stream systems, and on areas subjected to proportionately greater human influence compared with the EMAP sampling sites. Since EPA is interested in supporting the generation of BLM values that are protective of aquatic life, the lack of bias noted for the lower tails of the DOC concentration distributions is noteworthy.

4.3.2 Testing for Bias Using Data from the WSA

A more comprehensive evaluation of the effects of sampling bias on the NOCD can now be made using

the results of national statistically

- designed water quality sampling surveys. We assembled a database of organic carbon data from 1,313 randomly selected sites throughout the continental U.S

. collected for the WSA. GIS procedures were used to associate each site with t he Level III ecoregion corresponding to its location.

The 1,392 sites sampled for the WSA were identified using a probability

- based sample design, a technique in which every element in the population has a known probability of being selected for sampling (USEPA, 2006). This ensured that the results of the WSA reflect the full range of variation among wadeable streams across the U.S. The target population for the WSA was wadeable, perennial streams in the conterminous U.S. (lower 48 states). The WSA used th e National Hydrography Dataset (NHD), a comprehensive set of digital spatial data on surface waters (USGS

, 2012), to identify the location of wadeable perennial streams. Rules for site selection included weighting to provide balance

in the number of stream sites from each of the 1 st 

### through 5 th 

### SO size classes (Strahler, 1952, 1957),

### 64 and controlled spatial distribution to ensure that sample sites were distributed across the U.S.). The basic sampling design drew 50 sampling sites randomly distributed in each of the EPA Regions and WSA ecoregions. The unbiased site selection of the survey design ensures that assessment results represent the condition of the streams throughout the nation.

4.3.2.1 Selection of Statistical Test to Assess Potential Bias in DOC Data

The m ost appropriate statistical test for determining bias in the NOCD is the comparison of WSA and organic carbon database DOC data within each ecoregion as independent groups of data to determine if one group tends to contain larger values (Helsel and Hirsch, 2002; USGS, 2002). The WSA and organic carbon database DOC data are independent because there is no natural structure in the order of observations across groups. A nonparametric statistical test is most appropriate since no assumptions regarding normality of the data are required. As noted by Helsel and Hirsch (2002), nonparametric tests are, in general, never worse than their parametric counterparts in their ability to detect departures from the null hypothesis, and may be better. These considerations led us to select the rank

sum test, a nonparametric procedure for determining whether data are significantly different between two independent groups. This test is also known as the Wilcoxon Rank

- Sum Test or, alternatively, the

Mann

- Whitney U

- Test.

In its mos t general form, the rank

- sum test is a test to determine whether one group tends to produce larger observations than another group. It has as its null hypothesis:

H 0 

### : Prob [x > y] = 0.5

where x are data from one group and y are from another group (the probability of an x value being higher than any given y value is one

- half). The test is typically used to determine whether two groups come from the same population (same median and other percentiles), or alternatively whether they differ only in location (central value or median). If both groups of data are from the same population, about half of the time an observation from either group could be expected to be higher than that from the other, so the above null hypothesis applies. If the groups belong to d ifferent populations the null hypothesis does not apply.

In practice, the rank

- sum test takes several forms, depending upon the size of the smaller sample ( n observations ) and the larger sample ( m observations ). Walpole and Myers (1978; Section 13.2) prese nt
the details of four alternative forms of the rank

- sum test, depending on the sizes of n and m . The exact

form of the rank

- sum test is the only form appropriate for comparing groups of sample sizes of 10 or smaller per group. When both groups have sample s sizes greater than 10 ( n , m > 10), the large

- sample approximation may be used.

4.3.2.2 Rank

- Sum Test Comparing WSA DOC Data to NOCD

Table 17 presents the results of the rank

- sum test comparing Level III ecoregional DOC data from the WSA (USEPA, 2006b) and the N OCD. The left

- hand columns present statistics (sample size, median, and

Mann

- Whitney U
x 

### , and U y 

### ) for the ecoregion

- specific DOC data from the two datasets. The next six columns to the right present the test statistics for the appropriate form of the rank

- s um test. The right

hand column provides a summary interpretation of the test for each ecoregion indicating whether the null hypothesis (H 0 

### : DOC concentrations from both datasets are not different) should be rejected at the 5% level of significance, in favo r of the alternative hypothesis (H 1 

### : DOC concentrations are higher in the

### 65 national organic carbon database). In other words, rejection of the null hypothesis implies that DOC concentrations from the National Organic Carbon Database are biased high in that ecoregion relative

to the WSA data.

### 66 Table 17 . Results of r ank -s um t est c omparing Level III e coregional DOC d ata from WSA and NOCD

Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

1 Coast Range 38 1.41 2535.5 91 2.2 922.5 4.17 1.54E

- 05 reject H0

2 Puget Lowland 3 0.96 2289 835 6.6 216 - 2.48 6.63E

- 03 reject H0

3 Willamette Valley 2 2.47 84 66 2.9 48 - 0.65 2.57E

- 01 do not reject H0

4 Cascades 23 0.75 1673 100 1.4 627 3.39 3.46E

- 04 reject H0

5 Sierra Nevada 14 0.91 448 32 3.6 0 5.35 5.96E

- 08 reject H0

6 Southern and

Central

California Chaparral and

Oak Woodlands 32 2.5 12372 479 4.6 2957 5.82 0 reject H0

7 Central California Valley 2 4.2 302 180 13 58 - 1.65 4.98E

- 02 reject H0 (P~5%)

8 Southern

California

Mountains 41 1.66 245 6 8.9 1 - 3.89 5.03E

- 05 reject H0

9 Eastern Cascades Slopes

and Foothills 19 0.97 209 13 2.3 38 3.28 5.18E

- 04 reject H0

10 Columbia

Plateau 8 2.59 477 73 3.6 107 - 2.93 1.70E

- 03 reject H0

11 Blue Mountains 77 1.59 1625 26 3.1 376.5 4.74 1.07E

- 06 reject H0

12 Snake River

Plain no test

### 67 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

13 Central Basin and Range 42 1.93 47300 1553 3 17926 4.986 2.98E

- 07 reject H0

14 Mojave Basin and Range 2 2.65 67 35 5.6 3 - 2.15 1.58E

- 02 reject H0 (P~1.6%)

15 Northern

Rockies 19 1.54 9149 778 1.8 5634 1.77 3.81E

- 02 reject H0 (P~3.8%)

16 Idaho Batholith 19 1.21 495 29 2.4 56 4.63 1.85E

- 06 reject H0

17 Middle Rockies 70 1.43 3142 81 1.9 2529 1.14 0.126 do not reject H0

18 Wyoming Basin 29 2.29 4006 150 7.3 344 7.17 0 reject H0

19 Wasatch and

Uinta

Mountains 25 2.11 978 46 4.8 172 4.85 5.96E

- 07 reject H0

20 Colorado

Plateaus 24 2.22 15697 798 6.3 3455 5.34 5.96E

- 08 reject H0

21 Southern

Rockies 43 2.05 18637 1129 1.3 29911 - 2.59 4.83E

- 03 reject H0

22 Arizona/New

Mexico Plateau 7 1.83 1723 281 5.6 244 - 3.40 3.40E

- 04 reject H0

23 Arizona/New

Mexico

Mountains 31 1.94 984 37 5.3 163 5.02 2.38E

- 07 reject H0

24 Chihuahuan

Deserts 1 1.48 110 116 6 6 - 1.54 6.18E

- 02 do not reject H0

25 High Plains 6 3.5 2450 439 11 184.5 - 3.62 1.48E

- 04 reject H0

26 Southwestern

Tablelands 17 4.21 2246 167 6.3 593 3.95 3.90E

- 05 reject H0

27 Central Great

Plains 12 4.71 2189 228 7 547 3.50 2.31E

- 04 reject H0

28 Flint Hills 2 4.91 19 10 8.7 1 1 reject H0

### 68 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

29 Central Oklahoma/

Texas Plains 6 5.58 1098 289 6.7 636 - 1.12 1.32E

- 01 do not reject H0

30 Edwards

Plateau no test

31 Southern Texas

Plains no test

32 Texas Blackland

Prairies 2 7.37 880 829 6 778 - 0.15 4.40E

- 01 do not reject H0

33 East Central

Texas Plains 3 15.03 108.5 268 5 695.5 2.17 9.85E

- 01 do not reject H0

34 Western Gulf

Coastal Plain 9 7.47 1860 399 7 1732 - 0.183 0.427 do not reject H0

35 South Central

Plains 24 7.7 6744 523 7.7 5808 0.62 2.68E

- 01 do not reject H0

36 Ouachita

Mountains 6 2.2 963.5 196 3.7 212.5 - 2.66 3.88E

- 03 reject H0

37 Arkansas Valley 3 4.61 327 184 7 225 - 0.55 2.92E

- 01 do not reject H0

38 Boston

Mountains 3 2.18 19 21 0.8 44 1.09 8.62E

- 01 do not reject H0

39 Ozark Highlands 10 1.8 2248 233 4.1 82 - 4.98 3.26E

- 07 reject H0

40 Central Irregular

Plains 8 6.7 1772 434 6.3 1700 - 0.10 4.60E

- 01 do not reject H0

41 Canadian

Rockies 5 0.8 127.5 36 1.1 52.5 - 1.49 6.76E

- 02 do not reject H0

42 Northwestern

Glaciated Plains 13 9.27 362 36 18 106 2.90 1.87E

- 03 reject H0

43 Northwestern

Great Plains 81 7.45 43205 679 14 11794 8.41 0 reject H0

### 69 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

44 Nebraska Sand

Hills 1 6.46 0 4 3.2 4 0.2 do not reject H0

45 Piedmont 28 2.11 5379 308 3.4 3245 2.17 1.51E

- 02 reject H0 (P~1.5%)

46 Northern

Glaciated Plains 17 14.62 1443 142 15 971 1.315 9.42E

- 02 do not reject H0

47 Western Corn

Belt Plains 42 2.84 6200 193 5.1 1907 5.38 5.96E

- 08 reject H0

48 Lake Agassiz

Plain 13 10.38 1962 261 10 1431 0.95 1.71E

- 01 reject H0 (P~1.7%)

49 Northern

Minnesota

Wetlands 1 11.71 37 44 17 7 - 1.15 1.24E

- 01 do not reject H0

50 Northern Lakes

and Forests 20 12.28 3099 403 8.1 4961 - 1.74 4.05E

- 02 reject H0 (P~4%)

51 North Central

Hardwood

Forests 7 8.08 573.5 152 9.2 490.5 - 0.35 3.64E

- 01 do not reject H0

52 Driftless Area 11 2.4 509 49 7.6 30 4.58 2.38E

- 06 reject H0

53 Southeastern

Wisconsin Till

Plains 5 3 2067 439 8 128.5 - 3.40 3.41E

- 04 reject H0

54 Central Corn

Belt Plains 9 2.69 1498 202 6.1 320 - 3.29 5.07E

- 04 reject H0

55 Eastern Corn

Belt Plains 6 2.83 7199 1325 6.8 751 - 3.43 3.00E

- 04 reject H0

### 70 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

56 Southern Michigan/North

ern Indiana Drift

Plains 9 4.62 1967 287 7 616 - 2.67 3.77E

- 03 reject H0

57 Huron/Erie Lake

Plains 3 5.05 8246 3762 7.1 3040 - 1.38 8.33E

- 02 do not reject H0

58 Northeastern Highlands 23 3.54 79049 14044 1.4854 2E+05

- 4.24 1.13E

- 05 reject H0

59 Northeastern

Coastal Zone 10 4.01 561 101 4.2 449 - 0.58 2.82E

- 01 do not reject H0

60 Northern Appalachian

Plateau and Uplands 5 3.74 831 354 3.2 939 0.23 5.93E

- 01 do not reject H0

61 Erie Drift Plain 9 2.99 7834 901 6.2 275.5 - 4.82 7.32E

- 07 reject H0

62 North Central Appalachians 4 3.34 102 106 1.7 322 1.76 9.60E

- 01 do not reject H0

63 Middle Atlantic

Coastal Plain 2 18.54 16060 16726 3.4 17392 0.10 5.39E

- 01 do not reject H0

64 Northern

Piedmont 15 2.18 18010 1524 4 4850 3.84 6.11E

- 05 reject H0

65 Southeastern

Plains 18 2.55 49987 3801 4.3 18432 3.38 3.61E

- 04 reject H0

66 Blue Ridge 16 1.09 4915 686 0.9 6061 0.71 2.37E

- 01 do not reject H0

67 Ridge and Valley 27 1.56 10612 733 1.7 9180 0.64 2.61E

- 01 do not reject H0

68 Southwestern Appalachians 9 1.91 212.5 47 1.7 210.5 - 0.02 4.91E

- 01 do not reject H0

### 71 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

69 Central Appalachians 10 1.84 3651 864 1.6 4990 0.84 8.01E

- 01 do not reject H0

70 Western Allegheny

Plateau 19 2.17 25890 1735 4 7075 4.28 9.18E

- 06 reject H0

71 Interior Plateau 14 2.24 2153 559 0.4 5674 - 2.88 2.00E

- 03 reject H0

72 Interior River Valleys and Hills 14 3.86 3143 328 6.2 1449 2.34 9.70E

- 03 reject H0 (P~0.97%)

73 Mississippi

Alluvial Plain 10 8.31 1646 503 5.5 3384 1.87 9.69E

- 01 do not reject H0

74 Mississippi Valley Loess

Plains 1 1.26 21 21 5.4 0 - 1.66 4.90E

- 02 reject H0 (P~4.9%)

75 Southern

Coastal Plain 6 6.7 20141 4222 15.5 5191 - 2.50 6.18E

- 03 reject H0

76 Southern

Florida Coastal

Plain no test

77 North Cascades 54 0.82 1169 50 0.7 1531 1.18 1.19E

- 01 do not reject H0

78 Klamath

Mountains 43 0.77 343 8 2.6 1 - 4.43 4.74E

- 06 reject H0

79 Madrean Archipelago 3 1 27 9 7.9 0 3 reject H0

80 Northern Basin and Range 26 1.51 372.5 16 3.2 43.5 4.26 1.02E

- 05 reject H0

81 Sonoran Basin and Range 3 1.72 364 133 5.1 35 - 2.44 7.40E

- 03 reject H0

82 Laurentian

Plains and Hills 5 5.68 94 21 9.9 11 - 2.70 3.47E

- 03 reject H0

### 72 Level III Ecoregion Ecoregion

Name WSA dataset NOC database M- W test (n1>10 and n2>10) One large sample (n2>20) Exact test (n2<20) Exact test (n2<9) Interpretation of

test

1- sided @ 0.05

n median

DOC Ux n median

DOC Uy Z P Z P critical U (0.05 level of signif.) P (H0: same mean of distributions)

83 Eastern Great

Lakes and

Hudson

Lowlands 12 6.72 8228 1346 7 7925 0.11 4.55E

- 01 do not reject H0

84 Atlantic Coastal

Pine Barrens 2 12.62 150 243 5.7 336 0.93 8.24E

- 01 do not reject H0

### 73 4.3.3 Results and Implications of Bias Testing

The results of the rank

- sum test indicate that DOC concentrations from the NOCD are biased high ( i.e.

, the null hypothesis was not rejected) in 52 of 81 (64%) of Level III ecoregions in which comparable data were available (no comparison was possible in four ecoregions). For those ecoregions where the null hypothesis was not rejected, BLM users can be confident that the lower percentile DOC concentrations

listed in Table 16 are representative for that ecoregion.

For ecoregions where the null hypothesis was rejected, the result suggests that the DOC data from the national organic carbon database are from biased samples. Recall discussion of both database in

Sections 4 .3, 4 .3.1

, and 4 .3.2 that WSA is a random design sampling that ensures unbiased site

selection. Whereas the NOCD is more influenced by locations with known water quality impairments and reflect unequal sampling efforts potentially creating a bias. It is likely that the percentile DOC concentrations tabulated for those ecoregions in Table 16 also reflect this bi as towards high concentrations. This was confirmed by comparing the probability distributions of DOC concentrations in the ecoregions where n and m were large ( n , m > 30).

In large

- sample eco regions where the null hypothesi s was rejected by the rank

- sum t est ( E coregions 1,
6, 11, 13, 23, 43

, and 47), the probability distributions also show that the DOC concentration percentiles are substantially different, with the NOCD showing higher values. An example of such a comparison of DOC probability distribution is shown in Figure 3 1. On the other hand, in such large

sample ec oregions where the null hypothesi s was not rejected ( E coregions 17, 21

, and 77), the probability distributions show that the DOC concentration percentiles are comparable. An example of

such a probability distribution is shown in Figure 32 . In all cases where it was possible to compare the DOC probability distributions, the results of the rank

- sum test were confirmed.

### 74 Figure 31

. Comparison of probability distributions of DOC concentrations in E coregion 23

Figure 32 . Comparison of probability distributions of DOC concentrations in E coregion 77 1 10 100

- 2.5

- 2

- 1.5

- 1

- 0.5 0 0.5 1 1.5 2 2.5DOC (mg/L)



### z score Cumulative Frequency Distributions for DOC in ecoregion 23

WSA ecoregional DOC data EPA national organic carbon database



### 0.1

1 10

- 2.5

- 2

- 1.5

- 1

- 0.5 0 0.5 1 1.5 2 2.5DOC (mg/L)



### z score Cumulative F requency D istributions for DOC in ecoregion 77

WSA ecoregional DOC data
EPA national organic carbon database

### 75 Because using a DOC concentration that is biased high as input to the BLM may lead to a non

conservative (high) site

- specific copper criterion, it would be inappropriate to use the 10 th 

### percentile

DOC concentrations in Table 16 for ecoregions in which the data from the NOCD come from biased samples.

We have not addressed the issue of whet her the streams sampled for the WSA are representative in terms of DOC concentrations for all lotic (flowing) waters. It is possible that larger rivers may have DOC concentrations that are different from streams. For this reason, we recommend that the esti mated ecoregional DOC values be compared to data from EPA’s NRSA (NRSA, USEPA, 2013 b ; EPA 841

- D

- 13

- 001) . If necessary, adjustment of the estimated DOC values can be made at that time.

4.4 Comparing NOCD to WSA/NRSA DOC D ata
The representativeness of the DOC data in the NOCD was evaluated by statistically comparing the data, at the ecoregional level, with the combined DOC data from two smaller random statistical surveys of rivers and streams. The two smaller surveys were:

(1) the 2004

- 05 WSA (1,313 sites), and

(2) the 2008

- 09 NRSA (2,113 sites).

The NRSA was the first nationally consistent survey assessing the ecological condition of the full range of flowing waters in the conterminous U.S. The target population includes the Great R ivers (such as the Mississippi and the Missouri), small perennial streams, and urban and non

- urban rivers. Run

- of

- the

river ponds and pools are included, along with tidally influenced streams and rivers up to the leading edge of dilute sea water.

NRSA samp ling locations were selected by random selection. The locations of perennial streams were identified using the EPA

- USGS National Hydrography Dataset Plus (NHD

- Plus), a comprehensive set of digital spatial data on surface waters at the 1:100,000 scale. Info rmation about stream order was also

obtained from the NHD

- Plus. The 1,924 sites sampled for the NRSA were identified using a probability

based sample design. Details about the NRSA probabilistic sampling design are described in Section 1.1

of the NRSA: Fie ld Operations Manual (USEPA, 20 07 ; EPA

- 841

- B

- 07

- 009 ). Site selection rules included weighting to provide balance in the number of river and stream sites from each of the size classes. Site selection was also controlled for spatial distribution to make sure sample sites were distributed across the U.S. Among these randomly selected sample sites were 359 of the original 2004 WSA sites. These were revisited as part of the NRSA to examine whether conditions have changed. When sites were selected for sampling, r esearch teams conducted office evaluations and field reconnaissance to determine if the sites were accessible or if a river or stream labeled as perennial in NHD

- Plus was, in fact, flowing during the sampling season. If a river or stream was not flowing or was determined to be inaccessible, it was dropped from the sampling effort and replaced with a perennial river or stream from a list of replacement sites within the random design.

The DOC data from these two smaller datasets were combined and described, h ereafter, as WSA/NRSA data. GIS was used to determine which sampling sites were in each Level III ecoregion. The statistical

test used was the non

- parametric Wilcoxon 2

- sample test, with a null hypothesis that DOC

concentrations in the two different DOC sa mple datasets were equal. The alternative hypothesis was that DOC concentrations in the NCOD were significantly greater than those in the WSA/NRSA data,

### 76 indicating positive bias possibly due to over

- representation of impacted sites. The test was applied fo r each of 84 Level III ecoregions at alpha=0.05.

Table 18 below includes the number of data (n) in each ecoregion, and the 10 th 

### percentiles of DOC based upon data from the EPA DOC database (NOCD) and the combined WSA/NRSA data. For each ecoregion, the tabl e also provides the result of the Wilcoxon 2

- sample test, in terms of whether the null hypothesis (the two samples are equal) should be accepted or rejected. The null hypothesis was rejected in 59 of 83 ecoregions, indicating bias in DOC concentrations hig her in the national organic carbon dataset for the majority of ecoregions. In these 59 ecoregions, low

- end percentiles based on DOC concentrations in the WSA/NRSA data were selected as reasonably protective estimates of ecoregional DOC concentrations.

Ta ble 18. DOC concentrations (mg/L) in each Level III ecoregion based upon data from the NOCD and the combined WSA/NRSA data: number of data (n); 10th percentiles; and results of the Wilcoxon 2

- sample test

Ecoregion NOC database WSA/NRSA database H 0 (equal means )

n 10% n 10%

1 91 1.1 60 0.7 reject

2 835 2.5 8 0.3 6 reject

3 66 1.1 12 0.4 reject

4 100 0.5 37 0.3 reject

5 32 2.1 21 0.5 reject

6 479 2.1 42 0.8 reject

7 180 2.7 7 1.1 reject

8 6 4.4 43 0.7 reject

9 13 1.4 25 0.5 reject

10 73 2.0 22 1.0 reject

11 26 1.3 91 0.8 reject

12 50 2.2 6 1.2 reject

13 1553 1.5 82 0.7 reject

14 35 2.8 8 0.8 reject

15 778 1.0 39 0.8 reject

16 29 1.4 34 0.8 reject

17 81 0.2 94 0.7 accept

18 150 4.3 52 1.1 reject

19 46 1.8 41 0.9 reject

20 798 3.0 61 1.2 reject

21 1129 0.6 76 0.8 accept

22 281 2.6 27 0.7 reject

23 37 2.2 48 0.7 reject

24 116 2.3 10 1.4 reject

25 439 4.4 29 1.3 reject

### 77 Ecoregion NOC database WSA/NRSA database H 0 (equal means )

n 10% n 10%

26 167 3.3 47 1.9 reject

27 228 3.8 92 2.2 reject

28 10 4.9 8 1.2 reject

29 289 3.8 30 1.7 reject

30 200 1.0 4 1.0 accept

31 58 1.0 4 0.3 accept

32 829 2.0 9 3.1 accept

33 268 3.0 5 5.6 accept

34 399 4.0 18 2.8 accept

35 523 4.6 66 3.3 reject

36 196 1.1 18 0.7 reject

37 184 1.9 18 1.4 reject

38 21 0.4 7 0.5 accept

39 233 2.3 39 0.8 reject

40 434 4.0 32 3.3 reject

41 36 0.6 7 0.6 reject

42 36 5.1 43 2.5 reject

43 679 6.2 234 2.3 reject

44 4 1.4 4 1.0 accept

45 308 1.0 93 1.1 reject

46 142 9.9 28 6.1 reject

47 193 3.1 103 1.7 reject

48 261 7.6 26 5.4 reject

49 44 11.0 9 6.0 accept

50 403 3.7 77 2.7 accept

51 152 3.1 44 3.2 reject

52 49 3.1 49 1.1 reject

53 439 5.3 12 1.9 reject

54 202 2.7 21 1.8 reject

55 1325 3.6 30 2.1 reject

56 287 3.8 38 2.9 reject

57 3762 4.7 14 1.5 accept

58 14044 0.6 92 1.2 accept

59 101 2.6 81 2.7 accept

60 354 1.3 29 1.4 reject

61 901 5.1 26 1.8 reject

62 106 0.7 22 0.9 accept

63 16726 2.2 45 1.7 accept

64 1524 1.3 47 1.0 reject

65 3801 2.4 108 1.4 reject

66 686 0.5 40 0.6 accept

### 78 Ecoregion NOC database WSA/NRSA database H 0 (equal means )

n 10% n 10%

67 733 0.6 88 0.9 accept

68 47 0.9 17 0.8 accept

69 864 0.7 31 1.1 accept

70 1735 1.5 67 1.5 reject

71 559 0.1 54 1.1 accept

72 328 2.7 65 2.2 reject

73 503 3.4 107 2.8 reject

74 21 1.7 18 1.2 accept

75 4222 8.0 41 3.6 reject

76 1 na 0 na na

77 50 0.4 61 0.4 accept

78 8 1.7 56 0.6 reject

79 9 2.6 9 0.8 reject

80 16 1.8 49 1.0 reject

81 133 2.2 13 1.0 reject

82 21 5.5 18 2.8 reject

83 1346 1.0 32 2.6 reject

84 243 1.6 4 3.3 accept



### In the 24 ecoregions where the null hypothesis was not rejected (i.e., no significant difference in DOC

concentrations was found between datasets)

, the data were combined and the percentiles of the combined dataset were recalculated (Table 1 9 ). In these 24 ecoregions, low

- end percentiles based on

DOC concentrations in the combined data (NOCD and WSA/NRSA) were selected as reas onably protective estimates of ecoregional DOC concentrations.

Recommended DOC estimated values for 83 of the 84 ecoregions are summarized in Table 20 . In the remaining ecoregion (76; Southern Florida Coastal Plain), there were insufficient data in either dataset (NOC database or WSA/NRSA) to calculate DOC concentration percentiles.

Table 1 9 . DOC concentrations (mg/L) in 24 ecoregions where no significant difference in DOC concentrations was found between national organic carbon database (NOCD) and the WSA/NRSA datasets: number of data (n); 10 th 

### percentiles from combined NOCD & WSA/NRSA data

Ecoregion n DOC (mg/L)

10%

17 175 0.6

21 1205 0.6

30 204 1.0

31 62 1.0

32 838 2.0

### 79 Ecoregion n DOC (mg/L)

10%

33 273 3.0

34 417 4.0

38 28 0.5

44 8 1.0

49 53 10.4

50 480 3.5

57 3776 4.6

58 14136 0.6

59 182 2.7

62 128 0.7

63 16771 2.2

66 726 0.5

67 821 0.6

68 64 0.9

69 895 0.7

71 613 0.1

74 39 1.5

77 111 0.4

84 247 1.6



### Table 20. Recommended ecoregional DOC concentrations (mg/L) based upon combined data from the NOCD and the WSA/NRSA data in 83 Level III ecoregions: number of observations (n); 10 th



### percentiles; and source of data for each ecoregion

Ecoregion n DOC (mg/L)

10% Data Source

1 60 0.7 WSA/NRSA

2 8 0.3 WSA/NRSA

3 12 0.4 WSA/NRSA

4 37 0.3 WSA/NRSA

5 21 0.5 WSA/NRSA

6 42 0.8 WSA/NRSA

7 7 1.1 WSA/NRSA

8 43 0.7 WSA/NRSA

9 25 0.5 WSA/NRSA

10 22 1.0 WSA/NRSA

11 91 0.8 WSA/NRSA

12 6 1.2 WSA/NRSA

13 82 0.7 WSA/NRSA

14 8 0.8 WSA/NRSA

15 39 0.8 WSA/NRSA

### 80 Ecoregion n DOC (mg/L)

10% Data Source

16 34 0.8 WSA/NRSA

17 175 0.6 NOCD & WSA/NRSA

18 52 1.1 WSA/NRSA

19 41 0.9 WSA/NRSA

20 61 1.2 WSA/NRSA

21 1205 0.6 NOCD & WSA/NRSA

22 27 0.7 WSA/NRSA

23 48 0.7 WSA/NRSA

24 10 1.4 WSA/NRSA

25 29 1.3 WSA/NRSA

26 47 1.9 WSA/NRSA

27 92 2.2 WSA/NRSA

28 8 1.2 WSA/NRSA

29 30 1.7 WSA/NRSA

30 204 1.0 NOCD & WSA/NRSA

31 62 1.0 NOCD & WSA/NRSA

32 838 2.0 NOCD & WSA/NRSA

33 273 3.0 NOCD & WSA/NRSA

34 417 4.0 NOCD & WSA/NRSA

35 66 3.3 WSA/NRSA

36 18 0.7 WSA/NRSA

37 18 1.4 WSA/NRSA

38 28 0.5 NOCD & WSA/NRSA

39 39 0.8 WSA/NRSA

40 32 3.3 WSA/NRSA

41 7 0.6 WSA/NRSA

42 43 2.5 WSA/NRSA

43 234 2.3 WSA/NRSA

44 8 1.0 NOCD & WSA/NRSA

45 93 1.1 WSA/NRSA

46 28 6.1 WSA/NRSA

47 103 1.7 WSA/NRSA

48 26 5.4 WSA/NRSA

49 53 10.4 NOCD & WSA/NRSA

50 480 3.5 NOCD & WSA/NRSA

51 44 3.2 WSA/NRSA

52 49 1.1 WSA/NRSA

53 12 1.9 WSA/NRSA

54 21 1.8 WSA/NRSA

55 30 2.1 WSA/NRSA

56 38 2.9 WSA/NRSA

57 3776 4.6 NOCD & WSA/NRSA

### 81 Ecoregion n DOC (mg/L)

10% Data Source

58 14136 0.6 NOCD & WSA/NRSA

59 182 2.7 NOCD & WSA/NRSA

60 29 1.4 WSA/NRSA

61 26 1.8 WSA/NRSA

62 128 0.7 NOCD & WSA/NRSA

63 16771 2.2 NOCD & WSA/NRSA

64 47 1.0 WSA/NRSA

65 108 1.4 WSA/NRSA

66 726 0.5 NOCD & WSA/NRSA

67 821 0.6 NOCD & WSA/NRSA

68 64 0.9 NOCD & WSA/NRSA

69 895 0.7 NOCD & WSA/NRSA

70 67 1.5 WSA/NRSA

71 613 0.1 NOCD & WSA/NRSA

72 65 2.2 WSA/NRSA

73 107 2.8 WSA/NRSA

74 39 1.5 NOCD & WSA/NRSA

75 41 3.6 WSA/NRSA

77 111 0.4 NOCD & WSA/NRSA

78 56 0.6 WSA/NRSA

79 9 0.8 WSA/NRSA

80 49 1.0 WSA/NRSA

81 13 1.0 WSA/NRSA

82 18 2.8 WSA/NRSA

83 32 2.6 WSA/NRSA

84 247 1.6 NOCD & WSA/NRSA



### 4.5 Conclusions

EPA tested the 10 th 

### percentiles of ecoregional DOC concentrations against data from the Southern Rocky Mountains (Level III Ecoregion 21) as input to the copper BLM

. Broad ranges of errors (including

some that were larger than an order

- of magnitude) were observed in BLM predictions made with the DOC estimates, in comparison to predictions made with actual measured site data. Although the
copper crite ri a values predict ed using the parameter estimates for DOC were found to be protective in 90% of the cases, in many of these cases these predictions were overly

- protective (e.g., IWQC lower by

a factor of 4 to 5). For this reason, BLM users should be cautious when considering lower percentiles of

the distrib ution of DOC as estimates for missing input parameters to the BLM. In general, it is preferable to use site

- specific measurements of DOC as BLM input because: (1) copper toxicities (and BLM model predictions) are highly s ensitive to DOC concentrations and (2) reasonably protective DOC

concentrations can be difficult to estimate at th e ecoregional level, when data are limited.

For many ecoregions, the EPA recommended percentiles in Table 20 are based upon a relatively small number of DOC data, which can be a cause for concern in terms of the reliability of these values. For

### 82 example, in 47 ecoregions the DOC percentiles were calculated from 50 or fewer concentration values,

and in seven ecoregions the DOC percentiles were cal culated from fewer than 10 values. In the former case (n≤50), the lower 95% confidence limit of the 10 th 

### percentile cannot be calculated (Berthouex and Brown, 1994), while in the latter (n<9) the 10 th 

### percentile itself is below the lowest concentration

val ue. Because of these and other limitations on the DOC database and the importance of this parameter in criteria calculation, users are encouraged to sample for DOC as a basis for determining BLM input rather than using default parameters where possible.



## 5 SU MMARY AND RECOMMENDATIONS

The BLM predicts acute copper toxicity based on site

- specific water quality parameters, and calculates aquatic life criteria based on the predicted copper toxicity. The BLM requires 10 input parameters to

calculate copper criteria : temperature, pH, DOC, alkalinity

, calcium, magnesium, sodium, potassium, sulfate, and chloride , the last seven of which are also referred to as GI s

. G iven the broad geographical range over which the BLM is likely to be applied, and the limited availabil ity of data for input

parameters in many areas, a practical method to estimate missing water quality parameters was developed to support the use of the copper BLM for copper aquatic life criteria

.

I n this report we describe d three approaches EPA used to es timate default input parameters for GI and

DOC for BLM that could be used where site

- specific data are not available. EPA ’ s goal was to provide

estimates for these missing input parameters that are reasonably protective. EPA used geostatistics to predict ecoregional input parameters from national water quality databases, and developed correlations between GI parameters and conductivity. These estimates were further refined using

stream order.

Our analysis of national data indicates that there is no relationship between conductivity and pH, and geostatistical methods were found to produce similarly ambiguous results. Because pH is one of the most important BLM inputs for predicting criteria for copper, we conclude that site

- specific data for pH are needed for successful BLM application . Temperature is a commonly measured parameter and should be easy to obtain by users for input in the BLM.

5.1 Recommendations for BLM inputs for geo c hemical ions where site

- specific data are not

available

In Section 2 we us ed geostatistics to estimate missing GI parameter values based on geography . We supplemented the geostatistical approach by adding conductivity as an additional explanatory variable

to generate a more robust spatial estimate of the GI water quality inputs for the BLM because

c onductivity is one of the most widely monitored water quality indicators in the U.S

. and correlates

well with GIs

. We presented average predicted 10 th 

### percentile concentrations fo r the BLM GI water quality parameters Level III e coregions. W e further refined these estimates by considering the effect of

stream order (size) in Section 3

. We found that values of the GI estimates generally increased with stream order, a trend that was most apparent and consistent for higher order streams. Tables 8

, 9 , and

1 0 present best estimates of GI input parameters for the BLM. Estimated inputs are provided for each

GI in each ecoregion categorized by stream order for low, medium, and high ord er streams, respectively. EPA recommends these 10 th 

### percentile Level III e coregion, stream order group

- specific

values be used in the BLM whe re site

- specific data are not available.

### 83 5.2 Recommendations for BLM inputs for DOC where site

- specific data are not a vailable

In Section 4 we determined that the geostatistical and regression

- based approaches used to estimate GI input parameters for the BLM do not produce accurate site

- specific estimates for DOC. Because previous analyses indicate that DOC is the most important BLM input for estimating criteria for copper, we further refined our approach in Section 4 based on analyses using the NOCD to estimate lower

percentile DOC concentrations

. Based on statistical comparisons to an independent probabilistic dataset, we found that DOC concentrations from the NOCD are reasonably protective estimates of DOC for use as input parameters for the BLM for some ecoregions

. For other ecoregions, EPA recommends
using esti mates based on the WSA dataset. Recommended 10 th 

### percenti le DOC estimated values for 83 of the 84 ecoregions are summarized in Table 20 . In the remaining ecoregion (76; Southern Florida Coastal Plain), there were insufficient data in either dataset (NOC database or WSA/NRSA) to calculate
DOC concentration percen tiles. Because limitations in the DOC database and the importance of this parameter in criteria calculation, users are encouraged to sample for DOC as a basis for determining BLM input rather than using default parameters wherever possible.

5.3 Recommendations for BLM inputs for pH where site

- specific data are not available

In Section 2 we determined that geostatistical and regression

- based approaches used to estimate GI input parameters for the BLM did not produce accurate site

- specific estimate s for pH. Our analysis of national data indicates that there is no relationship between conductivity and pH, and geostatistical methods were found to produce similarly ambiguous results. Because pH is one of the most important BLM inputs for predicting cri teria for copper , we conclude that site

- specific data for pH are needed for successful BLM application

. Temperature along with pH is similarly recommended to acquire site

specific data for BLM application with the advantage of both of these been easy param eters to

measure.

5.4 Conclusions

The approaches described in this TSD can be used to provide reasonable default values for input parameters in the BLM to derive protective freshwater aquatic life criteria for copper when data are lacking. These data could als o be used to provide reasonable default values to fill in missing water quality input parameters in the application of other metal BLM models as well when data are lacking.

Default recommended values for GI parameters are 10 th 

### percentile ecoregional, stream

- order specific

values. Default recommended values for DOC are 10 th 

### percentile ecoregional values

. Both pH and temperature should be measured values when using the BLM. It should be noted that site

- specific data

are always preferable for use in the BLM and should be used to develop copper criteria via the BLM when possible. Users of the BLM are encouraged to sample their water body of interest, and to analyze the samples for the constituent (parameter) concentrations as a basis for determining BLM in puts
whe re possible.

### 84 

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### 87 

## Appendix A: An Examination of Spatial Trends in Surface Water Chemistry in the Continental United States: Implications for the Use of Default Values as Inputs to the Biotic Ligand Model for Prediction of Acute Metal Toxicity to Aquatic Organisms

Internal EPA Report (2006)

James N. Carleton EPA, Office of Water, Office of Science & Technology

.

A.1 Abstract

A large database of surface water chemistry monitoring data was examined to look for spatial trends in

five chemical constituents that are key inputs to a model for predicting metal toxicity to aquatic organisms. Continuous prediction maps of concentrations were generated using various kriging techniques to interpolate between site

- median values measured at several thou sand separate locations throughout the continental United States (U.S.) . Continuous concentration surfaces were then averaged over 8

- digit Hydrologic Unit Code (HUC) polygons to produce block

- averaged mean

estimates of site

- median concentrations. Pairwise comparisons indicated distinct trends between

various HUC

- averaged predicted constituents. The same analyses performed on data from 772 locations where all five constituents had been measured revealed similar relationships between

monitored constituents. P rincipal components analyses performed on these data sets showed that 80

to 90 % of the variance in both cases could be explained by a single component with loadings on three of the five constituents. The use of kriging to produce appropriate quantile maps for block

- averaging is suggested as a possible approach for developing regional values to use as default model inputs, when

site

- specific monitoring data are lacking.

A.2 Background

The U.S. Environmental Protection Agency is planning in the near future to rel ease proposed water quality criteria for copper ( Note: EPA’s BLM

- based Freshwater Copper Aquatic Life Ambient Water Quality Criteria document was released in 2007, EPA

- 822

- R

- 07

- 001 ) . These criteria are unlike most water quality criteria in that acceptable (safe) concentrations for aquatic life support, rather than being defined as simple numerical values that apply everywhere, will be addressed through the use of a chemical speciation model

– the Biotic Ligand Model (BLM) (EPA, 2003). The BLM calculates met al toxicity to aquatic organisms as a function of simultaneous concentrations of additional chemical constituents of water, for example other ions that can either complex with copper and render it biologically unavailable, or compete with copper for bindin g sites at the point of entry into a vulnerable organism (i.e. at the fish gill). While the BLM has the potential to improve the accuracy of metal ecotoxicity predictions, its use requires input concentrations of nine separate chemical constituents

and wat er temperature. Of these nine chemical constituents (Alkalinity (alk), calcium (Ca), magnesium (Mg), sodium (Na), sulfate (SO42

- ), potassium (K), chloride (Cl), dissolved organic carbon (DOC), and pH), model

- predicted toxicity is most sensitive to five: Ca , alk, pH, Na, and DOC. States or other entities wishing to use the BLM to assess compliance with the proposed criteria in specific waters, or to develop effluent permit limits, will therefore require monitoring information on a suite of chemical

### 88 constitue nts

– information that is not always available. One possible way to deal with such missing information is to develop reasonably protective default values for these various model inputs, especially the five to which the BLM is most sensitive. Given that amb ient surface water chemistry reflects, among other things, the influences of local soil types and land uses, it may make sense that any such defaults be developed on some kind of regional or local basis.

The exercise described in this report comprises a ge ospatial examination of a large amount of water chemistry monitoring data collected in recent years by the U

. S

. Geological Survey, and recorded in their National Water Information System (NWIS ) database. The data includes monitoring information

from severa l thousand separate surface water sampling locations throughout the U.S. (Figure A

- 1). The latitudes and longitudes of each sampling location are part of the data record. The primary objective of this analysis is to look for any obvious spatial trends in t ypical concentrations of the five most sensitive constituents, and to suggest procedures for making use of these trends to define regional default values for use as inputs to the BLM. For purposes of expediency, the geographic extent of this analysis

is li mited to the continental U.S

.

Figure A

- 1. NWIS sample collection locations in the continental U.S.

A.3 Description of Data

Although NWIS contained data from 207

, 153 sampling events at 13

, 824 individual sampling locations in the continental U.S. (Figure A

- 1 ), all 10 constituents of relevance to the BLM were not monitored at each location. For the five constituents of interest, the numbers of discrete sampling locations were as follows: alk, 5

, 900; Ca, 10

, 940; DOC, 3

, 726; Na, 10

, 424; pH, 11

, 780. Numbers of sampling events at individual locations ranged from 1 to 2

, 605, with a mean of 15, and a mode of one (i.e. most sites were

### 89 only sampled once). Examination of the spatial distribution of numbers of sampling events per site

reveals that the most intensive sa mpling tended to occur in Midwestern and western states (Figure A

2). Because environmental sampling data tend to be lognormally distributed, disparities in numbers of samples may tend to produce higher mean and median values at more

- frequently

- sampled loc ations. As spatial distributions of representative (e.g.

, median) concentrations are examined, it should be kept in mind that apparent geographic trends in concentration may be in part simply the result of uneven sampling intensity.

Figure A

- 2. Intensit y of sampling (number of separate sampling dates) at each NWIS site

A.4 Data Analysis

Because environmental data tend to be positively skewed, the median statistic was chosen as providing the best central

- tendency representation of each location’s concentration. For the purpose of looking for general spatial trends in the five constituents, the first step involved simply mapping the sampling locations as points, color

- graded by median concentration. Figure A

- 3, for example, shows some apparent trends in alkalinity across the country, with lower concentrations along the eastern seaboard, and higher concentrations in parts of the Midwest. Similar kinds of trends at the national

scale were also seen with the other constituents.

### 90 Figure A

- 3. Median measured alkalinity (mg/L as CaCO3) at NWI S locations

The next step in data visualization involve d the calculation of median concentrations averaged over

each 8

- digit HUC containing sampling locations. These display essentially the same information as the point displays (Figure A

- 3), but with a degree of smoothing and summarization provided by the spa tial averaging process, to make visual interpretation of general trends easier (Figure A

- 4).

Figure A

- 4. HUC

- averaged mean median observed alkalinity in the continental U.S.

The use of 8

- digit HUCs as the areal units over which to calculate representat ive concentrations for default BLM inputs makes some physical sense: HUCs are areas that are defined by some degree of

### 91 interconnection between associated surface water features. HUCs may be either watersheds in their own right or downstream sections of lar ger watersheds (Omernik, 2003). In either case, all flowing surface water that passes through a HUC eventually (in theory) passes through the same downstream “pour point”. One advantage of using HUCs is that they divide the land area into roughly equally s ized areas at a level of resolution roughly consistent with gross variations in median concentration (Figure

A

- 3). One problem with using HUCs for spatial aggregation is that not all HUCs contain NWIS sampling locations, as the blank areas in Figure A

- 4 ma ke clear. The third step in this analysis therefore involved the use of kriging to create continuous surfaces of interpolated concentrations that cover the entire area of interest. Spatial averaging of the results over each HUC was then used to provide est imates of expected concentrations for all HUCs, including those lacking NWIS samples.

For each of the five key constituents, the Geostatistical Analyst extension in ArcGIS was used to explore the data, and to look for sets of kriging model options that pro vided the best fit to the data. The criteria used to evaluate goodness of fit were as follows:

1. Mean Standardized Error as small as possible

2. RMSE as small as possible

3. Root

- Mean

- Square Standardized Error close to 1.0

4. RMSE and Average Standard Err or close together

Trial and error parameter selection was used to search for a set of model options that best attained each of these four goals simultaneously. For each constituent, 10 to 20 combinations were tried, until a best option for each emerged, as determined by judgment of the author. The results are as follows:

Alk : Universal kriging, log transformation, constant trend, 50% global, 50% local, spherical
semivariogram, no anisotropy.

Ca : Ordinary kriging, log transformation, constant trend, 50% gl obal, 50% local, exponential
semivariogram, anisotropy.

DOC : Universal kriging, log transformation, constant trend, 50% global, 50% local, hole

- effect
semivariogram, anisotropy.

Na : Universal kriging, log transformation, constant trend, 50% global, 50% loc al, hole

- effect
semivariogram, anisotropy.

pH : Ordinary kriging, no transformation, constant trend, 50% global, 50% local, spherical
semivariogram, no anisotropy.

Prediction surface maps were generated for each constituent using the above sets of kriging o ptions. Figure A

- 5, which displays the results for alkalinity, shows patterns that are generally consistent with those in the data (Figures A

- 3 and A

- 4). Figure A

- 6 shows the predicted alkalinities projected into three dimensions using ArcScene.

### 92 Figure A

- 5. Kriging prediction map of median alkalinity

Figure A

- 6. Kriging map of alkalinity, projected into vertical dimension

This technique demonstrates broad geographic trends most dramatically, for example emphasizing the fact that the highest alkalinities are apparently found in northern North Dakota and Montana. Figure A

7 shows the predicted values averaged over HUC polygon s by using the Zonal Statistics function of ArcGIS Spatial Analyst.

### 93 Figure A

- 7. Kriging

- based alkalinity predictions, averaged over 8

- digit HUC polygons

For HUCs containing NWIS sampling locations, linear regression plots of predicted v ersus measured

co ncentrations (Figure A

- 8) provided a check on the accuracy of the kriging predictions. R

- squared values for the five constituents were: 0.537 (alk), 0.238 (Ca), 0.686 (DOC), 0.351 (Na), and 0.139 (pH). In most cases, a handful of outliers appeared to be re sponsible for smaller

- than

- expected correlation

coefficients.

Figure A

- 8. Kriging

- predicted vs. calculated HUC

- averaged alkalinity; r 2 

### =0.537 HUC-Averaged Median Alkalinity

0 500 1000 1500 2000

0 500 1000 1500 2000

ObservedPredicted (kriged)

data 1:1 line regression line

### 94 A scatter plot matrix of cross

- constituent comparisons revealed some interesting, non

- random relationships bet ween HUC

- averaged concentrations (Figure A

- 9). For comparative purposes, a subset of 772 sampling locations was also identified, at which sampling for all five of the constituents had taken place. Coincident concentrations of all constituents allowed a sca tter plot matrix of this data (Figure A

- 10) to also be constructed. Similarities between the kinds of relationships in Figure A

- 9 and A

10 suggest that the predicted HUC mean median values are reasonable.

Figure A

- 9. Scatter plot matrix of median concen tration kriged predictions, averaged over 8

- digit HUCs regions covering the continental U.S.

### 95 Figure A- 10. Scatter plot matrix of median concentrations from 772 monitoring locations i n the

continental U.S.

In addition to scatter plot s, correlation coeffi cient matrices between constituents in each of the two data sets (HUC

- mean kriged median values and site median values for 772 locations) were generated (Table A- 1). Although not identical, the coefficients were generally similar between the two datasets, again suggesting that the kriging predictions are reasonable.

Table A- 1. Matrices of correlation coefficients bet ween constituent concentrations

2096 HUC

- averaged predicted median values

Alk DOC Na pH Ca

Alk 1

DOC - 0.01456 1

Na 0.327599 - 0.02661 1

pH 0.761675 - 0.27746 0.286512 1

Ca 0.698379 - 0.02585 0.531727 0.58514 1

772 site

- median values

Alk DOC Na pH Ca

Alk 1

DOC 0.019145 1

Na 0.327028 0.165445 1

pH 0.453161 - 0.24067 0.169238 1

Ca 0.842484 - 0.05097 0.387617 0.374592 1

### 96 Principal components analyses (PCA) were also run on both the HUC

- averaged predictions and the 772 sets of monitored constituent concentrations to look for linear combinations of variables that might explain most of the observed variation. Figures A

- 11 and A

- 12 show the resulting plots of the variance explained by each component, and Table A

- 2 lists the loadings of the components onto the original variables. The first component comprised 80 and 88% of the variance in the HUC

- based and site

- based analyses, r espectively. As Table A

- 2 indicates, this component loaded entirely onto alk, Na, and Ca in both cases. For the HUCs, component 1 was primarily loaded on Na, while for the sites, it primarily

loaded on alk.

Figure A

- 11. Variance plot from PCA of HUC

- aver age kriging

- predicted concentrations

Figure A

- 12. Variance plot from PCA of site

- median measured concentrations

### 97 Table A- 2. Loadings onto original variables from PCA on HUC

- averaged predictions and site

- median

concen trations

HUCs:

Comp.1 Comp.2 Comp.3 Comp.4 Comp.5

Alk 0.219 0.932 - 0.289

DOC 0.999

Na 0.965 - 0.25

pH - 0.999

Ca 0.142 0.263 0.954

Sites:

Comp.1 Comp.2 Comp.3 Comp.4 Comp.5

Alk 0.952 0.162 0.259

DOC - 0.997

Na 0.13 - 0.982 0.133

pH 0.999

Ca 0.277 - 0.955



### A.5 Developing Regional Defaults

Besides prediction maps of best

- estimate median concentrations, the Geostatistical Analyst can be used, with the same sets of kriging parameters listed previously, to generate quantile surface maps that rep resent reasonably protective inputs to the BLM than standard kriging predicted values. The five key inputs examined in this paper are all positively associated with BLM

- predicted LC50s. Thus, lower values of all of them tend to result in lower (i.e.

, more protective) site

- specific criteria. Lower quantile predictions can be used to produce protective regional default inputs. As an example, Figure A- 13 displays the 25 th 

### percentile prediction map for alkalinity. When these values are block

- averaged over the HUC polygons, the resulting alkalinities are lower than 67% of the site

- minimum alkalinities (Figure

A- 14) measured inside the same areas.

### 98 Figure A

- 13. Kriging 25th percentile map of median alkalinity

Figure A

- 14. Comparison of observed site

- minimum alkalinities with HUC

- mean 25th percentile kriging

- predicted values 0 200 400 600 800 1000

0 50 100 150 200 250

Alk (mg/L as CaCO3)measured and kriging-predictedHUC mean Alk

site minimum observed Linear (HUC-mean 25th percentile kriged conc.)

### 99 A.6 Discussion

The use of HUCs for spatial averaging of surface water concentrations is not without conceptual difficulties. First, only about 45% of HUCs are actual watersheds (Omernik, 2003) ; the rest receive drainage from additional upgradient areas. Concentrations measured in flowing waters reflect the soil, vegetation and land use properties of the aggregate upstream drainage areas, rather than of the sampling locations themselves (Smith e t al., 1997). Assignment of measured concentrations to a HUC through block averaging may understate the spatial relevance of the samples for HUCs that are only parts of watersheds. One way to address this concern might be to use, as the aggregation polygon s, only samples from watersheds that are entirely contained within single ecoregions (Omernik, 1987). However, this would have the unacceptable consequence of excluding large areas, and perhaps much of the data, from analysis. Another critical problem with this idea is that watershed boundaries for all of the NWIS sampling locations are not readily available, so there is currently no basis for deciding which points should be included or excluded. One advantage provided by the use of HUCs is that they

divide the entire land mass of interest in this case into roughly equal sized polygons, at a level of resolution that appears to be roughly compatible with that of observed concentration trends. Block averaging using other sets of similarly sized polygons, such as counties, might serve equally well for empirically capturing broad spatial variability in concentrations. However the resulting concentrations would be less useful because they would lack even the incomplete degree of organization by connected hydrology that HUCs provide.

A.7 Conclusions

Kriging

- predicted median concentrations of five water quality constituents, averaged over 8

- digit HUCs, showed similar inter

- constituent relationships as median concentrations from 772 specific sampling locations. PCA analy ses revealed that in both cases, most of the observed variability was related to variations in three of the five constituents: alk, Na, and Ca. Results suggest that block averaging of kriging predictions over irregularly spaced sampling points can provide estimates that preserve much of the interrelationships between different measured entities. The use of suitable low

quantile kriging predictions is suggested as a way to estimate reasonably protective concentrations to serve as regional default inputs to t he BLM

.

A.8 R eferences

Omernik, J., 1987. Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77:118

- 125.

Omernik, J., 2003. The misuse of hydrologic unit maps for extrapolation, reporting, and ecosystem

management. Journal of the American Water Resources Association 39(3):563

- 573.

Smith, R.A., Schwartz, G.E., and R.B. Alexander, 1997. Regional interpretation of water

- quality monitoring data. Water Resources Research 33912):2781

- 2798.

U.S. Environmental Protection Age ncy, 2003. The Biotic Ligand Model: Technical Support Document for its Application to the Evaluation of Water Quality Criteria for Copper, EPA 822

- R

- 03

- 027.

### 100 

## Appendix B: Approaches for Estimating Missing Biotic Ligand Model Input Parameters. Correlation approaches to estimate Biotic Ligand Model input parameters using conductivity and discharge as explanatory

variables

B.1 Introduction

Derivation of water quality criteria for copper and other metals from predictions of bioavailability generated by the Biotic Ligand Model ( BLM ) introduces a number of issues. For example, obtaining the data needed to apply the BLM may be problematic for many dischargers and receiving waters. The
BLM requires 1 0 input parameters to characterize water quality at a particular site; the most im portant ones for predicting copper bioavailability and toxicity include pH, dissolved organic carbon ( DOC )

, calcium, magnesium, sodium, alkalinity

, and temperature. In stream segments with only small dischargers, or possibly no dischargers at all, the data needed to apply the BLM may not be available. Water quality criteria that rely upon BLM predictions would be greatly facilitated by the development of practical approaches to estimate values for BLM water quality parameters, which could be applied

when da ta for one or more of these parameters are missing at a site.

Given the broad geographical range over which the BLM is likely to be applied, potentially over the entire Nation, and the limited information that is available for many areas, a practical meth od to estimate missing water quality parameters is needed. The geostatistical methods employed by the U

. S

. Environmental Protection Agency ( EPA ) (Carleton, 2006) presented a viable system to estimate missing water quality parameters required by the BLM. Th e prototype work developed by Carleton applied kriging to predict average concentrations of alkalinity, DOC, sodium, pH and calcium over hydrologic units (8

- digit Hydologgic Unit Codes [ HUCs ] ), using the U

. S

. Geological Service (USGS) National Water

Inform ation Service (NWIS) as the source of spatial data. Comparison of measured concentrations with kriging predictions were encouraging for several of the BLM water quality parameters, although the errors and uncertainties associated with these predictions wer e not fully explored.

The geostatistical approach utilizes knowledge of spatial correlation to project values of a water quality parameter at sites where it has not been measured. The accuracy of these projections depends upon the availability of sufficie nt and spatially

- proximate data for the specific parameter of interest. In addition, the seasonal and annual temporal variation in water quality must also be addressed in order to apply the BLM at a site. Water quality parameters often experience large cha nges during periods of snowmelt or intense rainfall. In many rivers and streams, the chemical composition and physical properties of water are following trends associated with increased land use in watersheds, water diversion for irrigation, regulation of river flow by dams, and other anthropogenic disturbances.

The acute BLM predicts an instantaneous acute copper criterion (i.e., a maximum short

- term, non

- toxic concentration of copper), which will vary according to changes in the water quality parameters. An appropriately protective copper criterion must therefore reflect the variability of water quality parameters at the site. In previous analyses we found that protective water quality criteria for copper

### 101 generally corresponded to approximately the 2.5 th 

### p ercentile of the distribution of instantaneous water quality criteria (IWQC) predicted by the BLM

. 5 

### BLM criteria predictions made for a site using the corresponding percentiles (i.e., 2.5%) of the water quality parameter distributions will be a

conservativ e approximation of this protective criterion. The sensitivity of criteria predictions to the most important BLM water quality inputs is proportional (sensitivity to DOC is ~100% 6 

### , [H + 

### ] is ~50%, calcium, magnesium and sodium is ~20%). Relevant site

- specific water quality parameters will be

values from the lower “tail” of the measured or estimated distributions.

There may be great value in supplementing the geostatistical approach with classical estimation methods, such as regression and correla tion. Examination of the NWIS data used to develop the geostatistical approach suggests that two variables

, discharge (flow rate) and conductivity

, may be useful for estimating BLM input water quality parameters. The USGS maintains the most comprehensive r outine water flow and water quality data for streams and rivers in the Nation. Discharge may be a relevant explanatory variable because the USGS measures or estimates flow on a daily basis for a large number of stream and river segments. Among water qualit y parameters, the data for conductivity are the most complete and cover the longest time period (Wang and Yin, 1997). The literature also indicates that conductivity is one of the most widely monitored water quality indicators

in the U

. S. In part, this is because conductivity measurements are usually included in automated multiparameter systems for monitoring changes in the quality of surface waters (Allen and Mancy, 1972).

Conductivity is useful as a general measure of stream water quality. Each stream tends to have a relatively constant range of conductivity that, once established, can be used as a baseline for comparison with regular conductivity measurements (USEPA, 1997). Conductivity in streams and rivers is affected primarily by the geology of the area through which the water flows. Streams that run through areas with granite bedrock tend to have lower conductivity because granite is composed of more inert materials that do not ionize (dissolve into ionic components) when washed into the water.

On t he other hand, streams that run through areas with clay soils tend to have higher conductivity because of the presence of materials that ionize when washed into the water. Ground water inflows can have the same effects depending on the bedrock they flow th rough.

Conductivity reflects the strength of major ions in water and is a good estimator of total dissolved solids (TDS). Linear relationships between conductivity and TDS have been developed for many USGS monitoring sites. Conductivity is also linearly re lated to the sum of cations (McCutcheon et al

. , 1993). In addition, conductivity measurements provide information about the total concentration of ionic species in a water sample (Tyson, 1988). Figure B

- 1 illustrates how conductivity relates to hardness an d anion concentrations in a river that has a rather saline base flow maintained by irrigation drainage and groundwater inflows. The chemical characteristics of the base flow are generally constant but they are subject to seasonal dilution by runoff. Relati onships between conductivity and chloride and sulfate concentrations are well defined. A similarly good association with hardness (calcium+magnesium) is

5 This was the median for 17 sites; the range was 1 to 36%.

6 100% sensitivity implies that a model prediction (in this case, the criteria predicted by the BLM) varies in direct proportion to the change in the value of a specified input parameter.

### 102 indicated

. Lines drawn by eye through the points for chloride and sulfate show slight curvature, but th e departure from linearity is insignificant. It seems evident that a record of conductivity at this station could be used to compute the other chemical characteristics of the water with a good level of accuracy for major ions, except at high flow when the relationships would not be as well defined (Hem, 1985).

Figure B

- 1. Relation of conductivity to chloride, hardness and sulfate concentrations in the Gila River at Bylas, Arizona

(reprinted from Hem, 1985)

Wang and Yin (1997) established conductivity a s a general water quality indicator based on spatial data. The concentration of major base metal cations in water explained the positive correlation between conductivity and hardness. This also explained a rather weak correlation between conductivity and t he pH value. Its relationship with other materials, however, most likely resulted from the dilution effect of stream flow. Conductivity was negatively correlated with discharge (ρ=

- 0.729), and the same was found for most water quality variables that were p ositively correlated with conductivity. With increasing stream flow, the concentration of the dissolved material decreased, as did the conductivity. Wang and Yin’s analysis suggests that conductivity could be used as a general indicator of water quality, w hich is positively related to dissolved materials and soluble metals. As it is

### 103 widely monitored and has relatively long records, conductivity has the potential to be a very useful variable for estimating missing water quality input parameters for the BLM.

We explored this possibility by assessing the degree of correlation between conductivity and each of the BLM water quality parameters. We used NWIS data from three contiguous states in the Western

U

. S

. (Colorado, Utah and Wyoming) for this analysis. These states were selected because of the large spatial and temporal variability observed in BLM water quality parameters, and because they provided us a tractable dataset for analysis.

Discharge was included as one of the parameters in this correlation analysis . However, discharge is most often used to explain water quality variation at a particular site (Hem, 1985). The concentration of dissolved solids in the water of a stream is related to many factors, but it seems obvious that one of the more direct and imp ortant factors is the volume of water from rainfall available for dilution and transport of weathering products. Presumably, therefore, the concentrations of dissolved solids should be an inverse function of the rate of discharge of water over all or at le ast most of the recorded range (Hem, 1985). Regressing water quality parameter measurements against discharge is a common practice in environmental engineering, and many references on this subject are available (McDiffett et al.,1989; Chanat and Hornberger , 2002; Christensen et al., 2005; Godsey and Kirchner, 2005). We should also point out that correlating the variation in water quality parameters to streamflow is also necessary for effluent dilution calculations associated with use of the BLM (for example , the probabilistic dilution framework incorporated in the BLM

- Monte software [HydroQual, 2001]).

B.2 Data
Data for discharge, conductivity, and BLM water quality parameters (temperature, pH, DOC, alkalinity calcium, magnesium, sodium, potassium, sulfate , and chloride, ) were retrieved from the USGS NWIS web interface ( http://nwis.waterdata.usgs.gov/usa/nwis/qwdata ). Data were selected for 790 stream
and river stations in Colorado, Utah

, and Wyoming reporting 100 or more water quality observations.

This latter c onstraint was imposed to eliminate the large number of stations reporting very few (often one) water quality observations. Even when the analysis was restricted to sites with more than 100 water quality observations, there were frequently a marginal number of data for the multiple parameters needed to measure between

- parameter correlations. We also restricted the analysis to observations made since 1975 to avoid the possible influence of pre

- Clean Water Act discharges on water quality.

Natural logarithms o f the discharge data were used in the analysis, because discharge was clearly lognormally distributed at the majority of sites. In cases where a parameter was measured simultaneously by more than one method (field pH versus laboratory pH, for example), the reported results were averaged for analysis. We did not consider other approaches for selecting data based on preference for a particular analytic method (Roberson et al., 1963).

B.3 Results

Table B

- 1 provides an inventory of the number of observations, and n umber of sites with data, for several of the parameters in the state of Colorado (these numbers reflect the full NWIS dataset, uncensored for minimum number of observations or date). From this table, it is apparent that a vast amount of conductivity data e xists, both in terms of the total number of observations and the number of sites reporting this parameter in comparison to the BLM water quality parameters. For example,

### 104 there are almost four times as many observations of conductivity as there are for calcium, and they are measured at more than twice the number of sites. Discharge data is similarly abundant.

Table B- 1. Number of observations and sites reported in NWIS for streams and rivers in Colorado

P arameter N umber of O bservations N umber of S ites

pH 62,005 3668

alkalinity 8136 839

calcium 45,490 2708

conductivity 168,110 6101

discharge 127,275 3340



### To quantify the relationship between conductivity levels and values of water quality parameters required by the BLM, we performed correlation analyses on the NWIS water quality data for the three states. We estimated correlations for several statistics that summarized the distribution of conductivity and water quality values at each station. These included median levels, as well as the first quartile and fifth quantile. The last two statistics represent the lower end of the distribution of parameter values at a site, and are appropriate statistics for calculation of BLM instantaneous criteria. A non

- parametric correlation (Spearman’s rank correlation) was employed to avoid the problems of unknown d ata distributions and possibly non

- linear relationships. To determine the statistical significance of the rank correlation coefficient (ρ), the significance level (P) was also calculated. The Spearman’s rank

correlation was also used to examine the relatio nship between stream discharge and the water quality

variables to reveal the effect of dilution.

For the median site concentrations, we found that six BLM water quality parameters, two

- thirds of the

nine variables examined in this study, had non

- zero rank correlation coefficients at the 0.001 significance level (Table B

- 2). As expected, strong positive correlations between conductivity and salt concentrations were found. For example, the correlation coefficients between conductivity and the

concentration of salt cations and anions (sodium, potassium, magnesium, calcium, sulfate and chloride) were all higher than 0.80. However, median site conductivity was not significantly correlated to several other important BLM parameters including pH, DOC

, and alkalinity . In terms of the site medians, there appears to be limited correlation between conductivity and the BLM water quality parameters. Furthermore, for the median site concentrations neither conductivity nor any of the BLM water quality parameters were significantly correlated to discharge.

### 105 Table B- 2. Results of Spearman rank tests for correlation ( ρ ) between median values of variables at

each site.

Probability values (P) are not exact due to the presence of ties in the data

Conductivity Discharge

Conductivity ρ : 0.012

P: 0.892

pH ρ : 0.175

P: 0.019 ρ : 0.441

P: 0.008

DOC ρ : 0.866

P: 0.333 ρ :

P:

Ca ρ : 0.867

P: <0.001 ρ :

- 0.371

P: 0.068

Mg ρ : 0.882

P: <0.001 ρ :

- 0.516

P: 0.008

Na ρ : 0.921

P: <0.001 ρ : 0.139

P: 0.695

K ρ : 0.846

P: <0.001 ρ :

- 0.128

P: 0.551

SO 4 ρ : 0.905

P: <0.001 ρ :

- 0.514

P: 0.010

Alkalinity ρ :

- 0.600

P: 0.350 ρ :

P:

Cl ρ : 0.827

P: <0.001 ρ :

- 0.866

P: 0.333



### We then repeated the correlation analysis for the site first quartiles (Table B- 3) and fifth quantiles (Table B

- 4). For both of these low

- end distribution statistics, all of the BLM water quality parameters were significantly correlated to conductivity, having non

- zero rank correlation coefficients at the 0.001 significance level, as listed in Tables B

- 3 and B

- 4. The correlation coefficients are lower for pH and DOC than for the salts and alkalinity, but are nevertheless significant. Apparently, the correlation structure between conductivity and the BLM water quality parameters is much stronger at the lower end of the site distributions. Ambiguity in correlations between conductivity and BLM water quality parameters disappears when low

- end distribution statistics are analyzed. As was the case for the median site

concentrations ; neither conductivity nor any of the BLM water quality parameters were correlated with discharge for the low

- end distribution statistics.

### 106 Table B- 3. Results of Spearman rank tests for correlation ( ρ ) between the first quartile of values at

each site.

Probability v alues (P) are not exact due to the presence of ties in the data

Conductivity Discharge

Conductivity ρ : 0.057

P: 0.144

pH ρ : 0.287

P: <0.001 ρ : 0.070

P: 0.168

DOC ρ : 0.618

P: <0.001 ρ :

- 0.149

P: 0.031

Ca ρ : 0.920

P: <0.001 ρ :

- 0.060

P: 0.305

Mg ρ : 0.935

P: <0.001 ρ :

- 0.107

P: 0.066

Na ρ : 0.910

P: <0.001 ρ :

- 0.075

P: 0.129

K ρ : 0.773

P: <0.001 ρ :

- 0.109

P: 0.075

SO 4 ρ : 0.941

P: <0.001 ρ :

- 0.068

P: 0.247

Alkalinity ρ : 0.829

P: <0.001 ρ : 0.099

P: 0.381

Cl ρ : 0.752

P: <0.001 ρ : 0.004

P: 0.958



### Table B- 4. Results of Spearman rank tests for correlation ( ρ ) between the fifth quantile of values at

each site.

Probability values (P) are not exact due to the presence of ties in the data

Conductivity Discharge

Conductivity ρ : 0.056

P: 0.213

pH ρ : 0.382

P: <0.001 ρ : 0.032

P: 0.579

DOC ρ : 0.558

P: <0.001 ρ :

- 0.107

P: 0.134

Ca ρ : 0.920

P: <0.001 ρ : 0.017

P: 0.791

Mg ρ : 0.929

P: <0.001 ρ :

- 0.056

P: 0.383

Na ρ : 0.845

P: <0.001 ρ :

- 0.089

P: 0.078

### 1 07 K ρ : 0.694

P: <0.001 ρ :

- 0.065

P: 0.353

SO 4 ρ : 0.908

P: <0.001 ρ : 0.017

P: 0.790

Alkalinity ρ : 0.784

P: <0.001 ρ : 0.184

P: 0.102

Cl ρ : 0.706

P: <0.001 ρ : 0.034

P: 0.671



### To further illustrate these correlations, scatter plot matrices (or SPLOMs) were prepared for the first quartiles (Figure B

- 2) and fifth quantiles (Figure B

- 3). SPLOMs show scatter plots for each combination of parameters, arrayed as a matrix, with parameters labeled along the borders of the plot. Histogr ams for each parameter are plotted on the main diagonal. The correlations between conductivity and each of the BLM water quality parameters are apparent by examining the second row (from the top) of scatter plots in Figures B

- 2 and B

- 3. Likewise, the lack of correlation between these parameters and discharge is apparent in the top row of the scatter plots in these same figures.

### 108 Figure B

- 2. Scatter plot matrix for first quartile of site

- specific data for discharge (LNDISCH), conductivity (COND), and BLM wa ter quality parameters LNDISCH LNDISCH COND PH DOC CA MG NA K SO4 ALK CL LNDISCH

COND COND

PH PH

DOC DOC

CA CA

MG MG

NA NA

K K

SO4 SO4

ALK ALK

LNDISCH CL

COND PH DOC CA MG NA K SO4 ALK CL CL

### 109 Figure B

- 3. Scatter plot matrix for fifth quantile of site

- specific data for discharge (LNDISCH), conductivity (COND), and BLM water quality parameters

To understand why the correlations between conductivity and the other BLM wat er quality parameters are so much stronger for the low

- end distribution statistics than for the medians, it is necessary to

examine the site

- specific data itself. Figure B

- 4 is a SPLOM of the conductivity, discharge, and BLM water quality parameter data fo r a representative USGS station in Colorado. The histograms for conductivity, salts, and alkalinity are remarkable in that the distribution of each is clearly bimodal (i.e.,

two separate peaks are evident in the histograms). This was observed for many of t he sites in this dataset (not shown). In Figure B

- 5, the conductivity and discharge data for this site are plotted as a time series, which reveals why the water quality data are bimodal: high values of conductivity (> 5,000 micromhos/cm) occur when streamf low discharge is low, and low values of conductivity (< 2,000 micromhos/cm) occur when the discharge is high. At this station (and many others in this region), streamflow discharge is high in the May

- June period coinciding with snowmelt at higher elevation s. Feth and others (1964) reported conductivities of melted snow in the Western US ranging from about 2 to 42 picomhos/cm. Thus, the low values of conductivity (as well as concentrations of the salts and alkalinity) are the result of annual dilution from s now melt. At most other times, conductivity and salt and alkalinity concentration values are much higher. Depending upon how the water quality samples LNDISCH LNDISCH COND PH DOC CA MG NA K SO4 ALK CL LNDISCH

COND COND

PH PH

DOC DOC

CA CA

MG MG

NA NA

K K

SO4 SO4

ALK ALK

LNDISCH CL

COND PH DOC CA MG NA K SO4 ALK CL CL

### 110 are allocated at a site, the median concentration of these parameters may fall in either mode of the

bimo dal distribution, resulting in quite different values that appear almost random. Fortunately, the

low

- end distribution statistics avoid this seeming randomness because they consistently reflect sampling from the lower mode of the concentration distribution

.

Figure B

- 4. Scatter plot matrix of BLM water quality parameter data from NWIS Station 384551107591901 (Sunflower Drain at Highway 92, near Read, Delta County, Colorado) TEMP TEMP LNDISCH COND PH DOC CA MG NA K SO4 ALK CL TEMP LNDISCH LNDISCH

COND COND

PH PH

DOC DOC

CA CA

MG MG

NA NA

K K

SO4 SO4

ALK ALK

TEMP CL

LNDISCH COND PH DOC CA MG NA K SO4 ALK CL CL

### 111 Figure B

- 5. Time series plot of conductivity (diamond symbols) and discharge (o pen circles connected by dashed line) at Station 384551107591901 (Sunflower Drain at Highway 92, near Read, Delta County, Colorado)

Figure B

- 4 also illustrates that, in terms of explaining site

- specific variability, discharge is a much better predictive variable for a number of the BLM water quality parameters than conductivity. Each of these parameters ( alkalinity calcium, magnesium, sodium, potassium, sulfate

, and chloride) is clearly correlated to discharge, but not to conductivity. Discharge correlati ons are observed at many locations, and are commonly used to project water quality for various applications (Hem, 1985).

B.4 Discussion

Incorporating classical water quality correlation approaches, using conductivity and discharge as explanatory variables, wit hin the geostatistical approach prototyped by EPA, appears promising. Conductivity, but not discharge, is significantly correlated to BLM water quality parameters between sites, especially for the low

- end distribution statistics of interest for criteria ca lculations. Since conductivity data is abundant and it correlates well to BLM water quality parameters, it is reasonable to incorporate conductivity in spatial projections of BLM parameters. This may simplify the geostatistical approach and allow more robu st spatial extrapolation of BLM water quality parameters.

Conversely, discharge is correlated to concentrations of a number of BLM parameters (salts and alkalinity) within many sites. Streamflow is a good explanatory variable for a number of the BLM water quality parameters (the salts and alkalinity) because their variabilities largely reflect dilution at high flow rates. Discharge data are also plentiful, so we believe that incorporating classical methods of 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04conductivity (micromhos/cm)

-20 0 20 40 60 80 100 120discharge (cfs)

### 112 correlating concentration to discharge may be a useful means to address within

- station variability for BLM water quality parameters.

It should also be recognized that geostatistical and/or correlation approaches appear to most often fail for those water quality parameters which are the most sensitive an d important to the BLM, namely DOC and pH. Additional sampling effort will likely be required to address these deficiencies. In the case of pH, it is worth noting that many surface water sampling crews carry electronic multiparameter

instruments which meas ure pH, conductivity

, and temperature simultaneously in the field. Therefore, data collection strategies which incorporate these three measurements may be especially effective.

Measurement of DOC is considerably more difficult and expensive. It may be wort hwhile to investigate whether ultraviolet (UV) absorption spectroscopy could be used as a surrogate measurement technique for DOC. The organic ligands that bind metals are humic and fulvic compounds (HydroQual, 2005). At least some of these compounds can b e measured by UV absorption spectroscopy or related methods (Kalbitz et al., 2000; Wang and Hsieh, 2001), which may be easier and less expensive than DOC analysis.

B.5 References

Allen, H.E. and K.H. Mancy. 1972. Design of measurement systems for water analys is. In: Ciaccio, L.L., ed. Water and water pollution handbook. Marcel Dekker, Inc. New York, N.Y.

Carleton, J.N. 2006. An Examination of Spatial Trends in Surface Water Chemistry in the Continental United States: Implications for the Use of Default Values as Inputs to the Biotic Ligand Model for Prediction of Acute Metal Toxicity to Aquatic Organisms U.S. Environmental Protection Agency, Office of Water, Office of Science & Technology.

Chanat, J.G., Rice, K.C. and G.M. Hornberger. 2002. Consistency of patt erns in concentration

- discharge plots. Water Resources Research, 38 (8): 22

- 1.

Christensen, V.G., Jain, X. and A.C. Ziegler. 2005. Regression Analysis and Real

- Time Water

- Quality Monitoring to Estimate Constituent Concentrations, Loads, and Yields in the L ittle Arkansas River, South

- Central Kansas, 1995

- 99. U.S. Geological Survey, Water

- Resources Investigations
Report 00

- 4126

USEPA. 2002. Development of Methodologies for Incorporating the Copper Biotic Ligand Model (BLM) into Aquatic Life Criteria: Applicat ion of BLM to Calculate Site

- Specific Fixed Criteria. Prepared by Great Lakes Environmental Center (GLEC) for U.S. Environmental Protection Agency, Office of Science and Technology, Health and Ecological Criteria Division, Contract No.68

- C

- 980134

Work Assi gnment 3

- 38. 63 pp.

Feth, J. H., Roberson, C. E., and W.L. Polzer. 1964. Sources of mineral constituents in water from granitic rocks, Sierra Nevada, California and Nevada: U.S. Geological Survey Water

- Supply Paper

1535

- L 70 p.

Godsey, S.E. and J.W. Kirchn er. 2005. Concentration

- Discharge Relationships Across Temporal and Spatial Scales, Eos Trans. AGU, 86(52), Fall Meet. Suppl., Abstract H22B

- 04.

Hem, J.D. 1985. Study and Interpretation of the Chemical Characteristics of Natural Water. U.S. Geological Surv ey, Water Supply Paper 2254.

### 113 HydroQual, Inc. 2001. BLM

- Monte User’s Guide, Version 2.0. HydroQual, Mahwah, NJ. October, 2001.

HydroQual, Inc. 2005. Biotic Ligand Model Windows Interface, Version 2.1.2, User’s Guide and Reference Manual. HydroQual, Mahwah, NJ. June, 2005.

Kalbitz, K., Geyer, S. and W. Geyer. 2000. A comparative characterization of dissolved organic matter by means of original aqueous samples and isolated humic substances. Chemosphere. 40(12):1305

- 12, June 2000.

McCutcheon, S.C., Martin, J.L. and T.O. Barnwell, Jr. 1993. Water Quality, Section 11, Handbook of Hydrology, David Maidment, Ed., McGraw

- Hill, New York.

McDiffett, W.F., Beidler, A.W., Dominick, T.F. and K.D. McCrea. 1989. Nutrient concentration

- stream discharge relationships during storm events in a first

- order stream. Hydrobiologia. 179 (2) July,

1989.

Roberson, C.E., Feth, J.H., Seaber, P.R., and P. Anderson. 1963. Differences between field and laboratory determinations of pH, alkalinity, and specific conductance of natural water. U.S. Geological Survey Professional Paper 475

– C, p. C212

– C215.

Tyson, J. 1988. Analysis: What analytical chemists do. London: The Royal Society of Chemistry.

USEPA. 1997. Volunteer Stream Monitoring: A Methods Manual. United States Environmental

Protectio n Agency, Office of Water (4503F), EPA 841

- B

- 97

- 003, November, 1997.

Wang, G.S. and S.T. Hsieh. 2001. Monitoring natural organic matter in water with scanning spectrophotometer. Environ. Int. 26(4):205

- 12.

Wang, X. and Z.Y. Yin. 1997. Using GIS to assess the relationship between land use and water quality at a watershed level. Environment International. 23(1): 103

- 114.

### 114 

## Appendix C: Development of Tools to Estimate Biotic Ligand Model Parameters

C.1 Introduction

The U

. S

. Environmental Protection Agency ( EPA ) explored using regression models that project BLM water quality parameters from conductivity data for sites where there may be few or no data available to characterize water. We demonstrated previously (USEPA, 2007) that conductivity (specific

conductance ) is significantly correlated to Biotic Ligand Model (BLM) water quality parameters between a large number of monitoring sites in three western states (Colorado, Utah

, and Wyoming), especially for the low

- end distribution statistics of interest for site

- sp ecific fixed water quality criteria calculations. Since conductivity data are also abundant, it is reasonable to incorporate conductivity in spatial projections of BLM parameters.

C.2 Regression Analysis

Water quality data were retrieved from the U

. S

. Geolog ical Survey (USGS) National Water Information System (NWIS; http://waterdata.usgs.gov/nwis/qw ). We focused our efforts on data collected from rivers and streams in the western states of Colorado, Utah, and Wyoming between 1984 and 2005.

Data from these thr ee states were selected because conductivity was known to vary substantially, and the legacy of past mining in the region made the contamination of waterbodies by trace metals a possibility. Data collected prior to 1984 was excluded because a number of the analytical methods used by USGS prior to that date have been replaced by methods with improved precision and lower

detection limits . Furthermore, only sites with 40 or more samples were included in the analysis. Data were retrieved for all BLM water quali ty input parameters including pH, dissolved organic carbon ( DOC ) (or total organic carbon ( TOC ) , if no DOC data were available) and the geochemical ions (GIs) . We also retrieved discharge measurements and filtered (dissolved) copper concentration data, alt hough these data were not included in the regression analysis.

In work described in Appendix B , we found that the correlation structure between conductivity and the BLM water quality parameters was much stronger at the lower end of the concentration distri butions.

For various low

- end distribution statistics, all of the BLM water quality parameters were significantly correlated to conductivity, having non

- zero rank correlation coefficients at the 0.001 significance level. The correlation coefficients for pH and DOC were lower than for the GIs , but were nevertheless significant. We exploited this feature of the data in our current work. For each site, we estimated the

10 th 

### percentile (i.e., the value exceeded by 90% of the data) of conductivities and the 10 th 

### percentile of BLM water quality parameter values. We then fit regression models to project 10 th 

### percentiles of BLM parameter values as a function of 10 th 

### percentiles of conductivities.

We also fit regression models to the full NWIS dataset (data for all r ivers and streams sampled in Colorado, Utah, and Wyoming between 1984 and 2005). This was done out of concern that the lower percentile data might be skewed due to sampling bias, censoring, fewer sites, etc. The results of both approaches are presented bel ow.

C.2.1 pH
The following regression model appeared to be optimum for projecting the 10 th 

### percentile of pH from

the 10 th 

### percentile of conductivity at the sites for which appropriate data were available:

ln(pH) = 1.85 + 0.0352·ln(EC)

### 115 We did not fit a regression model to the full NWIS dataset, because no trend was evident between conductivity and pH.

C.2.2 DOC
The following regression model appeared to be optimum for projecting the 10 th 

### percentile of DOC

concentrations from the 10 th 

### percentile of conductivity at the si tes for which appropriate data were

available:

ln(DOC) = 0.671·ln(EC)

– 1.60

As with pH, we did not fit a regression model to the full NWIS dataset, since no trend was evident between conductivity and DOC.

C.2.3 Alkalinity

The following regression model appeared to be optimum for projecting the 10 th 

### percentile of alkalinity

concentrations from the 10 th 

### percentile of conductivity at the sites for which appropriate data were

available:

ln(alkalinity) = 1.14·ln(EC)

– 4.68

For the full NWIS dataset, the following reg ression model was developed to project alkalinity concentrations from conductivity:

ln(alkalinity) = 0.652·ln(EC) + 0.530

C.2.4 Calcium

The following regression model appeared to be optimum for projecting the 10 th 

### percentile of calcium

concentrations from the 10 th 

### percentile of conductivity at the sites for which appropriate data were

available:

ln(Ca) = 1.14·ln(EC)

– 4.35

For the full NWIS dataset, the following regression model was developed to project calcium concentrations from conductivity:

ln(Ca) = 0.866·ln(EC)

- 1.51

C.2.5 Magnesium

The following regression model appeared to be optimum for projecting the 10 th 

### percentile of magnesium concentrations from the 10 th 

### percentile of conductivity at the sites for which appropriate

data were available:

ln(Mg) = 1.27·ln(EC)

– 4.81

For the full NWIS dataset, the following regression model was developed to project magnesium concentrations from conductivity:

ln(Mg) = 0.986·ln(EC)

– 3.48

### 116 C.2.6 Sodium

The following regression model appeared to be optimum for projecting the 1 0 th 

### percentile of sodium

concentrations from the 10 th 

### percentile of conductivity at the sites for which appropriate data were

available:

ln(Na) = 0.578·ln(EC)

– 2.62

For the full NWIS dataset, the following regression model was developed to project sodium concentrations from conductivity:

ln(Na) = 1.32·ln(EC)

– 4.96

C.2.7 Potassium

The following regression model appeared to be optimum for projecting the 10 th 

### percentile of potassium concentrations from the 10 th 

### percentile of conductivity at the sites for which app ropriate

data were available:

ln(K) = 0.882·ln(EC)

– 3.29

For the full NWIS dataset, the following regression model was developed to project potassium concentrations from conductivity:

ln(K) = 0.647·ln(EC)

– 3.04

C.2.8 Sulfate

The following regression model appe ared to be optimum for projecting the 10 th 

### percentile of sulfate

concentrations from the 10 th 

### percentile of conductivity at the sites for which appropriate data were

available:

ln(SO 4 

### ) = 1.16·ln(EC)

– 4.85

For the full NWIS dataset, the following regressio n model was developed to project sulfate concentrations from conductivity:

ln(SO 4 

### ) = 1.43·ln(EC)

– 4.47

C.2.9 Chloride

For the full NWIS dataset, the following regression model was developed to project chloride concentrations from conductivity:

ln(chloride) = 1. 39·ln(EC)

- 6.15

Unfortunately, there were an insufficient number of sites reporting chloride data for a regression model to be developed for the 10th percentile of chloride.

C.3 Application of Conductivity Regressions

There are a number of ways in which the conductivity regressions could be used to project BLM water quality inputs. However, the most important situation may be when a fixed copper criteria value must be calculated for a site where there may be little data available to characterize water quality. In such cases, the regressions allow some or all of the BLM water quality inputs to be projected from either (1)

### 117 a limited number of conductivity measurements or (2) a low

- end conductivity value estimated by

geosta tistical or other methods. The first approach, projecting BLM water quality inputs from conductivity measurements, will be demonstrated in this section for a limited number of test sites. The second approach, projecting the BLM water quality inputs based o n conductivities estimated by geostatistical methods, is demonstrated in the following section (Section C 4).

The regression models presented above for projecting BLM water quality inputs from conductivity were tested using data and BLM predictions from a number of sites. For each site, a fixed copper criteria value was calculated using the Monte Carlo method described in EPA 2002. The BLM version 2.2.3 was used for all BLM calculations. Fixed copper criteria values were determined by the Monte

Carlo metho d, utilizing site

- specific data for parameter distributions and variance

- covariance structure of all BLM water quality parameter inputs as well as filtered copper concentrations. The test sites (below) were selected on the basis of convenience, number of w ater quality observations, and geographic location.

The BLM water quality inputs projected from the conductivity regressions are low

- end percentiles appropriate for predicting the instantaneous criterion (IC) predicted by the BLM to estimate the fixed

site criteria (FSC) value. We suggested this approximation previously, based on the observation that protective FSC for copper generally corresponded to approximately the 2.5 th 

### percentile of the distribution of IC predicted by the BLM

. BLM estimates made for a site using the corresponding percentiles of the water quality parameter distributions will be a conservative approximation of this protective criteria values. For the present work, we are using this approach to test the 10 th 

### percentile water quality param eter values projected from the conductivity regressions.

Previously we noted that filtered copper concentrations were correlated to BLM input water quality parameters at many sites. Furthermore, we found that the degree of correlation between copper

concen trations and BLM input parameters appeared to be an important site

- specific factor in determining the relationship between the FSC and the IC. Copper concentrations are not required to run the BLM in its toxicity prediction mode, but they are used in the M onte Carlo method to determine the FSC. Because of this, we calculated the FSC both with and without (neglecting) the correlation between copper concentrations and BLM input parameters at each test site.

C.3.1 Naugatuck River, Connecticut

The USGS has sampled th e Naugatuck River near Waterville, Connecticut (Station 01208049) since
1967. Ninety

- one water samples collected since 1984 provided near

- concurrent measurements of all BLM water quality inputs and filtered copper concentrations. The water is low in hardne ss and alkalinity, slightly acidic (mean pH = 7.32), and fairly low in conductivity (10 th 

### percentile = 134 μS/cm at 25o C). Organic carbon concentrations are representative for rivers and streams in this region and nationwide (logmean TOC = 4.02 mg/L), and the filtered copper concentrations are low (logmean filtered copper = 3.62 μg/L). The FSC for copper at this site was calculated to be 11.4 μg/L when the correlation between copper concentrations and BLM parameters was considered, and 7.0 μg/L when

this c orrelation was neglected. Test results at this site are show in Table C

- 1

.

### 118 Table C

- 1. Copper Fixed Site Criterion predictions for the Naugatuck River, Connecticut using various

calculation methods

Calculation Method DOC (mg/L) pH Geochemical Ions Copper Fi xed

Site Criterion (μg/L)

Monte Carlo FSC with [copper] correlated to inputs (r =0.7) Data Data Data 11.4

Monte Carlo FSC with no [copper] correlation Data Data Data 7.0

IC calculated with 10 th % of input

data 2.8 (10 th % of data) 7.1 (10 th % of data) (10 th % of data) 6.4

IC calculated with input from

10 th % of conductivity and

correlations 5.49 (projected from correlations) 7.55 (projected

from correlations) (projected from correlations) 21.5

IC calculated with input from

10 th % of conductivity and correlations except DOC 2.7 (10 th % from L3 ecoregion) 7.55 (projected

from correlations) (projected from correlations) 10.5

IC calculated with input from

10 th % of conductivity and correlations except pH & DOC 2.7 (10 th % from L3 ecoregion) 7.1 (10 th % of data) (projected from correlations) 5.7

IC calculated with input from

10 th % of conductivity and correlations except pH & DOC 2.8 (10 th % of data) 7.1 (10 th % of data) (projected from correlations) 5.9



### The BLM was then applied to predict IC for copper, using low

- end percentiles of the measured BLM water quality inputs. Using the 10 th 

### percentile values of all measured input data, an IC of 6.4 μg/L was predicted. This IC is 43% smaller than the FSC calculated consider ing the copper correlation, but only 21% smaller than the FSC neglecting this correlation. When the 10 th 

### percentiles of all of the BLM water quality inputs were instead projected from conductivity using the regression models, the predicted ICs
were 21.7 μg /L (using the regressions based on 10 th 

### percentiles of the three

- state data) and 21.5 μg/L (using the regressions based on all of the data). These results illustrate two important points. First, the BLM predictions based on water quality inputs all projected from conductivity correlations are quite different from BLM predictions based on site data; this will be further considered below. Secondly, however, the BLM predictions based on projected water quality inputs do not really depend on which

correlations are used.

Clearly, this result shows that the regression models are unable to accurately project all BLM water quality inputs at this site. However, this was almost entirely due to inaccuracy in the pH and organic carbon projections. To demonstrate this, we recalculated the IC several times, using better estimates of the organic carbon and/or pH data, but all other BLM water quality inputs projected from conductivity using the regression models. The first recalculation was made using the 10 th 

### percentile of DOC from rivers and streams in the Northeastern Coastal Zone, the Level III ecoregion where the

### 119 Naugatuck River is located

. 7 

### In this case the predicted IC was 10.5 μg/L, a value much closer to the FSCs as well as the IC calculated using the 10 th 

### percentile values of all the measured input data. A second recalculation was made using the 10 th 

### percentile of the pH data, together with the ecoregional 10 th



### percentile of DOC and all other BLM water quality inputs projected from conductivity using t he regression models. Finally, a third recalculation was made in which the 10 th 

### percentiles of both pH and DOC data were input, with the remaining BLM water quality inputs projected from conductivity using the regression models. For both of these cases, th e ICs were within about 10% of the prediction made using the 10 th 

### percentile values of all the measured input data. In summary, if BLM predictions are made for copper IC using measured values of pH and organic carbon, minimal error results from
projecting the other BLM water quality inputs using conductivity and the regression models. As will be shown in the following sections, the same result was found for the other test sites.

The correlation between filtered copper concentrations and BLM parameters and o utput was quite strong at this location (r = 0.70 between filtered copper and IC predictions). As a result, the FSC corresponds to an elevated percentile (40%) of the IC predictions. If this correlation is neglected in the
Monte Carlo method, the FSC corre sponds to only the 14 th 

### percentile of the IC predictions. This suggests that the relationship between FSC and IC (in terms of the percentile of the IC distribution corresponding to the FSC) may be somewhat site

- specific. Regardless of this complication, th e conductivity regressions appear to project reliable low

- end percentile estimates of the BLM water quality inputs other than pH and organic carbon. This was demonstrated by repeating the analysis described above using 5 th 

### , 2.5 th



### , and 1 st 

### percentile input values and projections, each of which produced comparable results (not shown).

C.3.2 San Joaquin River, California

The USGS has sampled the San Joaquin River near Vernalis, California (Station 1130500) since 1950. Water samples collected since 1984 provided 283 near

- concurrent measurements of all BLM water quality inputs and 77 filtered copper concentrations. The water has moderate values of hardness and alkalinity, neutral pH, and moderately high conductivity (10 th 

### percentile = 307 μS/cm at 25o C). DOC

concentra tions are representative for rivers and streams in this region and nationwide (logmean DOC = 5.35 mg/L), and the filtered copper concentrations are low (logmean filtered copper = 1.75 μg/L). The FSC for copper at this site was calculated to be 39.1 μg/L, a nd the correlation between copper concentrations and BLM parameters was strong (r = 0.624 between filtered copper and IC predictions). This FSC value corresponds to the 46 th 

### percentile of the distribution of IC. When the FSC for copper was

recalculated ass uming no correlation between copper concentrations and BLM parameters, the value decreased to 11.1 μg/L (corresponding to the 4.5 th 

### percentile of the IC distribution). Test results at this

site are tabulated Table C

- 2

.

7 Ecoregi on and water body

- type specific DOC concentration percentiles were tabulated for the Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000), Technical Support Document Volume 2: Development of National Bioaccumula tion Factors (EPA

- 822

- R

- 03

- 030).

### 120 Table C

- 2. Copper Fixed Site Criterio n predictions for the San Joaquín River, California using various

calculation methods

Calculation Method DOC (mg/L) pH Geochemical Ions Fixed Site

Criterion (μg/L)

Monte Carlo FSC with [copper] correlated to inputs (r =0.6) Data Data Data 39.1

Monte Carlo FSC with no [copper] correlation Data Data Data 11.1

IC calculated with 10 th % of input

data 2.7 (10 th % of data) 7.5 (10 th % of data) (10 th % of data) 11.9

IC calculated with input from

10 th % of conductivity and

correlations 9.38 (projected

from correlations) 7.77 (projected

from correlations) (projected from correlations) 54.0

IC calculated with input from

10 th % of conductivity and correlations except DOC 2.79 (10 th % from L3 ecoregion) 7.77 (projected

from correlations) (projected from correlations) 16.0

IC calculated with input from

10 th % of conductivity and correlations except pH & DOC 2.79 (10 th % from L3 ecoregion) 7.5 (10 th % of data) (projected from correlations) 11.6

IC calculated with input from

10 th % of conductivity and

corr elations except pH & DOC 2.7 (10 th % of data) 7.5 (10 th % of data) (projected from correlations) 11.2



### The BLM was then applied to predict IC for copper, using low

- end percentiles of the BLM water quality inputs. Using the 10 th 

### percentile values of all measured input data, an IC of 11.9 μg/L was predicted, which is 70% smaller than the FSC calculated considering the copper concentration correlation but 7% higher than the FSC neglecting this correlation. When the 10 th 

### percentiles of all of the BLM water quality inputs were instead projected from conductivity using the regression models, the predicted IC were 50.0 μg/L (using the regressions based on 10 th 

### percentiles of the three

- state data) and 54.0 μg/L (using the regressions bas ed on all of the data). Again, the BLM predictions based on projected water quality inputs do not really depend on which correlations are used. And, as was the case at the Naugatuck River site, the regression models were unable to accurately project all BLM water quality inputs at this site, although the error is again almost entirely due to inaccuracy in the pH and organic carbon projections. As in the previous case, we demonstrated this by recalculating the IC several times, using better estimates of the organic carbon and/or pH data, but all other BLM water quality inputs projected from conductivity using the regression models. The first recalculation was made using the

10 th 

### percentile of DOC from rivers and streams in the Central California Valley, the L evel III ecoregion where the San Joaquin River is located. In this case the predicted IC was 16.0 μg/L, a value much closer to the uncorrelated FSCs as well as the IC calculated using the 10 th 

### percentile values of all the measured input data. A second reca lculation was made using the 10 th 

### percentile of the pH data, together with the ecoregional 10 th 

### percentile of DOC and all other BLM water quality inputs projected from conductivity using the regression models. Finally, a third recalculation was made in which the 10 th



### percentiles of both pH and DOC data were input, with the remaining BLM water qualit y inputs projected from conductivity using the regression models. For both of these cases, the ICs were within

### 121 about 5% of the prediction made using the 10 th 

### percentile values of all the measured input data. BLM predictions made for copper IC at this site using measured values of pH and organic carbon, but all other BLM water quality inputs projected using conductivity regressions, were found to be accurate in comparison to model predictions made using all measured input data.

C.3.3 South Platte River, Colorado

T he South Platte River has been sampled by the USGS at Denver, Colorado (Station 06714000) since 1972. Water samples collected since 1984 provided 93 near

- concurrent measurements of all BLM water quality inputs and 10 filtered copper concentrations. The water is moderately high in hardness and alkalinity, neutral pH, and moderate conductivity (10 th 

### percentile = 229 μS/cm at 25° C). Organic carbon concentrations are representative for rivers and streams in this region (logmean DOC = 5.50 mg/L), and the filter ed copper concentrations are low (logmean filtered copper = 3.27 μg/L). The FSC for copper at this site was calculated to be 35.4 μg/L. This FSC value corresponds to the 32 nd 

### percentile of the distribution of IC. Moderate correlation between copper concent rations and BLM parameters was observed at this site (r = 0.50 between filtered copper and IC predictions). When the FSC for copper was recalculated assuming no correlation between copper concentrations and BLM parameters, the value decreased to 20 μg/L (c orresponding to the 4.3 rd 

### percentile of the IC distribution). Test results at this site are shown in Table C -3.

Table C

- 3. Copper Fixed Site Criterion predictions for the South Platte River, Colorado using various

calculation methods

Calculation Method DO C (mg/L) pH Geochemical Ions Copper Fixed

Site Criterion (μg/L)

Monte Carlo FSC with [copper] correlated to inputs (r =0.5) Data Data Data 35.4

Monte Carlo FSC with no [copper]

correlation Data Data Data 20.0

IC calculated with 10 th % of input

data 4.1 (10 th % of data) 7.5 (10 th % of data) (10 th % of data) 17.3

IC calculated with input from 10 th % of conductivity and correlations 7.7 (projected from correlations) 7.7 (projected

from correlations) (projected from correlations) 37.5

IC calculated with input from 10 th % of conductivity and correlations
except DOC 4.5 (10 th % from L3 ecoregion) 7.7 (projected

from correlations) (projected from correlations) 21.6

IC calculated with input from 10 th % of conductivity and correlations except pH & DOC 4.5 (10 th % from L3 ecoregion) 7.5 (10 th % of data) (projected from correlations) 17.3

IC calculated with input from 10 th % of conductivity and correlations except pH & DOC 4.1 (10 th % of data) 7.5 (10 th % of data) (projected from correlations) 15.9

### 122 The BLM wa s applied to predict IC for copper, using low

- end percentiles of the BLM water quality inputs. Using the 10 th 

### percentile values of all measured input data, an IC of 17.3 μg/L was predicted, which is 51% smaller than the FSC calculated considering the coppe r concentration correlation but only 14% smaller than the FSC neglecting this correlation. When the 10 th 

### percentiles of all of the BLM water quality inputs were instead projected from conductivity using the regression models, the predicted IC was 37.5 μg/L . As was the case at the previous sites, the regression models were again unable to accurately project pH and organic carbon concentrations for input to the BLM. We demonstrated this by recalculating the IC several times, using better estimates of the orga nic carbon and/or pH data, but all other BLM water quality inputs projected from conductivity using the regression models. The first recalculation was made using the 10 th 

### percentile of DOC from rivers and streams in the Western High Plains, the Level III e coregion where the South Platte River is located. In this case the predicted IC was 21.6 μg/L, a value much closer to the uncorrelated FSCs as well as the IC calculated using the 10 th



### percentile values of all the measured input data. A second recalculation was made using the 10 th



### percentile of the pH data, together with the ecoregional 10 th 

### percentile of DOC and all other BLM water quality inputs projected from conductivity using the regression models. Finally, a third

recalculation was made in which the 10 th 

### percentiles of both pH and DOC data were input, with the remaining BLM water quality inputs projected from conductivity using the regression models. For both of these cases, the ICs were within 10% of the prediction made using the 10 th 

### percentile values of all the measured input data. As with the previous cases, the BLM predictions made for copper IC at this site using measured values of pH and organic carbon, but where all other BLM water quality inputs were projected using conductivity regressions, wer e found to be accurate in comparison to model predictions made using all measured input data.

C.3.4 Halfmoon Creek, Colorado

The USGS has sampled Halfmoon Creek near Malta, Colorado (Station 07083000) since 1959. Seventy

three water samples collected since 1984 provided near

- concurrent measurements of all BLM water quality inputs and 18 filtered copper concentrations. The water is very low in hardness and alkalinity, slightly acidic (mean pH = 7.76), and low in conductivity (10 th 

### percentile = 50.1 μS/cm at 25° C) . Organic carbon concentrations are low (logmean DOC = 0.92 mg/L), as are the filtered copper concentrations (logmean filtered copper = 1.75 μg/L). The FSC for copper at this site was calculated to be 1.56 μg/L, corresponding to the 6 th 

### percentile of the d istribution of IC. The correlation between copper concentrations and BLM parameters was negligible at this site, so the Monte Carlo FSC were not calculated twice (i.e., with and without the copper correlation) as was done at the other sites. Test

results a t this site are shown in Table C

- 4

.

### 123 Table C

- 4. Copper Fixed Site Criterion predictions for the Halfmoon Creek, Colorado using various

calculation methods

Calculation Method DOC (mg/L) pH Geochemical Ions Copper Fixed

Site Criterion (μg/L)

Monte Carlo FSC with [copper] correlated to inputs (r =0.01) Data Data Data 1.56

IC calculated with 10 th % of input

data 0.6 (10 th % of data) 7.2 (10 th % of data) (10 th % of data) 1.42

IC calculated with input from 10 th % of conductivity and correlations 2.8 (projected from correlations) 7.3 (projected

from correlations) (projected from correlations) 7.43

IC calculated with input from 10 th % of conductivity and correlations
except DOC 0.6 (10 th % from L3 ecoregion) 7.3 (projected

from correlations) (projected from correlations) 1.58

IC calculated with input from 10 th % of conductivity and correlations except pH & DOC 0.6 (10 th % from L3 ecoregion) 7.2 (10 th % of data) (projected from correlations) 1.39

IC calculated with input from 10 th % of conductivity and correlations except pH & DOC 0.6 (10 th % of data) 7.2 (10 th % of data) (projected from correlations) 1.39



### The BLM was then applied to predict IC for copper, using low

- end percentiles of the BLM water quality inputs. Using the 10 th 

### percentile values of all measured input data, an IC of 1.42 μg/L was predicted, only 9% smaller than the FSC. When the 10 th 

### percentiles of all of the BLM water quality inputs were instead projected from conductivity using the regression models, the predic ted IC was 7.43 μg/L. Again, this result clearly shows that the regression models are unable to accurately project all BLM water quality inputs at this site. As in the previous examples, this was almost entirely due to inaccuracy in the pH and organic carb on projections. As in the previous cases, we demonstrated this by recalculating the IC several times, using better estimates of the organic carbon and/or pH data, but all other BLM water quality inputs projected from conductivity using the regression models. The first recalculation was made using the 10 th 

### percentile of DOC from rivers and streams in the Southern Rockies, the Level III ecoregion where Halfmoon Creek is located. In this case the predicted IC was 1.58 μg/L, a value within

about 10% of the FSC as well as the IC calculated using the 10 th 

### percentile values of all the measured input data. A second recalculation was made using the 10 th 

### percentile of the pH data, together with the ecoregional 10 th 

### percentile of DOC and all other BLM water quality inp uts projected from conductivity using the regression models. Finally, a third recalculation was made in which the 10 th 

### percentiles of both pH and DOC data were input, with the remaining BLM water quality inputs projected from conductivity using the regress ion models. For both of these cases, the ICs were within about 2% of the prediction made using the 10 th 

### percentile values of all the measured input data. As in the previous examples, if BLM predictions are made for copper IC using measured values of pH and organic carbon, minimal error results from projecting the other BLM water quality inputs using conductivity and the regression models.

### 124 C.3.5 Summary of Site

- Specific Test Results

The results of this work can be summarized as follows:

Regression models were deve loped to project 10 th 

### percentiles of BLM water quality parameters from

the 10 th 

### percentile of conductivity distributions at sites in Colorado, Utah

, and Wyoming. The regression models were tested using data and copper BLM predictions for four sites, and pr oduced highly consistent results. The regression models for pH and DOC, the most sensitive of BLM water quality parameters, were not sufficiently accurate to make reliable BLM predictions. However, regression models for the GI parameters ( alkalinity, calci um, magnesium, sodium, potassium, sulfate,

and chloride, ) were reasonably accurate, as judged by comparison of model predictions made using projected values of the GI BLM input parameters to model predictions made using all measured input data. The regres sion models used to project GI parameters from conductivity were calculated two different ways; however, the BLM predictions of IC were not sensitive to this difference.

We were unable to find an estimate for site

- specific pH that was superior to the (admi ttedly poor) conductivity regression. To improve upon this estimate it was necessary to use actual site

- specific pH data. This appears to be the general case for reliable site

- specific BLM application.

For DOC, the ecoregion and water body

- type specific DO C concentration percentiles tabulated by EPA for the National Bioaccumulation Factors Technical Support Document appear to be far better

estimates of lower

- percentile DOC concentrations than the projections made using the conductivity regression. These tab ulations are based on an organic carbon database compiled prior to 2003 from a number of sources including EPA’s STOrage and RETrieval Data Warehouse ( STORET ) and the USGS NWIS. The utility of these tabulations could be improved by updating them to incorpo rate newer information. For example, EPA recently released data from the Wadeable Stream Assessment, which included DOC measurements from a statistically based random sample of ~2,000 streams. Other statistically

- based national water quality surveys, inclu ding national assessments of lakes and large rivers, will also be providing additional data in future years.

The Monte Carlo method developed to calculate FSC for copper was applied at each of the four sites,

both with and without the correlation between f iltered copper concentrations and the BLM water quality parameters that were found to be significant at three of the sites. We also approximated the FSC using the 10 th 

### percentile of the distribution of IC predicted by the BLM at each site. When copper concentration correlations were considered in the FSC calculations, the 10 th 

### percentile of the IC

distributions w as found to be highly conservative approximations of the F SC, underestimating the FSC by 44 to 70%. This is illustrated in Figure C

- 1 , which also shows the good agreement between IC predicted with the BLM using site

- specific data and IC predicted using measured pH and organic carbon but projected values of the GI BLM input parameters. Ecoregion and water body

- type specific DOC concentration percentiles (“L3

- DOC” in the figure below) were also an improvement over the projections based on conductivity regressions.

### 125 Figure C

- 1. Instantaneous Criteria (IC) predicted with the BLM using site

- specific data and IC predicted using measured pH and organic carbon and projected values of the GI BLM input

parameters

When copper concentration correlations were neglected in the FSC calculations, the 10 th 

### percentile of

the IC di stributions did a much better job approximating the FSC. This is shown in Figure C

- 2 . In this case, the 10 th 

### percentile of the IC distributions w as within 15% of the FSC. This figure also shows the good agreement between IC predicted with data and projecte d values of the GI BLM input parameters. 0 10 20 30 40 50 60

0 5 10 15 20 25 30 35 40 45

Copper FSC (ug/L) calculated with copper concentration correlationCopper IC (ug/L) IC (10th % of data)

IC (10th % of conductivity & correlations)

IC (10th % conduct/correl. except L3-DOC)

IC (10th % conduct/correl. except pH & DOC)

ISC = FSC

### 126 Figure C

- 2. 10 th 

### percentile of the IC distributions using data and projected (predicted) values of the GI
BLM parameters

The degree of correlation between filtered copper concentrations and BLM input water qualit y parameters appears to be an important site

- specific factor in determining the relationship between the

FSC and the IC. Figure C

- 3 plots the percentile of the IC corresponding to the FSC for each site as a

function of the correlation coefficient between t he copper concentrations and the IC, for two cases: (1) FSC calculated by the Monte Carlo method including the observed correlations between concentrations of copper and BLM input water quality parameters, and (2) FSC calculated with no

correlation between concentrations of copper and BLM input water quality parameters. In the first case (plotted with dark diamond symbols), the percentile of the IC corresponding to the FSC increases substantially (6 th 

### to 46 th 

### percentile) as the correlation coefficient betwe en the copper concentrations and the IC increases. If the correlation between concentrations of copper and BLM input water quality parameters is neglected (the second case, plotted in lighter square symbols), the percentile of the IC corresponding to the F SC is considerably lower (4.3 rd 

### to 14 th 

### percentile). This suggests that correlations between copper concentrations and BLM input parameters should be given careful consideration when calculating FSC. 0 10 20 30 40 50 60

0 5 10 15 20 25

Copper FSC (ug/L) calculated with no copper concentration correlationCopper IC (ug/L) IC (10th % of data)

IC (10th % of conductivity & correlations)

IC (10th % conduct/correl. except L3-DOC)

IC (10th % conduct/correl. except pH & DOC)

ISC = FSC

### 127 Figure C

- 3. Percentile of the IC corresponding to the FSC for each site as a function of the correlation

coefficient between the copper concentrations and the IC when the FSC is calculated with Copper correlation and when FSC is calculated without Copper correlation

C.4 Combining GI

- Conductivity Regressions with Geostatistical Techniques

Geostatistical techniques are attractive because they explain parameter variation arising from spatial correlations, which are otherwise ignored by (and may, in fact, violate the assumptions of) conventional statistics. BLM input water quality parameters (except for pH and DOC) are GIs, the concentrations of which vary in surface water due to dissolution, weathering, ground water

- surface water interactions, and other geologic processes in the watershed. Consequently, the concentra tions of GI parameters tend to vary according to the regional geology. For example, water hardness has noticeable geographic trends. Areas with limestone geology, such as in the prairie states, tend toward high hardness and alkalinity. Areas of with granit e geology, such as parts of the Northeast, tend toward low hardness and alkalinity. The estimation of GI parameter values based on geography thus seems possible. EPA has provided a prototype of a geostatistical approach 8 

### that demonstrated this potential.

T hat work applied kriging to predict median concentrations of five of the BLM water quality input parameters ( pH, DOC, alkalinity, sodium, and calcium) averaged over 8

- digit HUCs, using the USGS NWIS as the source of spatial data. Comparison of measured con centrations with kriging predictions were encouraging, especially for DOC and alkalinity. Geostatistical techniques to project BLM GI input 0 5 10 15 20 25 30 35 40 45 50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Correlation coefficient btwn. IC and filtered copper concentrationsPercentile of IC corresponding to FSC FSC calculated with copper correlation
FSC calculated with no copper correlation

### 128 parameters might well be developed from the same nationwide monitoring data used to develop a correlation approach. By the same token, geostatistical techniques based on these data may suffer the same problems experienced when developing the correlation approach. Most significantly, the NWIS data are not randomly distributed in either time or space, and the measurements of the BLM GI parameters are generally uneven (considerable differences in terms of the number of observations for different parameters) and/or inconsistent (i.e., relatively few concurrent measurements of BLM GI parameters).

There may be great value in supplementing the geostatistical approach with classical estimation methods, such as regression and correlation. Examination of the NWIS data suggests that conductivity may be useful for estimating BLM input water quality parameters in conjunction with ge ostatistics. The literature indicates that conductivity is one of the most widely monitored water quality indicators in the US. Among water quality parameters, the data for conductivity are the most complete and cover the longest time period (Wang and Yin, 1997). In part, this is because conductivity measurements are usually included in automated multiparameter systems for monitoring changes in the quality of surface waters (Allen and Mancy, 1972). A vast amount of conductivity data exists, both in terms of the total number of observations and the number of sites reporting this parameter in comparison to the BLM GI quality parameters. For example, NWIS data for the state of Colorado have almost four times as many observations of conductivity as for calcium, and they are measured at more than twice the number of sites. There are 20 times as many observations of conductivity as for alkalinity, and they are measured at more than seven times the number of sites. Since conductivity data are abundant, and correlate well to the BLM GI parameters (GLEC, 2007), it is reasonable to incorporate conductivity in spatial projections of BLM parameters. This may simplify the geostatistical approach and allow more robust spatial projections of BLM water quality parameters.

Alt hough combining GI

- conductivity regressions with geostatistical techniques seems promising for the reasons mentioned above, this approach ha d never been demonstrated. W e conducted a simple test using NWIS conductivity and hardness data from the state of Co lorado. We used data from Colorado because many more stations were sampled in comparison to the surrounding states.

The data were processed in a manner similar to the methods used to develop the regressions in

Section C. 2. For each station, we calculated the 10 th 

### percentiles of conductivity and hardness. A regression model was fit to the full dataset (data for all rivers and streams sampled in Colorado

between 1984 to 2005). The following regression model was developed to project hardness from conductivity:

ln(hardness) = 0.984·ln(EC)

– 0.870

We also kriged the 10 th 

### percentiles of conductivity and hardness, using latitude and longitude coordinates reported by USGS for each sampling station. Figure C

- 4 shows the kriged surface of the

10 th 

### percent ile of conductivity at all stations in Colorado, Utah

, and Wyoming. Data are far more abundant in Colorado, as shown by the density of the dots representing the locations of sampling
stations. Figure C

- 5 shows the kriged surface of the 10 th 

### percentile of h ardness at all stations in Colorado. Kriging was done using the Vertical Mapper program, version 3.1; no attempts were made to optimize the kriging of conductivity or hardness by parameter adjustment.

### 129 Figure C

- 4 . Kriged surface of the 10th percentile of conductivity at all stations in Colorado, Utah and Wyoming

Dots represent sampling stations; notice that data are far more abundant in Colorado

.

Figure C

- 5 . Kriged surface of the 10th percentile of hardness at all stations in Colorado

Our goal was to s ee whether combining the kriged conductivities with the conductivity

- hardness regression would project the 10 th 

### percentiles of hardness better than direct kriging of the hardness

### 130 data. For the combined kriging/regression approach, we determined the kriged conductivity values at all of the sampling locations and then projected the 10 th 

### percentiles of hardness at these locations using the regression equation. We also determined the directly

- kriged 10 th 

### percentiles of hardness at all of the sampling locations.

The hardness estimates obtained by each approach were then compared to the 10 th 

### percentiles of hardness measured at each station. The results of this comparison are shown graphically in Figure C

- 6

. Both approaches produce estimates of hardness that corr elate significantly with the measured data (correlation coefficient r= 0.80 for direct kriging of hardness; r= 0.950 for conductivity kriging + regression projection). However, the kriging+regression approach fits the hardness data substantially

better tha n direct kriging. To quantify this, we calculated the residual sum of squares (RSS), a composite measure of the discrepancy between the data and our alternative hardness estimates. The smaller this discrepancy is, the better the estimation will be. In natu ral log space, the RSS for the kriging+regression approach is 18.6 (135 degrees of freedom, or df) while the log

- space RSS for the direct kriging approach is 73.4 (136 df). Thus, for this test case substantially better estimates of the 10 th



### percentile of h ardness were made by the kriging/regression approach compared to direct kriging.

Figure C

- 6 . Comparison of the 10 th 

### percentile of hardness at all stations in Colorado with estimates based on (a) direct kriging of hardness data and (b) kriging of conduct ivity to station locations and projecting conductivity to hardness via re gression (“kriging/regression”)

As this test demonstrated, combining kriging with regressions to project BLM GI inputs from conductivity appears to improve the accuracy of estimates of parameters used as BLM inputs. Applying the conductivity kriging/regression projection approach on a br oader scale should be considered as a 1 100 10000

1 100 10000

10th percentile hardness (data)10th percentile hardness (kriged or projected) kriged 10th % of hardness

projected 10th % of hardness

1:1 line (perfect fit)

### 131 “next step” in developing tools to estimate BLM water quality parameters for sites where there may be few or no data available to characterize water quality. Since direct kriging of most BLM GI parameters has already b een done using data from NWIS, it will also be worthwhile to continue comparing the

alternative estimates to the observed data in order to obtain the best estimates.

We should also note that although the kriging/regression approach can be used to improve the accuracy of estimates of GI parameters used as BLM inputs, this approach cannot be expected to produce accurate site

- specific estimates for the two most important BLM inputs: pH and DOC. As

shown in Section C

. 3, accurate estimates of the GI parameters are less important than pH and DOC in terms of predicting appropriate site

- specific IC and FSC. Since our analysis of NWIS data indicates there

to be either little no trend between conductivity and pH, and direct kriging produced similarly ambiguous predictions, we must conclude that site

- specific data for pH must either be available or be collected for BLM application at a site. This may not be a significant obstacle, since pH data can be cheaply and readily acquired.

Lack of methods to accurately estimate DOC is a bigger problem, since measurements of this parameter are comparatively rare and DOC is a relatively expensive measurement to make. For DOC, analysis of NWIS data again indicates no trend with conductivity, so the kriging/regression approach is not appropriate for this parameter. However, other analys es conducted suggested that DOC could be kriged with some success. And, as was demonstrated for the test sites in Section C

. 3, the ecoregion and water body

- type specific DOC concentration percentiles tabulated by EPA for the National Bioaccumulation Factors Technical Support Document appear to offer reasonable estimates of lower

percentile DOC concentrations. Furth er development of these approaches for estimating site

- specific DOC appears worthwhile, for example by incorporating new data from the Wadeable Stream Assessment and other statistically

- based national water quality surveys.

C.5 References

Allen, H.E. and K.H. Mancy. 1972. Design of measurement systems for water analysis. In: Ciaccio, L.L., ed. Water and water pollution handbook. Marcel Dekker, Inc. New York, N.Y.

USEPA. 2002. Development of Methodologies for Incorporating the Copper Biotic Ligand Model into
Aqu atic Life Criteria: Application of BLM to Calculate Site

- Specific Fixed Criteria. GLEC Work
Assignment 3

- 38, Contract No. 68

- C

- 98

- 134. 63p plus figures.

USEPA. 2007. Approaches for Estimating Missing BLM Input Parameters: Correlation approaches to

estimate BLM input parameters using conductivity and discharge as explanatory variables. GLEC Work Assignment 2

- 34, Task 1, Subtask 1

- 7. Contract No. 68

- C

- 04

- 006. 18 p.

Wang, X. and Z.Y. Yin. 1997. Using GIS to assess the relationship between land use and water qu ality at a watershed level. Environment International. 23(1): 103

- 114.

### 132 

## Appendix D: Approaches for Estimating Missing Biotic Ligand Model Input Parameters: Projections of Total Organic Carbon as a Function of Biochemical Oxygen Demand

D.1 Introduction

The 2007 U pdate of the Ambient Water Quality Criteria for Copper (EPA

- 822

- R

- 07

- 001 ) employs the Biotic Ligand Model (BLM) to estimate bioavailability of this metal in toxicity tests used in Criterion

Maximum Concentration derivation , which require s data on the 10 in put parameters for the BLM, including dissolved organic carbon (DOC)

. D ata for DOC concentrations, in both effluents and receiving waters, are extremely limited. T he BLM is very sensitive to DOC concentrations (HydroQual, 2005),

which means that to ensure accurate predictions of copper bioavail a bility and toxicity reliable data on

DOC concentrations in the water are needed . Effluent DOC concentrations, which are necessary for application of the BLM to predict copper toxicity associated with a wastewater dis charge, are monitored by very few publicly

- owned treatment works (POTWs).

Projections of DOC concentrations from biochemical oxygen demand (BOD) values may be a viable solution for surmounting the lack of data on DOC. Effluent BOD (most typically 5

- day BO D) is monitored by most POTWs. We expect a positive correlation between BOD and DOC, because the two parameters are conceptually related. While DOC quantifies the concentration of many organic compounds dissolved in water, BOD is a routine surrogate test f or estimating the load of organic carbon into the environment. Ideally, one might expect an almost stoichiometric relationship between organic carbon (i.e., DOC) and the oxygen consumed during its metabolization (i.e., BOD). For instance, Fadini et al. (20 04) evaluated the possible replacement of BOD for DOC measurements in a number of different wastewater categories. A statistical relationship between effluent BOD and DOC would provide estimates of DOC concentrations, needed for application of the BLM, fro m routine BOD monitoring data. The effluent contribution to in

- stream DOC could then be estimated, for example, by using a dilution model for a site.

Evidence, from analyses of effluent monitoring data from the New York State Department of

Environmental Co nservation (NYSDEC) Contaminant Assessment and Reduction Project (CARP), suggests that most of the total organic carbon (TOC) in POTW effluent is in the form of DOC. Therefore, a regression between BOD and TOC could be used as a surrogate for the relations hip between BOD and DOC. The advantage of using TOC is the greater availability of data. TOC is reported for a significant number of major POTW dischargers.

D.2 Data

In 2006, m onitoring data from all major POTWs reporting TOC and 5

- day BOD in the United State s were downloaded from the U

. S

. Environmental Protection Agency (EPA) Permit Compliance System ( PCS ) web site http://www3.epa.gov/enviro/facts/pcs

- icis/search.html . Nine POTWs had 30 or more synchronous records of TOC and BOD, while 23 POTWs had at least 1 0 synchronous records. These numbers include both monthly average and maximum monthly values.

Review of the data indicated several extremely high (>1,000 milligrams per liter [ mg/L ] ) effluent TOC values for discharger CA0079243. We assumed that they prese nted errors in the reported unit, and divided them by 1,000 to convert from units of microgram per liter ( μg/L ) to mg/L. TOC and BOD

### 133 records were matched by POTW, location (e.g., upstream, downstream, influent or effluent), year and month. Thus, “synchrono us” measurements do not necessarily correspond to samples collected on the same day and time. The resulting table had 341 records.

D.3 Results

D.3.1 TOC and BOD at All Monitoring Locations

The first statistical evaluation involved data for all monitoring locations at the eight POTWs reporting 30 or more synchronous records of TOC and BOD. Table D- 1 presents the results of least squares regression of the average monthly data: TOC avg = a + b BOD av g . A scatter plot of this data is shown in
Figure D- 1. Table D- 2 presents the results of least squares regression of the maximum monthly data:

TOC max = a + b BOD max . A scatter plot of this data is shown in Figure D- 2. Bimodal distributions are

observed for TOC and especially BOD in this data set. It should be noted that the BOD concentrations of 200 mg/L or higher were measured in samples of untreated (influent) wastewater; TOC concentrations were also quite high in these samples. Both scatter plots (Figure s D- 1 and D- 2) show a fairly strong correlation between TOC and BOD in the combined data for all POTWs. The linear relationship between TOC and BOD is better defined in the average data (Figure D- 1).

Table D- 1. Least squares regression of average monthly TOC and BOD data for all monitoring

locations

POTW Location Intercept (a) Slope (b) r 2 df

CA0054372 Effluent Gross Value

CA0105295 Effluent Gross Value 7.551

- 0.379 0.009 41

CA0105295 Raw Sew/Influent 19.935 0.142 0.104 59

CA8000326 Effluent Gross Value*

CA8000383 Effluent Gross Value 4.952 0.725 0.344 31

CA8000383 Raw Sew/Influent 59.586 0.107 0.038 30

ID0020443 Upstream Monitoring*

ID0020443 Downstream Monitoring 3.500

- 0.400 0.190 2

ID0023981 Effluent Gross Value 6.268 0.281 0.196 26

ID0023981 Upstream Monitoring 3.200

- 0.300 0.127 3

LA0073521 Effluent Gross Value*

TN0023353 Effluent Gross Value 2.628 0.391 0.438 35

All POTWs All locations 4.828 0.237 0.873 243


### *note: POTW/location without regression results indicates less than 2 synchronous data records

### 134 Figure D- 1. Scatter Plot of Average Monthly Data (all Monitoring Locations)

Table D- 2. Least squares regression of maximum monthly TOC and BOD data for all monitoring

locations

POTW Location Intercept (a) Slope (b) r 2 df

CA0054372 Effluent Gross Value 9.420 0.499 0.154 29

CA0105295 Effluent Gross Value 8.567

- 0.114 0.007 50

CA0105295 Raw Sew/Influent 56.439 0.062 0.046 59

CA8000326 Effluent Gross Value 7.307 0.006 0.000 28

CA8000383 Effluent Gross Value 6.674 0.507 0.235 31

CA8000383 Raw Sew/Influent 149.293 0.047 0.052 30

ID0020443 Upstream Monitoring 0.300 1.175 0.039 4

ID0020443 Downstream Monitoring 3.500

- 0.400 0.190 2

ID0023981 Effluent Gross Value 6.210 0.208 0.202 28

ID0023981 Upstream Monitoring 3.200

- 0.300 0.127 3

LA0073521 Effluent Gross Value 12.989 - 0.818 0.110 18

TN0023353 Effluent Gross Value*

All POTWs All locations 11.183 0.196 0.700 302 

### TOCavg ~ BODavg (All Samples)

Biochemical Oxygen Demand (mg O2/L)Total Organic Carbon (mg C/L)

0 100 200 300 400 500050100 150 200

### 135 Figure D

- 2. Scatter Plot of Maximum Monthly Data (all Monitoring Locations )

Results of regression analyses revealed large differences in slopes of the linear model TOC = a + b BOD among locations and POTWs. Slopes for individual regressions ranged from

– 0.40 to 0.73 for average,

and from

- 0.82 to 0.50 for maximum BOD and TOC value s. Coefficients of determination (r 2 

### ) for the regressions were low; for most of them r 2 

### < 0.2. Pooling the data from all POTWs and locations

increased the r 2 

### to 0.87 for average and 0.70 for maximum BOD and TOC values.

Diagnosis of regression analyses re vealed that variance in both the average and maximum TOC rose with increasing values of biochemical oxygen demand. Such patterns were also evident from a simple inspection of the plots cited above. Homogeneity of variance, though, is a core assumption of o rdinary least squares regression, and its violation compromises the quality of results generated by the analysis.

The solution was to perform quantile regression analysis because it does not assume that variance of the response is homogeneous along the ran ge of the independent variable. The fitted model for the

50 th 

### quantile (median) was:

TOC avg = 5.5647 + 0.2088 BOD avg (243 df, R 1 

### = 0.77)

D.3.2 TOC and BOD at Effluent Monitoring Locations

Although the quantile regression model above provided a reasonable fit of the data at all monitoring locations, we were specifically interested in the relationship between TOC and BOD measured in POTW effluents. Therefore, we conducted a separate statistical analysis of effluent monitoring data from the

17 POTWs with more than one synchronous record of TOC and BOD retrieved from PCS. TOC and BOD records were again matched by POTW, year

, and month. The resulting data tabulation had 373

records. TOCmax ~ BODmax (All Samples)

Biochemical Oxygen Demand (mg O2/L)Total Organic Carbon (mg C/L)

0 500 1000 1500 20000100200300400

### 136 The results of least squares regression of the average monthly effluent data: TOC avg = a + b BOD avg are presented in Table D- 3. A scatter plot of this data is shown in Figure D- 3. Table D- 4 presents the results of least squares regression of the effluent maximum monthly data: TOC max = a + b BOD max . A scatter plot of this data is shown in F igure D- 4.

Table D- 3. Least squares regression of average monthly TOC and BOD data for effluent monitoring

locations

POTW 

### Intercept (a) Slope (b) r 2 df

CA0054372

CA0077691

CA0079103

CA0079243

CA0102822

CA0105295 7.5512 - 0.3789 0.009 41

CA0107492

CA0109991

CA8000073

CA8000326

CA8000383 4.9522 0.7254 0.344 31

ID0023981 6.2679 0.2808 0.196 26

LA0069868

LA0073521

TN0023353 2.6284 0.3909 0.438 35

TN0023531 37.6422 - 1.1348 0.076 3

TN0023574 7.4142 0.0882 0.016 25

All POTWs 5.8740 0.2859 0.245 174

### 137 Figure D

- 3. Scatter Plot of Average Monthly Data (Effluent Monitoring Locations) TOCavg ~ BODavg (All Samples)

Biochemical Oxygen Demand (mg O2/L)Total Organic Carbon (mg C/L)

0 10 20 30 40051015202530

### 138 Table D- 4. Least squares regression of maximum monthly TOC and BOD data for effluent monitoring

locations

POTW Intercept (a) Slope (b) r 2 df

CA0054372 9.4197 0.4993 0.154 29

CA0077691 4.2750 0.7008 0.422 6

CA0079103 23.3517 - 0.1278 0.035 16

CA0079243 5.7979 - 0.0367 0.008 8

CA0102822 6.3526 0.1780 0.195 28

CA0105295 8.5667 - 0.1143 0.007 50

CA0107492 5.9043 1.0676 0.027 2

CA0109991 3.0331 0.8458 0.167 11

CA8000073 9.0000 0.0000 0.000 8

CA8000326 7.3073 0.0056 0.000 28

CA8000383 6.6743 0.5070 0.235 31

ID0023981 6.2101 0.2083 0.202 28

LA0069868 7.6475 0.1139 0.535 5

LA0073521 12.9886 - 0.8184 0.110 18

TN0023353

TN0023531

TN0023574

All POTWs 6.6930 0.4311 0.276 299



### Figure D- 4. Scatter Plot of Maximum Monthly Data (Effluent Monitoring Locations) TOCmax ~ BODmax (All Samples)

Biochemical Oxygen Demand (mg O2/L)Total Organic Carbon (mg C/L)

0 20 40 60020 40 60 80

### 139 Results of the effluent regression analyses revealed large differences in slopes of the linear model TOC

= a + b BOD among POTWs. Slopes for individual regressions ranged from

– 1.13 to 0.73 for average,

and from

- 0.82 to 1.07 for maximum BOD and TOC values . Coefficients of determination (r 2 

### ) for the regressions were low; all r 2 

### < 0.55 and for most of them r 2 

### < 0.24. Low coefficients of determination were also recorded for regressions of TOC on BOD values from all POTWs (r 2 

### = 0.245 and 0.276, for average and maximum values, respectively). Further investigations of the effluent regression analyses were performed, because visual inspection of Figures D

- 3 and D

- 4 suggested the presence of outliers in

the data.

We examined the fit of the linear model, TOC avg = a + b BOD avg , by inspecting its residuals (Figure D

5). Studentized residuals were plotted against projected (fitted) TOC values in the left pane, and against quantiles of the standard normal distribution in the right pane. Four suspiciously

- low average

TOC points in Figure D

- 4 are labeled ‘324’, ‘308’, plus the two points adjacent to the latter (left pane). This plot reveals that residuals for high

- TOC points ‘489’ and ‘64’ are far larger in magnitude than residuals for the four suspiciously

- low points. Resi duals for these two points greatly deviate from the normal distribution (right pane). Furthermore, points ‘335’ and ‘336’ have much greater leverage than any other (leverages for ‘335’: 0.164, ‘336’: 0.127). Fitting the linear model without points ‘489’, ‘ 64’, ‘335’, and ‘336’ results in the following parameter values:

TOC avg = 6.0388 + 0.2171 BOD avg (r 2 

### = 0.185, 170 df) (Equation 1)

Figure D

- 5. Residuals of the line ar model, TOCavg = a + b BODavg

(Left: plot of Studentized residuals (studres) against projected (fitted) TOC values; Right: plot of stud i es against quantiles of the standard normal distribution).

It should be noted that this model (Equation 1) projects average TOC concentrations very similar to the regression based upon the uncensored data (i.e., within ± 2 mg/L), for the range of BOD concentrations of interest (less than 30 mg/L).

Diagnosis of the regression analysis, TOC max = a + b BOD max, revealed an excessively high residue for the (19, 91) point and very high leverage for the (71, 31) point. The projected regression line without those two points was : fitted(toc.lm)studres(toc.lm)

6 8 10 12 14 16 18-20246 489

64

336
335

324 308

Quantiles of Standard Normalstudres(toc.lm)

-2 -1 0 1 2-20246

### 140 TOC max = 6.6242 + 0.4095 BOD max (r 2 

### = 0.352, 297 df) (Equation 2)

This model (Equation 2) fits a single slope for all data. Our results, though, revealed large differences in slopes of reg ression lines among POTWs (Table D- 4). We tested the significance of such differences

with an F

- test, which required fitting two additional models, one with the same slope for all POTWs and the other with a distinct slope for each POTW. The F

- test compares model fits while taking into account the loss in degrees of freedom associated with the computation of multiple slopes. The

estimated F

- value ( F = 4.92, 13 df) was highly significant ( P < 0.001), indicating that distinct slopes are necessary to accurately project maximum TOC from maximum BOD values.

D.3.3 TOC and DOC at CARP Effluent Monitoring Locations

E ffluent discharge samples were collected from 11 New Jersey POTWs in 2000 and 2001 for the NYSDEC CARP project ( www.dec.state.ny.us/website/dow/bwam/CARP ). The se samples were analyzed for DOC, particulate organic carbon (POC)

, and total suspended solids (TSS) by the U.S. Geological Survey. TOC was calculated by adding together DOC and POC concentrations. The results are shown in

Table D- 5. Effluent DOC concentrations are generally much higher than POC because most of the particulate organic matter is removed from wastewater during secondary treatment. A scatter plot of the TOC and DOC data, Figure D- 6, shows the strong linear correlation between TOC and DOC that results from the predominance of DOC in effluent. These data are replotted in Figure D- 7 for TOC concentrations less than 50 mg/L.

Table D- 5. CARP organic carbon and total suspended solids (TSS) monitoring data for New Jersey discharger

DATE SITE DOC (mg /L) POC (mg/L) TOC (mg/L) TSS (mg/L)

Oct. 2

- 4, 2000 PVSC 43.0 10.5 53.5 51.4

MCMUA 0.10 6.75 6.85 36.3

BCMUA 22.2 10.6 32.8 54.1

JMEU 8.51 8.29 16.8 19.2

RVMUA 12.2 3.81 16.0 22.1

LRMUA 8.76 4.71 13.5 9.3

Dec. 11

- 15, 2000 PVSC 50.3 5.35 55.7 25.9

MCMUA 260 9.22 269 62.6

BCMUA 20.0 2.73 22.8 14.4

JMEU 23.0 5.23 28.2 31.1

RVMUA 23.4 8.73 32.1 42.0

LRMUA 10.4 11.4 21.8 55.2

NHH 14.0 3.07 17.1 22.5

NBC 28.6 6.67 35.3 23.3

NBW 21.8 3.38 25.2 7.8

NHWNY 18.7 5.66 24.3 18.1

SMUA 15.8 2.89 18.6 6.6

May 21

- 23, 2001 PVSC 34.5 14.2 48.7 41.1

BCMUA 15.0 9.17 24.1 11.9

### 141 DATE SITE DOC (mg /L) POC (mg/L) TOC (mg/L) TSS (mg/L)

RVMUA 9.26 10.2 19.5 12.0

LRMUA 14.7 9.34 24.1 10.9

EMUA 0.25 0.15 0.40 19.9

August 6

- 9, 2001 PVSC 123 8.74 132 35.6

MCMUA 20.6 5.34 25.9 22.6

BCMUA 109 15.4 125 45.6

JMEU 131 8.58 140 18.1

RVMUA 8.78 3.39 12.2 6.7

LRMUA 7.33 5.01 12.3 17.9

NBC 191 8.39 199 17.4

NBW 23.4 9.82 33.3 13.5

EMUA 14.7 5.33 20.1 7.5

NHWNY 17.7 12.1 29.8 13.8

SMUA 10.7 2.67 13.4 3.8



### Figure D- 6. Scatter p lot of TOC v ersus DOC in CARP e ffluent m onitoring d ata y = 0.9266x

R 2 = 0.9898

0 50 100 150 200 250 300

0 50 100 150 200 250 300

TOC (mg/L)DOC (mg/L)

### 142 Figure D

- 7. Scatter p lot of TOC v ersus DOC in CARP e ffluent m onitoring d ata (TOC <50 mg/L)

Least squares regression of the CARP effluent data (Table D

- 5) produces the following model:

DOC = 0.9266 TOC (r 2 

### = 0.9898, 32 df)

This regression was forced through the origin by constraining the intercept to be zero. At the limit of removal efficiency (i.e., as effluent TOC approaches zero), any remaining TOC should be in the form of
DOC, as mentioned above . This argument justifies forcing the regression through the origin. If only the data for which TOC falls in the expected range for effluent concentrations (TOC < 50 mg/L) are considered, the regression (again forced though the origin) is:

DOC = 0.7133 TOC (r 2 

### = 0.8913, 25 df)

For either case, the CARP effluent data show the strong linear relationship between TOC and DOC. Because TOC and DOC are linearly related in POTW effluent, the relationships between BOD and TOC reported above (Sections D. 1 and D. 2) also apply to DOC.

D.4 Discussion

Initially, we attempted to correlate BOD and TOC concentration measurements using data for all monitoring locations retrieved from PCS for major POTWs. We produced significant linear regression models for both average (Figure D

- 1) and maximum monthly (Figure D

- 2) data. Coefficients of determination were 0.87 and 0.70, respectively, for these data when combined for all locations. However, these correlations were substantially influenced by very high (i.e., greater than 50 mg/L)

concentrations of BOD and TOC measured in untreated wastewater. y = 0.7133x

R 2 = 0.8913

0 5 10 15 20 25 30 35 40

0 10 20 30 40 50 60

TOC (mg/L)DOC (mg/L)

### 143 When we repeated the statistical analysis using effluent monitoring data, we found large differences in the slopes of the linear model TOC = a + b BOD among POTWs. Low coefficients of determi nation were also recorded for regressions of TOC on BOD values from all POTWs (r 2 

### = 0.245 and 0.276, for average and maximum values, respectively). In part, this may reflect random errors in the measurements of BOD and TOC, since data quality issues includ ing loss of precision tend to be more frequent and significant at lower concentrations. The greater scatter in the plots of effluent BOD and TOC (Figures D

3 and D

- 4) may also reflect the limitations of working with the monthly average and maximum data

rep orted by PCS.

Direct inspection of the TOC data in Figures D

- 3 and D

- 4 is nevertheless instructive. Aside from some extreme high and low values, the great majority of effluent TOC concentrations are in the range of 5 to 10 mg/L, especially for effluents wi th BOD concentrations below 10 mg/L. This is true for both average and maximum monthly TOC values. Table D

- 6 presents summary statistics for average monthly effluent TOC, for all data as well as data categorized according to the following BOD ranges: ≤5 mg /L, 5 to 10 mg/L, 10 to 20 mg/L and > 20 mg/L. As noted in Table D

- 6, four very low TOC values (≤ 0.5 mg/L) were judged to be anomalies and were therefore censored from the data for these statistics. The same summary statistics are presented for maximum mo nthly effluent TOC in Table D

- 7. In this context, the regressions of TOC on BOD values from effluent samples at all POTWs are quite reasonable, despite the low coefficients of determination. For average monthly effluent data, the regression of TOC on BOD i s:

TOC avg = 5.8740 + 0.2859 BOD avg (r 2 

### = 0.245, 174 df)

### 144 Table D- 6. Summary statistics for POTW average monthly effluent TOC concentrations, categorized according to average monthly effluent BOD concentration

TOC avg BODavg level

≤5 mg/L >5

- 10 mg/L > 10

- 20 mg/L >20 mg/L All levels

Mean 6.75 8.45 9.83 13.32 7.98

Median 6.17 8.15 8.70 13.25 6.90

Standard Deviation 2.47 2.41 5.26 5.79 3.76

5 th quantile 4.90 6.08 4.46 6.53 4.96

95 th quantile 10.00 10.58 18.40 21.8 14.52

n 98* 32* 34* 8 172*



### *Four suspiciously

- low TOC values were censored from the data for these statistics

Table D- 7. Summary statistics for POTW maximum monthly effluent TOC concentrations, categorized according to maximum monthly effluent BOD concentratio n

TOCmax BODmax level

≤5 mg/L >5

- 10 mg/L >10

- 20 mg/L >20 mg/L All levels

Mean 7.95 7.88 14.73 20.57 10.08

Median 7.30 7.85 11.00 20.60 7.90

Standard Deviation 3.28 2.74 15.97 7.75 7.60

5 th quantile 5.50 3.00 2.00 11.00 5.4

95 th quantile 10.94 12.00 25.55 35.3 23.6

n 164 72 30 35 301



### Given the substantial limitations imposed by the data available from PCS, we believe that this regression gives reasonable estimates of TOC in POTW effluents. These are also probably the best

available estimates of effluent TOC for dilution calculations to determine DOC concentrations for use in the BLM (for example, the probabilistic dilution framework incorporated in the BLM

- Monte software [HydroQual, 2001]). As shown in Section D. 3, effluent DOC concentrations can be reliably predicted

fro m TOC values:

DOC = 0.7133 TOC (r 2 

### = 0.8913, 25 df)

It should be noted that the regressions presented here should not be applied to project water quality in natural receiving waters unimpacted by POTW effluent, because they are based solely on POTW

effluen t monitoring data. The characteristics of the constituents DOC, TOC

, and BOD, as well as the relationships between them, may be quite dissimilar between natural waters and effluents.

### 145 D.5 References

Fadini, S.F., Jardim, W.F., J.R. Guimarães. 2004 Evaluation of organic load measurement techniques in a sewage and waste stabilisation pond. Journal of the Brazilian Chemical Society. 15(1): 131

- 135 ( http://jbcs.sbq.org.br/jbcs/2004/vol15_n1/19

- 152

- 02.pdf ).

HydroQual, Inc. 2001. BLM

- Monte User’s Guide, Version 2.0. H ydroQual, Mahwah, NJ. October, 2001.

HydroQual, Inc. 2005. Biotic Ligand Model Windows Interface, Version 2.1.2, User’s Guide and Reference Manual. HydroQual, Mahwah, NJ. June, 2005.