&EPA
United States
Environmental Protection
Agency
 Office of Water

EPA820-R-15-106

  March 2016
Draft Technical Support Document:
Recommended Estimates for Missing
Water Quality Parameters for
Application in EPA's Biotic Ligand Model
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Disclaimer 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 using
the Biotic Ligand Model to protect aquatic life from toxic effects of copper. Under 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 change 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/aq I ife/copper/2007_index.cfm
Cover photo: Credit USEPA

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Acknowledgements
Luis A. Cruz
(Document Coordinator and Contributor)
United States Environmental Protection Agency
Health and Ecological Criteria Division
Washington, D.C. 20460

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

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Acronyms or Abbreviations
Acronym/Abbreviation Definition
ACR
ASE
BLM
BOD
CCC
CMC
CWA
df
DOC
Ecoregion
EMAP
EPA
FSC
Gl
CIS
GLEC
HUC
IQR
IWQC
LCL
LDC
LR
mg/L
NHD
NHD-Plus
NOCD
NRC
NRSA
NWIS
PCS
RPDs
PCA
PH
POC
POTWs
RMSE
Acute to Chronic Ratio
Average Standard Error
Biotic Ligand Model
Biochemical Oxygen Demand
Criterion Continuous Concentration
Criterion Maximum Concentration
Clean Water Act
Degrees of Freedom
Dissolved Organic Carbon
Areas in which ecosystems and the type, quality, and quantity of environmental
resources are generally similar. In this document is represented by EPA Level III
Ecoregions
Environmental Monitoring and Assessment Program
United States Environmental Protection Agency
Fixed Site Criteria
Geochemical Ions; ion parameters for the Biotic Ligand Model (Ca, Mg, Cl, Na, K, SO4,
alkalinity)
Geographic Information System
Great Lakes Environmental Center
Hydrologic Unit Code (a two to eight digit code that identifies each hydrologic unit)
Interquartile range
Instantaneous Water Quality Criteria
Lower Confidence Limit
Legacy Data Center (EPA historical STORE! database, recently renamed)
Linear Regression
Milligrams per liter
National Hydrography Dataset
National Hydrography Dataset Plus
National Organic Carbon Database (combined organic carbon data from two
databases: USGS WATSTORE and EPA STORE!)
National Research Council
National River and Stream Assessment
National Waters Information System
Permit Compliance System
Relative Percent Differences
Principal Components Analysis
Negative logarithm of the hydrogen ion concentration (in moles per liter); scale range
from 0 to 14
Particulate Organic Carbon
Publicly Owned Treatment Works
Root Mean Square Error

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Acronym/Abbreviation Definition
RSS
SPLOM
SO
STORE!
IDS
TSD
TSS
TOC
UCL
U.S.
uses
WATSTORE
WSA
WQC
u.S/cm
Hg/L
Residual Sum of Squares
Scatter Plot Matrices
Strahler Stream Order
EPASTOrage and RETrieval Data Warehouse (recently renamed Legacy Data Center,
LDC)
Total Dissolved Solids
Technical support document
Total Suspended Solids
Total Organic Carbon
Upper Confidence Limit
United States
United States Geological Survey
USGS National WATer Data STOrage and REtrieval System
(predecessor of NWIS)
Wadeable Stream Assessment
Water Quality Criteria
Microsiemens per centimeter
Micrograms per liter
IV

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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, the last seven of which are
also referred to as geochemical ions (Gl). Previously available hardness-based copper criteria
incorporated consideration only of the effects of hardness on bioavailability, while the 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, given the broad geographical range over which the BLM is likely to be
applied, and the limited availability of data for input parameters in many areas, a practical method to
estimate missing water quality parameters was 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 to develop these default value recommendations:
    •   Conducted geostatistics and conductivity analyses to predict Gl parameters
    •   Applied stream order to refine prediction of Gl parameters
    •   Mined the National Organic Carbon Database (NOCD) to estimate DOC
In brief, EPA found that an approach that used correlation (with conductivity and discharge as
explanatory variables), combined with geostatistical techniques (kriging), and a consideration of
stream order  produced the best estimates for BLM Gl parameters. Tables 8, 9, and 10 present
estimated inputs for each Gl and water hardness in each ecoregion categorized by stream order for
low, medium, and high order streams, respectively. Recommended Gl values are based on the 10th
percentile of ecoregional Level 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 provides estimates for DOC by ecoregion based on an analysis of a compilation of national organic
carbon databases. The 10th percentile of ecoregional  Level III values are recommended for DOC. There
was insufficient data to refine the DOC estimates 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 contents of each section in this report.
Section 1 provides an introduction to this study, including background 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 demonstrates that protective water

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quality criteria (WQC) for copper generally corresponded to a low percentile 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 estimate BLM Gl 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 Gl parameters.
Section 3 provides an analysis and discussion of the EPA approach to estimation of BLM Gl parameters
incorporating stream order as a variable, with a goal of providing BLM users with tables of
recommended Gl parameter estimates based upon both ecoregion and  stream order. For each Level III
ecoregion, we recalculated the 10th percentiles of the distributions of all daily water quality parameters
measured at all NWIS stations taking into account stream orders or ranges (groups) of stream orders
within each ecoregion. Values of the BLM Gl parameters 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. The NOCD was
compiled from a number of sources, including EPA's Storage and Retrieval Data Warehouse (STORE!)
and the United States Geological Survey's National Water Data Storage  and Retrieval System
(WATSTORE) (the predecessor of the National Waters Information System (NWIS)). The two other
databases, the Wadeable Stream Assessment (WSA) and the National River and Stream Assessment
(NRSA), were used to supplement and update the DOC analysis. Section 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 selection of 10th percentile DOC concentrations from either the
NOCD or the WSA or NRSA databases. The importance of field sampling for DOC is highlighted  in
Section 4 because 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 criteria 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.
                                                                                          VI

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Table of Contents
Disclaimer Page	i

Acknowledgements	ii

Acronyms or Abbreviations	iii

Executive Summary	v

Table of Contents	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 Gl PARAMETERS	5

   2.1   Data Source and Processing	5

   2.2   Geostatistical Analysis of National Data for Geochemical Ions	8
     2.2.1    Kriging of Conductivity Data	9
     2.2.2    Co-kriging of Gl 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 Parameters: pH and DOC	33

3    USING STREAM ORDER TO REFINE PREDICTION  OF Gl 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
                                                                                                         VII

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     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 Probability-Based Surface Water Sampling	50
   3.4   Summary	56
4    DOC ESTIMATION USING THE NATIONAL ORGANIC CARBON DATABASE	58
   4.1   Description oftheNOCD	58
   4.2   Recalculation of Ecoregional DOC Percentiles for Rivers and Streams	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 to 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 data 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.I     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 using conductivity and discharge as explanatory variables
              	100
   B.I     Introduction	100
   B.2     Data	103
                                                                                                         VIII

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   6.3      Results	103
   B.4      Discussion	Ill

   6.5      References	112

Appendix C:   Development of Tools to Estimate Biotic Ligand Model Parameters	114

   C.I   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   Sulfate	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 Gl-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 Demand	132

   D.I      Introduction	132

   D.2      Data	132

   D.3      Results	133
     D.3.1   TOCand BOD at All Monitoring Locations	133
     D.3.2   TOC and BOD at Effluent Monitoring Locations	135
     D.3.3   TOCand DOC at CARP Effluent Monitoring Locations	140

   D.4      Discussion	142

   D.5      References	145
                                                                                                             IX

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List of Figures
Figure 1. NWIS sample collection locations in the continental U.S. (Carleton, 2006)	6
Figure 2. Kriged prediction surface for 10th percentile of conductivity in the continental U.S. (sample locations in
    blue)	10
Figure 3. Co-kriged prediction surface for 10th percentile of calcium in the continental U.S	11
Figure 4. Co-kriged prediction surface for 10th 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 10th percentiles of data for conductivity (COND_SAM) and Gl
    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 10th percentiles of geostatistical predictions of conductivity
    (COND_KR) and BLM Gl 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  10th percentiles of conductivity versus data	25
Figure 9. Ecoregional averages of cokriged 10th percentiles of alkalinity versus data	26
Figure 10. Ecoregional averages of cokriged 10th percentiles of calcium versus data	27
Figure 11. Ecoregional averages of cokriged 10th percentiles of magnesium versus data	28
Figure 12. Ecoregional averages of cokriged 10th percentiles of sodium versus data	29
Figure 13. Ecoregional averages of cokriged 10th percentiles of potassium versus data	30
Figure 14. Ecoregional averages of cokriged 10th percentiles of sulfate versus data	31
Figure 15. Ecoregional averages of cokriged 10th 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 alkalinity 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  (10th 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

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Figure 27. BLM parameter estimates for each SO group in Ecoregion 54 (Central Corn Belt Plains)	50
Figure 28. Scatter plot of ecoregional 25th percentile conductivity for NWIS Data (SO Class 1-3) versus ecoregional
    25th percentile conductivity for Griffith data (mostly SO 1-4)	51
Figure 29. Scatter plot of ecoregional 25th percentile calcium concentration for NWIS data (SO class 1-3) versus
    ecoregional 25th percentile calcium concentration for Griffith data (mostly SO 1-4)	52
Figure 30. BLM predictions of copper criteria made with Gl water quality parameters based on ecoregional 25th
    percentile from NWIS data (SO class 1-3) versus ecoregional 25th percentile 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-l. 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/LasCaCO3) at NWIS locations	90
Figure A-4. HUC-averaged mean 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; r2=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-ll. 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-l. 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 quality 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 symbols) and discharge (open circles connected by dashed
    line) at Station 384551107591901 (Sunflower Drain at Highway 92, near Read, Delta County, Colorado)	Ill
                                                                                                               XI

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Figure C-l. Instantaneous Criteria (1C) predicted with the BLM using site-specific data and 1C predicted using
    measured pH and organic carbon and projected values of the Gl BLM input parameters	125
Figure C-2. 10th percentile of the 1C distributions using data and projected (predicted) values of the Gl BLM
    parameters	126
Figure C-3. Percentile of the 1C corresponding to the FSC for each site as a function of the correlation coefficient
    between the copper concentrations and the 1C when the FSC is calculated with Copper correlation and when
    FSC is calculated without Copper correlation	127
Figure C-4. Kriged surface 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 10th 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-l. 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 of 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
                                                                                                              XII

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List of Tables
Table 1. Summary of water quality data retrieved from NWIS .
Table 2. Model selection and cross validation statistics for geostatistical fitting of 10th percentiles of BLM Gl
    parameters	9
Table 3. Level III ecoregions of the U.S. organized according to broader Level I ecoregions	13
Table 4. Predicted 10th percentile concentrations for conductivity (u.S/cm), BLM Gl water quality parameters (mg/L)
    and hardness in each Level III ecoregion in the continental U.S	18
Table 5. Spearman rank correlation matrix for unbiased log means of 10th percentile concentrations measured in
    Level III ecoregions	24
Table 6. Spearman rank correlation matrix for unbiased log means of 10th percentile predicted (kriged/cokriged)
    concentrations in Level III ecoregions	24
Table 7. Correlation coefficients and linear regression (LR) statistics between ecoregional average 10th percentiles
    of data and geostatistical predictions	32
Table 8. Recommended 10th 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 10th 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 10th 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. Characteristics 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 ecoregion based upon data from the NOCD and the
    combined WSA/NRSAdata: 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);
    10th percentiles from combined NOCD & WSA/NRSAdata	78
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); 10th  percentiles; and  source of
    data for each ecoregion	79
Table A-l. Matrices of correlation coefficients between constituent concentrations	95
                                                                                                           Xlll

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Table A-2. Loadings onto original variables from PCA on HUC-averaged predictions and site-median concentrations	97
Table B-l. 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 (p) between median values of variables at each site	105
Table B-3. Results of Spearman rank tests for correlation (p) between the first quartile of values at each site	106
Table B-4. Results of Spearman rank tests for correlation (p) between the fifth quantile of values at each site	106
Table C-l. 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 Joaquin  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-l. 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 regression 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) monitoring data for New Jersey discharger	140
Table D-6. Summary statistics for POTW average monthly effluent TOC concentrations, categorized according to
    average monthly effluent  BOD concentration	144
Table D-7. Summary statistics for POTW maximum monthly effluent TOC concentrations, categorized according to
    maximum monthly effluent BOD concentration	144
                                                                                                            XIV

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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)(l) of 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)(l) of the CWA, 33
U.S.C. ง 1314(a)(l), directs the Administrator of EPA to publish water quality criteria that 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 developed 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 available 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 of
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 parameters 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 publish 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 BLM users can apply to estimate
values for missing input water quality parameters.

1.2  Input Data and the 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 limit
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 concentrations, such as
copper in freshwater, which will vary according 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 2.5th percentile of the

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distribution of instantaneous water quality criteria (IWQC) predicted by the BLM1 (USEPA, 2002). Thus,
predictions made for a site using 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 concentrations (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 provided 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 metal toxicity to aquatic
organisms: pH, dissolved organic carbon, alkalinity, calcium, and sodium. Continuous prediction maps
of concentrations were generated using various kriging techniques to interpolate between site-median
values measured at several thousand 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 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. 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  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. Refer to Appendix A for more
information.

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

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(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 conductivity is significantly correlated with BLM water quality parameters 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 conductivity in spatial projections of BLM parameters. Correlation
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) Software and Supporting Documents Preparation:
       Development of Tools to Estimate BLM Parameters (USEPA, 2008)
In order to predict parameters based  on geographic location, this 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 fora limited number of test sites. Regression models were developed to project 10th
percentiles  of BLM water quality input parameters from the 10th percentile of measured conductivity
distributions at sites in Colorado, Utah, and Wyoming. The 10th 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 produced highly consistent results. The regression  models for pH
and DOC, the most sensitive of the BLM water quality parameters, were not sufficiently accurate to
make reliable BLM parameter predictions. However, regression models for the Gl 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 ecoregion  (referred to herein as simply "ecoregion")
and water body type-specific DOC concentration percentiles tabulated  by EPA for the  National
Bioaccumulation Factors Technical Support Document (USEPA, 2003) appear to be far better estimates
of lower-percentile DOC concentrations than the estimates made using the conductivity regression.
EPA also provided a proof of concept  for the second approach, which was to see whether combining
the kriged conductivities with the conductivity-hardness regression would project the 10th percentiles
of hardness better than direct kriging of the hardness data. EPA found that both approaches produce
estimates of hardness that correlate significantly 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.

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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 necessary 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 demand (BOD) 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 gives reasonable
estimates of TOC in POTWs effluents and are likely the best available estimates of effluent TOC to
determine DOC 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:
    ซ  Section 2: Using Geostatistics and Conductivity to Predict Gl Parameters
    ซ  Section 3: Using Stream Order to Refine Prediction of Gl Parameters
    ซ  Section 4: Using the National Organic  Carbon Database to estimate DOC
EPA recommends that temperature and  pH be measured directly in the field.

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2   USING GEOSTATISTICS AND CONDUCTIVITY TO PREDICT Gl PARAMETERS
The following section describes studies that demonstrate how geostatistical techniques, coupled with
conductivity correlations, can be used to predict BLM input parameters for GIs when site-specific data
are unavailable. In a previous study (USEPA, 2008) EPA demonstrated that combining kriging with
regressions to estimate inputs based on conductivity improves the accuracy of Gl estimates. In this
section EPA has expanded on this approach and developed national estimates at the Level III ecoregion
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, pH, DOC, alkalinity, calcium, magnesium, sodium, potassium,
sulfate, and chloride, the last seven of which are GIs. 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 Gl parameters tend to vary according to the regional geology. For example, alkalinity
has noticeable geographic trends. Areas dominated  by 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 2008  proof of concept (in Appendix C) using geostatistics to
develop default missing Gl 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 conductivity data are
abundant and correlate well to the BLM Gl  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 implemented 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 Gl 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 range 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 Western
states tended to be sampled most intensively (Carleton, 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 representative (e.g., median) concentrations are examined, it should be  kept in mind that apparent

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geographic trends in concentration may be in part simply the result of uneven sampling intensity
(Carleton, 2006).
                                                  i	1	1	1	i	1	1—i	1
               Legend
                    NW1S sampling locations
               I    1 Lower48 STATES Albers
         Figure 1. NWIS sample collection locations in the continental U.S. (Carleton, 2006)
We focused our efforts 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 NWIS, 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, sodium, potassium, sulfate, and chloride. Data were
also collected on filtered  (dissolved) copper, and the spatial coordinates  (latitude and longitude) of
each sampling station were also retrieved. No data were collected on temperature.  Only the  Gl data
were included in the geostatistical analysis. A summary of the water quality data retrieved from NWIS
is provided in Table 1.

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Table 1. Summary of water quality data retrieved from NWIS
BLM Water Quality NWIS Parameter „ . . Number of
Parameter Description
Parameter Code Observations
Conductivity1
PH
Dissolver Organic
Carbon
Alkalinity
Calcium
Magnesium
Sodium
Potassium
Sulfate
Chloride
00094
00095
00400
00403
00681
00410
00417
00419
00418
00915
00925
00930
00935
00945
00940
Specific conductance, water,
unfiltered, field, microsiemens per
centimeter at 25 degrees Celsius
Specific conductance, water,
unfiltered, laboratory,
microsiemens per centimeter at 25
degrees Celsius
pH, water, unfiltered, field,
standard units
pH, water, unfiltered, laboratory,
standard units
Organic carbon, water, filtered,
laboratory, milligrams per liter
Acid neutralizing capacity, water,
unfiltered, fixed endpoint (pH 4.5)
titration, field, milligrams per liter
as calcium carbonate
Acid neutralizing capacity, water,
unfiltered, fixed endpoint (pH 4.5)
titration, laboratory, milligrams per
liter as calcium carbonate
Acid neutralizing capacity, water,
unfiltered, incremental titration,
field, milligrams per liter as calcium
carbonate
Alkalinity, water, filtered, fixed
endpoint (pH 4.5) titration, field,
milligrams per liter as calcium
carbonate
Calcium, water, filtered,
laboratory, milligrams per liter
Magnesium, water, filtered,
laboratory, milligrams per liter
Sodium, water, filtered, laboratory,
milligrams per liter
Potassium, water, filtered,
laboratory, milligrams per liter
Sulfate, water, filtered, laboratory,
milligrams per liter
Chloride, water, filtered,
laboratory, milligrams per liter
553,700
799
352,336
151,161
30,008
35,232
15,264
10,198
2,686
146,608
145,938
136,310
132,659
147,824
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 assurance procedures. All data were checked to
confirm that they contained numerical values without null (missing) records and remark codes were

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identified and reviewed. Minimum and maximum values for each parameter were confirmed to be
within expected ranges 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 following 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 10th percentile for that parameter at that station was censored to improve the reliability of
       the lower-tail (i.e., 10th) 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 report are based on the 10th 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 10th  percentile concentrations. We selected the 10th 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 protective water quality criteria (WQC) for copper generally corresponded to
approximately the 2.5th 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 10th percentile of water quality
parameters will also derive reasonably protective criteria, especially for small sample sizes where there
may be greater uncertainty at lower percentile estimates. The 10th percentile estimates presented in
this document were initially developed to implement the copper BLM published by EPA 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 Gl parameters. Using the 10th 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
serve 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 were
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;

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   •  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 value 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 incorporated in the semivariogram models.
Table 2. Model selection and cross validation statistics for geostatistical fitting of 10*
percentiles of BLM Gl parameters
,- ... .. Mean _ RMS Average
Geostatistical Number of . .. . Root mean .... . .
Parameter . . . standardized standardized standard
model samples square error
error error error
Conductivity
Alkalinity
Calcium
Magnesium
Sodium
Potassium
Sulfate
Chloride
Universal kriging
Universal cokriging
with conductivity
Universal cokriging
with conductivity
Universal cokriging
with conductivity
Universal cokriging
with conductivity
Universal cokriging
with conductivity
Universal cokriging
with conductivity
Universal cokriging
with conductivity
4833
1372
2590
2578
2439
2379
2650
2792
-0.01038
-0.001115
0.0001694
-0.002258
-0.002929
-0.001184
-0.0000225
0.001653
1361
36.62
26.81
15.92
156.3
3.488
114.5
375.2
1.081
1.09
1.186
1.16
1.583
1.429
1.29
1.51
1259
33.23
22.02
13.58
95.78
2.381
87.04
247
                                                                 ,th
2.2.1   Kriging of Conductivity Data
Universal kriging with a constant trend was used to map the surface of 10™ 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 10th
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.

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                                                                             10th Percenlite of Conductivity

                                                                                 (yS/cm)
                                                                                    6.0 - 53.9
                                                                                    53.9 - 73,3
                                                                                    73.3-121.2
                                                                                    121.2-238.9
                                                                                    238.9-5281
                                                                                 Bl $281 -1.239.1
                                                                                 j^H 1.2391-2,986.9
                                                                                 ^H 2.986.9 - 7.283.6
                                                                                 ^H 7,283.6 -17.845 7
                                                                                 ^H 17.8457-43.8100
                                        -.th
 Figure 2. Kriged prediction surface for 10  percentile of conductivity in the continental U.S. (sample
                                         locations in blue)
2.2.2  Co-kriging of Gl Data
Co-kriging was used to improve surface predictions of the BLM Gl 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 10th percentile BLM GIs 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 northeast. Prediction surfaces
for the other BLM Gl's are generally similar to those for conductivity and calcium.
                                                                                                10

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                                                                                    Universal Cokriging Surface
                                                                                      Prediction for Calcium
                                                                                     10th Percentile of Calcium
                                                                                        (mg/L)
                                                                                             0.1 -5.6
                                                                                             5.6 - 8.6
                                                                                             8.6-14.2
                                                                                             14.2 - 24.2
                                                                                             24.2 - 42.6
                                                                                            76.0-136.8
                                                                                           f 136-8 -247.6
                                                                                            247.6 - 449.4
                                                                                            449.4 -817.0
                                                    th
Figure 3. Co-kriged prediction surface for 10   percentile of calcium in the continental U.S
                                                                                                               11

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                                                                     4
Universal Cokriging Surface
  Prediction for Alkalinity
 10th Percentile of Alkalinity
   (mg CaCO3/L)
       1.0-8.2
       8.2 - 12.6
       12.6-19.8
       19.8-31.4
       31.4-50.1
   ^H 50.1 -80.4
   IB 80.4 - 129.2
   ^H 129.2-208.2
   ^H 208.2 - 335.8
   ^H 335.8-541.9
                                              ,th
     Figure 4. Co-kriged prediction surface for 10  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 guidance 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 the 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
components 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, water quality, and  hydrology. The map layer
in Figure 5 was compiled by EPA (USEPA, 2013a)
(http://www.epa.gov/wed/pages/ecoregions/level  iii iv.htm).
                                                                                               12

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Table 3. Level III ecoregions of the U.S. organized according to broader Level 1 ecoregions
Level 1 Ecological Regions
Level III Ecoregion Name of Ecoregion
Marine West Coast Forest
1
2
3
111
113
115
119
120
Coast Range
Puget Lowland
Willamette Valley
Ahklun and Kilbuck Mountains
Alaska Peninsula Mountains
Cook Inlet
Pacific Coastal Mountains
Coastal Western Hemlock-Sitka Spruce Forests
Northwestern Forested Mountains
4
5
9
11
15
16
17
19
21
41
77
78
105
116
117
118
Cascades
Sierra Nevada
Eastern Cascades Slopes and Foothills
Blue Mountains
Northern Rockies
Idaho Batholith
Middle Rockies
Wasatch and Uinta Mountains
Southern Rockies
Canadian Rockies
North Cascades
Klamath Mountains
Interior Highlands
Alaska Range
Copper Plateau
Wrangell Mountains
Mediterranean California
6
7
8
Southern and Central California Chaparral and Oak Woodlands
Central California Valley
Southern California Mountains
North American Deserts
10
12
13
14
18
20
22
Columbia Plateau
Snake River Plain
Central Basin and Range
Mojave Basin and Range
Wyoming Basin
Colorado Plateaus
Arizona/New Mexico Plateau
13

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Table 3. Level III ecoregions of the U.S. organized according to broader Level 1 ecoregions
Level 1 Ecological Regions
Level III Ecoregion Name of Ecoregion
24
80
81
Chihuahuan Deserts
Northern Basin and Range
Sonoran Basin and Range
Temperate Sierras
23
Arizona/New Mexico Mountains
Great Plains
25
26
27
28
29
30
31
34
40
42
43
44
45
46
47
48
Western High Plains
Southwestern Tablelands
Central Great Plains
Flint Hills
Central Oklahoma/Texas Plains
Edwards Plateau
Southern Texas Plains
Western Gulf Coastal Plain
Central Irregular Plains
Northwestern Glaciated Plains
Northwestern Great Plains
Nebraska Sand Hills
Piedmont
Northern Glaciated Plains
Western Corn Belt Plains
Lake Agassiz Plain
Eastern Temperate Forest
32
33
35
36
37
38
39
51
52
53
54
55
56
57
59
60
Texas Blackland Prairies
East Central Texas Plains
South Central Plains
Ouachita Mountains
Arkansas Valley
Boston Mountains
Ozark Highlands
North Central Hardwood Forests
Driftless Area
Southeastern Wisconsin Till Plains
Central Corn Belt Plains
Eastern Corn Belt Plains
Southern Michigan/Northern Indiana Drift Plains
Huron/Erie Lake Plains
Northeastern Coastal Zone
Northern Appalachian Plateau and Uplands
14

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Table 3. Level III ecoregions of the U.S. organized according to broader Level 1 ecoregions
Level 1 Ecological Regions
Level III Ecoregion Name of Ecoregion
61
63
64
65
66
67
68
69
70
71
72
73
74
75
82
83
84
Erie Drift Plain
Middle Atlantic Coastal Plain
Northern Piedmont
Southeastern Plains
Blue Ridge
Ridge and Valley
Southwestern Appalachians
Central Appalachians
Western Allegheny Plateau
Interior Plateau
Interior River Valleys and Hills
Mississippi Alluvial Plain
Mississippi Valley Loess Plains
Southern Coastal Plain
Laurentian Plains and Hills
Eastern Great Lakes and Hudson Lowlands
Atlantic Coastal Pine Barrens
Northern Forests
49
50
58
62
Northern Minnesota Wetlands
Northern Lakes and Forests
Northeastern Highlands
North Central Appalachians
Tropical Wet Forests
76
Southern Florida Coastal Plain
Southern Semi-Arid Highlands
79
Madrean Archipelago
Taiga
101
102
103
104
106
107
108
Arctic Coastal Plain
Arctic Foothills
Brooks Range
Interior Forested Lowlands and Uplands
Interior Bottomlands
Yukon Flats
Ogilvie Mountains
Tundra
109
110
112
114
Subarctic Coastal Plains
Seward Peninsula
Bristol Bay-Nushagak Lowlands
Aleutian Islands
15

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              Level III Ecorcgions of the Continental United States
                                      (Reviwd April 2013)
                               11 leallh and linvuoumenlal Ll'fttls
                                     !KMnnuii!;il Pralrclion Ajrcucy

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

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Using the differences in land and water interactions, regional variations in attainable water quality,
distinct biogeographical patterns (MacArthur, 1972), and similarities and differences 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 quality, 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 NRCof the National Academy of Sciences has similarly endorsed the use 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 characteristics 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. The 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 Oregon, 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 basin 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 water 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 etal., 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 points 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 10th percentile concentrations for conductivity and the Gl parameters for each
of the Level III ecoregions in the continental U.S. are presented in Table 4. For each of these
parameters,  there is considerable variation between the ecoregional averages nationally. Chloride
concentrations exhibit the greatest variation, with ecoregional 10th percentile averages that range from
0.7 to 573 milligrams per liter (mg/L). Alkalinity was the least variable, but the ecoregional 10th
percentile average concentrations still ranged from 12 to 163 mg/L.
                                                                                           17

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Table 4. Predicted 10* percentile concentrations for conductivity (u.S/cm), BLM Gl water
quality parameters (mg/L) and hardness in each Level III ecoregion in the continental U.S.
                          Unbiased log
                          mean of 10th
                          percentile
                          concentrations
Level III   Ecoregion
Ecoregion Name
Conductivity  Calcium Magnesium Sodium Potassium Alkalinity Chloride  Sulfate  Hardness1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Coast Range
Puget Lowland
Willamette Valley
Cascades
Sierra Nevada
Southern and
Central California
Chaparral and Oak
Woodlands
Central California
Valley
Southern California
Mountains
Eastern Cascades
Slopes and
Foothills
Columbia Plateau
Blue Mountains
Snake River Plain
Central Basin and
Range
Mojave Basin and
Range
Northern Rockies
Idaho Batholith
Middle Rockies
Wyoming Basin
Wasatch and Uinta
Mountains
Colorado Plateaus
Southern Rockies
Arizona/New
Mexico Plateau
Arizona/New
Mexico Mountains
Chihuahuan
Deserts
High Plains
Southwestern
102
80
91
107
195
600
378
772
212
166
142
273
426
976
90
91
300
446
426
639
259
697
879
2712
1770
2147
8.4
7.1
8.2
6.6
8.3
42
21
63
8.2
15
11
33
43
69
11
13
30
35
61
65
26
50
66
176
104
114
3.2
1.9
2.9
2.9
4.7
24
16
25
3.8
5.2
3.9
10
16
27
3.1
3.8
10
13
27
26
8.0
15
18
50
35
34
4.1
2.8
4.4
3.5
8.8
48
25
63
6.0
9.3
7.7
13
45
81
2.3
3.6
14
33
61
57
12
65
85
379
191
316
0.64
0.64
0.90
0.74
1.3
2.5
1.7
3.8
1.0
1.8
1.4
2.3
3.6
6.3
0.67
0.88
1.9
1.7
3.3
2.6
1.4
3.0
3.4
8.6
6.0
4.9
33
22
30
35
58
124
91
150
44
40
49
109
120
138
44
62
105
96
155
117
55
96
102
106
112
94
3.2
2.3
4.7
2.2
5.8
56
21
54
3.2
3.3
3.3
10
45
85
0.72
1.9
7.6
7.2
55
28
3.8
65
100
573
281
512
4.8
5.6
3.8
3.2
11
136
58
171
5.0
10
7.1
22
83
258
4.4
5.9
55
104
155
197
56
143
189
608
353
374
34.12
25.54
32.39
28.39
40.02
203.4
118.1
260
36.08
58.82
43.49
123.5
173.1
283.2
40.21
48.08
116
140.8
263.2
269.1
97.8
186.5
238.8
645
403.5
424.4
                                                                                                    18

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Table 4. Predicted 10* percentile concentrations for conductivity (u.S/cm), BLM Gl water
quality parameters (mg/L) and hardness in each Level III ecoregion in the continental U.S.
                          Unbiased log
                          mean of 10th
                          percentile
                          concentrations
Level III   Ecoregion
Ecoregion Name
Conductivity  Calcium Magnesium Sodium Potassium Alkalinity Chloride  Sulfate  Hardness1

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Tablelands
Central Great
Plains
Flint Hills
Central
Oklahoma/Texas
Plains
Edwards Plateau
Southern Texas
Plains
Texas Blackland
Prairies
East Central Texas
Plains
Western Gulf
Coastal Plain
South Central
Plains
Ouachita
Mountains
Arkansas Valley
Boston Mountains
Ozark Highlands
Central Irregular
Plains
Canadian Rockies
Northwestern
Glaciated Plains
Northwestern
Great Plains
Nebraska Sand Hills
Piedmont
Northern Glaciated
Plains
Western Corn Belt
Plains
Lake Agassiz Plain
Northern
Minnesota

1228
406
925
596
798
364
367
565
160
116
192
152
258
310
164
545
828
486
75
524
464
441
229

84
42
60
48
56
39
36
43
12
7.9
16
18
31
39
22
37
49
47
5.8
40
48
42
24

24
8.5
16
14
14
5.8
6.3
10
2.8
2.8
4.7
3.3
10
8.5
8.7
20
24
13
1.9
20
16
18
10

176
30
107
38
58
21
23
62
11
8.4
15
4.3
4.5
11
15
61
84
35
4.0
38
16
16
3.2

6.9
4.3
4.1
2.7
3.8
3.2
3.8
3.9
2.3
1.3
1.8
1.3
1.6
3.0
0.57
5.9
5.3
6.9
1.5
9.1
3.4
5.1
1.4

121
121
95
98
129
92
98
87
34
34
51
53
96
100
80
163
151
151
19
163
136
140
95

245
42
164
62
73
26
29
86
15
10
20
6.7
6.0
13
1.7
8.1
10
10
3.9
13
16
8.6
2.6

204
45
108
52
91
33
29
78
15
11
16
8.2
20
50
38
147
247
96
4.1
106
45
62
8.4

308.4
139.85
215.6
177.4
197.4
121.28
115.83
148.5
41.48
31.23
59.27
58.53
118.5
132.35
90.67
174.5
220.9
170.8
22.29
182
185.6
178.8
101
                                                                                                    19

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Table 4. Predicted 10* percentile concentrations for conductivity (u.S/cm), BLM Gl water
quality parameters (mg/L) and hardness in each Level III ecoregion in the continental U.S.
                          Unbiased log
                          mean of 10th
                          percentile
                          concentrations
Level III   Ecoregion
Ecoregion Name
Conductivity  Calcium Magnesium Sodium Potassium Alkalinity Chloride  Sulfate  Hardness1

50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
Wetlands
Northern Lakes and
Forests
North Central
Hardwood Forests
Driftless Area
Southeastern
Wisconsin Till
Plains
Central Corn Belt
Plains
Eastern Corn Belt
Plains
Southern
Michigan/Northern
Indiana Drift Plains
Huron/Erie Lake
Plains
Northeastern
Highlands
Northeastern
Coastal Zone
Northern
Appalachian
Plateau and
Uplands
Erie Drift Plain
North Central
Appalachians
Middle Atlantic
Coastal Plain
Northern Piedmont
Southeastern
Plains
Blue Ridge
Ridge and Valley
Southwestern
Appalachians
Central
Appalachians

166
295
348
510
546
463
463
467
97
176
271
364
184
793
208
121
121
163
151
193

19
31
34
39
53
49
52
52
11
8.3
33
31
13
6.7
21
7.4
11
17
13
16

6.5
12
15
20
24
15
15
15
1.9
2.0
7.3
8.1
3.9
2.3
5.8
2.2
3.2
4.5
3.2
5.6

2.5
5.6
5.1
16
14
11
14
13
5.7
14
37
19
7.1
6.8
10
5.5
3.0
4.6
2.5
4.6

0.78
1.7
1.6
2.1
1.7
2.0
1.9
2.2
0.69
1.3
1.3
2.3
1.0
1.8
1.9
1.5
1.3
1.4
1.3
1.3

83
115
107
112
124
116
134
125
24
15
53
64
41
15
39
19
23
33
42
34

3.4
8.9
10
31
30
23
28
27
10
22
64
29
11
83
17
15
3.4
6.3
3.2
4.5

6.1
15
17
25
46
32
29
32
7.4
8.4
22
38
15
22
15
7.2
6.0
15
11
33

74.15
126.7
146.5
179.5
230.9
184
191.5
191.5
35.29
28.95
112.43
110.71
48.49
26.18
76.28
27.52
40.62
60.95
45.62
62.96
                                                                                                   20

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Table 4. Predicted 10* percentile concentrations for conductivity (u.S/cm), BLM Gl water
quality parameters (mg/L) and hardness in each Level III ecoregion in the continental U.S.
                         Unbiased log
                         mean of 10th
                         percentile
                         concentrations
Level III   Ecoregion
Ecoregion Name
Conductivity  Calcium  Magnesium Sodium  Potassium Alkalinity  Chloride  Sulfate  Hardness1
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
Western Allegheny
Plateau
Interior Plateau
Interior River
Valleys and Hills
Mississippi Alluvial
Plain
Mississippi Valley
Loess Plains
Southern Coastal
Plain
Southern Florida
Coastal Plain
North Cascades
Klamath Mountains
Madrean
Archipelago
Northern Basin and
Range
Sonoran Basin and
Range
Laurentian Plains
and Hills
Eastern Great
Lakes and Hudson
Lowlands
Atlantic Coastal
Pine Barrens
276
237
326
255
102
726
682
93
156
625
298
991
104
294
261
23
27
34
14
10
18
49
6.5
8.7
42
26
64
4.8
34
7.4
7.3
5.9
12
5.5
2.9
5.0
6.6
2.3
4.6
11
8.2
24
0.78
6.8
2.7
10
4.4
11
17
3.9
20
25
2.3
4.0
45
20
115
2.5
21
10
1.7
1.5
2.4
2.6
1.6
2.0
2.4
0.64
0.66
2.8
2.7
4.4
0.48
1.3
1.7
46
65
87
44
38
41
103
27
44
92
89
121
12
61
23
14
7.0
17
22
4.5
390
43
1.2
2.1
39
15
131
2.7
40
16
47
20
46
13
8.4
38
15
4.9
3.5
78
24
192
4.4
26
11
87.43
91.69
134.2
57.55
36.89
65.5
149.56
25.68
40.61
150.1
98.62
258.4
15.198
112.88
29.57
1 Water Hardness calculated as equivalents CaCOS = 2.5 (Ca2*) + 4.1 (Mg2*)

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 Gl 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.
Scatter plot matrices were developed for the ecoregional averages of conductivity and Gl 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
                                                                                               21

-------
these figures 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 averages  in Figures 6 and 7
demonstrate that the geostatistical ecoregion predictions are reasonable.
           COND SAMOA SAM  MG SAM  NA SAM  K SAM  ALK SAM  CL SAM  SO4 SAM
                    .ป*•/:
                                                               	
                                                                                .CD
           COND SAMOA SAM  MG SAM  NA SAM  K SAM  ALK SAM  CL SAM  SO4 SAM
     Figure 6. Scatter plot matrix of ecoregional average 10th percentiles of data for conductivity
   (COND_SAM) and Gl parameters (calcium=CA_SAM, magnesium=MG_SAM, sodium=NA_SAM,
          potassium=K_SAM, alkalinity=ALK_SAM, chloride=CL_SAM, sulfate=SO4_SAM)
                                                                                        22

-------
         COND KR CA  CO   MG CO  NA CO   K  CO   ALK CO  CL  CO  SO4 CO
      O
      O,
      O
      O,
      8
      o,
      o

                                      	
                                                                                    O
                                                                                    O
                                                                                    o
                                                                                    o
                                                                                    o
                                                                                    o
                                                                                    8
                                                                                    o
         COND KR CA  CO   MG CO  NA CO   K  CO   ALK CO  CL  CO  SO4 CO
                                                  ->th
  Figure 7. Scatter plot matrix of ecoregional average 10  percentiles of geostatistical predictions of
      conductivity (COND_KR) and BLM Gl 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 averages 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 between the two datasets, again demonstrating that the geostatistical predictions are
reasonable.
                                                                                          23

-------
    Table 5. Spearman rank correlation matrix for unbiased log means of 10* percentile
    concentrations measured in Level III ecoregions
                  Conductivity  Calcium  Magnesium  Sodium  Potassium  Alkalinity  Chloride  Sulfate
Conductivity
Calcium
Magnesium
Sodium
Potassium
Alkalinity
Chloride
Sulfate
1
0.895
0.877
0.865
0.823
0.769
0.815
0.894

1
0.927
0.819
0.758
0.881
0.774
0.864


1
0.813
0.73
0.898
0.702
0.85



1
0.859
0.698
0.855
0.883




1
0.679
0.788
0.786





1
0.595
0.725






1
0.744







1
Table 6. Spearman rank correlation matrix for unbiased log means of 10* percentile
predicted (kriged/cokriged) concentrations in Level III ecoregions
Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate
Conductivity
Calcium
Magnesium
Sodium
Potassium
Alkalinity
Chloride
Sulfate
1
0.872
0.843
0.87
0.842
0.747
0.829
0.889

1
0.924
0.82
0.803
0.868
0.715
0.873


1
0.778
0.753
0.901
0.592
0.875



1
0.836
0.672
0.826
0.893




1
0.745
0.725
0.831





1
0.461
0.751






1
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), magnesium (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 predictions is high.
                                                                                              24

-------
         1500

         1000


          500
      ?
      8
                        100            1000
                       conductivity (uS/cm)



Figure 8. Ecoregional averages of kriged 10th percentiles of conductivity versus data
                                                                       25

-------
          160
          120
       8  80
       1   40
       CD
       D)
       JZ
                                           1    I  T
                                            I    I  I
                     alkalinity (mg CaC03/L)
                                     ,th
Figure 9. Ecoregional averages of cokriged 10 percentiles of alkalinity versus data
                                                                         26

-------
          200
          160

          120


           80
       I   40
      jj

       s
                            10             100

                         calcium (mg/L)
                                     ,th
Figure 10. Ecoregional averages of cokriged 10  percentiles of calcium versus data
                                                                       27

-------
         IT 30
         I 20

         I10
          ง>
          D)
          8
                                     1     i   i   i i  i r
                                     i     i   i   i i  i i
                         magnesium (mg/L)
Figure 11. Ecoregional averages of cokriged 10*  percentiles of magnesium versus data
                                                                         28

-------
          100
      f  10
       8
                1.0         10.0       100.0
                          sodium (mg/L)
                                     ,th
Figure 12. Ecoregional averages of cokriged 10  percentiles of sodium versus data
                                                                        29

-------
             10
              8
              6
              2
          D)
         JZ
1      I   I   TTT
                                            I    I  I   I I
                                                
-------
      3
      i
          100
      Jz
       8   10
                   1.0       10.0      100.0
                           sulfate (mg/L)
                                      ,th
Figure 14. Ecoregional averages of cokriged 10 percentiles of sulfate versus data
                                                                          31

-------
        100.0
     CD

     .g
     -5
          10.0
           1.0
                                                  I  -
             0.1      1.0     10.0    100.0  1000.0
                          chloride (mg/L)
                                     ,th
Figure 15. Ecoregional averages of cokriged 10  percentiles of chloride versus data
Table 7. Correlation coefficients and linear regression (LR) statistics between
ecoregional average 10th percentiles of data and geostatistical predictions
Correlation ,,. . P LR P
Parameter .... . LR coefficient ,„ ,. ... ,„,. ...
coefficient^ (2 Tail) constant (2 Tail)
Conductivity
Alkalinity
Calcium
Magnesium
Sodium
Potassium
Sulfate
Chloride
0.908
0.915
0.922
0.885
0.93
0.906
0.865
0.839
0.98
1.209
1.061
1.097
1.101
1.073
1.264
1.039
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
-20.992
-12.161
-1.994
-1.169
-4.78
-0.184
-13.599
-7.271
0.495
0.028
0.365
0.22
0.169
0.297
0.041
0.386
                                                                         32

-------
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 Gl
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 Gl
water quality inputs for the BLM because conductivity is one of the most widely 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 the average predicted 10th percentile concentrations for the
BLM Gl water quality  parameters, as presented in Table 4 by ecoregions. Because they are based on
the 10th  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 parameter selection
however would include consideration of stream order in GIs estimates. Section 3 presents further
refinements of estimates of the Gl parameters by stream order and EPA's recommendations for
default Gl parameters for the BLM when data are lacking.
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 estimates of the missing BLM water quality
parameters. If the regression projections differ from the geostatistical average predictions, the lower
(more conservative) estimate is recommended for application to ensure protection of aquatic life. As
always, users 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 for 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 Gl
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 Gl  parameters
than to pH and DOC predicting site-specific criteria for copper. Since our analysis indicates that there is
little 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 with 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 Document (USEPA, 2003) because they appeared  to offer reasonable
estimates of lower percentile DOC concentrations, and were based on substantially more DOC data
than were available in the NWIS. In Section 4 of this report we further tested these ecoregional DOC
concentrations for use in the BLM where site-specific data are not available.
                                                                                          33

-------
3   USING STREAM ORDER TO REFINE PREDICTION OF Gl 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 Gl parameters,
building on the ecoregional work described in Section 2.
Estimations of values for the Gl parameters (alkalinity, calcium,  magnesium, sodium, potassium,
sulfate, 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 Gl input parameter values. However, these values also vary due to
anthropogenic impact. In the case of conductivity and Gl 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 1st order (a stream with no
tributaries) to 12th order (the Amazon, at its mouth). First through 3rd 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 7th order or larger constitutes a river.  For example, the Ohio River is
8th order and the Mississippi River is 10th 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, 2015).
In this section we consider variability in GIs by determining the SO of each surface water sampling
location in the USGS NWIS2 database, and explore methods of incorporating SO variation in the
parameter estimates. Tables are provided in this section showing tabulations of parameter estimates
based upon both ecoregion and SO to maximize the accuracy of estimated input parameters.

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 snapped 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/NHDPIus/NHDPIusV2_home.php
                                                                                         34

-------
3.2  Estimating BLM Parameters for Ecoregions and SO
Estimated (10th percentile) BLM water quality parameters were presented in Section 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 ecoregion.

3.3  Results
The distribution of NWIS sampling locations by SO is presented in Figure 16. The largest proportion of
sampled locations (78%) was found to be in SO 1 through 4.
           25%
           20%
           15%
       U)
       OJ
       .*;
       •5   10%
       c
       o
       '•E
       o
       Q.
       O
       *    5%
            0%
                                        456
                                          Strahler stream order
              Figure 16. Distribution of NWIS surface water sampling locations 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 17), 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
                                                                                          35

-------
streams), 4 through 6 (mid-reaches) and 7 through 9 (rivers). There were no data for rivers with S0>9.
Grouping simplified the presentation of results and 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).

10000
05
A 1000
'•g
1 1ฐฐ
10


~
-
;
-
-
















I





1 13 2





I





3









I






t
*




I



4 46 5











{\


\\




6 7 79

I

T
I
T
i

8










I


I
*


9


~
-

-
-








Strahler stream order
           Figure 17. Box plot of estimated ecoregional conductivities as a function 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 25th and 75th percentile concentrations defined as the interquartile range (IQR)
(i.e., the box 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.
                                                                                           36

-------

65s
8 100
8
CD
_l
fr 1ฐ
'c
"CD
*L
1
.
~
-
-
r
: L

-


















*












I





























I








1 13 2 3 4 46 5 6






L








7
i i




\t\t\"
T "
' ;
-
* :

-
i i










79 8 9
Strahler stream order
Figure 18. Box plot of estimated ecoregional alkalinity concentrations as a function of SO
                            (Refer to note in Figure 17.)

^> 100.0
1
"c 100
8 '
8
E
.2 1.0
0
<3
0 1

i
La
-
:

-
~
-



















1 13 2


I







— ' L


I


















i
I

T




i

3 4 46 5 6
Strahler stream

i
i

T




I

7





L









i i
JL,
T
'
L






I


|

-
:

-
~
-











79 8 9
order
 Figure 19. Box plot of estimated ecoregional calcium concentrations as a function of SO
                            (Refer to note in Figure 17.)
                                                                                      37

-------

5 100.00
o
'•g 10.00
i 1.00
0)
i 0.10






—
-
f
-
-
-









: f






1 13 2

























I

_ —







_

II







3 4 46 5 6 7 79 8

I




L





9


—
-
E
-
-
-
-









Strahler stream order
Figure 20. Box plot of estimated ecoregional magnesium concentrations as a function of SO
                             (Refer to note in Figure 17.)

~ 1000.0
I'
.I 100.0
1
ง 10-ฐ
9 10


41
-
*
-
r
r
-








>



>


<
* *
T
If










1

1 1

1 13 2 3 4 46 5 6 7 79 8
Strahler stream order











i


i
I
i
i
9


.
-
-


-
-









  Figure 21. Box plot of estimated ecoregional sodium concentrations as a function of SO
                             (Refer to note in Figure 17.)
                                                                                     38

-------
|10.0
_O
ง
R 1-ฐ
|
s.
0.1

r
-

- -
-



-i _





_ r-














-1 _
















—








*
t *
1 13 2 3 4 46 5 6 7 79 8
Strahler stream order

I
i
T


I
9


-


-
-









Figure 22. Box plot of estimated ecoregional potassium concentrations as a function of SO
                             (Refer to note in Figure 17.)

_ 1000.00
g' 100.00
1
c
8 10.00
8
CD
$ 1.00
D
CO

0.10
^
: *
r
-

-
:
"
r
:
-






























J L





























~|




_






' T
1



1 *
r f 1 1
1 13 2 3 4 46 5 6 7 79
Strahler stream order
1

i
—


1





1
8
















1

1
-

j






1
9

.
-
-
-

-:
:
"
-
I
-
-
-:















  Figure 23. Box plot of estimated ecoregional sulfate concentrations as a function of SO
                             (Refer to note in Figure 17.)
                                                                                      39

-------

10000.00
^r
1" 1000.00
x— ^
c
0
IS 100.00
^_
ง
K 10.00
(D
1 1-00
6
0.10








_ 	 6
I G
r
I
;
-
r
-
E I
=-
_

](






r * ;
:
i









T

JE


1
3

i
< *
* * *
i


1 13 2 3 4 46 5 6



I
—
_
-
* :

* ]

r~
[


i r





t

-
-
- 1:
T =
-=
_
-=



i -














7 79 8 9
Strahler stream order
      Figure 24. Box plot of estimated ecoregional chloride concentrations as a function of SO
                                 (Refer to note in Figure 17.)
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, 10th percentiles of calcium increased with SO group in 68%
of ecoregions for low versus medium SO and 83% of ecoregions for medium versus high SO. In general,
parameter estimates (10th 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 grouped by SO and Level
III ecoregions in Tables 8 throughlO. 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 Level
III ecoregion and SO group.
                                                                                         40

-------
Table 8. Recommended 10*  percentile conductivity, GIs, and hardness estimates for SO Group 1
through 3 (number of stations shown in parentheses if n<10)
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
58
74
68
16
28
279
164
157
55
137
88
133
109
967
24
21
93
92
76
189
37
115
62
453
194
199
293
346
217
189
639
183
132
141
25
19
107
51
172
6.0
8.8
9.9
1.0
0.6
3.6
19
29
4.4 (8)
24.0
8.6(2)
13
9.4
15
3.1

6.9
22
59
59
3.5
13
6.3
43
43
18
21
50
30
25
48
26
24
13
0.9
0.9
23
0.9(7)
26
0.8
2.8
3.8
0.2
0.1
8.2
6.0
4.3
0.9 (8)
9.4
3.2 (2)
2.0
1.6
2.8
0.8

1.6
6.3
11
12
0.7
1.1
1.8
7.9
11
3.0
5.0
8.2
4.0
1.8
5.5
1.7
2.1
2.3
0.5
0.7
3.5
0.6 (7)
1.9
1.3
3.9
5.6
1.8
0.3
8.4
14
10
2.3(9)
10.2

6.1
2.7
6.0
0.9

1.5
4.7
5.1
19
0.8
2.3
3.7
35
31
63
9.9
4.4
17
1.6
47
5.9
5.8
9.8
1.9
0.9
23
0.63 (7)
1.3
0.1
0.5
1.5
0.2
0.1
0.9
1.8
1.5
0.4(9)
1.4

0.8
0.6
3.0
0.4

0.5
0.9
0.6
1.4
0.3
0.8
0.7
3.4
3.7
3.4
1.5
0.8
2.9
0.9
2.9
1.9
2.3
2.6
0.2
0.3
3.3
0.6(7)
0.7
44



38
73
120
70(7)
35(2)
127
169 (2)
35
45
90(7)
9.0

31

96
157
18
55
20
32
228
53
122
125
74
99

52
29
44
5.0
4.0
36
35
62
0.6
2.8
2.3
0.5
0.1
8.9
8.6
2.6
0.2
4.6

1.4
0.5
2.7
0.2

0.3
3.3
2.5
6.7
0.2
1.2
0.8
20
7.3
3.6
5.1
1.5
8.2
2.6
71
5.1
8.1
13
3.0
1.3
2.5
1.1
2.0
1.1
3.3
1.5
0.2
0.1
7.2
6.6
0.4
0.2
11

3.7
3.7
6.3
1.3

3.0
7.3
44
129
1.9
7.2
4.3
74
35
11
13
22
9.9
4.9
51
15
6.0
4.7
1.7
2.0
3.5
1.8
4.2
18.28
33.48
40.33
3.32
1.91
42.62
72.1
90.13
14.69
98.54
34.62
40.7
30.06
48.98
11.03

23.81
80.83
192.6
196.7
11.62
37.01
23.13
139.89
152.6
57.3
73
158.62
91.4
69.88
142.55
71.97
68.61
41.93
4.3
5.12
71.85
4.71
72.79
                                                                                    41

-------
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
223
93
256
327
156
44
400
380
295
402 (4)
69
137
432
502
574
420
219
446
25
69
61
131
32
108
134
25
12
81
32
36
125
179
180
102
52
75
430
14
19
340
78
203
20
13
23
17
21
2.7
32
41
31

5.1
28
42
27
51
40
28
35
1.2
3.6
4.7
13.0
1.8
1.1
12.0
0.7
0.5
4.7
1.9
2.8
5.6
10
17
6.9
3.2
6.6
41
1.6

30
6.3
19
4.6
3.8
7.3
16
3.2
0.8
13
11
15

1.0
11
12
8.0
22
11
7.4
10
0.4
1.1
1.2
2.5
0.7
0.8
4.6
0.5
0.3
2.0
1.2
0.8
4.0
1.8
5.3
2.7
1.4
1.5
1.8
0.4

6.2
1.1
2.4
8.4
0.4
7.4
26
6.5
2.5
15
4.8
4.9

1.2
2.0
2.9
11
8.4
7.8
3.6
6.8
0.3
6.2
1.5
8.0
0.8
2.8
7.0
1.5
0.7
2.0
1.3
0.4
2.7
1.1
5.2
3.4
2.5
4.5
7.8
0.4

25
4.3
10
2.8
0.1
1.6
3.7
4.7
1.2
7.8
1.3
2.5

0.3
0.9
0.7
2.0
1.2
1.5
0.9
2.3
0.2
0.9
0.4
1.1
0.3
0.8
1.3
0.3
0.3
0.7
0.7
0.4
1.4
0.7
1.7
2.0
1.5
0.5
0.3
0.2

2.8
2.2
2.8
46
60(9)
91
144
80(2)
12
94
83

227 (1)
32
53(4)
75
42
202
130
79
84
1.5
3.0
17
33
2.0
5.0
19
3.0
3.0
3.0
3.0
4.0
12
54
69
31
9.3
9.0
116
7.0

92(2)
24
52
5.2
0.1
1.3
2.7
0.7
2.4
5.4
12
4.3

0.4
3.3
4.8
25
28
19
6.3
19
0.4
10
1.5
7.8
0.8
5.5
10
2.6
0.7
1.8
1.4
1.0
2.4
2.2
3.5
2.0
2.6
9.0
30
0.2
2.1
13
0.2
2.6
23
1.6
14
119
5.5
1.9
60
15
13

1.2
4.5
12
22
44
16
16
33
4.2
5.8
6.6
8.3
4.4
3.2
11
0.7
1.1
7.1
3.6
8.1
16
2.8
21
2.9
3.9
1.5
0.1
0.7

23
2.5
6.1
68.86
48.08
87.43
108.1
65.62
10.03
133.3
147.6
139

16.85
115.1
154.2
100.3
217.7
145.1
100.34
128.5
4.64
13.51
16.67
42.75
7.37
6.03
48.86
3.8
2.48
19.95
9.67
10.28
30.4
32.38
64.23
28.32
13.74
22.65
109.88
5.64

100.42
20.26
57.34
42

-------
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
82
83
84
37
198
50
1.5
16
0.8
0.5
3.9
0.6
4.3
5.0
2.8
0.2
1.0
0.6

51
1.0
6.6
21
5.0
1.8
29
4.4
5.8
55.99
4.46
Table 9. Recommended 10*  percentile conductivity, GIs, and hardness estimates for SO group 4
through 6 (number of stations shown in parentheses if n<10)
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
52
49
62
35
18
316
67
93
52
83
52
95
124
688
34
22
123
145
135
260
74
215
289
240
220
367
351
298
351
377
447
311
334
125
3.6
5.3
7.1
3.5
0.9
9.1
6.5
9.0
5.5
8.6
3.7
13
12
58
3.5
2.4
10
15
34
38
6.5
25
31
24
14
39
39
68
39
44
54
35
33
10
1.0
1.2
2.5
1.0
0.1
4.8
2.5
1.5
0.8
3.2
0.8
2.5
3.4
13
1.1
0.4
2.5
4.0
9.1
9.6
1.3
4.3
9.5
5.1
3.4
9.1
7.1
14
6.8
12
9.4
2.8
5.0
3.5
2.0
1.3
4.3
2.8
0.6
5.4
2.9
8.4
2.4
4.0
1.6
4.9
9.6
86
0.9
1.3
1.4
7.1
6.7
16
1.9
9.5
5.6
18
8.7
29
11
9.6
20
6.5
10
12
16
13
0.2
0.1
0.8
0.4
0.2
1.0
0.9
1.0
0.5
0.9
0.7
1.2
1.8
7.9
0.4
0.4
0.6
0.9
1.3
1.5
0.6
1.3
1.1
1.5
1.6
2.8
5.1
2.1
2.7
0.8
1.8
2.8
2.1
2.7
15
16
29
16
5.0
32
33
17
22
33
16
40
68
225
16
10
44
57

107
18
62
101
80
84
79
79
150
80
140
142
94
45
40
1.6
0.8
4.6
0.8
0.4
2.3
1.7
3.2
0.9
1.4
0.3
2.2
3.9
55
0.2
0.2
0.5
1.4
7.3
4.0
0.4
2.4
3.5
7.0
4.1
11
5.6
7.4
20
10
12
12
23
12.2
2.2
1.8
2.8
0.8
0.4
4.1
3.2
6.0
2.2
3.1
0.7
3.8
8.1
86
1.2
0.6
2.2
16
9.5
55
3.2
18
2.4
21
29
56
16
39
19
13
29
22
28
3.1
13.1
18.17
28
12.85
2.66
42.43
26.5
28.65
17.03
34.62
12.53
42.75
43.94
198.3
13.26
7.64
35.25
53.9
122.31
134.36
21.58
80.13
116.45
80.91
48.94
134.81
126.61
227.4
125.38
159.2
173.54
98.98
103
39.35
                                                                                    43

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Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
67
24
55
41
160
258
133
285
342
232
44
480
390
422
75
78
125
221
389
520
413
389
489
38
81
101
178
50
65
175
43
14
89
42
115
108
145
251
99
46
57

4.2
1.0
3.8
8.4
19
33
19
22
28
27
3.0
32
43
40
7.9
7.9
19
15
36
49
43
44
56
4.9
5.1
12
20
3.9
3.6
13
2.8
1.0
7.9
4.0
6.8
11
18
25
6.4
2.3
2.2

1.1
0.8
2.3
1.0
1.8
5.7
4.5
9.2
8.5
3.8
1.2
17
10
18
2.4
2.4
6.5
6.3
19
22
12
14
15
0.9
1.4
2.5
4.7
0.9
0.9
4.7
0.9
0.3
2.0
0.9
1.5
3.1
2.6
7.7
2.4
1.0
1.0

6.5
1.1
5.2
1.2
1.6
6.8
0.7
12
13
9.4
3.0
30
7.0
8.5
2.3
1.2
2.3
4.2
9.1
7.4
5.6
10
10
2.9
7.8
5.3
8.4
4.0
3.9
9.9
2.2
0.8
2.9
1.0
1.7
4.2
1.4
8.2
3.9
2.2
3.8

1.5
0.5
1.3
0.8
0.9
2.7
0.2
2.3
2.7
7.0
1.3
8.6
1.7
4.1
0.7
0.4
0.9
1.5
2.2
1.0
1.9
1.5
2.6
0.5
1.1
1.0
1.8
0.6
1.2
1.2
0.8
0.5
1.0
0.8
0.6
1.2
1.0
2.1
2.2
1.1
0.5

10
4.0
9.6
13
73
64

141
145
184 (1)
13
153
109
170
25
37
102
50
138
148
162
133
108
5.0
10
20
47
5.0
8.0
48
6.0
4.0
14
16
9.0
11
53
61
34
11
1.0

3.5
1.5
2.0
1.5
2.3
6.7
0.1
2.3
2.2
2.5
2.9
9.1
8.5
7.0
2.3
0.9
3.0
6.4
18
22
19
18
22
4.3
11
3.9
61
2.6
6.6
15
3.4
0.4
3.4
1.4
1.8
4.5
2.8
10
3.0
2.0
6.1

5.0
2.0
2.0
3.2
3.6
20
1.9
34
45
2.7
2.9
89
19
32
4.5
2.7
3.6
10
20
37
22
21
31
6.1
7.3
8.4
11
6.1
3.2
13
1.7
1.1
8.8
4.4
8.8
22
3.7
30
5.0
1.0
1.7

15.01
5.78
18.93
25.1
54.88
105.87
65.95
92.72
104.85
83.08
12.42
149.7
148.5
173.8
29.59
29.59
74.15
63.33
167.9
212.7
156.7
167.4
201.5
15.94
18.49
40.25
69.27
13.44
12.69
51.77
10.69
3.73
27.95
13.69
23.15
40.21
55.66
94.07
25.84
9.85
9.6

44

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Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
77
78
79
80
81
82
83
84
44
92
371
204
146
29
97
41
4.7
7.9
33
15
30
3.5
11
0.8
1.5
3.2
7.1
5.7
5.7
0.7
1.9
0.5
1.1
4.0
25
4.1
11
2.1
3.2
2.4
0.2
0.6
2.0
0.8
2.0
0.4
0.7
0.7
34(3)
36
89
54
54
9.2
54
1.0
0.1
2.1
6.5
2.0
4.5
2.3
22
4.4
0.9
2.4
18
9.3
25
5.4
25
4.5
17.9
32.87
111.61
60.87
98.37
11.62
35.29
4.05
Table 10. Recommended 10* percentile conductivity, GIs, and hardness estimates for SO group 7
through 9 (number of stations shown in parentheses if n<10)
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
111

58
118

101
122


71
72
310
430
810
51

189
342
608
373

279

554
830
876
648
395
1194
12

5.0
13

9.0
10


5.7
8.5
37
38
64
5.2

20
35
55
39

28

60
64
56
61
41
71
3.4

1.6
3.6

4.5
5.0


1.5
1.5
10
10
23
1.5

5.6
11
20
12

4.9

11
20
20
16
8.9
19
4.3

3.4
3.7

4.9
6.2


2.0
3.3
13
32
69
1.4

3.7
14
44
25

15

76
60
61
43
15
132
0.8

0.6
0.9

0.9
1.0


0.7
0.7
2.5
5.6
3.2
0.5

1.1
1.3
2.2
1.7

1.9

4.3
4.6
3.5
6.2
6.4
4.7
56

20
52


56


16
32
122
175
121
20

69
119
145
102

80

107
127
128
96
119
89
2.3

2.7
1.7

1.7
3.5


0.8
0.8
11
15
55
0.4

1.3
2.5
13.1
9.7

4.5

49
16
24
25
10
210
6.3

2.3
6.9

3.1
4.8


4.2
5.0
30
27
181
3.1

13
45
149
85

37

145
184
187
112
36
130
43.94

19.06
47.26

40.95
45.5


20.4
27.4
133.5
136
254.3
19.15

72.96
132.6
219.5
146.7

90.09

195.1
242
222
218.1
138.99
255.4
                                                                                    45

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Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71

817
438
428
477
85
179
355

215
310

338
394

53
642
570
425


353
115
544
388
502

405

65



80
148
69

96
138

225
183

59
45
46
47
6.9
2.4
28

28
34

49
36

4.1
52
48
44


44
12
53
41
48

43

3.9



3.6
14
4.7

15
17

21
23

16
6.2
5.3
6.9
1.6
0.7 (3)
7.0

7.7
5.2

18
12

1.7
25
12
19


16
4.4
33
18
18

12

0.7



1.4
3.5
1.2

3.4
3.5

5.4
4.3

76
28
31
36
5.8
1.4(3)
29

2.8
6.4

26
24

6.0
49
15
14


7.2
2.9
7.9
9.7
20

9.5

8.5



5.1
4.6
3.7

4.7
4.1

9.8
3.2

4.1
3.9
4.7
4.3
1.4
1.7(3)
2.9

1.1
3.0

3.4
2.4

1.5
12
3.7
5.3


2.1
1.1
1.8
2.1
3.0

2.8

0.8



2.0
1.3
1.2

1.2
1.1

1.4
1.4

102
107
128
103
46

73

96
96

144
122

13
176
159
188


217
40

131
182 (4)

104

6.0



8.5
28
15

28
57(8)

29
56

88
37
42
49
5.0
20
30

3.4
7.3

8.7
6.2

5.0
22
11
9.9


10
4.4
19
16
32

20

13



6.5
8.0
4.1

5.7
5.7

10
3.8

141
38
28
35
8.0
21
28

6.0
24

87
74

7.3
149
44
61


13
5.0
22
25
33

30

6.0



7.4
20
5.7

12
12

44
13

213.1
137.92
136.73
145.79
23.81
8.87
98.7

101.57
106.32

196.3
139.2

17.22
232.5
169.2
187.9


175.6
48.04
267.8
176.3
193.8

156.7

12.62



14.74
49.35
16.67

51.44
56.85

74.64
75.13
46

-------
Ecoregion Conductivity Calcium Magnesium Sodium Potassium Alkalinity Chloride Sulfate Hardness
72
73
74
75
76
77
78
79
80
81
82
83
84
310
146

70




71
898
38
174

34
14

4.8




8.9 (3)
64
4.0
18

10
3.7

1.2




2.4 (3)
23
0.8
3.2

8.3
3.4

3.2




7.7(3)
80
1.9
6.1

2.3
1.7

1.0




2.1(3)
3.8
0.4
0.8

88
54

15





123
8.1
41

12
3.5

4.6




2.1(3)
69
2.4
10

23
6.0

3.9




5.1
160
4.5
12

126
50.17

16.92




32.09
254.3
13.28
58.12

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 25 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 26. Figure 27 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.
                                                                                          47

-------
     1000
      100
    01
    u
    01
    Q.
                                      D SO group 1-3

                                      • SO group 4-6


                                      D SO group 7-9
            Cond.
S04
Figure 25. BLM parameter estimates (10th percentile values) for each SO group in Ecoregion 46

                                (Northern Glaciated Plains)

        Key: Stream order: 1-3 are headwater streams, 4-6 are mid-reaches, and 7-9 are rivers.
                                                                                           48

-------
      1000
                                      D SO group 1-3
                                      • SO group 4-6
                                        SO group 7-9
             Cond.     Ca       Mg      Na      K      Alk.      Cl      SO4
Figure 26. BLM parameter estimates for each SO group in Ecoregion 83 (Eastern Great Lakes
                                      Lowland)
                                                                                      49

-------
        1000
         100
                                        a SO group 1-3
                                        • SO group 4-6

                                        D SO group 7-9
        01
        a.
               Cond.     Ca
Na
Alk.
Cl
SO4
  Figure 27. BLM parameter estimates for each SO group in Ecoregion 54 (Central Corn Belt Plains)
3.3.3   Comparison of Parameter Estimates to Results of Probability-Based Surface Water Sampling
There have been relatively few efforts to estimate Gl 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 probability-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
surveys, 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 were 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 NWIS data
for SO group 1 through 3 were compared to the corresponding estimates calculated by Griffith (2014).
While Griffith did not tabulate 10th percentiles, he did tabulate first quartile (i.e., 25th percentile)
                                                                                          50

-------
statistics for each ecoregion in supplemental material published with his article. Accordingly, we
            -th
                                                                                             ,th
calculated 25  percentiles of the ecoregional NWIS data in SO class 1 through 3 (in addition to the 10
percentiles) to facilitate this comparison. The 25th percentiles from the two datasets are compared for
conductivity in Figure 28 and calcium in Figure 29. The scatter plots reveal significant log-linear
relationships between 25th percentiles for the two datasets; the coefficient of determination (R2) was
0.668 for conductivity and 0.551 for calcium. For conductivity, the 25th 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.
         1000
                                     10                       100
                                   Ecoregional conductivity (NWIS data)
                                                                                      1000
Figure 28. Scatter plot of ecoregional 25th percentile conductivity for NWIS Data (SO Class 1-3) versus
              ecoregional 25th percentile conductivity for Griffith data (mostly SO 1-4)
                           Solid diagonal line represents 1:1 agreement.
                                                                                             51

-------
         1001
      TO
      •a

      1
     2.  10
     "S8
      c
      o
      c
      01
      u
      c
      o
      u
      TO
      O
      '
      8
                                   i                      10
                         Ecoregional calcium concentration, mg/L (NWIS data)
100
                                    rth
Figure 29. Scatter plot of ecoregional 25  percentile calcium concentration for NWIS data (SO class 1-
     3) versus ecoregional 25th percentile calcium concentration for Griffith data (mostly SO 1-4)
                           Solid diagonal line represents 1:1 agreement.
These results suggest reasonable overall consistency between datasets, as well 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 25th percentiles of conductivity and calcium in the low SO group
were so inconsistent with corresponding percentiles presented by Griffith. Table 11 presents salient
characteristics of the conductivity data for Ecoregion 19, including the number of stations, samples per
station, 25th percentile conductivity, the lowest station-specific 25th percentile conductivity in the NWIS
data, and other remarks. In that 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 conductivity data
for 32 stations, with a  single observation per station. The 25th percentile conductivity based on NWIS
data was 240 versus 22.9 microsiemens per centimeter (u.S/cm) based on Griffith's analysis of EPA
data. When recalculated for individual stations in Ecoregion 19 low SO group, the 25th percentile
conductivities varied widely, from 18.25 to 1590 u.S/cm. Griffith reported a median conductivity of 213
                                                                                            52

-------
u.S/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
Samples per station
Median samples per station
25th percentile conductivity (u.S/cm)
Range of station-specific 25th
percentile conductivity (u.S/cm)
Other remarks
62
1-189
22
240
18.25-1590
25th percentiles of conductivity reported by
Griffith were marginally higher than the
minimum station-specific 25th percentiles
calculated from the NWIS data
32
1
1
22.9

Median conductivity = 213
u.S/cm (19x the 25th 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, 10th and 25th percentiles of conductivity and/or calcium decreased with SO
       group, contrary to the general trend.
    •   In three ecoregions, 25th percentiles of conductivity reported by Griffith were marginally higher
       than the minimum station-specific 25th 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
Samples per station
Median samples per station
25th percentile conductivity (u.S/cm)
Range of station-specific 25th
percentile conductivity (u.S/cm)
Other remarks
34
1-129
2
350
29-846.5
10th and 25th conductivity percentiles decrease with SO
group;
Small number of NWIS stations;
25th percentiles of conductivity reported by Griffith were
marginally higher than the minimum station-specific 25th
percentiles calculated from the NWIS data
45
1
1
32


                                                                                          53

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Table 13. Characteristics of the conductivity data for Ecoregion 38 in the low SO group
NWIS data Griffith data
Number of stations
Samples per station
Median samples per station
25th percentile conductivity (u.S/cm)
Range of station-specific 25th
percentile conductivity (u.S/cm)
Other remarks
31
1-8
3
137
22-384
10th and 25th conductivity percentiles decrease with SO
group;
Small number of NWIS stations; 25th percentiles of
conductivity reported by Griffith were marginally higher than
the minimum station-specific 25th percentiles calculated
from the NWIS data
38
1
1
22.9


Table 14. Characteristics of the calcium data for Ecoregion 75 in the low SO group.
NWIS data Griffith data
Number of stations
Samples per station
Median samples per station
25th percentile calcium (mg/L)
Range of station-specific 25th
percentile calcium (mg/L)
Other remarks
360
1-177
17
13
0.02-91
10th and 25th calcium percentiles decrease with SO group
42
1
1
1.41


Table 15. Characteristics of the conductivity data for Ecoregion 78 in the low SO group.
NWIS data Griffith data
Number of stations
Samples per station
Median samples per station
25th percentile conductivity (u.S/cm)
Range of station-specific 25th
percentile conductivity (u.S/cm)
Other remarks
15
1-18
8
26
19-326.5
Small number of NWIS stations; 6 of 15 stations were Ashland
Creek (OR) or tributaries
45
1
1
98.4


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 sampled 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
                                                                                           54

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number of stations, and we included daily averages of all of these measurements in the calculation of
10th percentile estimates. In the probabilistic EPA surveys analyzed by Griffith, individual stations were
usually 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 well 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 percentiles 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 BLM
(version 2.1.2) using the 25th percentile Gl 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 25th
percentile of a Gl was not available, the value was projected from the 25th  percentile of conductivity
using regressions based on NWIS data. If the 25th 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 criteria using the 25th percentile Gl
estimates from NWIS and Griffith could be compared. The criteria estimated in these ecoregions  using
Gl input parameters from the two sources agree very well, as shown in Figure 30. The R2 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%.
                                                                                           55

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         1000
        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 GIs that are
input parameters to the BLM.
                                                                                          56

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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 25th percentiles for the two
datasets; the coefficient of determination (R2) was 0.668 for conductivity and 0.551 for calcium. For
conductivity, the 25th percentiles differed by more than a factor of 2 in 17% of the Level III ecoregions;
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 versus 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 percentiles 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 25th percentile Gl estimates as parameter inputs from NWIS and
Griffith's data. The criteria predicted using the two sets of Gl parameter inputs agreed favorably. The
R2 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 Gl parameter estimates are reasonably
conservative.
EPA incorporated SO variation in the parameter estimates to refine recommended input values for use
in the BLM. EPA found the 10th percentile ecoregion, SO group specific estimates to be reasonably
protective inputs and recommends their use where site-specific parameters are not readily available.
                                                                                          57

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4   DOC ESTIMATION USING THE NATIONAL ORGANIC 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 Technical 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 with
ecoregional statistics derived from the NOCD is provided below. This section also discusses how we
recalculated ecoregional DOC percentiles for rivers and streams, and then 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  Descrip tion of the NOCD
The NOCD is a compilation of pre-2003 organic carbon 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 background on the NOCD is available in USEPA 2003.
EPA's LDC database contains water quality monitoring data collected by academia, volunteer groups,
and tribes, as well as federal, state, and local agencies. Geographically,  the LDC data represent all 50
states 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 20th 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. Approximately 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 through 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). Additionally, 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 water 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 groundwater 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.
                                                                                         58

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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 data
were then processed and screened to ensure that only the most appropriate data would be retained.
This screening process is outlined below:
    ซ 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).
    ซ  "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 because 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 concentrations 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 and 60 mg/L in tributaries  draining
      wetland systems. Therefore, values for DOC above 60 mg/L were removed from the database.
The 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 impairments.
    • 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. Therefore some states are
      disproportionally represented, even when one considers the relative surface water area likely
      to be contained within each state.
    ซ WATSTORE and LDC generally contain more data from sampling sites in larger river and stream
      systems, and areas subjected to proportionately greater human influence compared with
      random statistical sampling.

4.2  Recalculation of Ecoregional DOC Percentiles for Rivers and Streams
Lower percentile (1st, 5th, 10th, and 25th percentiles) DOC concentrations were calculated 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 Berthouexand Brown
                                                                                         59

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(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 percentile values of DOC in U.S. streams and rivers by ecoregion, including 95%
confidence limits for percentile concentrations if n>20.
Level III
Ecoregion
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Ecoregion Name
Coast Range
Puget Lowland
Willamette Valley
Cascades
Sierra Nevada
Southern and Central California
Chaparral and Oak Woodlands
Central California Valley
Southern California Mountains
Eastern Cascades Slopes and
Foothills
Columbia Plateau
Blue Mountains
Snake River Plain
Central Basin and Range
Mojave Basin and Range
Northern Rockies
Idaho Batholith
Middle Rockies
Wyoming Basin
Wasatch and Uinta Mountains
Colorado Plateaus
Southern Rockies
Arizona/New Mexico Plateau
Arizona/New Mexico Mountains
Chihuahuan Deserts
High Plains
Southwestern Tablelands
Central Great Plains
Flint Hills
Central Oklahoma/Texas Plains
Edwards Plateau
Southern Texas Plains
Texas Blackland Prairies
n
(count)
91
835
66
101
32
480
180
6
13
73
26
50
1553
35
778
29
87
150
46
798
1129
281
37
116
439
167
228
10
289
200
58
829
1%

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Level III
Ecoregion
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
Ecoregion Name
East Central Texas Plains
Western Gulf Coastal Plain
South Central Plains
Ouachita Mountains
Arkansas Valley
Boston Mountains
Ozark Highlands
Central Irregular Plains
Canadian Rockies
Northwestern Glaciated Plains
Northwestern Great Plains
Nebraska Sand Hills
Piedmont
Northern Glaciated Plains
Western Corn Belt Plains
Lake Agassiz Plain
Northern Minnesota Wetlands
Northern Lakes and Forests
North Central Hardwood Forests
Driftless Area
Southeastern Wisconsin Till
Plains
Central Corn Belt Plains
Eastern Corn Belt Plains
S. Michigan/N. Indiana Drift
Plains
Huron/Erie Lake Plains
Northeastern Highlands
Northeastern Coastal Zone
Northern Appalachian Plateau
and Uplands
Erie Drift Plains
North Central Appalachians
Middle Atlantic Coastal Plain
Northern Piedmont
Southeastern Plains
Blue Ridge
Ridge and Valley
Southwestern Appalachians
Central Appalachians
Western Allegheny Plateau
Interior Plateau
n
(count)
268
399
523
198
184
21
233
434
36
36
679
4
309
142
193
261
44
403
153
49
439
202
1398
287
3825
14044
102
354
919
106
16730
1525
3813
699
733
47
864
1795
559
1%
1
2.8
1.45
0
0.49
<0.4
1.67
2.71
<0.3
<3
2.22
<1.4
0.4
4.03
0.44
3.1
<7.8
2
0.54
<1.7
2
1
0
1.4
0
0.25
0.05
0.6
0
0.41
1.1
0.43
1
0
0.1
<0.5
0.3
0
0.1
1%
(LCL)
<1
<1.7
<0.4
<0
<0.4
<0.4
<1.5
<1.4
<0.3
<3
0.72

<0
<3.3
<0.2
<0.8
<7.8
<0.1
<0
<1.7
<0.25
<0.7
0
<1.4
0
0.25
<0
<0.5
0
<0.4
1.01
0.3
0.51
0
0.1
<0.5
0.15
0
<0.1
1%
(UCL)
1.95
3
2.07
0.47
0.65
0.4
2
3
0.35
3.05
3.19

0.5
8.01
2.43
4.52
8.25
2.2
2.19
2.31
2.1
1.68
0
1.92
0
0.25
1.6
0.8
0
0.5
1.2
0.5
1.1
0
0.1
0.5
0.4
0
0.1
5%
2.6
3.7
3.92
0.8
0.8
0.4
2
3.5
0.39
3.09
4.4
<1.4
0.7
9.13
2.77
6.41
9.05
2.72
2.67
2.4
3.4
2.12
0
2.7
3.9
0.25
2.02
1
4.8
0.5
1.8
1
2
0.4
0.4
0.58
0.6
0.78
0.1
5%
(LCL)
2
3
3.2
0.4
0.55
<0.4
2
3.1
<0.3
<3
3.8

0.6
5.11
2.28
4.72
<7.8
2.36
1.42
<1.7
2.7
1.61
0
2.05
3.7
0.25
<0
0.87
4.6
<0.4
1.8
0.99
1.9
0.3
0.3
<0.5
0.5
0.2
0.1
5%
(UCL)
3
4
4
1.1
1.4
0.5
2.12
3.6
0.6
6.63
5.2

0.9
9.82
2.96
7.1
11
3.08
2.9
3.25
4.3
2.5
0.2
3.43
4.07
0.5
2.6
1.1
4.9
0.7
1.8
1
2.03
0.4
0.5
0.9
0.6
1
0.1
10%
3
4
4.6
1.1
1.9
0.42
2.3
4
0.57
5.05
6.2
<1.4
1
9.9
3.14
7.6
11
3.7
3.1
3.1
5.3
2.73
3.8
3.8
4.7
0.64
2.63
1.3
5.1
0.7
2.2
1.3
2.4
0.5
0.6
0.9
0.7
1.6
0.1
10%
(LCL)
3
4
4.1
0.86
0.85
<0.4
2
3.6
<0.3
<3
5.6

0.8
9.16
2.8
6.87
<7.8
3.06
2.7
<1.7
4.3
2.2
3
3.17
4.52
0.62
1.74
1.1
5
0.5
2.2
1.2
2.38
0.4
0.5
<0.5
0.7
1.4
0.1
10%
(UCL)
3
4
4.98
1.6
2.39
0.5
2.5
4
0.9
11.23
6.73

1.1
11
3.5
7.97
12
4.5
3.77
4.31
5.6
3
4
4.2
4.8
0.65
2.92
1.4
5.1
0.78
2.2
1.4
2.5
0.5
0.7
1
0.8
1.7
0.1
25%
3.83
5
5.7
2.38
3.73
0.5
3
4.9
0.93
13.25
9.9
1.4
1.7
12
3.9
9
13
5.8
4.95
4.5
6.8
3.9
5.2
5.4
5.8
0.95
3.28
2
5.5
0.98
2.7
2.2
3.3
0.6
1.05
1
1.1
2.7
0.3
25%
(LCL)
3.1
5
5.4
2.08
3
0.4
2.7
4.6
0.6
6.12
9.19

1.4
11
3.6
8.7
11
5.5
3.9
3.4
6.5
3.57
5
4.9
5.7
0.94
3
1.9
5.4
0.8
2.7
2.1
3.2
0.6
1
0.9
1.1
2.5
0.2
25%
(UCL)
4
5.9
6
2.8
4.4
0.74
3.15
5
1.03
16.34
10

2.1
12.86
4.3
9.22
14.88
6.2
5.6
5.8
7
4.8
5.3
5.7
5.9
0.97
3.63
2.3
5.6
1.2
2.7
2.4
3.3
0.7
1.1
1.38
1.2
2.9
0.3
61

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Level III
Ecoregion
72
73
74
75
76
77
78
79
80
81
82
83
84




Ecoregion Name
Interior River Valleys and Hills
Mississippi Alluvial Plain
Mississippi Valley Loess Plains
Southern Coastal Plain
Southern Florida Coastal Plain
North Cascades
Klamath Mountains
Madrean Archipelago
Northern Basin and Range
Sonoran Basin and Range
Laurentian Plains and Hills
Eastern Great Lakes and Hudson
Lowlands
Atlantic Coastal Pine Barrens
Lake Erie
Lake Michigan
Lake Ontario
Lake Superior
n
(count)
328
503
21
4223
-
50
8
9
16
133
21
1430
243
9
5
14
7
1%
1.32
1.7
<1.4
0.9
-
<0.2
<1.7
<2.6
<1.6
1.33
<4.6
0
1
<1.1
<2.6
<0.4
<1.2
1%
(LCL)
<0.15
<1
<1.4
0.9

<0.2



<1.3
<4.6
0
<0.9




1%
(UCL)
1.8
2.14
1.41
1.09

0.2



1.7
4.7
0
1.1




5%
2.1
2.82
1.41
5.3
-
0.26
<1.7
<2.6
<1.6
1.8
4.69
0
1.22
<1.1
<2.6
<0.4
<1.2
5%
(LCL)
1.9
2.4
<1.4
4.7

<0.2



1.38
<4.6
0
1.1




5%
(UCL)
2.4
3.1
2.61
6

0.4



2.1
5.68
0.2
1.5




10%
2.69
3.4
1.72
8
-
0.4
<1.7
2.6
1.81
2.2
5.52
1.9
1.6
1.18
<2.6
0.4
<1.2
10%
(LCL)
2.4
3.2
<1.4
7.6

<0.2



1.8
<4.6
1
1.32




10%
(UCL)
3.05
3.6
2.7
8.3

0.4



2.6
7.98
2
2




25%
4.2
4.3
2.7
12.1
-
0.48
2.3
4.05
2.5
3.85
8.45
5.1
2.6
1.4
2.6
2.2
1.4
25%
(LCL)
3.6
4.1
1.46
11.9

0.4



2.94
5.15
5
2.4




25%
(UCL)
4.6
4.5
4.72
12.3

0.5



4.4
9.3
5.5
3




As was the case for the BLM Gl 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 Environmental
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 Streams
Assessment (WSA), which included DOC measurements from a statistically based random sample of
approximately 2,000 wadeable, perennial 1st through 5th order streams (USEPA, 2006c). In Section
4.3.2, we compare the WSA data to the ecoregion-specific DOC 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). However, 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
                                                                                         62

-------
records (e.g., >110,000 DOC values), a representation of DOC values for all 50 states, 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 body 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 geographically 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 because 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 Allegheny 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 poorest at the higher end
of the distributions. At the lower tails of the distributions (e.g., 10th, 25th percentiles) the
WATSTORE/LDC DOC data are generally within 30% of the EMAP data (Ecoregion 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 (Ecoregions 47 and 70
are about a factor of 2 greater). This result is expected, given the greater focus of the WATSTORE/LDC
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. CIS procedures were used to associate each site with the 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 the 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 1st through 5th SO size  classes (Strahler, 1952, 1957),

                                                                                         63

-------
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 most 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 most 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:
       H0: 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 different 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) present
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  samples 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 NOCD. The left-hand columns present statistics (sample size, median, and
Mann-Whitney Ux, and Uy) 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-sum test. The right-
hand column provides a summary interpretation of the test for each ecoregion indicating whether the
null hypothesis (H0: DOC concentrations from both datasets are not different) should be rejected at the
5% level of significance, in favor of the alternative hypothesis (Hi: DOC concentrations are higher in the

                                                                                         64

-------
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.
                                                                                           65

-------
Table 17. Results of rank-sum test comparing Level III ecoregional DOC data from WSA and NOCD
 Level III     Ecoregion
 Ecoregion      Name
WSA dataset
                         n    median     Ux
                               DOC
NOC database
                   n   median   Uy
                         DOC
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
Exact test   Exact test Interpretation of
 (n2<20)     (n2<9)        test
                     1-sided @ 0.05
                                                 critical U (0.05
                                                 level of sign if.)
                                                       (HO: same mean
                                                       of distributions)
1
2

3
4
5


6



7

8


9


10
11

12

Coast Range
Puget Lowland
Willamette
Valley
Cascades
Sierra Nevada
Southern and
Central
California
Chaparral and
Oak Woodlands
Central
California Valley
Southern
California
Mountains
Eastern
Cascades Slopes
and Foothills
Columbia
Plateau
Blue Mountains
Snake River

Plain
38
3

2
23
14


32



2

41


19


8
77



1.41
0.96

2.47
0.75
0.91


2.5



4.2

1.66


0.97


2.59
1.59



2535.5
2289

84
1673
448


12372



302

245


209


477
1625



91
835

66
100
32


479



180

6


13


73
26



2.2
6.6

2.9
1.4
3.6


4.6



13

8.9


2.3


3.6
3.1



922.5
216

48
627
0


2957



58

1


38


107
376.5



4.17



3.39
5.35


5.82








3.28



4.74



1.54E-05



3.46E-04
5.96E-08


0








5.18E-04



1.07E-06




-2.48

-0.65








-1.65

-3.89





-2.93





6.63E-03

2.57E-01








4.98E-02

5.03E-05





1.70E-03






















































reject HO
reject HO

do not reject HO
reject HO
reject HO


reject HO



reject HO (P~5%)

reject HO


reject HO


reject HO
reject HO

no test

                                                                                                                                    66

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Central Basin
and Range
Mojave Basin
and Range
Northern
Rockies
Idaho Batholith
Middle Rockies
Wyoming Basin
Wasatch and
Uinta
Mountains
Colorado
Plateaus
Southern
Rockies
Arizona/New
Mexico Plateau
Arizona/New
Mexico
Mountains
Chihuahuan
Deserts
High Plains
Southwestern
Tablelands
Central Great
Plains
Flint Hills
42
2
19
19
70
29
25
24
43
7
31
1
6
17
12
2
1.93
2.65
1.54
1.21
1.43
2.29
2.11
2.22
2.05
1.83
1.94
1.48
3.5
4.21
4.71
4.91
47300
67
9149
495
3142
4006
978
15697
18637
1723
984
110
2450
2246
2189
19
1553
35
778
29
81
150
46
798
1129
281
37
116
439
167
228
10
3
5.6
1.8
2.4
1.9
7.3
4.8
6.3
1.3
5.6
5.3
6
11
6.3
7
8.7
17926
3
5634
56
2529
344
172
3455
29911
244
163
6
184.5
593
547
1
4.986

1.77
4.63
1.14
7.17
4.85
5.34
-2.59

5.02


3.95
3.50

2.98E-07

3.81E-02
1.85E-06
0.126
0
5.96E-07
5.96E-08
4.83E-03

2.38E-07


3.90E-05
2.31E-04


-2.15







-3.40

-1.54
-3.62




1.58E-02







3.40E-04

6.18E-02
1.48E-04


















1
















reject HO
reject HO
(P~1.6%)
reject HO
(P~3.8%)
reject HO
do not reject HO
reject HO
reject HO
reject HO
reject HO
reject HO
reject HO
do not reject HO
reject HO
reject HO
reject HO
reject HO
                                                                                                                                             67

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Central
Oklahoma/
Texas Plains
Edwards
Plateau
Southern Texas
Plains
Texas Blackland
Prairies
East Central
Texas Plains
Western Gulf
Coastal Plain
South Central
Plains
Ouachita
Mountains
Arkansas Valley
Boston
Mountains
Ozark Highlands
Central Irregular
Plains
Canadian
Rockies
Northwestern
Glaciated Plains
Northwestern
Great Plains
6


2
3
9
24
6
3
3
10
8
5
13
81
5.58


7.37
15.03
7.47
7.7
2.2
4.61
2.18
1.8
6.7
0.8
9.27
7.45
1098


880
108.5
1860
6744
963.5
327
19
2248
1772
127.5
362
43205
289


829
268
399
523
196
184
21
233
434
36
36
679
6.7


6
5
7
7.7
3.7
7
0.8
4.1
6.3
1.1
18
14
636


778
695.5
1732
5808
212.5
225
44
82
1700
52.5
106
11794






0.62






2.90
8.41






2.68E-01






1.87E-03
0
-1.12


-0.15
2.17
-0.183

-2.66
-0.55
1.09
-4.98
-0.10
-1.49


1.32E-01


4.40E-01
9.85E-01
0.427

3.88E-03
2.92E-01
8.62E-01
3.26E-07
4.60E-01
6.76E-02
































do not reject HO
no test
no test
do not reject HO
do not reject HO
do not reject HO
do not reject HO
reject HO
do not reject HO
do not reject HO
reject HO
do not reject HO
do not reject HO
reject HO
reject HO
                                                                                                                                             68

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)

44

45

46
47
48


49

50

51

52

53


54

55
Nebraska Sand

Hills
Piedmont
Northern
Glaciated Plains
Western Corn
Belt Plains
Lake Agassiz
Plain
Northern
Minnesota
Wetlands
Northern Lakes
and Forests
North Central
Hardwood
Forests
Driftless Area
Southeastern
Wisconsin Till
Plains
Central Corn
Belt Plains
Eastern Corn
Belt Plains

1

28

17
42
13


1

20

7

11

5


9

6

6.46

2.11

14.62
2.84
10.38


11.71

12.28

8.08

2.4

3


2.69

2.83

0

5379

1443
6200
1962


37

3099

573.5

509

2067


1498

7199

4

308

142
193
261


44

403

152

49

439


202

1325

3.2

3.4

15
5.1
10


17

8.1

9.2

7.6

8


6.1

6.8

4

3245

971
1907
1431


7

4961

490.5

30

128.5


320

751



2.17

1.315
5.38
0.95




-1.74



4.58










1.51E-02

9.42E-02
5.96E-08
1.71E-01




4.05E-02



2.38E-06

















-1.15



-0.35



-3.40


-3.29

-3.43










1.24E-01



3.64E-01



3.41E-04


5.07E-04

3.00E-04

























0.2























do not reject HO

reject HO

do not reject HO
reject HO
reject HO
(P~1.7%)

do not reject HO

reject HO (P~4%)

do not reject HO

reject HO

reject HO


reject HO

reject HO
                                                                                                                                             69

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)

56

57


58

59

60

61

62

63

64

65

66
67


68
Southern
Michigan/North
ern Indiana Drift
Plains
Huron/Erie Lake
Plains
Northeastern
Highlands
Northeastern
Coastal Zone
Northern
Appalachian
Plateau and
Uplands
Erie Drift Plain
North Central
Appalachians
Middle Atlantic
Coastal Plain
Northern
Piedmont
Southeastern

Plains
Blue Ridge
Ridge and
Valley
Southwestern
Appalachians

9

3


23

10

5

9

4

2

15

18

16
27


9

4.62

5.05


3.54

4.01

3.74

2.99

3.34

18.54

2.18

2.55

1.09
1.56


1.91

1967

8246


79049

561

831

7834

102

16060

18010

49987

4915
10612


212.5

287

3762


14044

101

354

901

106

16726

1524

3801

686
733


47

7

7.1


1.4854

4.2

3.2

6.2

1.7

3.4

4

4.3

0.9
1.7


1.7

616

3040


2E+05

449

939

275.5

322

17392

4850

18432

6061
9180


210.5






-4.24











3.84

3.38

0.71
0.64









1.13E-05











6.11E-05

3.61E-04

2.37E-01
2.61E-01




-2.67

-1.38




-0.58

0.23

-4.82

1.76

0.10









-0.02

3.77E-03

8.33E-02




2.82E-01

5.93E-01

7.32E-07

9.60E-01

5.39E-01









4.91E-01























































reject HO

do not reject HO


reject HO

do not reject HO

do not reject HO

reject HO

do not reject HO

do not reject HO

reject HO

reject HO

do not reject HO
do not reject HO


do not reject HO
                                                                                                                                            70

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)
69
70
71
72
73
74
75
76
77
78
79
80
81
82
Central
Appalachians
Western
Allegheny
Plateau
Interior Plateau
Interior River
Valleys and Hills
Mississippi
Alluvial Plain
Mississippi
Valley Loess
Plains
Southern
Coastal Plain
Southern
Florida Coastal
Plain
North Cascades
Klamath
Mountains
Madrean
Archipelago
Northern Basin
and Range
Sonoran Basin
and Range
Laurentian
Plains and Hills
10
19
14
14
10
1
6

54
43
3
26
3
5
1.84
2.17
2.24
3.86
8.31
1.26
6.7

0.82
0.77
1
1.51
1.72
5.68
3651
25890
2153
3143
1646
21
20141

1169
343
27
372.5
364
94
864
1735
559
328
503
21
4222

50
8
9
16
133
21
1.6
4
0.4
6.2
5.5
5.4
15.5

0.7
2.6
7.9
3.2
5.1
9.9
4990
7075
5674
1449
3384
0
5191

1531
1
0
43.5
35
11

4.28
-2.88
2.34




1.18


4.26



9.18E-06
2.00E-03
9.70E-03




1.19E-01


1.02E-05


0.84



1.87
-1.66
-2.50


-4.43


-2.44
-2.70
8.01E-01



9.69E-01
4.90E-02
6.18E-03


4.74E-06


7.40E-03
3.47E-03










3

















do not reject HO
reject HO
reject HO
reject HO
(P~0.97%)
do not reject HO
reject HO
(P~4.9%)
reject HO
no test
do not reject HO
reject HO
reject HO
reject HO
reject HO
reject HO
                                                                                                                                            71

-------
 Level III
Ecoregion
Ecoregion
  Name
WSA dataset
NOC database
M-Wtest(nl>10
  and n2>10)
One large sample
    (n2>20)
                           n     median     Ux
                                 DOC
                                      n    median   Uy
                                            DOC
Exact test    Exact test Interpretation of
 (n2<20)      (n2<9)        test
                      1-sided @ 0.05
                                                                           critical U (0.05
                                                                           level of sign if.)
                                                                            (HO: same mean
                                                                            of distributions)


83

84
Eastern Great
Lakes and
Hudson
Lowlands
Atlantic Coastal
Pine Barrens


12

2


6.72

12.62


8228

150


1346

243


7

5.7


7925

336


0.11




4.55E-01






0.93




8.24E-01












do not reject HO

do not reject HO
                                                                                                                                            72

-------
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 bias 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 ecoregions where the null hypothesis was rejected by the rank-sum test (Ecoregions 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 31. On the other hand, in such large-
sample ecoregions where the null hypothesis was not rejected (Ecoregions 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.
                                                                                          73

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                   Cumulative Frequency Distributions for DOC in ecoregion 23
1UU
1 10-
O
O
Q


* WSA ecoregional DOC data
" EPA national oraanic carbon database







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-2.5
                                                         1.5
2.5
                2    -1.5   -1    -0.5    0    0.5    1
                                      z score
Figure 31. Comparison of probability distributions of DOC concentrations in Ecoregion 23
                 Cumulative Frequency Distributions for DOC in ecoregion 77
j"
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0 WSA ecoregional DOC data
" EPA national oraanic carbon database







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                                                           1.5
 2.5
          -2.5   -2    -1.5   -1    -0.5    0    0.5    1
                                       z score
Figure 32. Comparison of probability distributions of DOC concentrations in Ecoregion 77
                                                                                   74

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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 10th 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 whether 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 estimated
ecoregional DOC values be compared to data from EPA's NRSA (NRSA, USEPA, 2013b; 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 Data
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 Rivers (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 sampling 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. Information 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: Field Operations Manual (USEPA, 2007; 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, research 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,  hereafter, as WSA/NRSA
data. CIS 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 sample datasets were equal. The alternative  hypothesis was
that DOC concentrations  in the NCOD were significantly greater than those in the WSA/NRSA data,

                                                                                         75

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indicating positive bias possibly due to over-representation of impacted sites. The test was applied for
each of 84 Level III ecoregions at alpha=0.05.
Table 18 below includes the number of data (n) in each ecoregion, and the 10th percentiles of DOC
based upon data from the EPA DOC database (NOCD) and the combined WSA/NRSA data. For each
ecoregion, the table 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 higher 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.
Table 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 H0 (equal means)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
91
835
66
100
32
479
180
6
13
73
26
50
1553
35
778
29
81
150
46
798
1129
281
37
116
439
1.1
2.5
1.1
0.5
2.1
2.1
2.7
4.4
1.4
2.0
1.3
2.2
1.5
2.8
1.0
1.4
0.2
4.3
1.8
3.0
0.6
2.6
2.2
2.3
4.4
60
8
12
37
21
42
7
43
25
22
91
6
82
8
39
34
94
52
41
61
76
27
48
10
29
0.7
0.36
0.4
0.3
0.5
0.8
1.1
0.7
0.5
1.0
0.8
1.2
0.7
0.8
0.8
0.8
0.7
1.1
0.9
1.2
0.8
0.7
0.7
1.4
1.3
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
reject
accept
reject
reject
reject
accept
reject
reject
reject
reject
                                                                                        76

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Ecoregion NOC database WSA/NRSA database H0 (equal means)
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
167
228
10
289
200
58
829
268
399
523
196
184
21
233
434
36
36
679
4
308
142
193
261
44
403
152
49
439
202
1325
287
3762
14044
101
354
901
106
16726
1524
3801
686
3.3
3.8
4.9
3.8
1.0
1.0
2.0
3.0
4.0
4.6
1.1
1.9
0.4
2.3
4.0
0.6
5.1
6.2
1.4
1.0
9.9
3.1
7.6
11.0
3.7
3.1
3.1
5.3
2.7
3.6
3.8
4.7
0.6
2.6
1.3
5.1
0.7
2.2
1.3
2.4
0.5
47
92
8
30
4
4
9
5
18
66
18
18
7
39
32
7
43
234
4
93
28
103
26
9
77
44
49
12
21
30
38
14
92
81
29
26
22
45
47
108
40
1.9
2.2
1.2
1.7
1.0
0.3
3.1
5.6
2.8
3.3
0.7
1.4
0.5
0.8
3.3
0.6
2.5
2.3
1.0
1.1
6.1
1.7
5.4
6.0
2.7
3.2
1.1
1.9
1.8
2.1
2.9
1.5
1.2
2.7
1.4
1.8
0.9
1.7
1.0
1.4
0.6
reject
reject
reject
reject
accept
accept
accept
accept
accept
reject
reject
reject
accept
reject
reject
reject
reject
reject
accept
reject
reject
reject
reject
accept
accept
reject
reject
reject
reject
reject
reject
accept
accept
accept
reject
reject
accept
accept
reject
reject
accept
77

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Ecoregion NOC database WSA/NRSA database H0 (equal means)
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
733
47
864
1735
559
328
503
21
4222
1
50
8
9
16
133
21
1346
243
0.6
0.9
0.7
1.5
0.1
2.7
3.4
1.7
8.0
na
0.4
1.7
2.6
1.8
2.2
5.5
1.0
1.6
88
17
31
67
54
65
107
18
41
0
61
56
9
49
13
18
32
4
0.9
0.8
1.1
1.5
1.1
2.2
2.8
1.2
3.6
na
0.4
0.6
0.8
1.0
1.0
2.8
2.6
3.3
accept
accept
accept
reject
accept
reject
reject
accept
reject
na
accept
reject
reject
reject
reject
reject
reject
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 19). In these 24 ecoregions, low-end percentiles based on
DOC concentrations in the combined data (NOCD and WSA/NRSA) were selected as reasonably
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 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); 10th percentiles from combined NOCD & WSA/NRSA data

Ecore ion n DOC (mg/L)
17
21
30
31
32
175
1205
204
62
838
0.6
0.6
1.0
1.0
2.0
                                                                                         78

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Ecore ion n DOC(mg/L)
33
34
38
44
49
50
57
58
59
62
63
66
67
68
69
71
74
77
84
273
417
28
8
53
480
3776
14136
182
128
16771
726
821
64
895
613
39
111
247
3.0
4.0
0.5
1.0
10.4
3.5
4.6
0.6
2.7
0.7
2.2
0.5
0.6
0.9
0.7
0.1
1.5
0.4
1.6
NOCD and the WSA/NRSA data in 83 Level III ecoregions: number of observations (n); 10*
percentiles; and source of data for each ecoregion
Ecoregion

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n

60
8
12
37
21
42
7
43
25
22
91
6
82
8
39
DOC (mg/L)

0.7
0.3
0.4
0.3
0.5
0.8
1.1
0.7
0.5
1.0
0.8
1.2
0.7
0.8
0.8
Data Source

WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
                                                                                       79

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Ecoregion
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57


34
175
52
41
61
1205
27
48
10
29
47
92
8
30
204
62
838
273
417
66
18
18
28
39
32
7
43
234
8
93
28
103
26
53
480
44
49
12
21
30
38
3776
DOC (mg/L)
10%
0.8
0.6
1.1
0.9
1.2
0.6
0.7
0.7
1.4
1.3
1.9
2.2
1.2
1.7
1.0
1.0
2.0
3.0
4.0
3.3
0.7
1.4
0.5
0.8
3.3
0.6
2.5
2.3
1.0
1.1
6.1
1.7
5.4
10.4
3.5
3.2
1.1
1.9
1.8
2.1
2.9
4.6

Data Source
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
80

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Ecoregion
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
77
78
79
80
81
82
83
84


14136
182
29
26
128
16771
47
108
726
821
64
895
67
613
65
107
39
41
111
56
9
49
13
18
32
247
DOC (mg/L)
10%
0.6
2.7
1.4
1.8
0.7
2.2
1.0
1.4
0.5
0.6
0.9
0.7
1.5
0.1
2.2
2.8
1.5
3.6
0.4
0.6
0.8
1.0
1.0
2.8
2.6
1.6

Data Source
NOCD&WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
WSA/NRSA
NOCD&WSA/NRSA
4.5  Conclusions
EPA tested the 10th 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 criteria values predicted 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 distribution 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 sensitive to DOC concentrations and (2) reasonably protective DOC
concentrations can  be difficult to estimate at the 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
                                                                                         81

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example, in 47 ecoregions the DOC percentiles were calculated from 50 or fewer concentration values,
and in seven ecoregions the DOC percentiles were calculated from fewer than 10 values. In the former
case (n<50), the lower 95% confidence limit of the 10th percentile cannot be calculated (Berthouex and
Brown, 1994), while in the latter (n<9) the 10th percentile itself is below the lowest concentration
value. 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   SUMMARY 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 GIs. Given  the broad geographical
range over which the  BLM is likely to be applied, and the limited availability 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.
In this report we described three approaches EPA used to estimate default input parameters for Gl 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 Gl 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 geochemical ions where site-specific data are not
     available
In Section 2 we used geostatistics to estimate missing Gl 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 Gl water quality inputs for the BLM because
conductivity is one of the most widely monitored water quality indicators in the U.S. and correlates
well with GIs. We presented average predicted  10th percentile concentrations for the BLM Gl water
quality parameters Level III ecoregions. We further refined these estimates by considering the effect of
stream order (size)  in Section 3. We found that values of the Gl estimates generally increased with
stream order, a trend that was most apparent and consistent for higher order  streams. Tables 8, 9, and
10 present  best estimates of Gl input  parameters for the BLM. Estimated inputs are provided for each
Gl  in each ecoregion categorized by stream order for low, medium, and high order streams,
respectively. EPA recommends these 10th percentile Level III ecoregion, stream order group-specific
values be used in the BLM where site-specific data are not available.
                                                                                          82

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5.2  Recommendations for BLM inputs for DOC where site-specific data are not available
In Section 4 we determined that the geostatistical and regression-based approaches used to estimate
Gl 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 estimates based on the WSA dataset. Recommended 10th percentile 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. 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 Gl
input parameters for the BLM did not produce accurate site-specific estimates 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 criteria 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 parameters 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 also 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 Gl parameters are 10th percentile ecoregional, stream-order specific
values. Default recommended values for DOC are 10th 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 inputs
where possible.
                                                                                         83

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Helsel, D.R. and R.M. Hirsch. 2002. Techniques of Water-Resources Investigations of the United States
       Geological Survey Book 4, Hydrologic Analysis and Interpretation. Section A3, Statistical
       Methods. U.S. Geological Survey. September 2002.  Publication available at:
       http://pubs.usgs.gov/twri/twri4a3/ (last accessed February, 2016).
Herlihy, A.T., D.P. Larsen, S.G. Paulsen, N.S. Urquhart, and B.J.  Rosenbaum. 2000. Designing a spatially
       balanced randomized site selection process for regional stream surveys: the EMAP mid-Atlantic
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       361-365.
Linton, T.K., W.H. Clement, W.F. Dimond, G.M. DeGraeve, and G.W. Saalfeld. 2007.  Development of a
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MacArthur, R.H. 1972. Geographical Ecology. New York: Harper & Row.

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McKay, L, Bondelid, T., Dewald, T., Johnston, J., Moore, R., and Rea, A. 2012. "NHDPIus Version 2: User
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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.I  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 thousand 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. Principal 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 release 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 metal
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 binding 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 water temperature. Of these nine chemical constituents (Alkalinity (alk), calcium (Ca), magnesium
(Mg), sodium (Na), sulfate (S042-), 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

                                                                                         87

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constituents - 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 ambient 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 geospatial 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 several thousand separate surface  water sampling locations throughout the U.S. (Figure A-l). 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 typical 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 limited to the continental U.S.
                      Legend
                          NWIS sampling locations
                         I Lower48 STATES Albers
1,020 Miles
 1
                Figure A-l. 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-l), 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
                                                                                           88

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only sampled once). Examination of the spatial distribution of numbers of sampling events per site
reveals that the most intensive sampling 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 locations.
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.
                   Legend
                   = of samples
                   COUNT
      Figure A-2. Intensity 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.
                                                                                          89

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                     Legend

                     MEDIAN_ALK
                      o 100-S6.00
                      O 88.01 - 195.00
                      O 195.01 • 382.00
                      * 382.01 - 2352.00
                      ป 2352.01 - 6860.00
                                           3DQ
                                                BOO
                                                          1,200 Miles
             Figure A-3. Median measured alkalinity (mg/L as CaCOS) at NWIS locations


The next step in data visualization involved 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 spatial
averaging process, to make visual interpretation of general trends easier (Figure A-4).
                    Legend
                    HUC-aveui(jed medial
                    AVG_HED_AL
                       o.oooo
                    |  | 0.0001 - 110.4444
                    |  | 110.4445- 188.5000
                    ^B 188.5001 - 305.0000
                    ^B 305.0001 > 776.2500
                    ^H 776.2501 - 1742.0000
300
     600
     	1	
1,200 Miles
  1
         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 representative concentrations for
default BLM inputs makes some physical sense: HUCs are areas that are defined by some degree of
                                                                                                  90

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interconnection between associated surface water features. HUCs may be either watersheds in their
own right or downstream sections of larger 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 sized
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 make 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 estimates 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 provided 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 Error 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% global, 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% local, 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 options.
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.
                                                                                           91

-------
                            Legend
                            Uiuveisal Kilying tfMk
                            Prediction Mi|>
                            Eilh_ซun_alb_te9tUNEDlAH_ALK]
                            Filled Couloirs
                               1DCQOQO- 1S.32053Q
                               19.320530-26.137335
                               29.137335-47.467863
                               47.457363-61.648392
                               81.648392- 145.456181
                            ^H 145.456161-264.536335
                            ^B 264,536835 -486770081
                            ^•l 486.770031 -901.510925
                            H 901.510925-1675.517456
                            ^H 1675.517456 -3120ฃ00000
D   275   550        1,100 Miles
 I	1	1	1	1	1	'	'	1
                         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 polygons by using the Zonal Statistics function of
ArcGIS Spatial Analyst.
                                                                                                           92

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                           Legend
                           HUC-avg. kriggedAlk
                           SCEN15_ALK
                           |  10.0-38.3
                           |  | 33.4-68,6
                           |  | 66.7-97.1
                           |  197.2-131.8
                             | 131.9-168.9
                           j^H 169.0-206.3
                           ^^ 206.4-243.1
                           P^ 243.2-287.1
                           BH 287.2 - 355.4
                           ••355.5-529.1
0   275   550
                  1,100 Miles
         Figure A-7. Kriging-based alkalinity predictions, averaged over 8-digit HUC polygons

For HUCs containing NWIS sampling locations, linear regression plots of predicted versus measured
concentrations (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 responsible for smaller-than-expected correlation
coefficients.

0) A
Predicted (krig





000
500
01
c


HUC-Averaged Median Alkalinity

^^•fcfcf4^^^^
) 500 1000 1500
Observed





20





00


            Figure A-8. Kriging-predicted vs. calculated HUC-averaged alkalinity; r =0.537
                                                                                                   93

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A scatter plot matrix of cross-constituent comparisons revealed some interesting, non-random
relationships between 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 scatter 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.
                                  010   30
                                                    5.5  6.5 7.5 8.5
                           Alk
                                    DOC
                                              Na
                                                       PH
                                                                Ca
                        0  200  400
                                           0  1000  2000
                                                             0 100 200 300
  Figure A-9. Scatter plot matrix of median concentration kriged predictions, averaged over 8-digit
                           HUCs regions covering the continental U.S.
                                                                                            94

-------
                         med alk
                                  med doc
                                            med na
                                                     med_ph
                                                              med ca
                        0 100 200 300
   Figure A-10. Scatter plot matrix of median concentrations from 772 monitoring locations in the
                                       continental U.S.
In addition to scatter plots, correlation coefficient 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-l). Although not identical, the coefficients were generally similar between the two datasets,
again suggesting that the kriging predictions are reasonable.
         Table A-l. Matrices of correlation coefficients between constituent concentrations
2096 HUC-averaged predicted median values
ca
Alk
DOC
Na
PH
Ca
1
-0.01456
0.327599
0.761675
0.698379
772 site-median values
Alk
Alk
DOC
Na
pH
Ca
1
0.019145
0.327028
0.453161
0.842484
DOC Na pH Ca

1
-0.02661 1
-0.27746 0.286512 1
-0.02585 0.531727 0.58514 1
^^^^^•H
DOC Na pH Ca

1
0.165445 1
-0.24067 0.169238 1
-0.05097 0.387617 0.374592 1
                                                                                           95

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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-ll 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, respectively. 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.
0
o
o -
i
o
o
8 -
Variances
20000 :
8
o -
0
0 -









prin3




Com p.1 Comp.2 Comp.3 Comp.4 Cctnp.5
       Figure A-ll. Variance plot from PCA of HUC-average kriging-predicted concentrations
                                              prin4

                              8
                              R
                                  Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
           Figure A-12. Variance plot from PCA of site-median measured concentrations
                                                                                          96

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 Table A-2. Loadings onto original variables from PCA on HUC-averaged predictions and site-median
                                        concentrations


Alk
DOC
Na
pH
Ca

Alk
DOC
Na
pH
Ca
HUCs:
Comp.l
0.219

0.965

0.142
Sites:
Comp.l
0.952

0.13

0.277

Comp.2
0.932

-0.25

0.263
Comp.2
0.162

-0.982



Comp.3
-0.289



0.954
Comp.3
0.259

0.133

-0.955

Comp.4 Comp.5

0.999

-0.999

Comp.4 Comp.5

-0.997

0.999

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 represent 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 LCSOs. 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 25th 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.
                                                                                            97

-------
                      Leyend
                      Univei ;,il hihjiinj 25th Perceiitile
                      Ou.inile M,i|.
                      [,ilk_s.im_.ill>].[rปE DIAII_ALK]
                      Filled <: outOlll s
                          1 • 20.8145657
                          20 B145657 -30.2422237
                          302422237-50.0567894
                          50 056 7S94-91.702003 5
                          91.7020035 -179.229732
                      ^B  179229782-363.191071
                      ^f  363.191071 -749.831299
                      ^f  749S31299-I.562.45I9
                      ^^  1^62.4519-327037549
                      ^H  3270.37549 - 6,8hLi
             275    550
              1	1	1
                    Figure A-13. Kriging 25th percentile map of median alkalinity
                        1000
                                         50
     100         150
  Alk(mg/l_asCaC03)
200
250
                 X   site minimum observed
•Linear (HUC-mean 25th percentile kriged cone.]
Figure A-14. Comparison of observed site-minimum alkalinities with HUC-mean 25th percentile
                                         kriging-predicted values
                                                                                                              98

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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 et 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 polygons,
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 analyses 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 the BLM.

A.8  References
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 Agency, 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.
                                                                                           99

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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.I  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 10 input  parameters to characterize water quality at a particular site; the most important
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 data 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 method 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. The 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
Information 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 were 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 sufficient 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 changes 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
                                                                                        100

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generally corresponded to approximately the 2.5th percentile 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
conservative 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 correlation. 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 routine 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 quality 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 the 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 through.
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 related 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-l illustrates how conductivity relates to hardness and
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. Relationships 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%.
6100% 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.


                                                                                          101

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indicated. Lines drawn by eye through the points for chloride and sulfate show slight curvature, but the
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).
          1600
          1400
          1200
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      •
    Chloride

      M
    Hardness


    Sul*ปte
                                                                           I	L
                        1000          200O         3000          *000          5OOO
                      SPECIFIC CONDUCTANCE. IN MICROMHOS PER CENTIMETER AT 25 DEGREES CELSIUS
                                                                                      6000
      Figure 11. Relation o* conductance ซo chloride, hardness, and suKale concentrations. Gila River at Bylas. Arir., October 1.
         . toScplcmber 10. W4.
Figure B-l. 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 as 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 the pH value. Its relationship with other materials, however, most likely resulted from
the dilution effect of stream flow. Conductivity was negatively correlated with discharge (p=-0.729),
and the same was found for most water quality variables that were positively 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, which is positively related to dissolved materials and soluble metals. As it is
                                                                                              102

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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 important 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 least 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 constraint 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 of 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-l provides an inventory of the number of observations, and number 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 exists, 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,

                                                                                         103

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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-l. Number of observations and sites reported in NWIS for streams and rivers in Colorado
Parameter
PH
alkalinity
calcium
Number of Observations
62,005
8136
45,490
Number of Sites
3668
839
2708

conductivity
discharge
168,110
127,275
6101
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 data
distributions and possibly non-linear relationships. To determine the statistical significance of the rank
correlation coefficient (p), the significance level (P) was also calculated. The Spearman's rank
correlation was also used to examine the relationship 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.
                                                                                          104

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 Table B-2. Results of Spearman rank tests for correlation (p) between median values of variables at
                                           each site.
              Probability values (P) are not exact due to the presence of ties in the data

Conductivity
pH
DOC
Ca
Mg
Na
K
S04
Alkalinity
Cl


p: 0.175
P: 0.019
p: 0.866
P: 0.333
p: 0.867
P: <0.001
p: 0.882
P: <0.001
p: 0.921
P: <0.001
p: 0.846
P: <0.001
p: 0.905
P: <0.001
p: -0.600
P: 0.350
p: 0.827
P: <0.001
Discharge
p: 0.012
P: 0.892
p: 0.441
P: 0.008
P:
P:
p: -0.371
P: 0.068
p: -0.516
P: 0.008
p: 0.139
P: 0.695
p: -0.128
P: 0.551
p: -0.514
P: 0.010
P:
P:
p: -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.
                                                                                           105

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Table B-3. Results of Spearman rank tests for correlation (p) between the first quartile of values at
                                          each site.
             Probability values (P) are not exact due to the presence of ties in the data

Conductivity
pH
DOC
Ca
Mg
Na
K
S04
Alkalinity
Cl


p: 0.287
P: <0.001
p: 0.618
P: <0.001
p: 0.920
P: <0.001
p: 0.935
P: <0.001
p: 0.910
P: <0.001
p: 0.773
P: <0.001
p: 0.941
P: <0.001
p: 0.829
P: <0.001
p: 0.752
P: <0.001
Discharge
p: 0.057
P: 0.144
p: 0.070
P: 0.168
p: -0.149
P: 0.031
p: -0.060
P: 0.305
p: -0.107
P: 0.066
p: -0.075
P: 0.129
p: -0.109
P: 0.075
p: -0.068
P: 0.247
p: 0.099
P: 0.381
p: 0.004
P: 0.958
Table B-4. Results of Spearman rank tests for correlation (p) 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
pH
DOC
Ca
Mg
Na
Conductivity

p: 0.382
P: <0.001
p: 0.558
P: <0.001
p: 0.920
P: <0.001
p: 0.929
P: <0.001
p: 0.845
P: <0.001
Discharge
p: 0.056
P: 0.213
p: 0.032
P: 0.579
p: -0.107
P: 0.134
p: 0.017
P: 0.791
p: -0.056
P: 0.383
p: -0.089
P: 0.078
                                                                                         106

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K
S04
Alkalinity
Cl
P:
P:
P:
P:
0.
0.
0.
0.
694
,001
908
,001
784
,001
706
,001
P:
P:
P:
P:
-0.065
0.353
0.017
0.790
0.184
0.102
0.034
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. Histograms
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.
                                                                                         107

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               LNDISCH  COND   PH    DOC   CA    MG   NA    K    SO4   ALK   CL
                            i I

                              Jj
                             .A'-.
E
                                                 ft
                                                 I
                                                           ^
               LNDISCH  COND   PH    DOC   CA    MG   NA    K    SO4   ALK   CL
Figure B-2. Scatter plot matrix for first quartile of site-specific data for discharge (LNDISCH),

                conductivity (COND), and BLM water quality parameters
                                                                                     108

-------
                     LNDISCH  COND   PH   DOC   CA    MG    NA    K    SO4   ALK   CL
                               ;i

                                        L
                                                          L
                     LNDISCH  COND   PH   DOC   CA    MG    NA
                                                            SO4   ALK   CL
    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 water 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 for 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 the 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 streamflow 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 elevations.
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 snow melt. At most other times, conductivity and salt
and alkalinity concentration values are much higher. Depending upon how the water quality samples
                                                                                          109

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are allocated at a site, the median concentration of these parameters may fall in either mode of the
bimodal 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.
                     TEMP LNDISCH COND  PH   DOC   CA   MG    NA   K   SO4  ALK   CL



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                              ฃ,1,


                                               r

                                                         I
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                     TEMP LNDISCH COND  PH   DOC   CA   HG    NA   K   SO4  ALK   CL
       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

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Figure B-5. Time series plot of conductivity (diamond symbols) and discharge (open 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 fora 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 correlations 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, within 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 calculations. 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 robust 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
                                                                                           111

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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 and 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 measure 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 worthwhile 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 be 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 analysis. 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 patterns 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 Little 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: Application 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 Assignment 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-L70p.
Godsey, S.E. and J.W. Kirchner. 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 Survey, Water Supply Paper 2254.

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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
       Protection 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 CIS to assess the relationship between land use and water quality at
       a watershed level. Environment International. 23(1): 103-114.
                                                                                        113

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

C.I  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-specific 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. Geological 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 three 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 quality 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, although 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 distributions.
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
10th percentile (i.e., the value exceeded by 90% of the data) of conductivities and the 10th percentile of
BLM water quality parameter values. We then fit regression models to project 10th percentiles of BLM
parameter values as a function of 10th percentiles of conductivities.
We also fit regression models to the full NWIS dataset (data for all rivers 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 below.

C.2.1   pH
The following regression model appeared  to be optimum for projecting the 10th percentile of pH from
the 10th percentile of conductivity at the sites for which appropriate data were available:
       ln(pH) = 1.85 + 0.0352-ln(EC)

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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 10th percentile of DOC
concentrations from the 10th percentile of conductivity at the sites 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 10th percentile of alkalinity
concentrations from the 10th percentile of conductivity at the sites for which appropriate data were
available:
       In(alkalinity) = 1.14-ln(EC) - 4.68
For the full NWIS dataset, the following regression model  was developed to project alkalinity
concentrations from conductivity:
       In(alkalinity) = 0.652-ln(EC) + 0.530

C.2.4  Calcium
The following regression model appeared to be optimum for projecting the 10th percentile of calcium
concentrations from the 10th 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 10th percentile of
magnesium concentrations from the  10th 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
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C.2.6  Sodium
The following regression model appeared to be optimum for projecting the 10th percentile of sodium
concentrations from the 10th 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 10th percentile of
potassium concentrations from the 10th percentile of conductivity at the sites for which appropriate
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 appeared to be optimum for projecting the 10th percentile of sulfate
concentrations from the 10th percentile of conductivity at the sites for which appropriate data were
available:
       ln(S04) = 1.16-ln(EC)-4.85
For the full NWIS dataset, the following regression model was developed to project sulfate
concentrations from conductivity:
       ln(S04) = 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:
       In(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)

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a limited number of conductivity measurements or (2) a low-end conductivity value estimated by
geostatistical 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 on conductivities estimated by
geostatistical methods, is demonstrated in the following section (Section C4).
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 method,  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 water 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 (1C) 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 2.5th percentile of the
distribution of  1C 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 10th percentile
water quality para meter 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
concentrations and BLM  input parameters appeared to be an  important site-specific factor in
determining the relationship between the FSC and the 1C. Copper concentrations are not required to
run the  BLM in its toxicity prediction mode, but they are used in the Monte 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 the 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  hardness and
alkalinity, slightly acidic (mean pH = 7.32), and fairly low in conductivity (10th percentile = 134 u.S/cm at
25- 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 u.g/L). The FSC for copper at this site was calculated to be 11.4 u.g/L when the
correlation between copper concentrations and  BLM parameters was considered, and 7.0 u.g/L when
this correlation was neglected. Test results at this site are show in Table C-l.
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 Table C-l. Copper Fixed Site Criterion predictions for the Naugatuck River, Connecticut using various
                                      calculation methods
Calculation Method DOC(mg/L) pH
Monte Carlo FSC with [copper]
correlated to inputs (r=0.7)
Monte Carlo FSC with no
[copper] correlation
1C calculated with 10th % of input
data
1C calculated with input from
10th % of conductivity and
correlations
1C calculated with input from
10th % of conductivity and
correlations except DOC
1C calculated with input from
10th % of conductivity and
correlations except pH & DOC
1C calculated with input from
10th % of conductivity and
correlations except pH & DOC
Data
Data
2.8 (10th % of data)
5.49 (projected
from correlations)
2.7 (10th % from L3
ecoregion)
2.7 (10th % from L3
ecoregion)
2.8 (10th % of data)
Data
Data
7. 1 (10th % of
data)
7.55 (projected
from
correlations)
7.55 (projected
from
correlations)
7. 1 (10th % of
data)
7. 1 (10th % of
data)
Geochemical Ions
Data
Data
(10th % of data)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
Copper Fixed
Site Criterion
(ug/L)
11.4
7.0
6.4
21.5
10.5
5.7
5.9
The BLM was then applied to predict 1C for copper, using low-end percentiles of the measured BLM
water quality inputs. Using the 10th percentile values of all measured input data, an 1C of 6.4 u.g/L was
predicted. This 1C is 43% smaller than the FSC calculated considering the copper correlation, but only
21% smaller than the FSC neglecting this correlation. When the 10th percentiles of all of the BLM water
quality inputs were instead projected from conductivity using the regression models, the predicted ICs
were 21.7 u.g/L (using the regressions based on 10th percentiles of the three-state data) and 21.5 u.g/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 1C 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 10th percentile of
DOC from rivers and streams in the Northeastern Coastal  Zone, the  Level III ecoregion where the
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Naugatuck River is located.7 In this case the predicted 1C was 10.5 |-ig/L, a value much closer to the FSCs
as well as the 1C calculated using the 10th percentile values of all the measured input data. A second
recalculation was made using the 10th percentile of the pH data, together with the ecoregional 10th
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 10th 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 about 10% of the prediction  made
using the 10th percentile values of all the measured input data. In summary, if BLM predictions are
made for copper 1C 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 output was quite
strong at this location (r = 0.70 between filtered copper and 1C predictions). As a  result, the FSC
corresponds to an elevated percentile (40%) of the 1C predictions. If this correlation  is neglected in the
Monte Carlo method, the FSC corresponds to only the 14th percentile of the 1C predictions. This
suggests that the relationship between FSC and 1C (in terms of the percentile of the  1C distribution
corresponding to the FSC) may be somewhat site-specific. Regardless of this complication, the
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 5th, 2.5th, and 1st 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 (10th percentile = 307 u.S/cm at 259 C). DOC
concentrations 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 u.g/L). The
FSC for copper at this site was calculated to be 39.1 u.g/L, and the correlation between copper
concentrations and BLM parameters was strong (r = 0.624 between filtered copper and 1C predictions).
This FSC value corresponds to the 46th percentile of the distribution of 1C. When the FSC for copper was
recalculated assuming no correlation between copper concentrations and BLM parameters, the value
decreased to 11.1 u.g/L (corresponding to the 4.5th percentile of the 1C distribution). Test results at this
site are tabulated Table C-2.
7 Ecoregion 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 Bioaccumulation Factors (EPA-822-R-03-030).
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 Table C-2. Copper Fixed Site Criterion predictions for the San Joaqufn River, California using various
                                      calculation methods
Calculation Method DOC(mg/L) pH
Monte Carlo FSC with [copper]
correlated to inputs (r =0.6)
Monte Carlo FSC with no
[copper] correlation
1C calculated with 10th % of input
data
1C calculated with input from
10th % of conductivity and
correlations
1C calculated with input from
10th % of conductivity and
correlations except DOC
1C calculated with input from
10th % of conductivity and
correlations except pH & DOC
1C calculated with input from
10th % of conductivity and
correlations except pH & DOC
Data
Data
2.7 (10th % of data)
9.38 (projected
from correlations)
2.79 (10th % from L3
ecoregion)
2.79 (10th % from L3
ecoregion)
2.7 (10th % of data)
Data
Data
7.5 (10th % of
data)
7.77 (projected
from
correlations)
7.77 (projected
from
correlations)
7.5 (10th % of
data)
7.5 (10th % of
data)
Geochemical Ions
Data
Data
(10th % of data)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
Fixed Site
Criterion
(ug/L)
39.1
11.1
11.9
54.0
16.0
11.6
11.2
The BLM was then applied to predict 1C for copper, using low-end percentiles of the BLM water quality
inputs. Using the 10th percentile values of all measured input data, an 1C of 11.9 u.g/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 10th percentiles of all of the BLM water
quality inputs were instead projected from conductivity using the regression models, the predicted 1C
were 50.0 u.g/L (using the regressions based on 10th percentiles of the three-state data) and 54.0 u.g/L
(using the regressions based 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 1C 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
10th percentile of DOC from rivers and  streams in the Central California Valley, the Level III ecoregion
where the San Joaquin River is located. In this case the predicted 1C was 16.0 u.g/L, a value much closer
to the uncorrelated  FSCs as well as the 1C calculated using the 10th percentile values of all the
measured input data. A second recalculation was made using the 10th percentile of the pH data,
together with the ecoregional 10th 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 10th
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
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about 5% of the prediction made using the 10th percentile values of all the measured input data. BLM
predictions made for copper 1C 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
The 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 (10th percentile = 229 u.S/cm at 25ฐ C). Organic
carbon concentrations are representative for rivers and streams in this region (logmean DOC = 5.50
mg/L), and the filtered copper concentrations are low (logmean filtered copper = 3.27 u.g/L). The FSC
for copper at this site was calculated to be 35.4 u.g/L. This FSC value corresponds to the 32nd percentile
of the distribution of 1C. Moderate correlation between copper concentrations and BLM parameters
was observed at this site (r = 0.50 between filtered copper and 1C predictions). When the FSC for
copper was recalculated assuming no correlation between copper concentrations and BLM
parameters, the value decreased to 20 u.g/L (corresponding to the 4.3rd percentile of the 1C
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
Copper Fixed
Calculation Method DOC (mg/L) pH Geochemical Ions Site Criterion
(ug/L)
Monte Carlo FSC with [copper]
correlated to inputs (r =0.5)
Monte Carlo FSC with no [copper]
correlation
1C calculated with 10th % of input
data
1C calculated with input from 10th %
of conductivity and correlations
1C calculated with input from 10th %
of conductivity and correlations
except DOC
1C calculated with input from 10th %
of conductivity and correlations
except pH & DOC
1C calculated with input from 10th %
of conductivity and correlations
except pH & DOC
Data
Data
4. 1 (10th % of data)
7.7 (projected from
correlations)
4.5 (10th % from L3
ecoregion)
4.5 (10th % from L3
ecoregion)
4.1 (10th % of data)
Data
Data
7.5 (10th % of
data)
7.7 (projected
from
correlations)
7.7 (projected
from
correlations)
7.5 (10th % of
data)
7.5 (10th % of
data)
Data
Data
(10th % of data)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
35.4
20.0
17.3
37.5
21.6
17.3
15.9
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The BLM was applied to predict 1C for copper, using low-end percentiles of the BLM water quality
inputs. Using the 10th percentile values of all measured input data, an 1C of 17.3 u.g/L was predicted,
which is 51% smaller than the FSC calculated considering the copper concentration correlation but only
14% smaller than the FSC neglecting this correlation. When the 10th percentiles of all of the BLM water
quality inputs were instead projected from conductivity using the regression models, the predicted 1C
was 37.5 u.g/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  1C 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 10th percentile of DOC from rivers and streams in the Western High
Plains, the Level III ecoregion where the South Platte River is located. In this case the predicted 1C was
21.6 u.g/L, a value much closer to the uncorrelated FSCs as well as the 1C calculated using the 10th
percentile values of  all the measured input data. A second recalculation was made using the 10th
percentile of the pH data, together with the ecoregional 10th 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 10th 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 10th percentile values of all
the measured input  data. As with the previous cases, the BLM  predictions made for copper 1C at this
site using measured values of pH and organic carbon, but where all other BLM water quality inputs
were projected using conductivity regressions, were 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 (10th percentile = 50.1 u.S/cm at 25ฐ C). Organic
carbon concentrations are low (logmean DOC = 0.92 mg/L), as are the filtered copper concentrations
(logmean filtered copper = 1.75 u.g/L). The FSC for copper at this site was calculated to be 1.56 u.g/L,
corresponding to the 6th percentile of the distribution of 1C. 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 at this site are shown  in Table C-4.
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  Table C-4. Copper Fixed Site Criterion predictions for the Halfmoon Creek, Colorado using various
                                     calculation methods
Calculation Method DOC(mg/L) pH
Monte Carlo FSC with [copper]
correlated to inputs (r =0.01)
1C calculated with 10th % of input
data
1C calculated with input from 10th %
of conductivity and correlations
1C calculated with input from 10th %
of conductivity and correlations
except DOC
1C calculated with input from 10th %
of conductivity and correlations
except pH & DOC
1C calculated with input from 10th %
of conductivity and correlations
except pH & DOC
Data
0.6 (10th % of data)
2.8 (projected from
correlations)
0.6 (10th % from L3
ecoregion)
0.6 (10th % from L3
ecoregion)
0.6 (10th % of data)
Data
7.2 (10th % of
data)
7.3 (projected
from
correlations)
7.3 (projected
from
correlations)
7.2 (10th % of
data)
7.2 (10th % of
data)
Geochemical Ions
Data
(10th % of data)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
(projected from
correlations)
Copper Fixed
Site Criterion
(ug/L)
1.56
1.42
7.43
1.58
1.39
1.39
The BLM was then applied to predict 1C for copper, using low-end percentiles of the BLM water quality
inputs. Using the 10th percentile values of all measured input data, an 1C of 1.42 u.g/L was predicted,
only 9% smaller than the FSC. When the 10th percentiles of all of the BLM water quality inputs were
instead projected from conductivity using the regression  models, the predicted 1C was 7.43 u.g/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 carbon projections. As in the previous cases, we demonstrated this by recalculating the
1C 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 10th 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  1C was 1.58 u.g/L, a value within
about 10% of the FSC as well as the 1C calculated using the 10th percentile values of all the measured
input data. A second recalculation was made using the 10th percentile of the pH data, together with the
ecoregional 10th 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 10th 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 about 2% of the
prediction made using the 10th percentile values of all the measured input data. As in the previous
examples, if BLM predictions are made for copper 1C 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.
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C.3.5   Summary of Site-Specific Test Results
The results of this work can be summarized as follows:
Regression models were developed to project 10th percentiles of BLM water quality parameters from
the 10th 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 produced
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 Gl parameters (alkalinity, calcium, magnesium, sodium, potassium, sulfate,
and chloride,) were reasonably accurate, as judged by comparison of model predictions made using
projected values of the Gl BLM input parameters to model predictions made using all measured input
data. The regression models used to project Gl parameters from conductivity were calculated two
different ways; however, the BLM predictions of 1C were not sensitive to this difference.
We were unable to find an estimate for site-specific pH that was superior to the (admittedly 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 DOC 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 tabulations 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
N WIS. The utility of these tabulations could be improved by updating them to incorporate 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,  including 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 filtered 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  10th percentile of the distribution of 1C  predicted by the BLM at each site. When copper
concentration correlations were considered  in the FSC calculations, the 10th percentile of the 1C
distributions was found to be highly conservative approximations of the FSC, underestimating the FSC
by 44 to 70%. This is illustrated in Figure C-l, which also shows the good agreement between 1C
predicted with the BLM using site-specific data and 1C predicted  using measured pH and organic carbon
but projected values of the Gl 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.
                                                                                         124

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                                 • 1C (10th % of data)
                                 • 1C (10th % of conductivity & correlations)
                                 O 1C (10th % conduct/correl. except L3-DOC)
                                 A 1C (10th % conduct/correl. except pH & DOC)
                                	ISC=FSC
                                 5     10    15    20    25    30    35    40
                                 Copper FSC (ug/L) calculated with copper concentration correlation
     Figure C-l. Instantaneous Criteria (1C) predicted with the BLM using site-specific data and 1C
     predicted using measured pH and organic carbon and projected values of the Gl BLM input
                                             parameters
                                                                                    ,th
When copper concentration correlations were neglected in the FSC calculations, the 10  percentile of
the 1C distributions did a much better job approximating the FSC. This is shown in Figure C-2. In this
case, the 10th percentile of the 1C distributions was within 15% of the FSC. This figure also shows the
good agreement between 1C predicted with data and projected values of the Gl BLM input  parameters.
                                                                                                 125

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            ,th
                                                    • IC(10th%ofdata)
                                                    • 1C (10th % of conductivity & correlations)
                                                    O 1C (10th % conduct/correl. except L3-DOC)
                                                    A 1C (10th % conduct/correl. except pH & DOC)
                                                   	ISC=FSC
                       0         5         10        15        20        25
                            Copper FSC (ug/L) calculated with no copper concentration correlation
Figure C-2.10  percentile of the 1C distributions using data and projected (predicted) values of the Gl
                                        BLM parameters
The degree of correlation between filtered copper concentrations and BLM input water quality
parameters appears to be an important site-specific factor in determining the relationship between the
FSC and the 1C.  Figure C-3 plots the percentile of the 1C corresponding to the FSC for each site as a
function of the  correlation coefficient between the copper concentrations and the 1C, 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 1C corresponding to the FSC increases
substantially  (6th to 46th percentile) as the correlation coefficient between the copper concentrations
and the 1C 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 1C
corresponding to the FSC is considerably lower (4.3rd to 14th percentile). This suggests that correlations
between copper concentrations and BLM input parameters should  be given careful consideration when
calculating FSC.
                                                                                             126

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                                FSC calculated with copper correlation
                                FSC calculated with no copper correlation
                        0      0.1     0.2     0.3     0.4     0.5     0.6     0.7     0.8
                                  Correlation coefficient btwn. 1C and filtered copper concentrations
 Figure C-3. Percentile of the 1C corresponding to the FSC for each site as a function of the correlation
  coefficient between the copper concentrations and the 1C when the FSC is calculated with Copper
                 correlation and when FSC is calculated without Copper correlation
C.4  Combining Gl-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 concentrations
of Gl 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 granite geology, such as parts of the Northeast, tend toward
low hardness and alkalinity. The estimation of Gl parameter values based on geography thus seems
possible. EPA has provided a prototype of a geostatistical approach8 that demonstrated this potential.
That 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 concentrations with kriging predictions
were encouraging, especially for DOC and alkalinity.  Geostatistical techniques to project BLM Gl input
                                                                                            127

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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 Gl
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 Gl
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 geostatistics. 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 Gl
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 Gl 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.
Although combining Gl-conductivity regressions with geostatistical techniques seems promising for the
reasons mentioned  above, this approach had  never been demonstrated. We conducted a simple test
using NWIS conductivity and hardness  data from the state of Colorado. 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 10th 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:
       In(hardness) = 0.984-ln(EC) - 0.870
We also kriged the 10th 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
10th percentile 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 10th percentile of hardness 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.
                                                                                        128

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 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 see whether combining the kriged conductivities with the conductivity-hardness
                            ith
regression would project the 10   percentiles of hardness better than direct kriging of the hardness
                                                                                          129

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data. For the combined kriging/regression approach, we determined the kriged conductivity values at
all of the sampling locations and then projected the 10th percentiles of hardness at these locations
using the regression equation. We also determined the directly-kriged 10th percentiles of hardness at
all of the sampling locations.
The hardness estimates obtained by each approach were then compared to the 10th 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 correlate 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 than 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 natural 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 10th
percentile of hardness were made by the kriging/regression approach compared to direct kriging.
            10000
           o
           Q.
           O
           at
           •i1
          =
              100 -
                      D  kriged 10th % of hardness
                      ป  projected 10th % of hardness
                      	1:1 line (perfect fit)
                                                 100
                                         10th percentile hardness (data)
                                                                                 10000
                               ->th
 Figure C-6. Comparison of the 10  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")
As this test demonstrated, combining kriging with regressions to project BLM Gl 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 broader scale should be considered as a
                                                                                            130

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"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 Gl parameters
has already been 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 Gl 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 Gl parameters are less important than pH and DOC in
terms of predicting appropriate site-specific 1C 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 analyses 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. Further 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
      Aquatic 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 CIS to assess the relationship between land use and water quality at
      a watershed level.  Environment International. 23(1): 103-114.
                                                                                         131

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

D.I  Introduction
The 2007 Update 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 requires data on the 10 input parameters for the BLM,
including dissolved organic carbon (DOC). Data for DOC concentrations, in both effluents and receiving
waters, are  extremely limited. The BLM is very sensitive to DOC concentrations (HydroQual, 2005),
which means that to ensure accurate predictions of copper bioavailability 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 discharge, 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 BOD) 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 for 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. (2004) 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, from 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 Conservation (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 relationship 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, monitoring data from all major POTWs reporting TOC and 5-day BOD in the United States
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 10 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 presented errors in the reported unit, and
divided them by 1,000 to convert from units of microgram per liter (u.g/L) to mg/L. TOC and  BOD

                                                                                      132

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records were matched by POTW, location (e.g., upstream, downstream, influent or effluent), year and
month. Thus, "synchronous" 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-l presents the results of least squares
regression of the average monthly data: TOCoi/g = a + b BODavg. A scatter plot of this data is shown in
Figure D-l. Table D-2 presents the results of least squares regression of the maximum monthly data:
TOCmox = a + b BODmox. 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 (Figures D-l 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-l).
    Table D-l. Least squares regression of average monthly TOC and BOD data for all monitoring
                                         locations

CA0054372
CA0105295
CA0105295
CA8000326
CA8000383
CA8000383
ID0020443
ID0020443
ID0023981
ID0023981
LA0073521
TN0023353
All POTWs

Effluent Gross Value
Effluent Gross Value
Raw Sew/Influent
Effluent Gross Value*
Effluent Gross Value
Raw Sew/Influent
Upstream Monitoring*
Downstream Monitoring
Effluent Gross Value
Upstream Monitoring
Effluent Gross Value*
Effluent Gross Value
All locations
Intercept (a)

7.551
19.935

4.952
59.586

3.500
6.268
3.200

2.628
4.828
Slope (b)

-0.379
0.142

0.725
0.107

-0.400
0.281
-0.300

0.391
0.237
r2

0.009
0.104

0.344
0.038

0.190
0.196
0.127

0.438
0.873


41
59

31
30

2
26
3

35
243
    ^note: POTW/location without regression results indicates less than 2 synchronous data records
                                                                                       133

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                               TOCavg ~ BODavg (All Samples)
             •e  o
             03  O
             o  •<-
             .9
             c
             03
             O)
                55-
                             100        200        300        400


                                 Biochemical Oxygen Demand (mg O2/L)
                                                                     500
         Figure D-l. 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
CA0054372
CA0105295
CA0105295
CA8000326
CA8000383
CA8000383
ID0020443
ID0020443
ID0023981
ID0023981
LA0073521
TN0023353
All POTWs
Location
Effluent Gross Value
Effluent Gross Value
Raw Sew/Influent
Effluent Gross Value
Effluent Gross Value
Raw Sew/Influent
Upstream Monitoring
Downstream Monitoring
Effluent Gross Value
Upstream Monitoring
Effluent Gross Value
Effluent Gross Value*
All locations
Intercept (a)
9.420
8.567
56.439
7.307
6.674
149.293
0.300
3.500
6.210
3.200
12.989

11.183
Slope (b)
0.499
-0.114
0.062
0.006
0.507
0.047
1.175
-0.400
0.208
-0.300
-0.818

0.196
	
0.154
0.007
0.046
0.000
0.235
0.052
0.039
0.190
0.202
0.127
0.110

0.700
d^l
29
50
59
28
31
30
4
2
28
3
18

302
                                                                                       134

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                                 TOCmax ~ BODmax (All Samples)
                o
                .9
                                500       1000        1500

                                    Biochemical Oxygen Demand (mg O2/L)
                                                              2000
           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 values. Coefficients of determination (r2) for the
regressions were low; for most of them r2 < 0.2. Pooling the data from all POTWs and locations
increased the r2 to 0.87 for average and 0.70 for maximum BOD and TOC values.
Diagnosis of regression analyses revealed 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 ordinary
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 range of the independent variable. The fitted model for the
50th quantile (median) was:
      TOCavg = 5.5647 + 0.2088 BODoi/g  (243 df, R1 = 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.
                                                                                        135

-------
The results of least squares regression of the average monthly effluent data: TOCoi/g = a + b BODavg
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: TOCmox = a + b BODmox. A
scatter plot of this data is shown in Figure D-4.
  Table D-3. Least squares regression of average monthly TOC and BOD data for effluent monitoring
                                            locations
                                    Intercept (a)   Slope (b)
                      CA0054372
                      CA0077691
                      CA0079103
                      CA0079243
                      CA0102822
                      CA0105295
                      CA0107492
                      CA0109991
                      CA8000073
                      CA8000326
                      CA8000383
                      ID0023981
                      LA0069868
                      LA0073521
                      TN0023353
                      TN0023531
                      TN0023574
                      All POTWs
7.5512
4.9522
6.2679
2.6284
37.6422
7.4142
-0.3789
0.009
41
0.7254
0.344
31
0.2808
0.196
26
0.3909
0.438
35
-1.1348
0.076
0.0882
0.016
25
5.8740
0.2859
0.245
174
                                                                                            136

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                          TOCavg ~ BODavg (All Samples)
        E>
        2  o
                              o
                          10           20           30

                             Biochemical Oxygen Demand (mg O2/L)
                                                               40
Figure D-3. Scatter Plot of Average Monthly Data (Effluent Monitoring Locations)
                                                                                     137

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















CA0054372
CA0077691
CA0079103
CA0079243
CA0102822
CA0105295
CA0107492
CA0109991
CA8000073
CA8000326
CA8000383
ID0023981
LA0069868
LA0073521


9.4197
4.2750
23.3517
5.7979
6.3526
8.5667
5.9043
3.0331
9.0000
7.3073
6.6743
6.2101
7.6475
12.9886

0 Slope (b) r
0.4993 0
0.7008 0
-0.1278 0
-0.0367 0
0.1780 0
-0.1143 0
1.0676 0
0.8458 0
0.0000 0
0.0056 0
0.5070 0
0.2083 0
0.1139 0
-0.8184 0
^^ฃ1^1
154 29
422 6
035 16
008 8
195 28
007 50
027 2
167 11
000 8
000 28
235 31
202 28
535 5
110 18
TN0023353
TN0023531
TN0023574

All POTWs
6.6930
0.4311 0
276 299

Total Organic Carbon (mg C/L)
0 20 40 60 80
ill

ง> c
0
TOCmax
o
o
o
n 0 ฐฐ
O
l^^3^
^0 ฐ ฐ ฐ
}
~ BODmax (All Samples)
o
^-^
o
ฐ o
1 1
20 40
Biochemical Oxygen Demand (mg O2/L)
—-—~~^^ o
60
        Figure D-4. Scatter Plot of Maximum Monthly Data (Effluent Monitoring Locations)
                                                                                      138

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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 (r2) for the
regressions were low; all r2 < 0.55 and for most of them r2 < 0.24. Low coefficients of determination
were also recorded for regressions of TOC on BOD values from all POTWs (r2 = 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, TOCoi/g = a + b BODavg, 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. Residuals 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:
       TOCavg = 6.0388 + 0.2171 BODavg
                                     (r2 = 0.185, 170 df) (Equation 1)
          CD


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      O
      O
      "I
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                                      14
                                   16
18
5-1012
                 Figure D-5. Residuals of the linear model, TOCavg = a + b BODavg
(Left: plot of Studentized residuals (studres) against projected (fitted) TOC values; Right: plot of studies
                      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 ฑ 2 mg/L), for the range of BOD concentrations
of interest (less than 30 mg/L).
Diagnosis of the regression analysis, TOCmox = a + b BODmox, 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:
                                                                                          139

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      TOCmox = 6.6242 + 0.4095 BODmox       (r2 = 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 regression 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
Effluent 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). These 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

Oct. 2-4, 2000





Dec. 11-15, 2000










May 21-23, 2001


PVSC
MCMUA
BCMUA
JMEU
RVMUA
LRMUA
PVSC
MCMUA
BCMUA
JMEU
RVMUA
LRMUA
NHH
NBC
NBW
NHWNY
SMUA
PVSC
BCMUA
DOC (mg/L)
43.0
0.10
22.2
8.51
12.2
8.76
50.3
260
20.0
23.0
23.4
10.4
14.0
28.6
21.8
18.7
15.8
34.5
15.0
POC (mg/L)
10.5
6.75
10.6
8.29
3.81
4.71
5.35
9.22
2.73
5.23
8.73
11.4
3.07
6.67
3.38
5.66
2.89
14.2
9.17
TOC (mg/L)
53.5
6.85
32.8
16.8
16.0
13.5
55.7
269
22.8
28.2
32.1
21.8
17.1
35.3
25.2
24.3
18.6
48.7
24.1
TSS (mg/L)
51.4
36.3
54.1
19.2
22.1
9.3
25.9
62.6
14.4
31.1
42.0
55.2
22.5
23.3
7.8
18.1
6.6
41.1
11.9
                                                                                       140

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                    RVMUA
                                                C(mg/L) POC(mg/L)  TOC(mg/L)  TSS (mg/L)
                         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
    300
    250 -
                                                                       250
                                                                                    300
      Figure D-6. Scatter plot of TOC versus DOC in CARP effluent monitoring data
                                                                                            141

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          40
          35 -
          30 -
          25 -
        O
        g
          15 -
          10 -
           5 -
                       10
                                   20           30
                                            TOC (mg/L)
                                                           40
                                                                      50
                                                                                  60
    Figure D-7. Scatter plot of TOC versus DOC in CARP effluent monitoring data (TOC <50 mg/L)
Least squares regression of the CARP effluent data (Table D-5) produces the following model:
       DOC = 0.9266 TOC   (r2 = 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   (r2 = 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.I 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-l) 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.
                                                                                        142

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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 determination were
also recorded for regressions of TOC on BOD values from all POTWs (r2 = 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 including 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
reported 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 with 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 monthly 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 is:
       TOCoi/g = 5.8740 + 0.2859 BODoi/g  (r2 = 0.245, 174 df)
                                                                                        143

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 Table D-6. Summary statistics for POTW average monthly effluent TOC concentrations, categorized
                    according to average monthly effluent BOD concentration
                                  ODavg
                                 <5 mg/L
              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
              rth
              5  quantile
4.90
6.08
  4.46
   6.53
   4.96
              95  quantile
10.00
10.58
  18.40
   21.8
   14.52
                                 98*
          32*
          34*
                      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 concentration
                                  BODmax lev
             TOCmax
                                  <5 mg/L
                                           mg/L    mg/L
              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  quantile
5.50
3.00
2.00
11.00
5.4
             95  quantile
10.94
12.00
25.55
35.3
23.6
                                  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
from TOC values:
       DOC = 0.7133 TOC   (r2 = 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
effluent 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.
                                                                                           144

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D.5  References
Fadini, S.F., Jardim, W.F., J.R. Guimaraes. 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://ibcs.sbq.org.br/ibcs/2004/voll5  nl7l9-152-02.pdf).
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.
                                                                                        145

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