United States
Environmental Protection
Agency
Office of Water (4607)
Washington, D.C. 20460
EPA-815-D-00-001
May 2000
} v>EPA Arsenic Occurrence in Public
Drinking Water Supplies
Public Comment Draft
U.S. EPA Headquarters Library
Mail code 3201
1200 Pennsylvania Avenue NW
Washington DC 20460
$
4 EPA
815-
D-
00-001
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U.S. EPA Headquarters Library
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1200 Pennsylvania Avenue NW
Washington DC 20460
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Table of Contents
e
List of Tablesc iii
*
List of Figures iv «
List of Acronyms v
Disclaimer vii
Acknowledgments viii
Executive Summary ix
1. Introduction 1-1
1.1 PURPOSE OF THIS DOCUMENT 1-2
1.2 ORGANIZATION OF THE DOCUMENT 1-2
2. Sources of Arsenic .2-1
2.1 PHYSICAL AND CHEMICAL PROPERTIES OF ARSENIC 2-1
2.1.1 Environmentally Relevant Arsenic Species 2-1
2.2 NATURAL SOURCES OF ARSENIC 7 2-4
2.3 ANTHROPOGENIC SOURCES OF ARSENIC 2 -11
3. Fate and Transport of Arsenic 3-1
3.1 RELATIONSHIP OF FATE AND TRANSPORT PROPERTIES TO SOURCE INTAKE .. 3 -1
3.2 RELATIONSHIP OF FATE AND TRANSPORT PROPERTIES TO TREATMENT AND
DISTRIBUTION 3-2
4. Sources of Data on Arsenic Occurrence in Drinking Water Supplies 4-1
4.1 ARSENIC COMPLIANCE DATABASE (ACD) 4-1
4.1.1 Safe Drinking Water Information System (SDWIS) 4-3
4.1.2 State Compliance Monitoring Databases 4-4
4.1.3 Building the ACD from SDWIS and State Compliance
Monitoring Databases 4-20
4.2 COMPARISON DATABASES 4-22
4.2.1 National Arsenic Occurrence Survey (NAOS) Database 4-22
4.2.2 USGS Arsenic Databases 4-25
4.2.3 National Inorganics and Radionuclides (NIRS) Database 4-26
4.2.4 Metropolitan Water District of Southern California <£"
(Metro) Database 4-26
4.3 OTHER DATABASES 4-27
4.3.1 1969 Community Water Supply Survey 4-27
4.3.2 1978 Community Water Supply Survey 4-28 '•
4.3.3 Rural Water Survey 4-28
4.3.4 National Organics Monitoring Survey : 4-28
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4.3.5 Western Coalition of Arid States Research Committee
Arsenic Occurrence Study 4-29
4.3.6 Association of California Water Agencies Database (ACWA) 4-29
5. Arsenic Occurrence Patterns in the United States 5-1
5.1 STRATIFICATION BY SOURCE WATER TYPE 5-1
5.2 STRATIFICATION BY SYSTEM SIZE 5-1
5.3 STRATIFICATION BY SYSTEM TYPE 5-9
5.4 REGIONAL STRATIFICATION 5-9
5.5 ARSENIC DISTRIBUTIONS AT THE STATE LEVEL 5-16
5.6 SUMMARY OF PATTERNS OF ARSENIC OCCURRENCE 5-20
6. National Occurrence Estimates 6-1
6.1 ARSENIC NATIONAL OCCURRENCE PROJECTION METHODOLOGY 6-1
6.1.1 System Means 6-2
6.1.2 State Exceedance Probability Distributions 6-3
6.1.3 Regional Exceedance Probability Distributions 6-4
6.1.4 National Exceedance Probability Distributions 6-6
6.1.5 Number of Systems Exceeding Alternative MCLs 6-8
6.2 ARSENIC NATIONAL OCCURRENCE ESTIMATES RESULTS 6-9
6.2.1 Community Water Supply Systems 6-9
6.2.2 Non-Transient, Non-Community Water Supply Systems 6-9
6.3 COMPARISON OF ACD, NAOS, WADE MILLER, AND USGS OCCURRENCE
ESTIMATES 6-15
6.4 UNCERTAINTY ANALYSIS 6-20
6.4.1 Purpose of Uncertainty Analysis 6-20
6.4.2 Uncertainty Analysis Methodology 6-20
6.4.3 Uncertainty Analysis Results 6-22
7. Intra-systemVariability 7-1
7.1 PURPOSE OF ANALYSES 7-1
7.2 AVAILABLE DATA 7-2
7.3 ANALYTICAL METHODS AND RESULTS 7-3
7.3.1 Empirical Average Coefficient of Variation 7-3
7.3.2 Pooled Log-Space Variance 7-8
7.3.3 Regression Model Coefficient of Variation 7-14
7.3.4 Variance Function Models Where the Coefficient of Variation
Depends on the Mean 7- 14
7.3.5 Estimating the Probability POE Mean Exceeds 2 ^g/L 7-15
7.4 SUMMARY OF INTRA-SYSTEM ANALYSES 7-19
8. Temporal Variability 8-1
8.1 PURPOSE OF ANALYSIS 8-1
8.2 AVAILABLE DATA AND RESULTS 8-1
9. References 9-1
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Appendices
Appendix A: Revised Regression on Ordered Statistics Methodology
Appendix B: Analysis Results
APPENDIX B-l: Box PLOTS
APPENDIX B-2: LOG-NORMAL PROBABILITY PLOTS OF SYSTEM MEANS
APPENDIX B-3: STATE EXCEEDANCE PROBABILITY DISTRIBUTIONS
APPENDIX B-4: INTRA-SYSTEM LOG-NORMAL PROBABILITY PLOTS
Appendix C: Summaries of Pre-1980 Data Sets
Appendix D: Database Specifications and Data Conditioning
APPENDIX D-l: INDIVIDUAL STATE DATABASE SPECIFICATIONS
APPENDIX D-2: ACD DATABASE SPECIFICATIONS
APPENDIX D-3: DATA CONDITIONING PROCESS
List of Tables
ES-l Estimated Arsenic Occurrence in U. S. Ground Water CWS xv
ES-2 Estimated Arsenic Occurrence in U. S. Surface Water CWS xvi
2-1 Physical and Chemical Properties of Arsenic 2-2
2-2 Inorganic and Organic Arsenic Compounds 2-3
2-3 Arsenic Concentrations in Environmental Media 2-7
2-4 Arsenic in Igneous and Sedimentary Rocks 2-8
2-5 Common Minerals of Arsenic 2-8
2-6 Estimated Natural Average Arsenic Releases to the Atmosphere 2-10
2-7 Summary of Current and Past Uses of Arsenic 2-11
4-1 Summary of Arsenic Data Sources 4-2
4-2 Overview of State Compliance Monitoring Data 4-5
4-3 State Data Sets Excluded from ACD 4-8
4-4 CWS Systems in. SDWIS and State Compliance Monitoring Data 4-11
4-5 Summary of Reporting Limits for Eight States 4-13
4-6 Overview of Complete Data Sets versus Data Subsets 4-16
4-7 Reduction in Coverage Associated with Data Subsets 4-17
4-8 Summary of Numbers of Samples per System
for State Compliance Monitoring Data 4-18
4-9 States in the Seven NAOS Regions 4-23
5-1 Distributions of System Means for Community Water Systems 5-2
5-2 Log-Means by System Size Category, Ground Water Systems 5-7
5-3 Log-Means by System Size Category, Surface Water Systems 5-8
5-4 Arsenic Occurrence in Ground Water CWS and NTNCWS Systems 5-10
5-5 Comparison of Regional Ground Water Arsenic Occurrence Estimates Based
on USGS Data All States in Region vs. States Represented in ACD 5-15
6-1 Cutoff Points for Ground Water and Surface Water, in vg/L 6-5
6-2 Regional Exceedance Probability Distribution Estimates 6-7
6-3 Estimated National Exceedance Probabilities for
Ground Water and Surface Water " 6-10
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6-4 Estimated Arsenic Occurrence in U. S. Ground Water CWS 6-11
6-5 Estimated Arsenic Occurrence in U. S. Surface Water CWS 6-12
6-6 Estimated Arsenic Occurrence in U. S. Ground Water NTNCWS 6-13
6-7 Estimated Arsenic Occurrence in U. S. Surface Water NTNCWS 6-14
6-8 Comparison of ACD, NAOS, and USGS Arsenic Occurrence Estimates 6-16
7-1 Summary of Data for Intra-system Analyses 7-2
7-2 Summary of Data for System Coefficients of Variation 7-5
7-3 Mean CV based on Empirical Analyses 7-5
7-4 Probability of an Observation > 2 ngfL from a Log-normal
Distribution with Mean of 1 /ug/L and Various CV 7-15
List of Figures
ES-l States With Suitable Arsenic Compliance Monitoring Data xiv
2-1 Environmental Transfer of Arsenic 2-6
4-1 States with Suitable Arsenic Compliance Monitoring Data 4-7
5-1 Boxplots of System Means for Community Water Systems
in the State of California 5-5
5-2 States with Arsenic Compliance Data in the Seven Regions 5-13
5-3 Boxplots of System Means for Community Water Systems
in the United States, by State ."'. 5-17
5-4 System Means of GW Arsenic Concentrations for NJ,
Log-normal Probability Plot 5-18
5-5 System Means of GW Arsenic Concentrations for NH,
Log-normal Probability Plot 5-19
6-1 Comparison of Arsenic Exceedance Probabilities, GW and SW Systems 6-15
6-2 Comparison of Ground Water Systems Exceeding
Arsenic Concentrations of 5^g/L 6-18
6-3 Comparison of Ground Water Systems Exceeding
Arsenic Concentrations of 20 ^g/L 6-19
6-4 Comparison of Ground Water 95% Confidence Intervals for 3 Distribution Types .6-23
6-5 Comparison of Surface Water 95% Confidence Intervals for 3 Distribution Types .6-24
6-6 Comparison of Ground Water 95% Confidence Intervals for Right-tailed ROS vs.
Empirical Methods 6-25
6-7 Comparison of Surface Water 95% Confidence Intervals for Right-tailed ROS vs.
Empirical Methods 6-26
7-1 Scatterplot of Coefficient of Variation versus
Mean Number of Observations per POE (GW-CWS Only) 7-6
7-2 Scatterplot of Coefficient of Variation versus
Mean Number of Observations per POE (SW-CWS Only) 7-7
7-3 Scatterplot of Coefficient of Variation versus
Mean Concentration per PWSID (both-CWS Only) 7-9
7-4 Scatterplot of Coefficient of Variation versus
Mean Concentration per PWSID (GW-CWS Only) 7-10
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7-5 Scatterplot of Coefficient of Variation versus
Mean Concentration per PWSID (SW-CWS Only) 7-11
7-6 POE Means of CWS GW and SW Arsenic Concentrations
for a System in California, Log-normal Probability Plot 7-12
7-7 Standardized Log POE Means of All CWS
Arsenic Concentrations, Normal Probability Plot 7-16
7-8 Standardized Log POE Means of All CWS GW
Arsenic Concentrations, Normal Probability Plot 7-17
7-9 Standardized Log POE Means of All CWS SW
Arsenic Concentrations, Normal Probability Plot 7-18
8-1 Standard Deviation in Relation to Well Depth 8-2
8-2 Coefficient of Variation in Relation to Mean Arsenic Level 8-3
AA
ACD
ACWA
AES
ANOVA
AOED
ASDTR
AWWA
CCA
CV
CWS
CWSS
DMAA
DSMA
EPA
FIFRA
FRDS
GW
IAOED
ICP
MCL
Metro
MMAA
MS
MSMA
NAS
NAOS
NIPDWR
NIRS
NOF
List of Acronyms
Atomic Adsorption
Arsenic Compliance Database
Association of California Water Agencies
Atomic Emission Spectrometry
Analysis of Variance
Arsenic Occurrence and Exposure Database
Agency for Toxic Substances and Disease Registry
American Water Works Association
Chromated Copper Arsenate
Coefficient of Variation
Community Water Supply
Community Water Supply Surveys
Dimethylarsinic Acid
Disodium Methanearsonate
Environmental Protection Agency
Federal Insecticides, Fungicides, and Rodenticides Act
Federal Reporting Data System
Ground Water
Intermediate Arsenic Occurrence and Exposure Database
Inductively Coupled Plasma
Maximum Contaminant Level
Metropolitan Water District of Southern California
Monomethyl-Arsionic Acid
Mass Spectrometry
Monosodium Methanearsonate
National Academy of Sciences
National Arsenic Occurrence Survey
National Interim Primary Drinking Water Regulations
National Inorganics and Radionuclides Survey
Natural Occurance Factor
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NOMS
NPDWR
NPL
NRC
NTNCWS
NWIS
OGWDW
POE
PWSs
PWSID
RDS
RIA
ROS
RWS
SDWA
SDWIS
SW
TMA
TNCWS
TOC
TRI
USEPA
USGS
VOC
WATSTORE
WESCAS
WITAF
National Organics Monitoring Survey
National Primary Drinking Water Regulations
National Priorities List
National Research Council
Non-Transient Non-Community Water Supply
National Water Information System
Office of Ground Water and Drinking Water
Points of Entry
Public Water Supplies
Public Water Supplies Identification Number
Raw Data Sets
Regulatory Impact Analysis
Regression on Order Statistics
Rural Water Survey
Safe Drinking Water Act
Safe Drinking Water Information System
Surface Water
Trimethylarsine
Transient Non-Community Water Supplies
Total Organic Carbon
Toxics Release Inventory
United States Environmental Protection Agency
United States Geological Society
Volatile Organic Compounds
USGS's Water Quality Database
Western Coalition of Arid States
Water Industry Technical Action Fund
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Disclaimer
This report is a draft, issued in support of a proposed National Primary Drinking Water
Regulation for arsenic. It is intended for public comment and does not represent final Agency
policy. EPA expects to issue a final version of this report in 2001, reflecting corrections due to
external peer review and public comment on the proposed rule and supporting documents.
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Acknowledgments
This report was prepared for EPA under the supervision of Andrew Schulman and
Jennifer Wu of the Office of Ground Water and Drinking Water. Ben Smith also contributed.
Timothy Barry, Irene S. Dooley, Henry Kahn, Jade Lee, and Elizabeth Margosches served as
internal EPA consultants.
ISSI Consulting Group, Inc. served as the primary contractor, under the direction of
David Kaczka. Jonathan Cohen and Robert Iwamiya, of ICF Consulting Group, Inc., a
subcontractor, performed the statistical analysis.
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Executive Summary
The Safe Drinking Water Act (SDWA), 42 U.S.C. §§300f-300j, originally enacted in
1974, directs the U. S, Environmental Protection Agency (EPA) to identify and regulate
contaminants in public drinking water. Section 1412(b)(12)(A) of SDWA, as amended by the
1996 Amendments, required EPA to propose a National Primary Drinking Water Regulation for
arsenic by January 1,2000, and to issue a final regulation by January 1,2001. One of the
elements that supports the development of the proposed regulation is information on arsenic
occurrence in drinking water, specifically estimates of the size of populations and number of
systems that are affected by different levels of arsenic in drinking water. This report presents
IS SI Consulting Group's arsenic occurrence analysis, which was prepared for the EPA Office of
Ground Water and Drinking Water (OGWDW) under Contract 68-C7-0005.
Sources of Arsenic in the Environment
Arsenic is released to the environment from a variety of natural and anthropogenic
sources. In the environment, arsenic occurs in rocks, soil, water, air, and in biota. Average
concentrations in the earth's crust reportedly range from 1.5 to 5 mg/kg (Cullen and Reimer,
1989). Higher concentrations are found in some igneous and sedimentary rocks, particularly in
iron and manganese ores (Welch et al, 1988). In addition, a variety of common minerals contain
arsenic, of which the most important are arsenopyrite (FeAsS), realgar (AsS), and orpiment
(As2S3). Natural concentrations of arsenic in soil typically range from 0.1 to-40 mg/kg, with an
average concentration of 5 to 6 mg/kg (National Academy of Sciences (NAS), 1977). Through
erosion, dissolution, and weathering, arsenic can be released to ground water or surface water.
Geothermal waters can be sources of arsenic in ground water, particularly in the Western United
States (Nimick et al., 1998, Welch etal, 1988). Other natural sources include volcanism and
forest fires.
Anthropogenic sources of arsenic relate to its use in the lumber, agriculture, livestock,
and general industries. Most agricultural uses of arsenic are banned in the United States.
However, organic arsenic is a constituent of the organic herbicides monosodium
methanearsonate (MSMA) and disodium methanearsonate (DSMA), which are currently applied
to cotton fields as herbicides (Jordan et al., 1997). Organic arsenic is also a constituent of feed
additives for poultry and swine, and appears to concentrate in the resultant animal wastes (NAS,
1977). The potential impact of arsenic in animal wastes used to fertilize crops is uncertain.
Most of the arsenic used in the United States is for the production of chromated copper
arsenate (CCA), the wood preservative (Reese, 1998). CCA is used to pressure treat lumber and
is classified as a restricted use pesticide by the USEPA. A significant industrial use of arsenic is
the production of lead-acid batteries, while small amounts of very pure arsenic metal are used to
produce the semiconductor crystalline gallium arsenide, which is used in computers and other
electronic applications.
Arsenic is also released from industrial processes, including the burning of fuels and
wastes, mining and smelting, pulp and paper production, glass manufacturing, and cement
manufacturing (USEPA, 1998b). In addition, past waste disposal sites may be contaminated with
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arsenic. Arsenic is a contaminant of concern at 916 of the 1,467 sites on the National Priorities
List (NPL) (Agency for Toxic Substances and Disease Registry (ATSDR), 1998). Sites included
on the NPL have the potential to release contaminants to ground water or surface water in the
vicinity of the site.
Anthropogenic releases of arsenic to the environment can be estimated from Toxics
Release Inventory (TRI) data. These data indicate that 7,947,012 pounds of arsenic and arsenic-
containing compounds were released to the environment in 1997, a significant increase from
3,536,467 pounds in 1995 (USEPA, 1999a). The increase primarily occurred at one facility,
where arsenic on-site land releases increased by 3.58 million pounds from 1995 to 1997 because
of a change in the facilities smelting process that was implemented to reduce sulfur dioxide
emissions. The TRI data do omit some potentially significant arsenic sources, including arsenic
associated with the application of herbicides and fertilizers and arsenic released from mining
facilities and electric utilities.
Arsenic Fate and Transport
Once arsenic released from natural or anthropogenic sources enters ground water or
surface water, a variety of processes affect its fate and transport. These include oxidation-
reduction reactions, transformations, ligand exchange, and biotransformations. The factors that
affect these reactions include oxidation state of the arsenic, oxidation-reduction potential (Eh),
pH, concentrations of iron, metal sulfides, and sulfides, temperature, salinity, and the distribution
and composition of biota (ATSDR, 1998; Robertson, 1989; Welches/., 1988). The
predominant forms of arsenic in ground water and surface water are arsenate (+5) and arsenite
(+3). Arsenite is generally associated with anaerobic conditions. Oxidation state appears to be
the most important factor that determines the fate and transport of arsenic through drinking water
treatment systems. Arsenate is more easily removed because of its ionic charge, and activated
alumina, ion exchange, and reverse osmosis technologies can achieve relatively high arsenic
removal rates. These technologies do not achieve comparable removal rates for arsenite.
Oxidization of arsenite to arsenate can improve removal efficiencies. Treatment efficiencies may
also be affected by water pH, depending on the technology applied, and competing ions. Higher
pH tends to decrease removal rates (Rubel and Hathaway, 1987); high sulfate, fluoride, and
phosphate concentrations also tend to decrease removal rates (Jekel, 1994).
Data Sources on Arsenic Occurrence in Drinking Water Systems
There are a variety of sources of information on arsenic in drinking water. This study is
based largely on arsenic data from 25 State compliance monitoring data sets, and information on
individual system characteristics that are provided in the Safe Drinking Water Information
System (SDWIS). Figure ES-1 presents the States for which compliance monitoring data were
available. As this figure shows, the Midwestern, South Central, North Central, and Western
regions of the United States are well represented, but fewer compliance monitoring data sets are
available for the States in the New England, Mid-Atlantic, and Southeastern regions.
These compliance monitoring data sets offer several benefits. For many States, they
represent almost every ground water and surface water community water supply (CWS) system
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in the State. In total, the compliance monitoring data in these databases represent nearly 19,000
of the approximately 53,000 CWS systems in the United States. In addition, a smaller number of
non-transient, non-community water supply (NTNCWS) systems are also represented in the State
compliance monitoring data sets. These data sets contain multiple samples from individual
systems, which can facilitate analysis of the variability in arsenic levels over time, or from
location to location, or point-of-entry to point-of-entry, within individual systems. However,
several of these data sets include samples that are censored1 within, rather than below, the
regulatory range of interest. To manage these multiple reporting levels and to calculate system
mean arsenic levels, regression on order statistics was used in the data analyses presented in
Chapters 5, 6, and 7 of this report.
Other arsenic databases are available for information on arsenic. Some are suitable for
the development of national arsenic occurrence estimates, and others are less suitable for this
purpose. The databases which may be suitable for the development of arsenic occurrence
estimates include the National Arsenic Occurrence Survey (NAOS), the United States Geological
Society (USGS) ambient ground water arsenic databases, the National Inorganics and
Radionuclides Survey (MRS), and the Metropolitan Water District of Southern California
Survey (Metro). NAOS is based on a representative proportional stratified sampling design. It
includes 517 raw water samples from ground water and surface water systems in the United
States. The analytical method used had a detection level of 0.5 jUg/L, below the regulatory range
of interest (2.0 to 50 fJ-gfL). Arsenic removal efficiencies associated with the treatment
technologies in place in each system were used to calculate expected finished water arsenic
concentrations. The USGS database is another relevant source of information on arsenic
occurrence. It contains approximately 20,000 ambient ground water samples collected
throughout the United States. These samples were analyzed with a consistent method which has
a detection level of 1 vg/L, and according to consistent quality control and quality assurance
protocols. Both the NAOS and the USGS databases are used as comparison tools in Chapter 6 of
this report.
Two other databases offer data that could be used to estimate arsenic occurrence, but
were not used in this report. The NIRS includes samples from approximately 1,000 ground water
systems in the United States. Most of the NIRS samples were collected from ground water
systems that serve fewer than 3,300 people, and most of the samples (95 percent) are censored at
5 ,ug/L. Metro contains 144 samples which were primarily collected from ground water and
surface water systems in the United States that serve populations of at least 50,000 people.
These samples have a low detection limit, but are not associated with an individual public water
supply identification number (PWS ID). For these reasons, the NIRS and Metro databases were
not used in the development of arsenic occurrence estimates presented in this report.
Because several other databases contain very old arsenic sample results and a high
proportion of the results were censored, they were not used in this occurrence analysis. These
1 Censored data are samples with contaminant concentrations reported as less than the analytical detection limit.
Actual contaminant concentrations in these samples may be positive, and may range from zero to the detection limit.
In the case of a naturally occurring contaminant, such as arsenic, contaminant concentrations may be exceedingly
low, but are rarely zero.
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databases include the 1969 and 1978 Community Water Supply Surveys (CWSS), the Rural
Water Survey (RWS), the National Organics Monitoring Survey (NOMS). Because these results
were quite old and highly censored, they were not used in this occurrence estimation. Another
database, the Western Coalition of Arid States (WESCAS) database, also was not used in this
analysis, because the data did not necessarily represent arsenic levels at individual PWSs, and
because the data conventions used appeared to have been inconsistent from State to State.
Patterns of Arsenic Occurrence in Drinking Water
The data were analyzed with respect to a variety of potential stratification variables,
including source water type, system size and type, and regional stratification. Distributions of
arsenic in ground water and surface water systems were clearly different; therefore, the
occurrence analyses were stratified on the basis of source water type. Analyses indicated that
arsenic occurrence is not associated with system size in any meaningful or consistent pattern. In
addition, arsenic levels for CWS did not appear to differ from NTNCWS. Therefore, data were
not stratified on the basis of size or system type. Other authors, who have evaluated regional
differences in arsenic occurrence, concluded that arsenic levels may differ from region to region.
Regional stratification was applied in these occurrence analyses. State compliance monitoring
data sets were stratified into the 7 regions that were identified by Frey and Edwards (1997). This
stratification scheme is convenient because the State compliance monitoring data can be easily
sorted and evaluated by State. Regional stratification would be unnecessary if data were
available for all 50 States. However, because average arsenic concentrations may differ from
region to region, and because the representation of States within each region differs from region
to region, regional stratification was applied to control these differences and to yield more
accurate occurrence estimates.
Predicted Number of Ground Water and Surface Water Systems Exceeding Potential
Regulatory Levels
Using the State compliance monitoring data, estimates of the proportions and numbers of
systems that may exceed specific maximum contaminant level (MCL) alternatives were
developed. Separate estimates were developed for ground water and surface water systems,
although both estimates were developed through a similar five-step process. In the first step,
system mean concentrations are estimated for each system in the compliance monitoring
database. Second, estimates of exceedance probabilities were developed for each State. Third,
State estimates were grouped and weighted, in order to develop regional arsenic occurrence
estimates. Fourth, regional estimates were weighted to develop national estimates of the
proportion of systems which are likely to have mean system arsenic levels above specific
concentrations of interest. Fifth, estimated exceedance probabilities were multiplied by the total
number of ground water or surface water systems in the United States to estimate the total
number of systems with various system mean arsenic concentrations. As shown in Tables ES-1
and ES-2, these estimates are based on the number of systems in specific size categories.
Under these estimates, 11,953 ground water CWS systems are estimated to have mean
arsenic levels that exceed 2 Aig/L. The number of systems with mean arsenic levels above the
potential MCL alternatives decreases rapidly as the potential MCL alternative concentration
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increases, but 2,384 systems are predicted to have mean arsenic levels greater than 10 ug/L, and
902 systems are predicted to have mean arsenic concentrations greater than 20 ^g/L. Arsenic
occurrence is projected to be significantly lower in surface water systems. For example, 1,069
surface water CWS systems are predicted to have arsenic levels above 2 jUg/L, 82 surface water
CWS systems are predicted to be out of compliance with an arsenic MCL of 10 jug/L, and 28 are
predicted to exceed an alternative MCL of 20 ng/L.
These arsenic estimates resemble other recently generated arsenic occurrence estimates.
At concentrations of 5 and 10 /*tg/L, these exceedance estimates are quite similar to estimates
developed by Frey and Edwards (1997). These estimates are also similar to, although slightly
lower than, the number of ground water systems that are estimated to be impacted at
concentrations of 2, 5, and 10 /ug/L based on the USGS database. However, the arsenic
occurrence estimates presented in this report are higher that estimates that were developed in
1992 for the USEPA (Wade Miller, 1992).
An uncertainty analyses was conducted to determine the potential amount of error in the
exceedance probability estimates. Therefore, to determine 95 percent confidence intervals, it was
necessary to perform a statistical simulation to quantify the potential sources of uncertainty.
Three sources of uncertainty were identified and simulated: 1) sampling variability, within and
between systems; 2) the fill-in of censored observations in the estimation of system means; and
3) fitting of log-normal distribution to populations of system means within each State.
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Intra-system Variability
The purpose of the intra-system analysis is to facilitate prediction of the number of
points-of-entry or POE that will be affected by various MCL alternatives. Compliance with the
arsenic standard is measured at the point-of-entry to the distribution system, and individual
systems can have multiple points-of-entry. Arsenic levels in POEs drive compliance costs and
risk reduction benefits more directly than do system mean arsenic levels. Data are not available
that would allow the development of directly representative estimates of arsenic occurrence.
Instead, this report uses compliance monitoring data with POE identifiers to quantify a
relationship between POE means and system means, so that the number of POE means in the
United States that are likely to exceed specific regulatory alternatives can be calculated from a
distribution of system means. This relationship was quantified as an estimated coefficient of
variation (CV), or relative standard deviation. Four analyses were used to develop estimates of
system CV, including an empirical model, a pooled log-normal model, a set of variance function
models, and a regression model. The empirical model and the regression model yielded similar
estimates of CV (approximately 64 percent), and the variance function models and pooled log-
normal models yielded higher CV (approximately 108 percent). These estimates are believed to
represent appropriate middle and high end estimates of the true average system CV level. The
CV values that were calculated under these analyses are being applied in a regulatory impact
analyses (RIA) conducted under a separate work assignment to estimate the number of POE that
may exceed regulatory alternatives.
Note on Corrections to the Analysis (May 2000)
This report reflects EPA's draft occurrence analysis as of September 1999. This analysis
was used to support EPA's regulatory impact analysis (RIA) of the draft revised arsenic rule, to
be issued in June 2000. The results in this report have therefore not been revised since
September 1999, in order to maintain consistency with the RIA. However since September 1999,
EPA has found and corrected some errors in its Arsenic Compliance Database (ACD), and is
expecting to make further corrections as more information becomes available. In particular, as of
May 2000, EPA is aware of corrections to the ACD which will cause the estimated fraction of
systems exceeding various mean concentrations, in Table 6-3, to increase by up to 0.3%. EPA is
also revising this report in response to an external peer review. Among other changes, these
revisions will cause the estimates of intra-system coefficients of variation, in Table 7-3, to
decrease by 10-40%. The final version of this report, to be issued in support of the final arsenic
rule, will reflect these revisions, as well as other corrections that may arise in the interim.
Public Comment Draft
xvu
May 8, 2000
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1. Introduction
Under the Safe Drinking Water Act (SDWA), 42 U.S.C. §§300f-300j, originally enacted
in 1974, arsenic is a regulated drinking water contaminant. In 1975, U.S. Environmental
Protection Agency (USEPA) issued a National Interim Primary Drinking Water Regulation
(NIPDWR) for arsenic at 50 ug/L (USEPA, 1975). This value is the current Maximum
Contaminant Level (MCL) for arsenic in drinking water. In 1986, Congress converted the
NIPDWR for arsenic to a National Primary Drinking Water Regulation (NPDWR), and directed
USEPA to revise National Primary Drinking Water Regulations. In 1994, following a consent
decree in a suit2 between USEPA and Citizens Concerned about Bull Run, Inc., a citizens' group,
USEPA organized an internal workgroup for the purpose of addressing risk assessment,
treatment, analytical methods, arsenic occurrence, exposure, costs, implementation issues, and
regulatory options for arsenic. In 1995, USEPA deferred the proposal of the regulation in order
to better characterize the human health effects associated with chronic low-level exposure to
arsenic and treatment costs. In accordance with Safe Drinking Water Act (SDWA), as amended
in 1996, Section 1412(b)(12)(A) directs USEPA to propose a National Primary Drinking Water
Regulation for arsenic by January 1,2000, and to issue a final regulation by January 1,2001.
Arsenic (As) is a metallic element that occurs at low concentrations in most rocks and
soils (Yan-Chu, 1994). To a small extent, it occurs in the elemental state; however, higher
concentrations of arsenic principally occur in mineral complexes with metals and other elements
(Welch et ai, 1988). For example, arsenic is a common impurity in the sulfide ores of lead,
copper, and zinc. Arsenic is released into the environment from natural processes such as the
weathering and dissolution of arsenic-containing minerals and ores (Yan-Chu, 1994). In addition
to its release from natural sources, arsenic is released from a variety of anthropogenic sources
(USEPA, 1998b), including:
Manufacturing of metals and alloys
• Petroleum refining
• Pharmaceutical manufacturing
• Pesticide manufacturing and application
• Chemicals manufacturing
• Burning of fossil fuels
• Waste incineration
These anthropogenic releases of arsenic can elevate environmental arsenic concentrations.
Human exposure to arsenic can result in a variety of chronic and acute effects. In
particular, there is evidence that associates chronic arsenic ingestion at low concentrations with
increased risk of skin cancer, and that arsenic may cause cancers of the lung, liver, bladder,
2 Miller v. USEPA, No. 89-CV-6328 (D. Ore., filed August 31,1989).
Public Comment Draft I - 1 May 8, 2000
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kidney, and colon (ATSDR, 1998). Because of the human health risks associated with arsenic.
USEPA regulates the level of arsenic in drinking water.
The objective of this study for the USEPA's Office of Ground Water and Drinking Water
(OGWDW) is to estimate arsenic occurrence in public water supplies (PWSs) in relation to
various MCL alternatives. Estimates of the number of people exposed to various concentrations
of arsenic in drinking water will be presented separately, and are not included in this report. The
arsenic occurrence estimates are also bounded by 95 percent confidence intervals. These
estimates will be significant in the development of the proposed arsenic regulation.
1.1 PURPOSE OF THIS DOCUMENT
This report summarizes the results of the arsenic occurrence analysis conducted by ISSI
Consulting Group, with its subcontractor, ICF Incorporated, for the USEPA's OGWDW. The
estimates of arsenic occurrence presented in this report differ from those presented in other
studies because of their strong reliance on existing compliance monitoring data that were
voluntarily provided to USEPA and its contractors. Supplementary data sources are used to
estimate arsenic occurrence where there are gaps in available compliance monitoring data. In
addition, new techniques have been applied to evaluate the statistical distributions of arsenic in
drinking water to estimate percentages of regulatory exceedances, to estimate the variability in
arsenic levels within systems, and to estimate the relative uncertainty associated with these
predictions.
1.2 ORGANIZATION OF THE DOCUMENT
This report is organized in seven sections that are relevant to the estimation of arsenic
occurrence in drinking water in the United States. The remaining sections of this document are
organized as follows:
Chapter 2: Sources of Arsenic identifies naturally occurring and anthropogenic sources of
arsenic in the environment, with a particular focus on sources of arsenic to
drinking water.
Chapter 3: Fate and Transport of Arsenic presents information on the physical and chemical
characteristics of arsenic and the relation between those properties and the
presence of arsenic in source waters. In addition, this section presents an
overview of the potential fate and transport of arsenic within treatment and
distribution systems.
Chapter 4: Sources of Data on Arsenic Occurrence in Drinking Water Supplies presents a
summary of the approaches used to identify and select data on arsenic occurrence
for use in this occurrence assessment. In addition, this section presents summary
information on the data sources that were used in the occurrence assessment.
Public Comment Draft
1-2
May 8, 2000
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Chapter 5: Arsenic Occurrence Patterns in the United States discusses the analyses that were
applied to identify patterns in the data and the conclusions developed as a result of
those analyses.
Chapter 6: National Occurrence Estimates presents estimates of the number of systems
projected to exceed specific arsenic levels, describes the method used to develop
these estimates, and discusses the uncertainty in these estimates.
Chapter 7: Infra-system Variability Assessment provides overview of the variations in arsenic
levels from location to location within public water supply systems.
Chapter 8: Temporal Variability Analysis examines the variability of arsenic concentrations
over time in a source.
Public Comment Draft I - 3 May 8, 2000
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2. Sources of Arsenic
This section discusses the physical and chemical properties of arsenic, and the natural and
anthropogenic sources of arsenic to the environment, particularly sources that may affect
drinking water in the United States. The primary natural sources include the earth's crust, soil
and sediment, geothermal activity, and volcanic activity. The most significant anthropogenic
sources are agricultural, industrial, and mining activities.
2.1 PHYSICAL AND CHEMICAL PROPERTIES OF ARSENIC
Arsenic (As) is a silver-gray brittle crystalline solid (Budavari, et al. 1989). It also exists
in black and yellow amorphous forms. Arsenic appears in Group 15 on the periodic table in the
first long period with a d-shell just below the valence shell. Arsenic has an atomic weight of
74.9216 and an atomic number of 33. Silver-gray arsenic has a specific gravity of 5.73; a melting
point of 817 °C (28 atm) and sublimes at 613 °C. The yellow amorphous form of arsenic has a
specific gravity of 1.97. Elemental arsenic can be present as a metalloid, although arsenic has an
elemental structure similar to non-metals. In the vapor state, arsenic occurs as a tetrameric
molecule (As4). In high oxidation states arsenic displays covalent tendencies, while in low
oxidation states it shows ionic tendencies (Ferguson, 1990). The physical and chemical
properties of arsenic are summarized in Table 2-1.
The valence states of As are: -3, 0, +1, +3, and +5 (Welch et al, 1988). Elemental
arsenic (valence 0) is rarely found under natural conditions. The +3 and +5 states are found in a
variety of minerals and in natural waters. Many of the chemical behaviors of arsenic are linked
to the ease of conversion between +3 and +5 valence states (National Research Council (NRC),
1999). The valence state affects the toxicity of arsenic compounds. While arsine (-3) is the most
toxic, the following are successively less toxic: organo-arsines, arsenites (+3), arsenates (+5),
arsonium metals (+1), and elemental arsenic (0).
2.1.1 Environmentally Relevant Arsenic Species
Arsenic occurs naturally as a constituent of a number of different compounds in both
marine and terrestrial environments. Arsenic species are classified as either organic or inorganic.
If carbon is present within the compound it is considered to be an organic arsenic species. Table
2-2 includes a summary of both organic and inorganic species of arsenic which may be found in
food and water.
Inorganic Arsenic
Inorganic arsenic, with +5 (arsenate) and +3 (arsenite) oxidation states, is more prevalent
in water than organic arsenic (Irgoiic, 1994; Clifford and Zhang, 1994). The dominant arsenic
species depends on pH and redox conditions. In general +5 predominates under oxidizing
conditions and +3 predominates under reducing conditions (ATSDR, 1998; Clifford and Zhang,
1994).
Public Comment Draft 2-1 • May 8,2000
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Table 2-1
Physical and Chemical Properties of Arsenic
CAS Number
Atomic Number
Atomic Weight
Melting Point at 28 atm
Boiling Point
Critical Temperature
Heat of Vaporization
Critical Pressure
Density (at 14°C)
Most Stable Isotope
Covalent Radius
Atomic Radius
Ionic Radius
Vapor Pressure
7440-38-2
33
74.92
817°C
613°C
1,400°C
11.2kcal/g-atom
22.3 MPa
5.727 g/cm3
75As
1.1 9 angstroms
1.39 angstroms
2.22 angstroms
1 mm (375°C)
10mm(437°C)
100mm(518°C)
Excerpted from Budavari el al, 1989
Public Comment Draft
2-2
May 8. 2000
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Table 2-2
Inorganic and Organic Arsenic Compounds
Name
Arsanilic acid
Arsenous acid
Arsenic acid
Monomethylarsonic acid
Methylarsonous acid
Dimethylarsinic acid
Dimethyiarsinous acid
Roxarsone
Trimethylarsine
Trimethylarsine oxide
Tetramethlarsonium ion
Arsenocholine
Arsenobetaine
Arsenic-containing ribo-sides
Abbreviation
—
As(ffl)
As(V)
MMAA
MMAA(IH)
DMAA
DMAA(m)
-
TMA
TMAO
Me4As*
AsC
AsB
Arsenosugar X-XV
Arsenolipidb
Chemical Formula
C6H8AsNO3
H3AsO3
H3AsO4
CH3AsO(OH)2
CH3As(OH)2[CH3AsO]n
(CH3)2AsO(OH)
(CH3)2AsOH [«CH3)2As)2O]
C6H6AsN06
(CH3)3As
(CH3)3AsO
(CH3)4As*
(CH3)3As*CH2CH2OH
(CH3)3As~CH2COO-
Exceipted from NRC, 1999
Examples of inorganic arsenic compounds found in the environment include oxides (i.e.
As203, As2O5, R3AsO)n, R^AsCKOH),.,, (n=l,2)) and sulfides (As^, AsS, HAsS2, HAsS330
(Cullen and Reimer, 1989). Inorganic arsenic species which are stable in oxygenated waters
include arsenic acid (As(V)) species (i.e. H3AsO4, H2AsO4", HAsO42" and AsO43"). Arsenous acid
(As(in)) is also stable as H3AsO3 and H2AsO3" under slightly reducing aqueous conditions.
In addition to geochemical factors, microbial agents can influence the oxidation state of
arsenic in water, and can mediate the methylation of inorganic arsenic to form organic arsenic
compounds. Microorganisms can oxidize arsenite to arsenate, reduce arsenate to arsenite, or
reduce arsenate to arsine (Cullen and Reimer, 1989). Bacterial action also oxidizes minerals
such as orpiment (As2S3), arsenopyrite (FeAsS), and enargite (Cu3AsS4) releasing arsenate.
Under aerobic conditions, the common aquatic bacterium Pseudomonas fluorescens reduces
3 R=H, Me, Cl, etc.
Public Comment Draft
2-3
May 8, 2000
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arsenate to arsenite. In a river in New Zealand, investigators found the predominant oxidation
state of arsenic varied seasonally because of (at least in part) the bacterium Anabaena
oscittaroides which reduces arsenate to arsenite. Arsenite was found to predominate in spring
and summer months, while arsenate was prevalent at other times of the year.
Organic Arsenic
Organic arsenic compounds such as Monomethylarsonic acid (MMAA), Dimethylarsinic
acid (DMAA), Trimethylarsine (TMA), and Trimethylarsine oxide (TMAO) are generally
associated with terrestrial settings, however, some are found in water (NRC, 1999). Organic
arsenic is produced naturally in the environment in natural gas (ethylmethylarsines), shale oil, in
water when microorganisms metabolize inorganic arsenic, and in the human body, as a result of
enzyme activity in the liver (USEPA, 1993; Berger and Fairlamb, 1994).
In studies of arsenic speciation in natural waters reviewed by the National Research
Council (1999), organic arsenical compounds were reported to have been detected in surface
water more often than in ground water. Surface water samples reportedly contain low but
detectable concentrations of arsenic species such as MMAA, DMAA, Arsenocholine (AsC),
TMAs, and species similar to Arsenobetaine (AsB). Methylarsenicals have been reported to
comprise as much as 59% of total arsenic in lake water. In some lakes, DMAA has been reported
as the dominant species, and concentrations appear to vary seasonally as a result of biological
activity within waters, with the highest concentrations observed in May and June.
2.2 NATURAL SOURCES OF ARSENIC
Arsenic occurs in the environment in rocks, soil, water, air, and hi biota; and
concentrations of arsenic in a variety of environmental media are presented in Table 2-3. The
following sections discuss important natural sources of arsenic hi the environment. Most arsenic
hi the environment exists in rock or soil (ATSDR, 1998). Because arsenic occurs naturally in
rock, soil and sediment, these sources are particularly important determinants of regional levels
of arsenic hi ground water and surface water. Natural sources of arsenic are discussed below,
and anthropogenic sources are discussed subsequently hi Section 2.3, and the cycling of arsenic
hi the environment is depicted hi Figure 2-1.
Earth's Crust
Arsenic is the twentieth most abundant element hi the earth's crust (ATSDR, 1998; NAS,
1977). Concentrations of arsenic hi the earth's crust vary, but average concentrations are
generally reported to range from 1.5 to 5 mg/kg (ATSDR, 1998; Cullen and Reimer, 1989; NAS,
1977). Arsenic is a major constituent of many mineral species in igneous and sedimentary rocks;
Table 2-4 presents concentrations of arsenic hi igneous and sedimentary rocks. Among igneous
rock types, the highest arsenic concentrations are found hi basalts.
Public Comment Draft
2-4
May 8. 2000
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Sedimentary rocks, particularly iron and manganese ores, often contain higher average arsenic
concentrations than igneous rocks (Welch et al, 1988).
Table 2-5, above, lists common minerals that contain arsenic. Arsenopyrite (FeAsS),
realgar (AsS), and orpiment (As2S3) are the most important of these minerals, and they are
commonly present in the sulfide ores of other metals including copper, lead, silver and gold
(Yan-Chu, 1994). Arsenic may be released from these ores to soil (Yan-Chu, 1994), surface
water (Mok and Wai, 1989), ground water (Welch et al, 1988), and the atmosphere (ATSDR,
1998).
In their evaluation of the regional distribution of arsenic in ground water in the Western
United States, Welch et al, (1988) evaluated the association between aquifer geology and arsenic
concentrations in ground water. Higher arsenic concentrations (ground water concentrations
greater than 50 Mg/L) were associated with sedimentary deposits derived from volcanic rocks.
These geological conditions occurred at a few locations in the Western Mountain Ranges
(notably near Reno, Nevada, and Eugene, Oregon), and in the Alluvial Basins. Within the
Alluvial Basins, elevated arsenic concentrations were associated with sediments derived from
volcanic rocks rather than non-sedimentary and unmineralized volcanic rock'. Weathering of the
volcanic rocks may result in the concentration of arsenic onto ferric oxyhydroxide that are
deposited with sediments (Welch et al, 1988). Lower arsenic concentrations are associated with
regions underlain by carbonate rocks and volcanic basalts. The regions with moderate arsenic
levels include parts of the Alluvial Basins in western Utah and eastern Nevada and the Columbia
Lava Plateau.
Public Comment Draft 2-5 May 8, 2000
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Magma Arsenic
(Water-Solubte)
'ill I
T IBualArseric
Unavailable Arsenic Coal
InsolubteSalts OH
SurfeceAdsorbed Wnerafe
OigaricallyBouxf
Figure 2-1. Environmental Transfer of Arsenic
Public Comment Draft
2-6
May 8, 2000
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Table 2-3
Arsenic Concentrations in Environmental Media
Environmental Media
Air
Rain from unpolluted ocean air
Rain from terrestrial air
Rivers
Lakes
Ground water
Sea water
Soil
Stream/river sediment
Lake sediment
Igneous rock
Metamorphic rock
Sedimentary rock
Biota - Green Algae
Biota - Brown Algae
Arsenic Concentration
Range
1.5-53
0.019
0.46
0.20-264
0.38-1,000
< 1.0 -> 1,000
0.15-6.0
0.1-1,000
5.0-4,000
2.0-300
0.3-113
0.0-143
0.1-490
0.5-5.0
30
Units
ng/m"3
jug/L
A*g/L
Vg/L
VgfL
Vg/L
A*g/L
mg/kg
mg/kg
-• mg/kg
mg/kg
mg/kg
mg/kg
mg/kg
mg/kg
Excerpted from NAS, 1977.
Public Comment Draft
2-7
May 8, 2000
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Table 2-4 Arsenic in Igneous and Sedimentary Rocks
Rocks
Igneous Rocks:
Ultrabasic
Basalts, gabbros
Andesites, dacites
Granitic
Silicic volcanic
Sedimentary Rocks:
Limestones
Sandstones
Shales and clays
Phosphorites
Sedimentary iron ores
Sedimentary manganese ores
Coal
No.
Analyses
37
146
41
73
52
37
11
324
282
110
-
1,150
Arsenic Concentration
Range Usually
Reported
0.3-16
0.06-113
0.5-5.8
0.2-13.8
0.2-12.2
*•
0.1-20
0.6-120
0.3^90
0.4-188
1-2,900
(up to 1.5%)
0-2,000
(mg/kg)
Average
3.0
2.0
2.0
1.5
3.0
1.7
2.0
14.5a
22.6
400
-
4-13
Adapted from NAS, 1977
'Excludes one sample containing As at a concentration of 490 mg/kg.
Table 2-5 Common Minerals of Arsenic
Arsenopyrite, FeAsS
Lollingite, FeAs2
Orpiment, A^Sj
Realgar, As4S4
Chloanthite, NiAsj
Niciolite, NiAs
Adapted from Ferguson, 1990.
Smalite,
Cobaltite, CoAsS
Gersdorffite, NiAsS
Tennantite, 4CU2SAS2SS3
Proustite, 3Ag2SAs2S3
Enargite,
Public Comment Draft
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MayS, 2000
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In other parts of the United States, high (greater than 50 ,ug/L in ground water)
concentrations of arsenic are recognized to be associated with other geological formations. In
Eastern Michigan and Northeastern Wisconsin, high arsenic concentrations are associated with
sulfide mineral deposits in sedimentary rocks (Westjohn et ai, 1998; Simo et ai, 1996), and hi
the upper Midwest, higher arsenic concentrations are associated with iron oxide rich sedimentary
deposits. Moderate to high arsenic concentrations (10 to 50 /^g/L in ground water) in portions of
the Northeastern United States (Massachusetts to Maine) appear to be related to sulfide minerals
in the bedrock aquifers (Marvinney et al, 1994).
Soil and Sediment
Arsenic concentrations in soils depend in part on the parent materials from which the
soils were derived, although they may be enriched by other sources, including anthropogenic
sources. Typical natural concentration ranges are 0.1 to 40 mg/kg, with an average concentration
of 5-6 mg/kg (NAS, 1977). The level of arsenic in soil derived from basalts tends to be higher
than in soils of granitic origin, and concentrations of 20 to 30 mg/kg may be found in soils
derived from sedimentary rocks (Yan-Chu, 1994). In areas of recent volcanism, soils average
arsenic concentrations are approximately 20 mg/kg. Very high natural concentrations of arsenic
(up to 8,000 mg/kg) may occur in soils that overlay deposits of sulfuric ores (NAS, 1977).
Arsenic can be found in soil in the inorganic state bound to cations, and it can also be found
bound to organic matter. Arsenic may be transferred to surface water and ground water through
erosion and dissolution; plants may also uptake arsenic. Because arsenic can be fixed in
inorganic and organic compounds in soil, soil may also be a sink for arsenic.
In bottom sediments of rivers and lakes, concentrations of arsenic in surface sediments
tend to exceed those found in deeper sediments due to diagenetic cycling (Nimick et al, 1998).
Recent anthropogenic arsenic releases may also result in the elevation of arsenic concentrations
in surface sediments. This phenomenon has been observed in Lake Michigan (NAS, 1977). In
the Madison and Missouri River Basins, Nimick et al., documented average arsenic
concentrations in bottom sediments that ranged from 7 to 102 mg/kg. These concentrations
differed substantially over the length of the river system. In Lake Michigan, average
concentrations of arsenic in surficial sediment were reported to be 12.4 mg/kg (NAS, 1977). In
sediments contaminated by mining and industrial activities, arsenic concentrations can be greatly
enriched. In creeks affected by mining activities in Idaho, Mok and Wai (1989) found arsenic
concentrations that ranged from 42.1 to 2550.4 mg/kg. In shallow waters, wave action and
seasonal high flow scouring can result in resuspension of arsenic rich surface sediments, whereas
in deeper lakes, arsenic may be permanently sequestered in sediment (Nimick et al., 1998).
Arsenic may also be released from bottom sediments as a result of microbial action.
Arsenic in soil and sediment may undergo microbial degradation or transformation. In
soil, arsenic in the form of arsenates, arsenites, monomethyl-arsionic acid (MMAA) or
dimethylarsinic acid (DMAA) may be biotransformed to arsine gases (Yan-Chu, 1994). These
arsine gases are subsequently volatilized to the environment. In sediment, biologically mediated
methylation of arsenates increases the solubility of arsenic, and may increase arsenic
concentrations in water (Mok and Wai, 1994). Conversely, the biologically mediated
Public Comment Draft 2 - 9 May 8, 2000
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demethylation of DMAA and MMAA can result in the formation of arsenates, which are
strongly adsorbed onto sediments.
Geothermal Waters
/
Geothermal water can be sources of arsenic in surface water and ground water. Welch et
al, (1988) identified 14 areas in the Western United States where arsenic conditions in water
exceed 50 #g/L because of known or suspected geothermal sources. In these areas, dissolved
arsenic concentrations ranged from 80 to 15,000 Mg/L. Welch et al, found that mean dissolved
arsenic concentrations in geothermal ground waters are higher than mean arsenic concentrations
in non-thermal ground waters in any of the physiographic provinces in the United States. Flow
of arsenic-enriched geothermal water from hot springs may result in high concentrations of
arsenic in surface water systems. In Yellowstone National Park, the arsenic concentrations in
geysers and hot springs range from 900 to 3,560 f^g/L (Stauffer and Thompson, 1984). Waters
from these sources cause elevated arsenic levels in the Madison and Missouri Rivers far
downstream of the park boundaries (Nimick et al., 1998). As a result, cities that use water from
portions of the Missouri River for municipal supply must treat it to reduce arsenic
concentrations. Geothermal sources of arsenic are primarily located in the Western United
States.
Other Sources
Natural emissions of arsenic associated with volcanic activity and forest and grass fires
are recognized to be significant. Indeed, volcanic activity appears to be the largest natural source
of arsenic emissions to the atmosphere (ATSDR, 1998). Estimates of natural releases (of which
volcanic arsenic emissions are the primary source) show significant range, and are summarized in
Table 2-7.
Table 2-6
Estimated Natural Average Arsenic Releases to the Atmosphere
Study
Tamaki and Frankenburger, 1992
Pacynaetal., 1995
Loebenstein, 1994
Estimated annual natural releases (metric tons)*
44,100
1,100-23,500
2,800-8,000
While at least one study suggests that natural arsenic emissions slightly exceed industrial
emissions (Tamaki and Frankenburger, 1992), other studies suggest that industrial emissions of
arsenic are significantly greater than natural emissions (Pacyna et al.f 1995; Loebenstein 1994).
Thus, the relative contributions of volcanic sources, other natural sources, and anthropogenic
sources to the atmosphere have not been definitively established.
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In summary, the primary natural sources of arsenic in the United States include arsenic in
geological formations (rock as well as soil and sedimentary deposits) and arsenic associated with
geothermal waters. In geological formations, higher arsenic concentrations tend to be associated
with sedimentary rocks derived from acidic to intermediate volcanics, which occur primarily in
parts of the Western United States, and from sulfide minerals and iron and manganese oxides
associated with sedimentary rocks, which occur in the parts of the Northeastern and Upper
Midwestern United States. Geothermal activity may affect arsenic levels in ground water and
surface water in some regions of the United States, particularly in the Western United States. In
addition to these recurrent sources, volcanos and forest fires may also result in the sporadic
release of large amounts arsenic to the environment.
2.3 ANTHROPOGENIC SOURCES OF ARSENIC
From man-made sources, arsenic is released to terrestrial and aquatic environments and to
the atmosphere. The anthropogenic impact on arsenic levels in these media depends on the level
of human activity, the distance from the pollution sources, and the dispersion and fate of the
arsenic that is released. This section discusses the major current and past anthropogenic sources
of arsenic, which are wood preservatives, agricultural uses, industry, and mining and smelting. It
also provides an overview of other sources of arsenic in the environment. Table 2-8 provides an
overview of the use of arsenic in specific economic sectors. It is important to note that some of
these uses are banned in the United States. After these sources are discussed, an overview of
anthropogenic arsenic releases to the environment, based on Toxics Release Inventory (TRI)
data, is provided.
Table 2-7
Summary of Current and Past Uses of Arsenic
Sector
Lumber
Agriculture
Livestock
Medicine
Industry
Uses
Wood preservatives
Pesticides, insecticides, defoliants, debarking agents, soil sterilant
Feed additives, disease preventatives, animal dips, algaecides
Antisyphilitic drugs, treatment of trypanosomiasis, amebiasis,
sleeping sickness
Glassware, electrophotography, catalysts, pyrotechnics, antifouling
paints, dye and soaps, ceramics, pharmaceutical substances, alloys
(automotive solder and radiators), battery plates, solar cells,
optoelectronic devices, semiconductor applications, light emitting
diodes in digital watches
Source: Azcue and Nriagu, 1994.
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Wood Preservatives
About 90% of the arsenic that is consumed in the United States on an annual basis is used
to manufacture the wood preservative chromated copper arsenate (CCA) (Reese, 1999; Reese,
1998). CCA is an inorganic arsenic compound and consists of arsenic, chromium and copper.
There are a number of CCA type products in the market, and the precise molecular composition
may vary from formulation to formulation. Different arsenic compounds are used as active
ingredients in CCA, including arsenic acid (H3AsO4), arsenic pentoxide (As2O5), and sodium
arsenate (Na2HAsO4). These arsenical ingredients in CCA are primarily produced from arsenic
trioxide (As2O3), although arsenic trioxide itself is not used as a wood preservative in CCA
treatment solutions. EPA classifies CCA as a restricted use pesticide (USEPA, 1997a, USEPA,
1984). CCA is used to pressure treat lumber, which is typically used for construction of decks,
fences, and other outdoor applications. As a result, CCA consumption is related to the
performance of the housing industry. Currently, there are three primary manufacturers of CCA,
and these firms are located hi the Southeastern United States (Reese, 1998).
The current releases of arsenic to the environment from the manufacture and use of CCA
are included in the Toxics Release Inventory, which is discussed below. Earlier releases of
arsenic to the environment at some wood preserving sites have resulted in localized
contamination of environmental media, and several wood preserving sites are included on the
NPL list. In addition, there is some evidence that some arsenic may be released to soil from
CCA treated lumber (USEPA, 1997a). EPA is evaluating the release of arsenic from treated
wood. However, no data are currently available that indicates releases of arsenic from treated
lumber could affect drinking water quality.
Agricultural Uses
Past and current agricultural uses of arsenic and arsenic compounds include pesticides,
herbicides, insecticides, defoliants, and soil sterilants (Azcue and Nriagu, 1994). Arsenic
compounds were also used in animal dips and are currently used in raising livestock as feed
additives and for disease prevention (Azcue and Nriagu, 1994). These uses of arsenic
compounds are discussed below.
Arsenic is a constituent of organic agricultural pesticides that are currently used in the
United States. The most widely applied organoarsenical pesticide is monosodium
methanearsonate (MSMA), which is used to control broadleaf weeds (Jordan et al., 1997).
MSMA was the twenty-second most commonly applied conventional pesticide in the United
States in 1995 (USEPA, 1997b). In 1995, approximately 4 to 8 million pounds of MSMA were
applied. It is primarily applied to cotton; small amounts of disodium methanearsonate (DSMA)
and cacodylic acid are also applied to cotton fields as herbicides. MSMA and DSMA have also
been used for the postemergence control of crabgrass, Dallisgrass, and other weeds in turf (NAS,
1977), and cacodylic acid is used for weed control and monocotyledonous weeds.
Organoarsenicals hi soil are metabolized to alkylarsines and arsenate by soil bacteria (USEPA,
1998b). No published data are currently available on the leaching or transport of MSMA or
DSMA from soil.
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Inorganic arsenic was also a constituent of a variety of pesticides, herbicides, and
fungicides. The last agricultural use of inorganic arsenic, which was the use of arsenic acid
pesticide on cotton, was voluntarily canceled in 1993 (USEPA, 1998a, 58 FR 64579). Other
inorganic arsenic-containing pesticides included sodium arsenate, calcium arsenate, copper
acetoarsenite (Paris Green), copper arsenate, and magnesium arsenate. These were used to
control potato beetles, boll weevils, grasshoppers, moths, bud worms, and other insects in the
United States (Thompson, 1973). Inorganic arsenic compounds (arsenic acid, arsenic trioxide,
and sodium arsenate) are currently only used in sealed ant bait and wood preservatives (USEPA,
1995). Formerly registered uses of inorganic arsenic under the Federal Insecticides, Fungicides,
and Rodenticides Act (FIFRA) were pesticides, insecticides, rodenticides, cotton dessicants, anti-
fouling paints for boat hulls, soil sterilants, and herbicides for crab grass and other weeds4.
These uses were voluntarily canceled by the product manufacturers by 1993. Sodium arsenite
was used as cattle and sheep dips (Azcue and Nriagu, 1994), but its is now banned in the United
States (USEPA, 1999b). Other inorganic arsenic pesticides that are banned in the United States
include calcium arsenate, copper arsenate, and lead arsenate. The use of arsenic trioxide and
sodium arsenate is severely restricted.
Arsenic-based inorganic pesticides were applied to various agricultural crop lands, and
the historical application of these pesticides has contaminated soil with arsenic residues. The use
of lead arsenate as an insecticide for worms and moths resulted in contamination of soils in apple
orchards (Davenport and Peryea 1991; Maclean and Langille, 1981; Hess and Blanchar, 1977).
Steevens, et al, (1972) showed that the use of sodium arsenate as a potato defoliant raised total
arsenic concentrations in Wisconsin potato field soils. The leaching of arsenic from agricultural
topsoil to subsoil has been reported (Peryea, 1991; NAS, 1977). Leaching is more likely to occur
in sandy soils than in organic soils, and sodium or calcium arsenates tend to leach faster than
aluminum or iron arsenates (NAS, 1977). Fertilization of fields with phosphates may increase
the rate of leaching of arsenic from soils (Davenport and Peryea, 1991; Woolson et al., 1973),
and can increase arsenic concentrations in the subsoil and shallow ground water (Peryea and
Kammereck, 1997; Davenport and Peryea, 1991; Peryea, 1991). This historical pesticide use is
not expected to have resulted in widespread ground water contamination. Peters et al., (1999)
found that arsenic levels in drinking water did not correlate with agricultural activities in the
State of New Hampshire. Fuhrer et al., (1996), however, reported that filtered water arsenic
concentrations in the Yakima River Basin were higher (3 mg/L, exceeding the 90th percentile) in
agriculturally affected areas than in other areas (< 1 mg/L). These authors suggested high arsenic
concentrations in orchard soils from past lead-arsenate applications were the likely source of the
arsenic in surface water.
Organic arsenic (e.g., roxarsone and arsanilic acid) is a constituent of feed additives for
poultry and swine for increased rate of weight gain, improved feed efficiencies, improved
pigmentation, and disease treatment and prevention (e.g., swine dysentary, chronic respiratory
disease, specific infections) (21 CFR 558.530; 21 CFR 558.62). These additives undergo little or
no degradation before excretion (NAS, 1977). Arsenic concentrations in animal wastes are
4 Based on queries of the EPA Office of Pesticide Programs Pesticide Product Information System databases
conducted by ISSI Consulting Group in July, 1999. This database is searchable online at
http://www.cdpr.ca.gov/docs/epa/epamenu.htm.
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reported to range from 4 to 40 mg/kg (Isaac et ai, 1978). Application of arsenic containing
wastes as fertilizer to field plots is reported to elevate arsenic concentrations in soil water. Two
other studies, however, reported that the fertilization of crops with poultry litter did not change
the arsenic content of soils or crops (Smith et al, 1992; Morrison, 1975). Therefore, potential
effect of arsenic from animal wastes upon ground water and surface water is not well understood.
Industrial Uses and Releases
Arsenic and arsenic compounds are used in a variety of industrial applications. Arsenic
metal is used in the production of posts and grids for lead-acid storage batteries, and is used in
the formulation of some copper alloys (Reese, 1998). As maintenance-free automotive batteries,
which contain little or no arsenic, replace lead-acid storage batteries, the demand for arsenic
metal is expected to decrease. An arsenic containing compound, crystalline gallium arsenide, is
produced from very pure arsenic metal. Crystalline gallium arsenide is a semiconducting
material used in computers, optoelectronic devices and circuits, and other electronic applications.
Industrial processes including the burning of fossil fuels, combustion of wastes
(hazardous and non-hazardous), mining and smelting (discussed separately below), pulp and
paper production, glass manufacturing, and cement manufacturing can result in emissions of
arsenic to the environment (USEPA, 1998b). Coal-burning power plants may emit aerosols and
fly ash that contain arsenic (Yan-Chu, 1994). These arsenic-containing particles may be
deposited from the atmosphere onto soil and surface waters. The amount of arsenic that is
emitted by power plants and fossil fuel combustion is not included in current Toxics Release
Inventory data.5 However, utility emissions of arsenic and several other metals will be added to
the TRI in 1999 (USEPA, 1999a).
Past waste disposal practices have impacted arsenic concentrations in ground water and
surface water at waste disposal sites. Arsenic has been identified as a contaminant of concern at
916 of the 1,467 Superfund National Priority List (NPL) hazardous waste sites (ATSDR, 1998),
and arsenic is listed as the highest priority contaminant on the ATSDR/EPA list of the hazardous
substances at NPL Sites (ATSDR, 1997). There is a potential for releases of arsenic from waste
sites to affect ground water or surface water in the vicinity of the waste sites.
Mining and Smelting
Arsenic can be obtained from two of its ores, arsenopyrite and lollingite, by smelting in
the presence of air around 650-700 °C (Kirk-Othmer, 1992), or arsenic trioxide (As203) in flue
dust from the extraction of lead and copper can be captured (Ferguson, 1990). Subsequently,
arsenic trioxide can be used to produce other arsenic compounds or purified to elemental arsenic.
Arsenic trioxide was produced for commercial use in the United State at the ASARCO
smelter in Tacoma, Washington, until 1985, at which time the smelter ceased operations
5 The Toxic Release Inventory is a database of toxic releases in the United States compiled annually from SARA
Title HI Section 313 reports.
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(ATSDR, 1998). The USEPA Office of Air Quality Planning and Standards indicates that
primary and secondary6 lead smelters, primary copper smelters, and secondary aluminum
operations are potential sources of arsenic (USEPA, 1998b). There are presently three active
primary lead smelters, two of which are located in Missouri and one is in Montana. In addition,
there are 19 active secondary lead smelters located throughout the United States. The seven
primary copper smelters are located in Arizona (3), New Mexico (2), Texas (1), and Utah (1).
Secondary aluminum operations locations were not identified in USEPA 1998b.
Arsenic may be emitted to the atmosphere from metals smelters, and deposited with
precipitation downwind of the smelter. In Washington, Crecelius (1975) found higher
concentrations of arsenic in rain and snow downwind of a smelter (17 A*g/L) than in rain in
unpolluted areas (< 1 /^g/L). Such atmospheric deposition could affect arsenic concentrations in
soil and other environmental media. Miesch and Huffman (1972) detected increased
concentrations in soil downwind of a smelter in Helena, Montana.
High concentrations of arsenic may occur in areas that are near or affected by current or
historical mining activities. Sulfide-bearing rocks are often mined for gold, lead, zinc, and
copper, and arsenic is frequently found as an impurity in the sulfide ores of these metals. The
drainage from abandoned mines and mine wastes is typically acidic, and dissolved arsenic
concentrations can be as high as 48,000 ^g/L in mine drainage (Welch et al., 1988). In mining
areas, the arsenopyrite (FeAsS) that occurs in association with ores and arsenic bearing pyrite is a
common source of dissolved arsenic. The mineral orpiment, realgar, and arsenic-rich iron oxides
are other sources of dissolved arsenic (Welch et al, 1988) at mining sites.
Other Uses and Sources
There is some evidence that some volatile organic compounds (VOCs) in ground water
may facilitate the release of arsenic from aquifer materials to ground water. Ground water that is
affected by VOCs like petroleum products and other landfill wastes may be sufficiently reduced
to result in elevated dissolved iron-oxide concentrations. Under these reducing conditions,
aquifer materials may be a source of dissolved arsenic in ground water (Ogden, 1990).
From the Civil War until approximately 1910, arsenic was used as an embalming fluid,
and elevated concentrations of arsenic were detected in ground water at cemeteries in Iowa and
New York (Konefes and McGee, 1996). Therefore, it appears that cemeteries may be sources of
localized arsenic contamination in ground water. However, the extent of or potential for arsenic
contamination associated with ground water in cemeteries has not been broadly evaluated.
Historically, inorganic and organic arsenic compounds were used as therapeutic agents.
The first recognized use of inorganic arsenic as a therapeutic agent was in 1786; Fowler's
solution, which contained approximately 1 percent arsenic trioxide, was a common arsenic
containing medicinal (NRC, 1999). Inorganic arsenic was used to treat symptoms of skin
diseases such as eczema and psoriasis, malarial and rheumatic fevers, asthma, pernicious anemia,
* Primary smelters produce metals from raw ores, whereas secondary smelters reclaim metals from used and
recycled materials, such as scrap metal and used batteries.
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leukemia, Hodgkin's disease, and pain. Because of concern over inorganic arsenic's toxic
effects, therapeutic use of inorganic arsenic ceased in the 1970s. Organic arsenicals were used
for the treatment of spirochetal and protozoal diseases through the first half of the twentieth
century. Salvarsan (arspheramine) was a common anti-syphilitic from 1907 until its use was
supplanted by penicillin in the 1940s and 1950s. Organic arsenic compounds were used for the
treatment of amebiasis and trypanosomaisis. Melarsoprol (organic arsenic compound) continues
to be used for treatment of trypanosomaisis. In addition, arsenic trioxide is being researched as
therapy for acute promyelocytic leukemia.
Releases to the Environment Reported in the TRI1997
The Toxics Release Inventory (TRI) is a national database that identifies facilities, and
chemicals manufactured and used at identified industrial facilities. The TRI database also
identifies the amounts of these chemicals that are released to the environment annually as a result
of routine operations, accidents, and other one-time events, and the amounts of chemicals that are
managed in on- and off-site waste for each year since 1987. Annually, certain facilities must
report basic information about their facilities and operations, and about the amounts of listed
toxic chemicals used, released, recycled, or otherwise managed at the facility. Facilities must
report on arsenic and arsenic compound use, release, management, and disposal. Therefore, the
TRI data provides a significant amount of information regarding industrial releases of arsenic to
the environment, and trends in those releases since 1987. Information on arsenic releases to the
environment, based on recent TRI data, is summarized below.
TRI facilities reported managing a total of 14,898,807 pounds of arsenic and arsenic
compound containing wastes that were either production or non-production related in 1997
(USEPA, 1999a). Total on- and off-site releases in 1997 were reported to be 7,947,012 Ibs. The
majority of these releases (6,046,473 Ibs) were on-site, and 95 percent of on-site releases were to
land (5,766,252 Ibs). The majority of the arsenic was released to the land via surface
impoundments or non-Resource Conservation and Recovery Act (RCRA) Part C landfills; less
than 1 percent of the arsenic and arsenic compounds waste is disposed of in RCRA Subtitle C
landfills. After releases to land, releases to air are the second largest component of on-site
releases, with a total of 199,918 Ibs of arsenic and arsenic compounds released from stacks, point
sources, and fugitive or non-point sources. In addition, 76,170 Ibs were reported to have been
injected underground and 4,133 Ibs were reported to have been released to surface water in 1997.
Of the 1,900,539 Ibs that were disposed of off-site, most (1,460,728 Ibs) were disposed of to
landfills and surface impoundments, and significant amounts were released by underground
injection (209,716 Ibs) or were solidified or otherwise stabilized (149,416 Ibs). Small amounts
were stored or transferred to wastewater treatment plants.
Data contained in the TRI indicate that releases of arsenic and arsenic compounds from
TRI reporting facilities to the environment have increased in recent years. From 1995 to 1997,
TRI data indicates that total on-site and off-site releases of arsenic have risen from 3,536,467 Ibs
to 7,947,012 Ibs. The increase primarily occurred at one facility, where arsenic on-site land
releases increased by 3.58 million pounds from 1995 to 1997 because of a change in the facilities
smelting process that was implemented to reduce sulfur dioxide emissions. However, from 1995
to 1997 the quantity of arsenic in air emissions, underground injection, releases to land, and
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transfers to off-site disposal have all risen, while only the quantity of arsenic discharged to
surface water is reported to have decreased (USEPA, 1999a). Total releases of arsenic to the
environment in 1997 also exceed those for the base year (1988) for TRJ data, when total on-site
and off-site emissions of arsenic were 6,911,043 Ibs.
Although the TR1 data include estimated emissions from many sources of anthropogenic
sources of arsenic to the environment, the data do not include several potentially significant
sources of arsenic emissions, and therefore the data it contains should be interpreted with some
care. For example, TRJ release data do not include arsenic in organoarsenical herbicides that are
applied to cotton fields. Several other potentially significant sources of arsenic emissions will
begin reporting to the TRI in 1999 for the year 1998, including coal and oil burning electrical
utilities, coal mining, and metals mining (USEPA, 1999). The addition of these industrial
sectors should improve the accuracy of reporting of emissions of arsenic and arsenic compounds.
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3. Fate and Transport of Arsenic
3.1 RELATIONSHIP OF FATE AND TRANSPORT PROPERTIES TO SOURCE INTAKE
Arsenic concentration in fresh waters shows considerable variation with geological
composition of the drainage area and the level of anthropogenic input. The fate and transport of
arsenic in ground water and surface water are discussed separately below:
Ground water
Arsenic may be released to ground water in a variety of ways, which include, but are not
limited to, weathering of earth's crust and soil materials, discharge from industrial processes, and
overland runoff from agricultural and urban areas. In water, arsenic can undergo a series of
transformations, including oxidation-reduction reactions, ligand exchange, and
biotransformations (ATSDR, 1998; Welch et ai, 1988). Several factors have been identified
which effect the fate and transport processes in ground water. These include the oxidation state
of the arsenic, oxidation-reduction potential (Eh), pH, iron concentrations, metal sulfide and
sulfide concentrations, temperature, salinity, and distribution and composition of the biota
(ATSDR, 1998; Roberston, 1989; Welch et al, 1988). The predominant form of arsenic is
usually arsenate (As+5), although arsenite (As+3) may be present under some conditions (Irgolic,
1994; Welch et ai, 1988). However, the NRC (1999) noted that arsenite might be more
prevalent than anticipated.
Surface water
The processes that affect arsenic fate and transport in surface water are analogous to those
that are operative in ground water systems. Thus, the factors that affect arsenic transformations
and transport include the oxidation state of the arsenic, oxidation-reduction potential (Eh), pH,
iron concentrations, metal sulfide and sulfide concentrations, temperature, salinity, and
distribution and composition of the biota (ATSDR, 1998). However, there are additional factors
that affect arsenic fate and transport in surface water systems. These include total suspended
sediment (Nimick et al, 1998; Waslenchuk, 1979), seasonal water flow volumes and rates
(Nimick et al, 1998; Waslenchuk 1979), and time of day (Nimick et al, 1998).
Sorption of arsenic to suspended sediment may strongly affect the fate and transport of
arsenic in surface water systems. Where pH and arsenic concentrations are relatively high, and
total suspended sediment levels are relatively low, sorption processes may be less important
(Nimick etal, 1998). However, where suspended sediment loads are higher, arsenic
concentrations are lower, and pH levels are lower, arsenic is more likely to be present in the
suspended particulate phase rather than the dissolved phase. Particulate phase arsenic may settle
to bottom sediment in reservoirs and areas with low flow levels. The sorption of arsenic onto
suspended sediment is a mechanism for the removal of dissolved arsenic from surface water.
Sorption is greater when the amount of suspended sediment is greater. In surface water, lakes
may interrupt the downstream transport of particulate sorbed arsenic. In deeper lakes,
remobilization of arsenic from the sediment may be minimal, whereas in shallower lakes, arsenic
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may be remobilized faster from wind induced wave action and high-flow scouring. Large and
deep reservoirs are more likely to be long-term sinks for arsenic.
Seasonal variations of arsenic concentrations have been observed in surface water
systems, and these variations appear to be related to the source of the arsenic and the flow of the
river. In the Madison River, where relatively constant inputs of arsenic originate from
geothermal sources, arsenic concentrations at one point ranged from 110 to 370 /^g/L in samples
collected between 1986 and 1995 (Nimick et at, 1998). The highest concentrations occurred
during periods of low flow, and the lowest concentrations occurred during periods of high flow.
Waslenchuk (1979) measured arsenic concentrations in rivers in the Southeastern United States,
where average arsenic levels are far lower than in the Madison River. In the rivers that
Waslenchuk studied, seasonal variations of as great as 0.2 /^g/L were observed around average
concentrations that ranged from 0.15 to 0.45 Mg/L. These variations were related to seasonal
precipitation levels, which flushed higher concentrations of arsenic into the rivers in the spring.
Arsenic concentrations in surface water may also change during one day, as a result of
changes in water pH that are attributable to incoming solar radiation and photosynthesis (Nimick
et al, 1998). These consistent daily changes are called diurnal variability. Because of
photosynthesis, the water pH tends to increase later in the day, and dissolved arsenic
concentrations also tend to increase. Diurnal variations in dissolved arsenic concentrations of as
much as 21-percent were observed at three of the five sites on the Madison and Missouri Rivers,
but diurnal variability was not seen at the other two sites. Thus, diurnal variability may affect
arsenic concentrations hi some surface water sources.
3.2 RELATIONSHIP OF FATE AND TRANSPORT PROPERTIES TO TREATMENT AND
DISTRIBUTION
A variety of processes and factors affect the fate and transport of arsenic within public
water supply treatment systems. The most important factor appears to be the oxidation state
(arsenate or arsenite). The presence of competing ions, especially sulfate and fluoride, are also
important, as is pH. This section discusses the effects of these factors upon arsenic
concentrations and removal from water in treatment systems.
Arsenic in water commonly occurs as arsenate, As (V), or arsenite, As (III). The
chemical species that are formed depends upon the oxidation-reduction conditions and the pH of
the source water. The common soluble species of arsenate are H3AsO4, H2AsO4'', HAsO42', and
AsO43'; whereas the common soluble species of arsenite are H3As03 and H2AsO3'~. At typical
pHs, the predominant arsenite form is the neutral species (H3AsO3), while the predominant
arsenate species are the anions H2AsO4]" and HAsO42". Because of its ionic charge, arsenate is
more easily removed from source waters than arsenite. In particular, activated alumina, ion
exchange, and reverse osmosis may achieve relatively high arsenate removal rates, but they show
lower treatment efficiencies for arsenite.
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Arsenite can be oxidized to arsenate, and this can improve arsenic removal efficiencies.
In water that contains no ammonia or total organic carbon (TOC), chlorine rapidly (in less than 5
seconds at chlorine concentrations of 1.0 mg/L) oxidizes approximately 95 percent of arsenite to
arsenate (Clifford, 1986). The reaction through which chlorine oxidizes arsenite is:
[2]
The presence of ammonia and TOC slows this oxidation process. Monochloramine at the
concentration of 1.0 mg/L oxidized 45 percent of arsenite to arsenate. Oxygen may slowly
oxidize arsenite to arsenate, although this reaction occurs slowly in laboratory studies. Shen
(1973) indicated that potassium permanganate can oxidize arsenite. Potassium permanganate
oxidizes arsenite according to the following reaction:
3As3+ + 2KMnO4 •* 2MnO4 + 3 As5
[3]
Therefore, it appears that chlorine and potassium permanganate are the most effective
processes to oxidize arsenite to arsenate. The oxidation of arsenic from its Irivalent state to its
pentavalent state can allow treatment plants to increase the removal efficiencies of treatment
technologies.
The water pH also affects the removal efficiencies of treatment technologies for arsenic,
and therefore, the level of and persistence of arsenic it drinking water. For example, activated
alumina removes arsenic most efficiently at pH 6, but yields lower treatment efficiencies at pH 9
(Rubel and Hathaway, 1987). Removal efficiencies for alum coagulation tend to decrease at pHs
greater than 7.0 (Gulledge and O'Connor, 1973).
Competition for adsorption sites with other ions may also affect the persistence and
removal of arsenic from source water in treatment plants. In particular, sulfate in source waters
may reduce the efficiency of arsenic removal. Sorg (1990) showed that waters with high sulfate
levels correlate with lower arsenic removal by ion exchange technologies. Anion exchange
resins preferentially adsorb sulfate over arsenic. Because of this preference for sulfate over
arsenic, under some conditions displacement of arsenic can result in peaks of arsenic in effluent
which exceed the concentrations of arsenic in source water influent. Jekel (1994) reported that
the competitive effects of sulfate, fluoride, and phosphate may reduce the effectiveness of
activated alumina treatments, particularly when the competitors are in the range of 0.1 to 2
mg/kg, and arsenic removal to the ppb level is required. If the competing ions are present in
small concentrations, activated alumina can be applied successfully at slightly acidic pH ranges
(pH 5.5 to 6.0).
In summary, three factors are particularly important to the fate of arsenic in treatment and
distribution systems. These include the oxidation state, the pH of the source water, and the
presence of other ions which may compete for adsorption sites in treatment technologies. The
most significant factor that affects the fate and transport of arsenic in treatment and distribution
systems appears to be the arsenic oxidation state. Arsenate is removed more efficiently than
arsenite.
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4. Sources of Data on Arsenic Occurrence in
Drinking Water Supplies
The potential health concerns associated with arsenic in drinking water have long been
recognized, and therefore a significant amount of data is available on source water and treated
drinking water arsenic concentrations. These data sources, which include national, regional, and
State databases, differ substantially in size, content, and quality. Table 4-1 lists the national data
sources which were available for development of arsenic occurrence estimates, and provides
general information about the characteristics of these data sources.
An important source of information for estimating arsenic occurrence on a national basis
is compliance monitoring data collected in accordance with the SOW A. The estimates of arsenic
occurrence and intra-system variability that are presented in Chapters 6 and 7 of this report were
developed using such data. In this Chapter, Section 4.1 discusses the development of this
database from sets of compliance monitoring data submitted to the USEPA, and from
information contained in the Safe Drinking Water Information System (SDWIS). In this report,
this database is referred to as the Arsenic Compliance Database (ACD).
Other arsenic occurrence surveys also provide potentially important sources of arsenic
occurrence information. Recently developed databases include the National Arsenic Occurrence
Survey (NAOS) and the United States Geological Survey Arsenic Database (USGS). These
databases are described in Section 4.2, and they are used in Chapter 6 to provide comparisons
with the occurrence projections developed using ACD. Other databases that could be used as
comparison tools include the Metropolitan Water District of Southern California (Metro) and the
National Inorganics and Radionuclides Survey (NIRS). These databases are also described in
Section 4.2.
In addition to the compliance monitoring data and the databases that have been used as
comparison tools in this report, a variety of other data sources are available that provide arsenic
occurrence information. However, these data sources were not used in this report for various
reasons. These databases, which include the Rural Water Survey (RWS), the 1969 and 1978
Community Water Supply Surveys (CWSS), the National Organics Monitoring Survey (NOMS),
the occurrence data gathered by the Western Coalition of Arid States (WESCAS) and the
Association of California Water Agencies (ACWA) database, are briefly described in Section
4.3, and the reasons why these databases were deemed unsuitable for use in this occurrence
estimation are presented.
4.1 ARSENIC COMPLIANCE DATABASE (ACD)
The Arsenic Compliance Database was developed to support the USEPA's effort to
estimate arsenic occurrence in the United States. The database includes information from
SDWIS and from State compliance monitoring data sets that were provided to ISSI by the
USEPA. Section 4.1.1 describes SDWIS.
Public Comment Draft 4 - 1 May 8, 2000
-------
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Section 4.1.2 describes the State compliance monitoring databases. Section 4.1.3 explains how
SDWIS and State compliance monitoring data sets were used to build the ACD.
4.1.1 Safe Drinking Water Information System (SDWIS)
SDWIS provides a complete inventory of information on all public and private water
supply systems in the United States. The SDWIS database is used for compliance tracking and to
allocate SDWA grant monies. Violations of the current arsenic MCL of 50 Atg/L are recorded in
the SDWIS database, but measured concentrations below the MCL are not recorded. While this
information is useful for determining the number of systems that have violated current Federal
arsenic drinking water limits, it is not suitable for estimating arsenic concentrations in the range
of2to50Aig/L.
SDWIS is a reliable source of information on the characteristics of individual public
water systems (PWSs) in the United States. While the universe of PWSs in the United States
does change, the SDWIS information is checked for accuracy on an annual basis. Near the end
of the calendar year, the SDWIS inventory is frozen and distributed to State drinking water
programs for verification of the numbers and types of systems (Science Applications
International Corporation (SAIC), 1999). The core verified data in the SDWIS inventory
include:
• System name and address;
Federal identification number (SDWIS ID number or PWSID);
* Source water type;
• Ownership category;
• Population served; and
• Regulatory classification (system type).
In regard to these categories, it is important to note that source water type can include
ground water, ground water under the influence of surface water, and surface waters. Small
systems typically have only one source, while larger systems may have multiple sources.
Although most large systems are served by either a ground water or a surface water source, some
systems do receive water from a mix of ground water and surface water sources. In SDWIS, any
water system with a continuous source of surface water is defined as a surface water system, even
if 75 percent, or 99 percent, of the water is from a ground water source. Therefore, systems that
rely entirely on surface water sources and blended water systems are coded as surface water
systems in SDWIS, and systems that rely entirely on ground water sources are coded as ground
water systems in SDWIS.
SDWIS characterizes the population served on the basis of retail customers only. The
SDWIS inventory populations do not include the number of people served with water that is
wholesaled by any individual utility (SAIC, 1999).
Ownership categories are limited to public and private, while regulatory classification
includes community water supplies (CWS), non-transient, non-community water supply systems
Public Comment Draft 4 - 3 May 8, 2000
-------
(NTNCWS) and transient non-community water supplies (TNCWS). The arsenic occurrence
estimates presented in this report are for CWS and NTNCWS.
In summary, the SDWIS database is a very useful source of information on the
characteristics of individual systems. In fact, as discussed in Section 4.1.3, SDWIS was a key
resource for information on the characteristics of individual systems for the development of
ACD. However, it does not contain information on the levels of arsenic in individual systems
that can be used to estimate arsenic occurrence within the range of interest.
4.1.2 State Compliance Monitoring Databases
An important source of data for estimating arsenic levels in drinking water is compliance
monitoring data that is collected from the drinking water utilities in accordance with the SDWA,
to comply with the current arsenic MCL of 50 ug/L. Such data was available from 31 States,
although data from only 25 States was found to be suitable for inclusion in this occurrence study.
Table 4-2 presents an overview of the suitable compliance monitoring data for each State. Figure
4-1 shows the geographical distribution represented in the 25 State's compliance monitoring
databases. The characteristics of the 25 suitable individual state databases are documented in
Appendix D-l. While the States for which compliance monitoring data are available are
distributed throughout the United States, this figure shows that the States are. not evenly
distributed. In particular, few data sets are available for States in the New England, Mid-
Atlantic, and Southeastern United States. In contrast, the Midwestern, North Central, South
Central, and Western Regions appear to be fairly well represented. The States in these regions
are described on Section 5.5; see figure 5-2 for a map of the United States.
A number of data sets were considered for inclusion in the ACD database, but were not
included for various reasons. Table 4-3 lists the States for which at least one data set was
excluded from the ACD database, and the specific reason why the data set was excluded. Data
sets from 16 States were excluded from the database. However, 10 of these States submitted
multiple data sets; the single most representative data set from each of these States was chosen
and included in ACD. In most cases, these States submitted multiple compliance monitoring
data sets, and the most recent data sets represented the largest number of systems. The later
compliance monitoring data sets also tended to have the lowest detection levels, although this
was not true in all cases. In one case, Maine, the older data set was included because the newer
data set only reported arsenic levels above 20 jig/L. Minnesota had two data sets, one containing
compliance monitoring data, and one containing data that was not from PWS systems (data from
private domestic wells or non-public water supply wells). In this case, only the compliance
monitoring data was included. For six States, the available data sets were unsuitable and were
not included in the State compliance monitoring database. Only one set of data was available for
each of these States, thus, these States are not represented in the compliance monitoring database.
The reasons for exclusion of these data sets are listed in Table 4-3. ISSI contacted
representatives of the State agencies that provided these data sets, but was unable to resolve the
problems with these data sets, or obtain newer, suitable data sets.
Public Comment Draft
4-4
May 8, 2000
-------
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Table 4-3
State Data Sets Excluded from ACD
State
Alaska
Arizona
California
Florida
Idaho
Iowa
Louisiana
Maine
Michigan
Minnesota
Oregon
Pennsylvania
North Dakota
South Dakota
Texas
Utah
Reason Data Set Excluded
Multiple data sets submitted; only the most recent data set selected.
Multiple data sets submitted; only the most recent data set selected.
Multiple data sets submitted; only the most recent data set selected.
Data set unsuitable; all results censored, no detection limit reported.
Data set unsuitable; no PWS ID numbers were provided.
Data set unsuitable; no reporting limits; results rounded to nearest 10 Mg/L.
Data set unsuitable; no reporting limits; only detected values provided, results
rounded to nearest 10 ^g/L.
Selected older data set; recent data set censored at 20 j/g/L.
Multiple data sets submitted; only the most recent data set selected.
Selected data set associated with PWSID numbers.
Multiple data sets submitted; only the most recent data set selected.
Data set unsuitable; all results censored, reporting limit of 50 pgfL.
Multiple data sets submitted; only the most recent data set selected.
Data set unsuitable; no result code flag was provided.
Multiple data sets submitted; only the most recent data set selected.
Multiple data sets submitted; only the most recent data set selected.
The compliance monitoring data sets were submitted voluntarily to the USEPA by State
drinking water agencies, either directly or through other organizations (e.g., AWWA, EPA Office
of Research and Development, EPA Regional Office, Association of Public Health Laboratories),
during this study and during earlier studies. These data sets generally included the following
information:
• System name and address;
PWS ID number;
Sample collection date;
• Result; and
• Detection limit, if arsenic was not detected in the sample.
Public Comment Draft
4-8
May 8, 2000
-------
Because these data were collected for compliance purposes, the samples were assumed to
have been collected from the points-of-entry (POE) into the distribution system, which is
representative of each well or source after treatment. Thus, the arsenic values represent finished
water, and should be representative of the arsenic levels to which consumers are exposed.
Compliance with the arsenic standard is measured at the POE. Therefore, these data are directly
relevant to the estimation of regulatory costs and benefits for the RIA.
Representation of Systems in Each State
As shown in Table 4-2, the characteristics of the data differ from State to State. Table 4-
4 presents number of CWS systems contained in the State data sets as a percentage of the total
number of CWS systems in the State based on SDWIS. Purchased water systems were excluded
from the occurrence data analysis. For purchased water systems, EPA allows the State to decide
how each system will conduct monitoring. In some cases, monitoring may be done by only the
wholesaling system. The SDWIS database, however, only contains data on retail populations and
does not indicate to which systems a wholesaler provides its water. As a result, we do not know
to what extent purchased water systems are representative of other water systems in the State's
database. Using the purchased water system data to develop the State probability estimates could
bias the estimates by double counting individual system results. Developing results based on
non-purchased water system results ensures that independent data form the basis for the State
estimates.
Table 4-4 shows that both ground and surface water systems are well represented in the
compliance monitoring databases. On average, the compliance monitoring data represent 81
percent of the ground water systems in each State, whereas the compliance monitoring data
represent a slightly higher portion (88 percent) of surface water systems in each State. For 17
States, at least 90 percent of ground water system are represented. California, Maine, and Utah
had the lowest coverage, on a percentage basis; yet, in each of these States, more than 100
systems are represented, and more than 1,000 systems are represented in California. Among
surface water systems, 19 States data sets include at least 90 percent of PWS, and the lowest
percentage of coverage is provided by the data sets for California, Maine, Michigan, and Utah.
The total number of ground water and surface water systems represented (17,103 and
2,437, respectively) is far higher than the number available from other national data sources.
Analytical Methods and Reporting Limits
Data for 11 of the States are censored7 at a single reporting limit, but for 14 States, data
are censored at multiple reporting limits that range from 1 -10 ^g/L- Where there are a range of
reporting limits, it suggests that arsenic samples may have been analyzed with more than one
7 Censored data are samples with contaminant concentrations reported as less than the analytical detection limit.
Actual contaminant concentrations in these samples may be positive, and may range from zero to the detection limit.
In the case of a naturally occurring contaminant, such as arsenic, contaminant concentrations may be exceedingly
low, but are rarely zero.
Public Comment Draft 4 - 9 May 8, 2000
-------
sample method. Several of the State data sets include method numbers associated with
individual samples. Based on these method numbers, the following analytical techniques, which
are approved for compliance monitoring at the arsenic MCL of 50 [AgfL?, are represented among
the compliance monitoring data sets (systems must use one of these approved methods for
compliance monitoring at a certified laboratory):
• Inductively coupled plasma (ICP) - Atomic Emission Spectrometry (AES)
ICP-Mass Spectrometry (MS)
• Platform Graphite Furnace - Atomic Adsorption (AA)
• Graphite Furnace AA; and
• Hydride Generation AA.
Because these samples were collected for compliance monitoring, it is assumed that
appropriate sample collection and laboratory quality assurance and quality control protocols were
followed.
Time trends were found in the reporting limits for 8 of the 14 States with multiple
reporting limits when the data were sorted by date. These data are summarized in Table 4-5.
These data show that reporting limits have decreased in five of the eight States, and increased in
three of the States. In Arizona, the majority of samples collected between 1988 and 1995 had
reporting limits of 10 ,ug/L, but between 1996 and 1998, the primary reporting limit declined to 5
Aig/L. In California, Minnesota, New Mexico, and Oregon, reporting limits were sharply lower in
the later time periods indicated in Table 4-5. In these four States, reporting limits declined to s2
in the later time periods. In contrast, reporting limits in Alaska rose from &2 ^g/L to 5
after 1994. In both Illinois and Utah, moderate increases in reporting limits were seen in
the later time periods. No time trends were observed in the data for the remaining 6 States -
Indiana, Kentucky, Michigan, New Jersey, North Carolina, and Ohio - with multiple reporting
limits.
8 40 CFR Section 141.23.
Public Comment Draft
4- 10
May 8, 2000
-------
Table 4-4
CWS Systems in SDWIS and State Compliance Monitoring Data
State
AK
AL
AR
AZ
CA
IL
IN
KS
KY
ME
MI
MN
Source Type
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
Systems in
SDWIS"
494
144
273
69
394
75
742
45
2,784
517
1,139
115
798
53
519
101
107
172
309
60
1,149
68
895
24
Systems in State
Data Set2
494
140
260
67
380
74
694
45
1,212
331
1,112
115
652
50
513
101
96
151
110
29
648
43
869
23
Percent
Coverage
100%
97%
95%
97%
96%
99%
94%
100%
44%
64%
98%
100%
82%
94%
99%
100%
90%
88%
36%
48%
56%
63%
97%
96%
Public Comment Draft
4-11
May 8, 2000
-------
Table 4-4 (Continued)
CWS Systems in SDWIS and State Compliance Monitoring Data
State
MO
MT
NC
ND
NH
NJ
NM
NV
OH
OK
OR
TX
Source Type
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
Systems in
SDWIS'
1,096
95
541
56
1,853
185
208
21
612
38
489
30
576
31
244
34
982
160
462
217
663
139
3,451
329
Systems in State
Data Set"
789
91
536
56
1,776
185
208
19
506
38
439
30
536
30
220
31
898
130
462
217
599
139
2,934
326
Percent
Coverage
72%
96%
99%
100%
96%
100%
100%
90%
83%
100%
90%
100%
93%
97%
90%
91%
91%
81%
100%
100%
90%
100%
85%
99%
Public Comment Draft
4-12
May 8, 2000
-------
Table 4-4 (Continued)
CWS Systems in SDWIS and State Compliance Monitoring Data
State
UT
Total Systems
Source Type
GW
SW
GW
SW
Systems in
SDWIS'
326
39
21,105
2,804
Systems in State
Data Set"
131
24
17,103
2,473
Percent
Coverage
40%
62%
81%
88%
Note:
* The total number of non-purchased public and private systems. Ground water under the influence of surface water
systems are included as surface water systems.
Table 4-5
Summary of Reporting Limits for Eight States
State
AK
AZ
CA
IL
MN
NM
OR
UT
Year Range
1980-94
1995-97
1988-95
1996-98
1980-94
1995-97
1992-94
1995-98
1992-93
1994-97
1980-93
1994-99
1990-92
1993-98
1980-88
1989-96
Total
Number of
Samples
3142
441
5612
1477
24948
6780
738
2983
636
3142
514
2214
1355
1225
1667
1678
Tota! ND
Samples
1954
324
3664
612
18664
2417
617
2344
452
1760
357
609
1164
1041
1058
1337
Total ND,
<:2
1954
0
143
19
2483
2289
617
2157
271
1760
51
548
8
975
961
292
Total ND,
2.01 to s5
0
324
1426
567
6218
80
0
161
181
0
270
59
1156
68
22
948
Total ND,
5.01 to slO
0
0
3218
26
9963
48
0
14
0
0
36
2
0
0
75
97
Public Comment Draft
4-13
May 8, 2000
-------
Table 4-6 compares the arsenic occurrence data contained in the complete State data sets
with the occurrence data contained in the subsets for ground water and surface water systems.
Because the subsets have lower detection levels, it was expected that the subsets would have
lower censoring levels and more accurate system mean arsenic concentrations. For all eight
States, subsetting reduces the percent of samples censored for ground water systems. The largest
reductions occurred in the States of Arizona (15.8 percent), California (7.6 percent), New Mexico
(5 percent), and Utah (10.7 percent). For each of these States, subsetting the data excludes a
significant number of samples with reporting limits of 10. For the other four States, reporting
levels in the original data sets are generally lower than those in the original data sets for Arizona,
California, New Mexico, and Utah; thus explaining the smaller reductions in percentage of
samples that are censored for the Alaska, Illinois, Minnesota, and Oregon. For surface water
systems, subsetting reduces the percent of samples that are censored for six of the eight States,
with the most significant decreases also occurring in the States of Arizona (23.2 percent),
California (7.2 percent), New Mexico (14.7 percent), and Utah (9.5 percent). The percent of
censored surface water samples rose slightly for the States of Illinois (0.5 percent) and Oregon
(1.8 percent); these two States had the highest levels of censoring among the eight States.
The minimum and average system arsenic concentrations for both ground water and
surface water systems are generally lower for the data subsets than for the complete State data
sets. This difference is probably a result of the conventions that were applied to estimate system
mean arsenic concentrations. These conventions are presented in Section 6.1.1 of this report.
The convention that may have the greatest impact regards the calculation of the system mean
arsenic concentration when there are four or fewer samples that were positive, or five or more
positive detects that were all equal. For example, consider a hypothetical system in California
with 12 samples, including 2 positive detects at concentrations of 1.5 and 1.8 //g/L, five non-
detects at 2 jUg/L and five non-detects at 10 Afg/L. The samples with reporting limits of 10 were
collected prior to 1995, while the positive detects and the samples with a reporting limit of 2
^g/L were collected after 1995. In accordance with the data convention described above, the
mean arsenic concentration for this system is 4.76 ngfL when the entire data set is considered,
and is 1.19 tfi/L when the recent data subset is considered. Thus, as this example indicates, the
higher reporting levels associated with the complete data sets can result in higher individual
system mean concentrations. In turn, higher system mean concentrations result in higher State
mean arsenic concentrations. Because a sample with a lower reporting limit provides a more
accurate indication of the arsenic concentration in drinking water, systems means calculated from
such samples are believed to be more accurate than system means calculated from samples with
higher reporting limits.
The largest differences in these system level arsenic concentrations tended to occur in the
States of Arizona, California, Oregon, and Utah. Subsetting the data resulted in several
significant differences in the maximum system mean arsenic levels in some States. Among
ground water systems, maximum system arsenic concentrations rose by 21 and 10 /^g/L in Alaska
and Utah, respectively, and decreased by 17 and 115 //g/L in Arizona and California,
respectively. For surface water systems, maximum system mean arsenic concentrations
decreased by 48.2 /^g/L in Arizona, and increased by 19.9 /ug/L in California. Table 4-7 shows
the difference in the number of systems that are represented in the complete State data sets
Public Comment Draft
4-14
May 8. 2000
-------
relative to the number of systems that are represented in the subsets of data. The highest
reductions in coverage for ground water occurred the States of Arizona, California, Illinois, and
Oregon. However, as shown in Table 4-6, each of the data subsets for these States contains a
large number of systems. For surface water systems, the greatest reductions in coverage occurred
in Arizona and California. Again, even with the reduction in coverage, these data sets provide a
significant amount of data for arsenic occurrence estimation.
As a result of the association between reporting limits and time in the 8 States, the
statistical analyses that are reported in Chapters 5 and 6 were conducted in using both the entire
set of data for each of the eight States, and subsets of data for each State that covers the period
when reporting limits were lower. However, the occurrence estimates presented in Chapter 6 are
based on the data subsets.
Public Comment Draft 4-15 May 8, 2000
-------
Table 4-6
Overview of Complete Data Sets versus Data Subsets
State &
Source
Type
AKGW
AZGW
CAGW
ILGW
MNGW
NMGW
ORGW
UTGW
AKSW
A2SW
CASW
ILSW
MNSW
NMSW
ORSW
UTSW
Data Set
Ail
1980-94
All
1996-98
AH
1995-97
All
1992-94
All
1994-97
All
1994-99
All
1993-98
All
1980-88
All
1980-94
All
1996-98
All
1995-97
All
1992-94
All
1994-97
All
1994-99
All
1993-98
All
1980-88
Number of
Systems
494
479
694
287
1212
824
1112
811
869
835
536
521
599
335
131
121
140
137
45
32
331
235
115
112
23
23
30
29
139
132
24
24
Number of
Samples
2353
2065
5031
968
19690
5622
3023
1306
3695
3068
2783
2011
1515
770
1646
781
1189
1044
2071
511
7946
2488
620
233
83
72
286
132
874
522
801
468
Average
[As]*
5.778
5.787
7.700
9.341
5.352
3.979
2.371
2.205
2.749
2.755
4.106
4.012
3.440
2.792
3.506
3.014
1.526
1.472
7.154
4.547
3.606
2.359
1.001
0.775
0.880
0.884
1.367
1.024
2.136
1.170
2.732
1.962
Min. [As|*
0.035
0.029
0.423
0.263
0.287
0.104
0.016
0.006
0.050
0.040
0.065
0.051
0.278
0.039
0.532
0.072
0.147
0.158
1.991
1.899
0.579
0.208
0.433
0.293
0.553
0.632
0.333
0.147
0.447
0.205
0.531
0.105
Max.
[As|*
93
114
118
101
210
95
67
59
66
66
140
. 140
51
56
25
35
21.8
25.7
63.5
15.3
35.7
55.6
5.8
3.2
1.6
1.3
4.0
5.0
10.6
4.9
17.0
20.6
Percent
Samples
ND
57.1%
54.8%
63.5%
47.7%
76.1%
68.5%
76.5%
74.5%
58.5%
55.5%
31.1%
26.1%
78.0%
76.5%
68.8%
58.1%
80.7%
78.3%
52.9%
29.7%
84.1%
76.9%
93.4%
93.9%
83.1%
80.6%
63.9%
49.2%
96.3%
98.1%
73.8%
64.3%
*CWS system mean arsenic concentrations in A*g/L-
Public Comment Draft
4-16
May 8, 2000
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Table 4-7
Reduction in Coverage Associated with Data Subsets
State
AK
AZ
CA
IL
MN
NM
OR
UT
Ground water
- 3.0 %
- 54.9 %
-13.9%
- 26.4 %
- 3.8 %
- 2.6 %
- 39.8 %
-3.1%
Surface Water
- 2.1 %
- 28.9 %
- 18.6 %
- 4.35 %
- 0.0 %
- 3.23 %
- 5.1 %
- 0.0 %
Multiple Samples for Individual PWS
One of the unique aspects of the compliance monitoring data sets is that many of them
provide multiple samples for many PWS. Table 4-8 summarizes the average, minimum, and
maximum number of samples per PWS in each State data set, by source water type. In general,
there are more samples per system for surface water systems than for ground water systems. This
is consistent with expectations, because surface water systems are required to monitor more
frequently than ground water systems. In several States, some systems are represented by more
than a hundred samples. Within individual systems, some of these samples have been collected
on the same day, while others have been collected over many years. In addition, samples can
come from multiple POE in a system. In some States, these samples have been censored at
multiple reporting limits, while in other States, all samples are censored at the same reporting
level. Having multiple samples for individual PWSs offers several benefits, but also presents
some challenges.
U.S. EPA Headquarters Library
Mail code 3201
1200 Pennsylvania Avenue NW
Washington DC 20460
Public Comment Draft
4-17
May 8, 2000
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Table 4-8
Summary of Numbers of Samples per System for State Compliance Monitoring Data
State
AK
AL
AR
AZ
CA
IL
IN
KS
KY
ME
MI
MN
MO
MT
NC
ND
NH
NJ
NM
NV
OH
OK
OR
TX
UT
Ground Water Systems
Min. (N)
1
1
1
1
1
I
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean (N)
3
6
2
3
7
2
1
5
5
3
2
2
1
2
5
1
2
4
3
1
4
2
2
2
6
Max. (N)
23
49
7
51
i72
21
12
39
14
77
253
20
4
14
55
5
45
65
80
2
71
78
28
63
44
Surface Water Systems
Min. (N)
I
1
1
1
1
1
1
1
2
2
1
2
1
1
1
1
2
3
1
1
6
1
1
1
1
Mean(N)
7
9
4
16
10
2
4
7
13
3
2
2
1
4
13
1
6
11
4
1
12
2
4
5
20
Max. (N)
178
56
20
121
114
6
19
11
28
9
16
3
2
22
46
1
15
108
14
2
80
64
15
52
249
Public Comment Draft
4-18
May 8, 2000
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With multiple samples over time, it is possible to average samples in order to develop a
better estimate of system arsenic levels. In addition, having multiple samples for individual PWS
can support analyses of variability in arsenic concentrations over time and from location to
location within individual PWS. For example, the assessment of infra-system variability
presented in Chapter 7 is based on the analyses of arsenic occurrence distributions from location
to location within individual PWS. However, when systems are represented by different
numbers of samples, it may be necessary to estimate a single system level statistic for each
system so that arsenic levels are comparable from system to system. This aggregation ensures
that a system with 50 samples can be represented and compared to a system with only one
sample. In this report, samples for individual systems have been averaged using the procedure
discussed in Section 6.1.1.
Water System Type
Many of the compliance monitoring data sets provide data on arsenic occurrence hi
NTNCWS systems as well as CWS systems. In fact, as shown on Table 4-2, data sets for 17
States contain some data from NTNCWS systems, although two of these State data sets each
contain information for only two systems. For 12 States, more than 100 ground water NTNCWS
systems are represented. Far fewer NTNCWS surface water systems are represented, but the
total population of NTNCWS surface water systems in the United States is quite small (545 non-
purchased NTNCWS systems). Information about NTNCWS systems is not available in the
other arsenic occurrence databases discussed in Section 4.2 and 4.3.
Point-of-Entry (POE) Data
The compliance monitoring data sets are unique from other data sets in that most of them
contain multiple data points for individual PWS systems. For individual PWS systems, these
samples may have been collected over a many year period; these samples may have been
collected from different points-of-entry to the system; in addition, these samples may have
multiple reporting limits. Multiple samples offer unique benefits, and pose specific challenges,
for the estimation of arsenic occurrence. Earlier surveys of arsenic occurrence generally included
one sample per system represented, and therefore, estimates based on these data amounted to
cross-sectional views of arsenic occurrence. However, where there are multiple samples and the
data are of sufficient quality, data may be used to understand variability in arsenic concentrations
over time (temporal variability) and from location to location within PWS systems (intra-system
variability). Therefore, the compliance monitoring data may be used to support national
estimates of arsenic occurrence that include temporal and intra-system variability.
For individual systems, compliance with the arsenic standard is measured at the POE into
the distribution system. A POE represents a well or entry point to the distribution system,
therefore a location where treatment may need to be installed if they have arsenic levels above
the MCL. Systems may have more than one POE. Generally larger systems have more POE;
also, individual POE or treatment plants may be supplied by a network of wells. A system with
multiple POE may need to install multiple treatment systems, depending on the contaminant
Public Comment Draft 4-19 May'8,2000
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concentrations at each POE. Therefore, to estimate compliance costs, it would be ideal to know
the number of POE in each system, and link each sample result to a specific POE ID. However, a
complete inventory of individual POE is not available, and many of the compliance monitoring
data sets do not include POE ID.
POE ID are available for the following States:
• Arkansas;
• Alabama;
• California;
• Indiana;
• Illinois;
New Mexico;
• Oklahoma; and
Utah.
These data were used to assess infra-system variability in arsenic concentrations. The
infra-system variability estimation and results are discussed in Chapter 7 of this report.
4.1.3 Building the ACD from SDWIS and State Compliance Monitoring Databases
Two separate databases comprise ACD, and these databases were developed from the
information contained in the individual State compliance data sets and SDWIS. These databases
are named GRAND.CPT and INTRA.CPT, and they were created in SAS. GRAND.CPT was
designed to support statistical evaluations and the development of national occurrence
projections presented in Chapters 5 and 6, and it contains the data sets of all 25 States.
INTRA.CPT was developed to support the assessment of intra-system variability presented in
Chapter 7, and it includes the eight data sets which include POE indentifiers. GRAND.CPT
database includes 120,207 records, and INTRA.CPT contains 54,851 record. Each record in
these databases correlates with a finished drinking water sample from a PWS. Appendix D-2
provides a list of the variables in these databases, and the characteristics of those variables. All
of the records in INTRA.CPT are included in GRAND.CPT, although INTRA.CPT include two
additional variables, which are the POE identifier and the POE type. INTRA.CPT was
developed to improve computational efficiency for the intra-system analyses; it include only
information that is relevant to those analyses, and was developed with minimal additional effort.
Initially, ISSI compiled the raw State compliance monitoring data sets in an Access
database named AOED. The initial data conditioning processes are described in Appendix D-3.
Subsequently, the Access database was converted to a SAS format, and the GRAND and INTRA
databases were created. In addition, raw compliance monitoring data sets were received for the
States of Alabama, Arkansas, Illinois, Indiana, Oklahoma, and Oregon after the Access database
had been converted to SAS, and these compliance monitoring data sets were loaded directly into
SAS.
Public Comment Draft
4-20
May 8, 2000
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Simply stated, the data conditioning process standardized the coding of variables from
State to State. For example, different States reported sample collection dates in different
formats. Through the data conditioning process, dates were standardized to a consistent format.
Similarly, result flags were initially inconsistent. Some States reported ND (not-detect), others
LT (less than), some N and D, and some marked non-detects with the "<" symbol. After
confirming the meaning of each flag with the data contact in the State, these flags were
standardized to N and D for non-detects and detects. The result and the reporting limit variables
were standardized to pg/L, because some States provided data in y^g/L and others reported data in
mg/L. PWS source type was standardized to GW and SW (systems coded as ground water under
the influence of surface water were coded to surface water), and type of water system was
standardized to CWS and NTNCWS.
SDWIS was an important tool for the development of the arsenic databases, and it was
used in a variety of ways. Each of the suitable State compliance monitoring databases provided
SDWIS PWSID numbers. By linking the State compliance monitoring data to SDWIS by PWSID
number, we were able to complete missing data where necessary, update variables which could
change over time (such as population served or if a PWS was active or inactive), and check for
discrepancies between the compliance monitoring data and the SDWIS data. When
discrepancies were identified between State data and SDWIS data, the State data were corrected
to reflect the SDWIS data. By doing so, the databases were corrected in a manner that was
consistent from State to State. In relation to the total number of record in the database (120,207),
few records required correction. In 55 cases, records did not identify whether or not a PWS was
a supplier of purchased or a non-purchased water; in six cases, SDWIS and State data sets
disagreed about the type of water system (CWS versus NTNCWS); and in 36 cases, SDWIS and
State data sets disagreed about the system source water type.
Several other conventions were applied during the development of the GRAND and
INTRA databases, and these conventions are discussed below:
1. Sample analyses conducted prior to 1980 were deleted. This is roughly the year that new
analytical techniques had become widely available and less precise colorimetric analysis
was phased out.
2. Analyses with no PWSID number, or no analytical result, or from systems identified in
SDWIS as either inactive or as suppliers of purchased water,9 were deleted.
3 Analyses with results greater than 1000 //g/L were deleted, because these values were
believed to be the result of data entry or data conversions errors, rather than
representative of actual arsenic concentrations in drinking water. As a result of this
convention, three arsenic values were deleted, which were two surface water samples
from a public water supply system in Alaska with values of 3,100 and 3,400 Mg/L, arid
one sample from a surface water system in California with a value of 4,950
* Purchased water systems do not withdraw the water that they supply to their customers directly from a ground
water or surface water source, but instead they purchase it from another water system.
Public Comment Draft 4-21 May 8,2000
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4. Arsenic values reported as "zero" or "ND" were considered to represent an analytical
result below the reporting limit. If the State did not disclose the reporting limits for the
samples, and reporting limits could not be determined based on conversations with
appropriate State representatives, reporting limits were assigned based on where the
majority of the lowest measured results clustered. The later convention was applied to
data from the States of Alabama and Oregon. For the State of Indiana, reporting limits
were assigned that correlated with laboratory analysis methods. Appropriate reporting
limits were provided by the State representative.
5. Sample results that were non-detect with reporting limits greater than 10 ppb were
deleted. This convention only affected Michigan, in which arsenic results were associated
with four report limits (0.3, 1.0, 2.0, and 50 ppb). Removing arsenic results associated
with a reporting limit of 50 ppb excluded 21% of Michigan data.
6. The State of Missouri reported only positive results. ISSI contacted the State, and the
Missouri Department of Health provided PWSIDs for all systems that monitored but had
no arsenic detects through the three year monitoring time period for which the positive
detects were provided. Furthermore, the State representative indicated that these systems
were non-detect at the reporting limit of 1 f^gfL. These data were then combined with the
positive results.
4.2 COMPARISON DATABASES
Four databases are available for use as comparison tools, including the NAOS, USGS,
NIRS, and Metro databases. All of these databases are national in scope, but the characteristics
of these databases differ substantially. Likewise, the survey methods that were used to collect
samples for NAOS and NIRS were quite different, and samples that are represented in USGS and
Metro databases were not collected in accordance with a specific survey method. Each of these
databases are described below, and comparisons of national arsenic occurrence estimates based
on the ACD, NAOS, and USGS databases are presented in Chapter 6.
4.2.1 National Arsenic Occurrence Survey (NAOS) Database
The Water Industry Technical Action Fund (WITAF)10 sponsored the National Arsenic
Occurrence Survey (NAOS), which was a nationwide arsenic survey (Frey and Edwards, 1997).
The NAOS was designed to be quantitatively representative of different source water types,
system sizes, and natural occurrence patterns (based on a derived natural occurrence factor -
discussed below), and to have a low detection limit (0.5
NAOS was based on a representational survey design. PWSs were selected from within
three representational groups, including:
10 WITAF includes the following organizations: American Water Works Association, National Association of Water
Companies, Association of Metropolitan Water Agencies, National Rural Water Association, and National Water
Resources Association..
Public Comment Draft
4-22
May8. 2000
-------
• source type (ground water and surface water);
• system size (small: 1,000 to 10,000 people served; and large: >10,000 people served);
and,
• natural occurrence factor (NOF) level (which includes consideration of geographic
region).
The NAOS survey design used the NOF to qualitatively represent the relative probability
of arsenic occurrence in water sources. For each State, separate ground water and surface water
NOF levels were estimated. USGS's water quality database (WATSTORE) was used to derive
surface water NOF levels for all States except Indiana, and ground water NOFs for 35 States. The
remaining State source types were not represented in WATSTORE. Metro data was used to
confirm the NOFs based on WATSTORE and to derive the missing NOF designations.
NOF levels were designated as low, medium, or high, and were based on total scoring in
four criteria. These elements were:
1) the probability of left censored (below detection limit) data;
2) the probability of measured observations below 5 y^g/L;
3) the probability of measured observations above 20 /^g/L; and
4) local interest in arsenic in source water, as indicated by the total number of samples.
For each criterion, potential scores were 5,15, or 25 points. Final NOF assignments for
ground water and surface water sources were based on the sum of the scores for these criteria.
These NOF levels served as a basis for identifying distinct regional arsenic occurrence patterns.
The seven regions that were identified, and the States included in each region, are shown below:
Table 4-9
States in the Seven NAOS Regions
Region
New England
Mid-Atlantic
Southeast
Midwest Central
South Centra!
North Central
Western
States in Region
CT, NH, NJ, NY, MA, ME, RI
DE, KY, PA, MD, NC, SC, VA, WV
AL, FL, GA, MS, TN
OH, 1A, IL, IN, MI, MN, WI
AR, CO, KS, LA, MO, NE, NM, OK,
MT, ND, SD, WY
AK, AZ, CA, HI, ID, NV, OR, UT, A\
TX
'A
Public Comment Draft
4-23
May 8. 2000
-------
NAOS was designed to include approximately 800 samples that were selected to
proportionally represent the survey design criteria (system sizes, source type, and geographic
region). A number of samples came from the same PWS, but from different facilities or wells
within the PWS. Systems were mailed a survey that requested their participation. Large systems
were selected from the Water Industry Database, and small systems were selected from the
Federal Reporting Data System(FRDS). Approximately the same number of small and large
systems were sampled. Based on source water type distributions contained in FRDS, the ratio of
surface water to ground water systems was 55:45 for large systems, and 30:70 for small systems.
To achieve geographic representativeness, sample sites were selected from within each region
(based on availability of sites within each region).
Sample kits with instructions were mailed to the selected utilities with a questionnaire on
specific details regarding the sample. While 809 sample kits were mailed out, 517 samples were
submitted to the investigators by water utilities (a 63.9 percent response rate). The response rate
for large utilities (70 percent) was slightly higher than for small ones (58 percent). The
investigators found that comparable portions of large and small systems responded within each
survey stratum. Samples were collected from the utilities' raw water sources. During data
handling, raw water results were multiplied by a removal efficiency factor associated with the
treatment train in place at the utility to calculate the likely finished water arsenic concentration.
There were a total of 435 predicted finished water arsenic level samples in the NAOS database
(161 surface water samples and 274 ground water samples). There were an additional 54 surface
water samples that did not have predicted finished water arsenic levels because there was no
treatment information available. Some of the PWSs in the survey had multiple water sources.
Based on responses to the questionnaire, the investigators concluded that the participating
utilities did not introduce bias into the survey results by selecting sources with known arsenic
concentrations.
The analytical method used for the NAOS analyses had a detection limit of 0.5 ^g/L, and
therefore, the data set has a lower percentage of non-detects than NIRS and many of the early
data sets. In ground water, arsenic was detected in 58% of samples, and in surface water,
detectable arsenic levels were reported in 73% of samples.
However, the data set has two potential drawbacks. The NAOS data set does not provide
PWS identification numbers, so it is difficult to identify the facilities which may be represented
by more than one sample. In addition, the estimated finished concentrations are not direct
measurements of finished arsenic concentrations. There is a potential for true finished
concentrations to differ from the estimated finished concentrations. The NAOS database was
used as a comparison tool to check arsenic occurrence projections developed from the ACD.
Public Comment Draft 4 - 24 May 8,2000
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4.2.2 USGS Arsenic Databases
The USGS has collected data on arsenic concentrations in ground water from locations
throughout the United States. These data were collected from a variety of wells, some of which
are used for supply of drinking water (about 10% of samples), and others for research,
agriculture, industry and domestic supply, as a part of a variety of Federal, State, and local
projects (Focazio et al., in press). These data were not collected specifically to develop national
estimates of arsenic in drinking water; however, the USGS databases provide approximately
20,000 samples (from approximately 20,000 locations throughout the country; 1 sample per well
or spring) that are potentially representative of ground water systems. Analyses were performed
by hydride-generation and atomic-adsoption spectrometry and have a consistent reporting limit of
1 ng/L. Thus, the USGS data provide a significant amount of information about arsenic
concentrations in ground water.
The USGS data is included in four distinct databases, which were derived from existing
databases. Under an interagency agreement with the USEPA, USGS used some of these
databases to project national estimates of arsenic occurrence in community water supplies with
ground water sources. These estimates are discussed in Chapter 6 of this report, and the four
databases which USGS developed are described below.
The Public Supply Database includes data derived from SDWIS on all ground, surface,
and purchased water CWS. The sources of the CWS were reviewed and characterized to identify
systems that are served partially or entirely with ground water. Systems that were at least
partially served by ground water sources were retained, and those that were totally dependent on
surface water (including purchased surface water) were excluded from the database.
The USGS Arsenic Point Database includes arsenic data from the USGS National
Water Information System (NWIS). It includes the results of analyses of filtered water samples
from 20,000 ground water wells and springs throughout the United States from 1973 to 1998.
Codes are provided which allow separation of potable and non-potable water sources. Where
NWIS contained multiple samples from individual wells, the most recent sample was included in
Arsenic Point Database. This database also provides information on water use, well
construction, and basic water quality parameters.
The USGS Arsenic Database of Selected Counties includes counties in which five or
more arsenic samples were present in the Arsenic Point Database, and counties that include five
or more arsenic values through a process of radial extrapolation. For counties with less than five
arsenic values, the radial extrapolation procedure ascribed all samples within a 50 kilometer (31
mile) radius of each county center to that county. Counties with five or more arsenic analyses
within the search radius were included in the database, and the arsenic samples identified by the
radial extrapolation were assumed to be representative of the county. There were a total of
17,496 samples for 1,528 counties in this database. When these data were associated with the
Public Supply Database, this database represented 76% of all large systems and 61% of all small
systems.
Public Comment Draft
4-25
May 8. 2000
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The fourth USGS database is the USGS Arsenic Database of AH Counties. This
database draws upon information in the Selected Counties database, and the Ground Water Atlas
of the United States. USGS used the Ground Water Atlas to identify the major aquifers in each
county, and thereby to the type of aquifer that ground water suppliers in each county withdraw
water from. When median arsenic concentrations were calculated for counties in specific
aquifers, they were extrapolated to other counties without arsenic data that use these aquifers.
Thus, arsenic values were extrapolated for the remaining counties in the United States.
From the USGS Arsenic Database of Selected Counties, the USGS developed the arsenic
occurrence estimates for ground water that are presented in Chapter 6. USGS estimated the
percent exceedances for each county by calculating the percentage of data points in each county
(with 5 or more data points) exceeding specific arsenic concentrations, from 1 ug/L to 50 ug/L.
Then USGS associated the percentages for each county with the number of systems in these
counties (from the USGS public supply database). This information was aggregated for all of the
appropriate counties to derive the national estimates for ground water systems.
4.2.3 National Inorganics and Radionuclides (MRS) Database
The USEPA's National Inorganics and Radionuclides Survey (NIRS) is a national
database of 983 samples of finished drinking water from community ground-jvater systems. The
survey was designed to be a nationally stratified proportional probability sample, and it was
stratified by system size to be nationally representative (Longtin, 1988). Two percent of the
PWSs in the United States in each size stratum were sampled. Local utilities sent field-preserved
samples collected between 1984 and 1986 from 48 States and Puerto Rico to USEPA
laboratories for analysis. Because most of the ground water systems in the United States are
small, the majority of systems represented in NIRS are very small.
NIRS contains sufficient information to support arsenic occurrence projections, and the
database contains all of the critical data elements. The NIRS data, however, are censored at 5.0
A£g/L, so the database does not provide information through the full regulatory range of interest.
In addition, because of the relatively high reporting level, 95% of the arsenic results are censored,
and therefore, projections of arsenic occurrence at levels below 5.0 ,ug/L based on NIRS data
includes a high level of uncertainty. For large and very large strata, 100% of the results are
censored.
4.2.4 Metropolitan Water District of Southern California (Metro) Database
The Metro database contains 144 arsenic samples from PWSs selected from American
Water Works Association (AWWA) Water Industry Database. The database generally represents
large PWSs (> 10,000), and the utilities represented serve more than a third (36%) of the U.S.
population. This database contains 57 ground water and 87 surface water sample results. The
detection limit was 0.5 ug/L and detected concentrations ranged from I Mg/L to 39 /ug/L in
ground water and 1 jug/L to 5 ,ug/L in surface water. Most of the 144 samples contain some
detectable arsenic; only 33% of the Metro sample results are censored.
Public Comment Draft
4-26
May 8, 2000
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The Metro data is a potentially important source of arsenic occurrence information,
although it, like the NAOS database, also has limitations. Metro data represents a subset of
facilities for which there are few samples in NIRS, but it provides little information about arsenic
occurrence in small water systems. Metro data also provides arsenic occurrence information
throughout the regulatory range of interest, because of the low detection level. However, this
data set does not include PWS identification numbers for the sampled facilities. Therefore, the
Metro database cannot be used to estimate arsenic occurrence together with other databases,
where the information in the two databases may overlap.
4.3 OTHER DATABASES
Several other databases that provide arsenic occurrence information are available,
however, these databases are not used in this report. These databases include the 1969 and 1978
Community Water Supply Surveys (CWSS), the Rural Water Survey (RWS), the National
Organics Monitoring Survey (NOMS), the Western Coalition of Arid States (WESCAS)
database, and the Association of California Water Agencies (ACWA) database. These databases,
with the exception of WESCAS and ACWA, were not used in this report because the data that
they contain are considered to be too old to accurately represent current conditions for arsenic
occurrence. Older arsenic data may not represent current conditions for several reasons. The
samples may have been analyzed with a less accurate laboratory method, thejaw water sources
may have changed, and treatment systems may have been installed. For example, filtration
treatment added to surface water systems to comply with the Surface Water Treatment Rule
would tend to decrease arsenic concentrations through incidental removal of arsenic. WESCAS
data was not used because the data that are contained do not necessarily represent arsenic levels
at individual PWSs, and because the data conventions and handling appear to have been
inconsistent from State to State. ACWA data were not used because compliance monitoring data
was available for the State of California. These databases are discussed below.
4.3.1 1969 Community Water Supply Survey
The U.S. Public Health Service conducted the 1969 CWSS to assess water supply
facilities and drinking water quality in the nation. Samples were collected from randomly
selected sites in the distribution systems of the participating PWSs. A total of 969 finished water
samples were collected from 678 ground water supplies, 109 surface water supplies, and 182
mixed sources (purchased water or unspecified source). Of these water samples, analytical
results for arsenic were available for 673 ground water samples, and 106 surface water samples.
Ninety-five percent of the ground water samples and 92 percent of the surface water samples
were censored. Only 33 ground water detections and 9 surface water detections occurred. The
results of the 1969 CWSS survey are summarized in Appendix C. Because this data set was
collected prior to 1980, and the analytical results it contains may be less accurate than more
recent data sets, these data will not be used to project arsenic occurrence and exposure estimates.
Public Comment Draft 4 - 27 May 8. 2000
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4.3.2 1978 Community Water Supply Survey
USEPA conducted a second CWSS in 1978 to determine the occurrence of organic and
inorganic compounds in public water supplies. Approximately 500 water supplies provided
drinking water samples. As a result of analytical anomalies and difficulties, the 1978 CWSS
only contains 259 pound water and 94 surface water analytical results for arsenic. From each
PWS, one to five samples of raw, finished, and/or distribution water were collected from each
supply sampled. Due to reporting inconsistencies, distributional and finished sample results were
averaged together and the raw water data were not used. A total of 49 non-censored arsenic
results and 3 non-censored surface water results were observed. The 1978 CWSS arsenic
occurrence results are summarized in Appendix C. Like the 1969 CWSS, this data set is
composed primarily of censored data: 82 percent of ground water sample data and 92 percent
surface water sample results are censored. This data set is also considered to be less accurate
than data collected after 1980, and will not be used to estimate arsenic occurrence and exposure
for this study.
4.3.3 Rural Water Survey
Between 1978 and 1980 the RWS was conducted to evaluate the status of drinking water
in rural America. A total of 71 ground water and 21 surface water samples were analyzed for
arsenic from the 648 PWSs surveyed. A total of 23 non-censored ground water and 2 non-
censored surface water arsenic results were observed. The arsenic occurrence data contained in
the RWS are summarized in Appendix C. Sixty-eight percent of the ground water analytical
results and 92 percent of surface water data were censored. The RWS data are deemed
insufficient to project arsenic occurrence and exposure estimates because they were collected
prior to 1980.
4.3.4 National Organics Monitoring Survey
In 1976 and 1977, USEPA conducted NOMS to provide data to support the development
of MCLs for organic compounds in drinking water. Trace elements were analyzed in finished
water samples from 113 PWSs; of this data arsenic analytical results were provided for 15
ground water and 86 surface water samples. A summary of arsenic data from NOMS is
presented in Appendix C. A total of 6 non-censored ground water and 19 non-censored surface
water arsenic results were observed. Sixty percent of ground water analytical results and 78
percent of the surface water analytical samples were censored. These data are deemed
insufficient to project arsenic occurrence and exposure estimates because they were collected
prior to 1980.
Public Comment Draft
4-28
May 8, 2000
-------
4.3.5 Western Coalition of Arid States Research Committee Arsenic Occurrence Study
In 1997, the Western Coalition of Arid States (WESCAS) Research Committee
conducted an arsenic occurrence study. The study consisted of arsenic data obtained from
Arizona, New Mexico, and Nevada. The primary purpose of this study was to collect data on
low levels of arsenic in ground water, particularly in small systems. California data were also
provided to WESCAS by ACWA for the study, but were not included in this database. The
WESCAS data have been manipulated and aggregated, and the data points that are contained do
not necessarily represent arsenic occurrence at individual PWSs. For some States, data were re-
defined to median values by area/or provider. However, the Arizona data were aggregated by
county. In addition, PWS identifications were not provided for data from Arizona and Nevada.
Because these data have been manipulated and are not comparable to other data sources, and
because relatively comprehensive compliance monitoring data are available for Arizona, New
Mexico, and Nevada, the WESCAS data were not used to project arsenic occurrence estimates
presented in Chapter 6.
4.3.6 Association of California Water Agencies Database (ACWA)
Association of California Water Agencies (ACWA) (Kennedy Jenks Consultants, 1996)
conducted a survey of low level arsenic occurrence in the State of Califomia.to determine the
potential impact of a revised arsenic standard upon California water supply systems. More than
1500 samples (1378 ground water and 166 surface water samples) were collected between 1992
and 1994, and these analyses had detection levels of 0.1 to 1 vg/L. Arsenic was present in a
greater percentage of ground water samples than surface water samples; 28 percent of the ground
water samples were censored, and 52 percent of the surface water samples were censored.
Arsenic concentrations in ground water were slightly higher than in surface water. The
maximum concentrations in these samples were 52 and 30 /*g/L, respectively, for ground water
and surface water samples. Most of the systems represented in this database are medium, large,
or very large PWS systems located in the southern part of the State. The survey also included
information from selected ACWA members and also from Central and West Basin Municipal
Water Districts and Southern California Water Company. This database was not used to develop
occurrence estimates because compliance monitoring data were available for the State of
California. The ACWA database does not provide PWSID numbers for each of the systems that
it contains, and therefore these data cannot be linked to SDWIS.
Public Comment Draft 4 - 29 May 8, 2000
-------
-------
5. Arsenic Occurrence Patterns in the United States
This section provides a discussion of the distribution of arsenic in drinking water in the
United States. Patterns of arsenic occurrence are discussed with respect to system water source
type, system size based on population served, system classification, and from region to region in
the United States. The data discussed in this section come from the ACD. The statistical
techniques applied to identify patterns in the available data are described, as are the results of
these analyses. In addition, the results of other relevant analyses that address patterns in arsenic
occurrence data are discussed and used to verify these analyses. Finally, these sections explain
the basis for decisions that were made with regard to data stratification for the development of
national arsenic occurrence estimates.
5.1 STRATIFICATION BY SOURCE WATER TYPE
Previous arsenic occurrence studies (Frey and Edwards, 1997; Wade Miller, 1992 and
1989) have stratified drinking water systems on the basis of source water type into ground water
and surface water systems. This stratification is appropriate because these studies indicated that
arsenic concentrations in ground water and surface water differ. Therefore, source water type is a
source of data heterogeneity. Stratification on the basis of source water type may also facilitate
analysis of regulatory impacts, because ground water and surface water systems are regulated
separately. Typical ground water and surface water systems differ in entry point configuration
and treatment train (SAIC, 1999). Therefore, the occurrence analyses presented in Chapter 6
have been stratified on the basis of source water type.
As Table 5-1 shows, distributions of system mean arsenic levels in ground water and
surface water systems differ in each State in the ACD database. In most of the 25 States for
which compliance monitoring data are available, mean arsenic concentrations are higher in
ground water systems than in surface water systems. For example, in Alaska ground water
systems, the average system arsenic level was 5.79 Mg/L, while in surface water systems, the
average system arsenic level was 1.47 //g/L. Arizona is another example, for ground water
systems, the average system arsenic level was 9.34 Aig/L, while in surface water systems, the
average system arsenic level was 4.55 A/g/L. In addition, while distributions of arsenic in ground
water and surface water are relatively similar at the 25th percentile in many States, the maximum
system arsenic levels are higher in ground water than surface water systems in 24 of 25 States.
5.2 STRATIFICATION BY SYSTEM SIZE
An important question is whether or not public water system size, based on population
served, is a determinant of average system arsenic concentrations. If arsenic concentrations are
associated with system size, then it may be appropriate to stratify the data on this basis when
developing occurrence estimates. However, if arsenic concentrations are not associated with
system size, incorrectly stratifying the data by system size could reduce the accuracy of the
arsenic occurrence estimates.
Public Comment Draft 5 -1 May 8, 2000
-------
Table 5-1
Distributions of System Means for Community Water Systems
State
AK
AL
AR
AZ
CA
IL
IN
KS
KY
ME
MI
MN
MO
Source
Type
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
Systems
(N)
479
137
260
67
380
74
287
32
824
235
811
110
652
50
513
101
96
15!
110
29
643
43
835
23
789
91
25th
Percentile
0.64
0.59
0.43
0.63
2.5
2.5
2.13
2.85
1.08
0.93
0.19
0.58
0.03
0.5
0.90
0.75
0.88
1.43
0.60
0.53
0.46
0.29
0.55
0.77
0.24
0.07
Median
1.50
0.82
0.59
0.74
2.50
2.50
4.91
3.40
1.71
1.36
0.49
0.72
0.09
0.5
1.53
0.90
1.28
1.70
0.83
0.67
1.5
0.55
0.94
0.85
0.42
0.17
Mean
5.79
1.47
0.66
0.77
2.52
2.52
9.34
4.55
3.98
2.36
2.21
0.78
0.26
0.69
2.62
1.11
1.46
1.78
3.03
1.20
5.28
0.93
2.76
0.88
0.76
0.37
75th
Percentile
5.80
1.41
0.77
0.88
2.5
2.5
11.0
4.70
3.67
1.90
l.iO
0.87
0.24
0.5
3.2
1.25
1.70
2.06
2.25
0.89
6.72
0.80
2.30
0.94
0.70
0.41
Maximum
113.5
25.7
7.0
1.81
7.0
3.67
101.6
15.25
95
55.60
59.1
3.18
7.1
4.0
65.14
3.64
4.5
4.86
53
6.70
89
8.93
65.82
1.25
34.78
3.6
Public Comment Draft
5-2
May8, 2000
-------
Table 5-1 (Continued)
Distributions of System Means for Community Water Systems
State
MT
NC
ND
NH
NJ
NM
NV
OH
OK
OR
TX
UT
Source
Type
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
Systems
(N)
536
56
1776
185
208
19
506
38
439
30
521
29
221
31
898
130
462
217
335
132
2934
326
121
24
25th
Percentile
0.56
0.6
2.36
2.14
0.50
0.70
2.30
3.04
0.23
0.60
0.65
0.37
2.08
0.46
2.34
2.27
0.87
0.53
0.48
0.56
0.43
1.08
0.60
0.41
Median
0.75
0.89
3.18
2.86
1.60
1.0
3.48
3.58
0.48
0.92
1.88
0.62
5.0
1.43
3.57
2.67
1.50
0.83
1.15
0.93
0.98
1.27
1.53
0.66
Mean
1.89
1.61
3.52
3.09
4.71
1.11
6.06
3.70
0.92
1.07
4.01
1.02
11.91
3.38
4.36
2.82
2.95
1.29
2.79
1.17
2.35
1.60
3.01
1.96
75th
Percentile
1.5
1.97
4.20
3.82
4.10
1.40
4.76
4.16
1.04
1.30
4.43
0.88
13.50
3.8
5.25
3.23
3.00
1.25
2.91
1.50
2.2
1.72
2.74
1.26
Maximum
45.75
7.81
69.51
7.56
51.4
2.40
107.9
6.93
14.0
2.81
140.29
5.04
150.0
39.0
42.96
6.21
78.45
36.35
56.0
4.90
64.99
20.7
35.0
20.56
Public Comment Draft
5-3
May 8, 2000
-------
The earlier USEPA occurrence estimate (Wade Miller, 1992) considered this question,
but did not stratify based on system size because there were too few detects in the NIRS, CWSS,
NOMS, and RWS data that were used for this estimate to stratify on this variable. However,
Frey and Edwards (1997) stratified their data into small systems (those serving 1,000 to 10,000
people) and large systems (those serving more than 10,000 people)." Thus, additional analyses
were conducted to evaluate if arsenic concentrations are associated with system size. The
following section describes the analyses that we conducted on arsenic occurrence and system size
using the ACD data. The system size categories of interest, based on population served, are:
25-100;
101-500;
501-1,000;
1,001-3,300;
3,301-10,000;
10,001-50,000; and
50,00 lor greater.
First, we visually compared the distributions of arsenic in ground water and surface water
systems in the seven system size categories, for each State, and nationally. Separate box plots
were prepared for ground water and surface water in each State. Figure 5-1. shows an example
plot from California. Plots for each State in ACD are included in Appendix B-l.
In each plot, system size categories are plotted on the horizontal axis, and arsenic
concentrations are plotted on the log scale on the vertical axis. Each distribution is represented
graphically, and is composed of system mean arsenic concentrations. The procedure for
calculating system mean arsenic concentrations is presented in Section 6.1. The lower and upper
ends of each box represent the 25th and 75th percentiles of each distribution. The line near the
middle of each box is the median, and the darkened circle is the mean. The "whiskers" above
and below each box illustrate the pattern of system means in the tails of the distribution, and are
composed of straight lines and plus signs. Lines represent system means from the quartiles to the
5th and 95* percentiles. Points in the upper and lower 5 percent tails are shown as plus signs.
Below each distribution is the number of systems in the sample.
The box plots provide qualitative comparison of the distributions of mean arsenic levels
across the size categories. For most States, these plots demonstrate that the means, medians, and
quartile ranges of the distributions are similar across the size categories for both pound water
and surface water. This is particularly true when there are relatively large numbers of systems
represented in each of the distributions, as is the case in California.
11 Frey and Edwards (1997) used a stratified survey design, of which system size was a stratification variable.
They did not specifically evaluate whether or not arsenic distributions are different for systems of different sizes.
Public Comment Draft
5-4
May8, 2000
-------
100.000-
1
^ 10.000-
_o
Arsenic Concentrat
0 r-1
U O
0 0
o o
to
1
g 0.010-
o
«•
0.001-
100.000
i
^r 10.000-
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177 139 73 114 109 149 59
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System Size
*
Surface Water (SW) Systems
*
* X *
MM *
*
19 27 21 46 38 31 44
1 1 1 i 1 II
25 - 200 101 * 500 SOI - 1000 1001 - 3 300 3301 - 10000 10001 - 50000 50001 +
System Size
. 5-1. Bnxplnts nf System Mwms Ry System Size for U.S. EPA Headquarters Liu .
Community Water Systems in the State of California Mail cod© 3201
Number of Systems Indicated Below Boxplot •) 2QO Pennsylvania Avenue NV'
Washington DC 20460
-------
The distributions tend to be less stable when they are based on smaller sample sizes, but this is
expected. Because the distributions are stable across size categories, this qualitative analysis
suggests that system mean arsenic concentrations are probably not associated with system size in
either ground water or surface water systems.
The box plots also suggest that arsenic distributions with system size categories are not
inconsistent with log-normal distributions. Many of these distributions, when plotted in log-
space, appear to be symmetrical around their medians. The distribution of system mean arsenic
levels in ground water and surface water in each State is discussed separately in section 5.5,
below.
In addition to the qualitative analysis of arsenic distributions for systems in different size
strata, we applied analysis of variance (ANOVA) to quantitatively test if arsenic concentration is
dependent on system size. For this analysis, a log-space mean and variance were calculated for
each system size strata in each of the seven NAOS Regions, using all systems in each stratum
and region. ANOVA was used to quantify the statistical significance of the differences among
the size strata means in each Region. These results are presented in Table 5-2 and 5-3. For each
Region, p values < 0.05 suggest that the means of the size strata may be significantly different.
These results suggest that mean arsenic concentrations may differ significantly from
stratum to stratum. For ground water systems, these differences may be significant in five
regions. For surface water systems, these differences appear to be significant in four regions.
However, the stratum ranks in these tables show that mean arsenic concentrations do not vary in
a consistent pattern from region to region. For example, for ground water systems, arsenic
concentrations in Region 1 appear to decrease as system size increases, while in Regions 2 and 5,
arsenic concentrations appear to increase as system size increases. In the four remaining regions,
no systematic patterns are evident. Similarly, no systematic patterns are evident for surface water
systems in any region.
The ANOVA methods do not incorporate the potential for different amounts of
uncertainty in the log means, which are attributable to different sample sizes with different
censoring levels and rates. Thus, the ANOVA results may not confirm that arsenic levels are
associated with stratum size. Because of this concern, and because there was no consistent
pattern from stratum to stratum, we chose not to stratify the data on the basis of system size.
Public Comment Draft
5-6
May 8, 2000
-------
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5.3 STRATIFICATION BY SYSTEM TYPE
Another potential stratification variable is systems type. Systems considered for this
analysis could be either CWS or NTNCWS. CWS are public water systems that serve at least 15
service connections used by year-round residents or regularly serve at least 25 year-round
residents.12 NTNCWS are public water systems that are not CWS and that regularly serve at
least 25 of the same persons more than 6 months of the year.13 The majority of NTNCWS
systems serve less than 3',300 people. The ACD database contains data for NTNCWS systems in
17 States, although only two systems are included for each of the States of Nevada and Utah.
Therefore, Nevada and Utah were not included in the analysis due to the very small sample size.
Basic statistics were calculated for ground water CWS and NTNCWS systems in each of these
States, and these statistics are presented in Table 5-4.
These data indicate that arsenic distributions in NTNCWS are quite similar to arsenic
distributions in CWS. In general, the means, standard deviations, and level of censoring for
CWS in a particular State are very close to the levels observed in NTNCWS in that State. In
some States, mean levels are slightly higher in CWS systems, whereas in others, mean levels are
slightly higher in NTNCWS systems. However, there is no clear pattern in the data, and the
differences appear to be relatively minor, suggesting that any differences are due to random
variation rather than systematic underlying differences. The standard deviations of the CWS and
NTNCWS overlap, indicating that they are probably not significantly different. As a result, it
appears that arsenic data for CWS systems reasonably represent arsenic levels in NTNCWS
systems in corresponding States. Arsenic occurrence projections for NTNCWSs reported in
Chapter 6 of this report are based on exceedance estimates from CWS systems.
5.4 REGIONAL STRATIFICATION
Natural arsenic sources, such as soil and rock, may be significant sources of arsenic in
drinking water. Therefore, regional differences in geology, hydrology, and hydrogeology may
result in significant differences in arsenic occurrence from region to region. Frey and Edwards
(1997) studied regional differences in arsenic occurrence using USGS WATSTORE data and the
Metro database. As discussed in Chapter 4, they calculated arsenic natural occurrence factors
(NOFs), and identified seven different regions that appeared to have distinct arsenic occurrence
characteristics based on these NOFs. USGS used these regions in its estimation of arsenic
occurrence, and presented comparisons of its findings with Frey and Edwards' findings.
12 40 CFR Section 141.2.
13 Ibid.
Public Comment Draft 5 - 9 May 8, 2000
-------
Table 5-4
Arsenic Occurrence in Ground Water CWS and NTNCWS Systems
State1
AK
AL
AZ
CA
IN
KS
MI
MN
MO
NC
ND
NJ
NM
System Type
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
Number of
Systems
479
332
260
40
287
78
824
126
652
547
513
78
648
278
835
659
789
227
1776
534
208
22
439
883
521
126
Mean
5.79
5.15
0.66
0.66
9.43
6.66
3.98
4.53
0.26
0.24
2.62
2.01
5.28
4.80
2.76
2.72
0.76
0.25
3.52
1.84
4.71
5.10
0.92
1.32
4.01
5.93
Std. Dev.
11.77
14.9
0.54
0.73
13.52
9.89
7.37
9.01
0.66
1.33
3.80
2.17
8.98
8.65
5.50
5.34
1.91
1.54
2.65
2.54
8.02
10.91
1.38
2.55
8.28
8.10
Censoring
' (%)z
34.9
53.3
94.6
95.0
41.1
57.7
46.0
57.1 -
97.5
97.3
13.8
20.5
23.9
24.8
44.8
51.3
90.7
97.4
80.9
93.6
23.6
36.4
88.4
86.7
26.9
28.6
Public Comment Draft
5-10
May 8, 2000
-------
Table 5-4 (Continued)
Arsenic Occurrence in Ground Water CWS and NTNCWS Systems
State1
OR
TX
System Type
CWS
NTNCWS
CWS
NTNCWS
Number of
Systems
335
85
2934
642
Mean
2.79
2.24
2.35
2.81
Std. Dev.
5.40
3.16
4.51
9.42
Censoring
(%)2
74.3
80.0
68.7
67.3
Note:
1 States without NTNCWS systems (AR, IL, KY, ME, MT, NH, OH and OK) are not listed in Table 5-4.
2 Percent censoring is defined as the percentage of systems with all datapoints censored. Under this definition, a
system with 10 samples, including one detect and 9 non-detects, would not be considered censored.
The NAOS regions are displayed in Figure 5-2. The States in each region for which
compliance data are available are shaded. This figure illustrates that there are significant
differences in the coverage that ACD provides for these regions. While almost every state is
represented in the Western Region, South Central Region, and the Midwest Central Region,
fewer States are covered in the Southeast Region, the Mid-Atlantic Region, and the New England
Region. Each of these Regions is represented by no more than three states, and the Southern
Region is only represented by Alabama. Frey and Edwards (1997) found that arsenic levels were
generally higher in the Western and Southern Central Regions, and were lower in the Southeast,
the Mid-Atlantic, and the New England Regions.
Where there are regional differences in contaminant occurrence patterns, and differences
in the level of coverage among those regions, regional stratification can improve the accuracy of
the national estimates. The NAOS Regions, which are delineated according to political
boundaries rather than physiographic provinces, are convenient for use with the ACD database,
which can easily be used to analyze arsenic occurrence on a State or regional basis. Use of the
NAOS Regions is also convenient because it facilitates comparisons of arsenic occurrence
presented in this report with arsenic occurrence levels that were reported by Frey and Edwards
(1997) and by USGS (Focazio et al, in press). Therefore, the regional stratification scheme of
the NAOS survey was applied to ACD during the development of the arsenic occurrence
estimates that are presented in Chapter 6.
The following States in the ACD database represent each of the NAOS Regions:
• New England: Maine, New Hampshire, and New Jersey;
• Mid-Atlantic: Kentucky and North Carolina;
• Southeast: Alabama;
• Midwest: Illinois, Indiana, Ohio, Michigan, and Minnesota;
• North Central: Montana and North Dakota;
Public Comment Draft
5-11
May 8. 2000
-------
• South Central: Arkansas, Kansas, Missouri, New Mexico, Oklahoma, and Texas; and
• Western: Alaska, Arizona, California, Nevada, Oregon, and Utah.
An analysis was conducted to evaluate potential bias introduced into the occurrence
estimates (presented in Chapter 6) by the use of the 25 States data from ACD to represent these
regions. This analysis was based on ground water data from the USGS database, and was
designed to semi-quantitatively assess the extent to which an individual State appears to reflect
arsenic occurrence in other States in the region, and in the region as a whole.
In this analysis, percent exceedances were estimated at concentrations of 2,5, 10,20 and
50 Aig/L for each region, using USGS data for the States which are represented in ACD in that
particular region. Two databases developed by the USGS were used to support this analysis.
The first database was derived from the USGS Arsenic Database of Selected Counties (see
Section 4.2.2). From this database, exceedance probabilities were estimated by calculating the
percentage of data points in each county exceeding specific arsenic concentrations.
Public Comment Draft
5-12
May8, 2000
-------
-------
USGS created the second database from information contained in SDWIS, and this
database provides, for each PWS in the United States, State and county Federal Information
Processing Standard (FIPS) codes14 correlated to the PWS location. The products of the county
exceedance probabilities contained in the first database and the number of PWS in each county,
determined from the second database, are the numbers of PWS in each county that may exceed
specific arsenic concentrations. Summing across counties yields the total number of systems in
the State, and the number of systems that are likely to exceed specific arsenic concentrations.
The occurrence estimates were weighted by the number of systems in each State, based on
SDWIS.
Two sets of regional exceedance probabilities were estimated from the USGS State
exceedance probabilities. The first Regional exceedance probability was based on the USGS
data for the State in the region represented in ACD. The second regional exceedance probability
was based USGS data for all of the States in the Region. For example, in the New England
Region, the first estimate was based on USGS data for the States of Maine, New Hampshire, and
New Jersey. Then, the second exceedance estimates were base on the USGS data for all of the
States in the New England Region.
As a result, two sets of occurrence estimates were developed for ground water in each
region based on USGS data, and these two regional estimates were compared. Table 5-5 presents
these regional comparisons, together with national estimates that were developed as the weighted
sum of the regional estimates. We hoped that analyzing these data would provide some
information about the use of some States to represent a region. However, it should be noted that
the USGS data is qualitatively different from the ACD data. Therefore, the power of the USGS
data to predict the potential accuracy of the ACD regional arsenic occurrence estimates may be
limited. The USGS data may have different spatial coverage from the ACD data. For example,
the USGS data may contain data from investigations that focused on specific areas in some
States. Some States may have data from a small number of counties. In addition, the USGS data
are not finished water samples collected from PWS facilities.
For Regions 1,4, and 5, the USGS data indicate that the States which are represented in
ACD may be reasonably representative of arsenic occurrence in the entire region. In Region 2,
these data suggest that the States which are contained in ACD may have slightly lower arsenic
occurrence concentrations than regional average concentrations when all States are considered.
The States in Regions 6 and 7 that are included in ACD may overestimate regional average
arsenic occurrence concentrations. In Region 3, data are inconsistent, and suggest that Alabama
data will overestimate regional arsenic occurrence at 2 Aig/L, and will underestimate regional
arsenic occurrence at concentrations at or above 5
14 FIPS codes, which are unique identifieis for each State (two letter abbreviations or two digit numbers) and
county (five digit identification number) in the United States, are assigned by the U.S. Postal Service.
Public Comment Draft 5-14 May 8,2000
-------
Table 5-5
Comparison of Regional Ground Water Arsenic Occurrence Estimates Based on USGS Data
Ail States in Region vs. States Represented in ACD
Region
1
2
3
4
5
6
7
National
Data Set
AH States
ACD States
All States
ACD States
Ail States
ACD States
All States
ACD States
All States
ACD States
All States
ACD States
All States
ACD States
All States
ACD States
>2/j.gfL
13.97%
12.61%
15.99%
8.47%
7.78%
10.96%
26.20%
29.11%
19.89%
17.83%
47.12%
57.98%
41.78%
47.33%
23.32%
23.88%
>5^g/L
6.83%
6.83%
5.02%
2.98%
2.82%
0.00%
14.09%
17.36%
9.56%
8.44%
32.00%
43.08%
25.25%
29.64%
12.25%
13.15%
>10^g/L
3.41%
1.96%
2.18%
0.96%
1.53%
0.00%
7.37%
9.58%
4.78%
4.55%
25.12%
35.90%
15.41%
19.05%
6.97%
7.82%
>20^g/L
0.63%
0.43%
0.62%
0.16%
0.89%
0.00%
2.49%
3.38%
1.74%
1.73%
16.37%
24.75%
6.87%
9.03%
2.92%
3.56%
>50Mg/L
0.30%
0.00%
0.04%
0.00%
0.17%
0.00%
0.48%
0.67%
0.35%
0.21%
8.74%
13.41%
2.22%
3.04%
0.92%
1.17%
National arsenic occurrence estimates, based on all of the States for which USGS
contains data (USGS does not include any samples from Vermont or Hawaii, but does represent
the remaining 48 States) are quite similar to the national arsenic occurrence estimates, based on
the USGS data in the 25 States that are represented in ACD. Both of these estimates were
derived from the respective regional estimates, weighted by the number of ground water systems
in each of the regions. At concentrations of 2 to 50 /wg/L, the estimates based on USGS data for
the 25 States that are represented in ACD are higher than those based on all of the States in
USGS; however, at each concentration of interest, the occurrence estimates differ by less than
one percent. The greatest differences occur at concentrations of 5 and 10 ^g/L, where the
estimates based on the data for the 25 States in ACD are 0.90 and 0.84 percent higher,
respectively, than those based on data for all of the States. These results suggest that the
estimates based on the ACD data, that are presented in Chapter 6 of this report, are probably not
significantly biased at the national level by the lack of complete representation within each
region. The potential overestimation based on data for some regions is balanced by the potential
underestimation of data for other regions. The USGS data also suggest that the data for the 25
Public Comment Draft 5-15 May 8, 2000
-------
States in ACD may slightly, although not significantly, overestimate national occurrence of
arsenic in drinking water. Therefore, this semi-quantitative analysis of the USGS data suggests
that the arsenic occurrence estimates presented in Chapter 6 may be slightly conservative.
5.5 ARSENIC DISTRIBUTIONS AT THE STATE LEVEL
We further investigated the nature of arsenic occurrence in ground water and surface
water in the individual States through graphical analyses of the distributions of systems means.
Figure 5-3 presents boxplots of the distributions of system means for ground water and surface
water systems in each of the 25 states with compliance monitoring data. The procedure for
calculating system mean arsenic concentrations is presented in Section 6.1, and the symbols that
comprise each boxplot distribution are described in Section 5.2 of this report. The number of
systems represented in each distribution is noted below each boxplot. These boxplots suggest
that arsenic concentrations in drinking water may vary significantly from State to State. Among
ground water systems, the State of Indiana has the lowest arsenic concentration levels, and the
States of Arizona and Nevada appear to have highest concentrations. Among surface water
systems, arsenic concentrations were lowest in Missouri, and were highest in the States of
Arizona, North Carolina, New Hampshire, Nevada, and Ohio. It should be noted that the
distributions for North Carolina, New Hampshire, and Ohio may be influenced by the
combination of relatively high detection limits (typically 5 to 10 Mg/L) and high levels of
censoring (arsenic was not detected in any samples from 71 to 78 percent of the surface water
systems in these three States).
Figure 5-4 shows a log-normal probability plot ground water systems in the State of New
Jersey, and Figure 5-5 shows a log-normal probability plot for ground water systems in the State
of New Hampshire. Similar plots for the remaining States in the ACD database are included in
Appendix B-2.
Public Comment Draft
5-16
May 8, 2000
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100.000 4
c 10.000
_o
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a
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"5
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0.100
s o.oio
0.001
GttTund Water (
wr Systems
*
479 380 824 • 6 2 96 648 789 1776 506 521 898 335 121
260 287 811 ' 513 110 835 536 208 439 221 462 2934
O O O t \3
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States
100.000
1
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1.000 -
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s
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09
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Surface Water (SW) Systems
!
137 74 235 SO 151 43 91 185 38 29 130 132 24
67 32 110 101 29 23 56 19 30 31 217 326
\ ^ o t
States
FTGITRR 3-3. Boxplots nf System Means for
Community Water Systems by State
Number of Systems Indicated Below Boxplot
-------
h CO
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In these plots, means for uncensored PWS ID are represented with diamonds, and open
circles are the corresponding fitted values from the log-normal model. Closed circles are the
fitted values for completely censored systems. When the line of the uncensored system means
falls close to the line of the fitted values from the log-normal model, this is a qualitative
indication that the distribution of system means does not strongly depart from the log-normal
distribution.
In most cases, the distributions of system means are fairly linear, and do not appear to be
inconsistent with log-normal distributions. In some cases, the fit of the data is least strong in the
tails of the distributions. The log-normal probability plot for ground water systems in New
Hampshire appeared to depart most strongly from log-normality. The sharp angle that appears on
the probability plot near the first quartile of the distribution may be related to differences
between measured values and censored system values.
5.6 SUMMARY OF PATTERNS OF ARSENIC OCCURRENCE
The analyses that are presented in this Chapter were designed to support decisions related
to the selection of an appropriate method for estimating arsenic national arsenic occurrence.
These analyses show that, in order to develop more representative estimates of arsenic
occurrence, data should be stratified by source water type, and by region, but not on system type
or system size. In addition, these data suggest that arsenic occurrence at the State level is
relatively log-normally distributed. As a result of these findings, in Chapter 6, arsenic data are
stratified accordingly, and are assumed to be log-normally distributed within States.
Public Comment Draft
5-20
May 8. 2000
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6. National Occurrence Estimates
This Chapter presents estimates of the number of systems that have mean arsenic levels
equal to or greater than specific MCL15 alternatives or concentrations of interest. First, the model
used to estimate exceedance probabilities is defined, and the assumptions that were made and the
data conventions that were applied are defined. Next, the specific number of systems that are
predicted to exceed potential MCL alternatives are presented for community water supply
systems and non-transient, non-community water supply systems. Then, the exceedance
probabilities calculated using ACD are compared to those developed using the NAOS and USGS
databases. The last section of this Chapter discusses an uncertainty analysis that was conducted
on the ACD-based arsenic occurrence estimates, and presents the results of that analysis.
Confidence intervals generated as a result of the uncertainty analysis were applied to the
exceedance estimates to provide a measure of the variability associated with these estimates.
6.1 ARSENIC NATIONAL OCCURRENCE PROJECTION METHODOLOGY
The methodology applied to develop estimates of arsenic occurrence in ground water and
surface water systems has five steps. The result of these steps is the derivation of arsenic
occurrence probability distributions, and the estimation of numbers of systems that may exceed
specific concentrations of interest. This process fits the unique data structure of the compliance
monitoring data in the ACD database. The five steps include:
• Calculate system arithmetic means;
• Calculate State exceedance probability distributions for ground water and surface water;
• Apply weighting and develop regional exceedance probability distributions for ground
water and surface water;
• Apply weighting and develop national exceedance probability distributions for ground
water and surface water; and
• Estimate numbers of systems exceeding levels of interest as the product of the national
probability distributions and the total number of ground water or surface water systems.
In addition, it should be noted that confidence intervals for the system exceedance
estimates are developed through the uncertainty analysis that is presented in Section 6.4. These
confidence intervals provide a measure of the variability or confidence associated with the
national arsenic occurrence estimates, and they are applied in Section 6.2 of this report.
15 An MCL is the maximum level of a contaminant that is allowable in public drinking water supplies. When EPA
sets an MCL for a contaminant, the PWS must ensure that the level of this contaminant is maintained at or below its
MCL.
Public Comment Draft 6 - 1 May 8, 2000
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6.1.1 System Means
For many States, the ACD database includes multiple samples for large numbers of
drinking water systems, whereas for others, this database includes only one or two samples. The
database tends to contain more samples for larger systems. In order to develop estimates of
arsenic occurrence, each system in the sample population should be represented by a single
value. Otherwise, the results would be biased to reflect systems with more samples.
To avoid this difficulty, we calculated a mean arsenic concentration (or censoring point
for systems without any positive detections) for each system represented in our sample
population. System means were calculated according to the following conventions:
• If all values in the system were positive (and uncensored), the mean of the detected values
was calculated for the system;
• If the system included at least five positive values that were not all equal but some values
were censored, the system mean was calculated by regression on order statistics (ROS) as
described in Appendix A;
• If the system included four or few samples that were positive, or five or more values that
were positive and equal, censored samples results were set equal to i4 the detection level,
and the system mean was calculated; and
• If all samples from a system were censored, the system was considered to be non-detected
at the mode of the reporting limits.
These conventions were established in order to develop the most accurate estimate of
each system mean, based on information available about that system. ROS has been shown to
provide accurate estimates of distributional parameters, including the mean, for sample sets that
include sufficient observations (Helsei and Cohn, 1988). However, ROS cannot be applied if
there are too few positive detections, or if all of the detected values are equal. In these cases, a
substitution method is the only alternative, and we believed substitution of Yt the detection level
to be less biased than substitution of zero or the detection level. In each case above, the system
mean was calculated, and the system was considered to be positive at the mean.
For some systems, however, all samples were censored. In these cases, the systems were
considered to be censored at the mode of the reporting limits. Together with the systems in which
arsenic was positively detected, these censored systems were used to develop State exceedance
probability distributions for arsenic concentrations.
6.1.2 State Exceedance Probability Distributions
State exceedance probability distributions indicate the probability that a randomly chosen
PWS from any specific State will have a mean arsenic concentration greater than a particular
concentration of interest. Using the sample set of system means that were derived for each State
from the compliance data in ACD, exceedance probability distributions were developed
separately for ground water and surface water systems in each state.
Public Comment Draft
6-2
May 8,2000
-------
Several methods could be used to estimate exceedance probability distributions in each
state. These include empirical and parametric estimates, using all or only a subset of the
estimated means to estimate the parameters. Empirical distributions estimate the probability of
exceeding any threshold as the observed fraction of the estimated system means .that exceed that
threshold. Empirical estimates are simple to compute, and they do not require any assumptions
about the form of the distribution being estimated. But a disadvantage of this type of estimate is
that it "jumps" by a discrete amount at each datum, and does not predict well either above or
below the range of the observed data. For example, it estimates zero probability (or in some
versions, a small but fixed probability) of ever seeing a system mean of any size larger than the
largest system mean observed so far.
Parametric estimates assume that the distribution follows a particular form, such as log-
normal. The distribution has parameters which are estimated from the data, by, for example,
maximum likelihood (Cohen, 1991) or an adapted ROS (Appendix A). At the cost of assuming a
particular distributional form, the parametric estimate gives smoothly changing probability
estimates even outside the range of the observed data. It also yields a simple computational form
for the estimates, which is useful as an input to further analyses, such as the cost models used in
the regulatory impact analyses (RIA) (although it should be noted that the RIA relies on National
exceedance probability distributions rather than State exceedance probability distributions).
Another reason to use a parametric fit is that, in the State distributions, the data do not consist of
true system means; rather, they are only estimated means, as described in the previous section.
An empirical estimate preserves the errors in these estimates, while a parametric fit tends to
smooth them over.
Because of these advantages, parametric distributions were fit to the distribution of
estimated system means in each State. In particular, log-normal distributions were used, and
these provide a reasonably good fit in most cases. Appendix B-2 shows log-normal probability
plots for each State and source water type, in which log-system means are plotted against their
corresponding normal quantiles. Each plot also shows a regression line fitted to the data in the
plot. If the data in a plot are truly log-normally distributed, they should lie close to the fitted line.
Examination of the plots in Appendix B-2 suggests that in some States and source types,
two distinct populations are present: the plotted system means form a broken line, instead of a
straight line that would indicate a single log-normally distributed population. This effect is most
apparent in the plot for New Hampshire ground water (see Figure 5-5), where the plotted system
means form two lines of different slopes, with the breakpoint at 5 jUg/L on the vertical axis. In
New Hampshire, there are no detected concentrations below 5 ^ug/L, and the detection limit for
all non-detected concentrations is 5 ftg/L. Therefore, any system mean that is estimated to be
less than 5 jUg/L must have been estimated from a large portion of data that was "filled in" below
5 #g/L from non-detects, as described in the previous section. The probability plot implies that
these systems may form a different distribution than the systems above 5 //g/L, which
presumably have fewer filled-in observations. The implication is that when a large fraction of
the observations from a system are non-detects, filling in the missing observations may not
reproduce the State's arsenic distribution accurately.
Public Comment Draft 6-3 May 8, 2000
U.S. EPA Headquarters Library
Mail code 3201
1200 Pennsylvania Avenue NW
Washington DC 20460
-------
In light of this evidence, a cutoff point was established for each State and source water
combination, and system means that were less than this cutoff point were not used in fitting the
log-normal distributions. The cutoff points were mostly set equal to the most common detection
limit for the State and source water type. In a few cases cutoff points were set lower than the
modal detection limit, where there were not enough system means above the detection limit to
give reasonably stable parameter estimates. The cutoff points for State and source water types
are presented in Table 6-1.
Using a parametric approach that we have named right-tailed ROS, log-normal
distributions were fit to the remaining system means in each State and source water type, by
fitting a regression line to the data in the plots of Appendix B-2, that is, to log-system means
greater than the cutoff point plotted against their normal quantiles. The exceedance probability
for any given log-system mean arsenic concentration was then estimated by using the fitted
regression line to find the normal quantile corresponding to that concentration, and computing
the standard normal probability associated with the quantile (see Equation A.3 in Appendix A).
The estimated probability distributions for each State and source water type are presented in
Appendix B-3.
6.1.3 Regional Exceedance Probability Distributions
The third step to developing national occurrence projections was to develop regional
exceedance probability distributions. Separate probability distributions were developed for
arsenic occurrence in ground water and surface water in each of seven regions. The seven
regions are based on those identified by Frey and Edwards (1997), and the States in each region
are discussed in Section 5.4.16 Regional exceedance probability distributions were developed as
the weighted sum of the exceedance probability distributions derived for each State with
compliance monitoring data in the region.17 As such, for Region Y represented by data from t
States, where t is any integer, the Regional distribution was calculated as:
Where:
(Nsl
*s tbe probability that a PWS system in Region Y will exceed arsenic concentration
n>x.si > n>x.s2 to n>x,st are me number of purchased and non-purchased water CWS in States
1 to t that are predicted to have mean arsenic levels greater than x; and
Ns, , N^ to NS, are the total number of purchased and non-purchased water CWS in States
1 to*.
16 Note that any similarities between the boundaries of the NAOS Regions and the boundaries of EPA's Regional
Offices is purely coincidental.
17 The number of systems exceeding specific arsenic concentrations in each state are self-weighted quantities.
Public Comment Draft 6 - 4 May 8, 2000
-------
Table 6-1
Cutoff Points for Ground Water and Surface Water, in
State
AL
AK
AR
AZ
CA
IL
IN
KS
KY
ME
Ml
MN
MO
Ground Water
1
2
0*
5
2
2
2
2
>2
2
2
2
2
Surface Water
1
2
0*
5
2
1
1
2
>2
1
1
0*
1
State
MT
NC
ND
NH
NJ
NM
NV
OH
OK
OR
TX
UT
Ground Water
>2
5*
2
5
1
2
5
10
2
2*
2 ...
2
Surface Water
>2
5*
1
2*
1
2
5
2*
2
2*
2
1
* Cutoff point set below modal reporting limit. Modal reporting limits for these States are (GW and SW modal RL
are the same except where noted): AR (5); MN (1); NC (10); NH (5); NJ (GW: 5; SW: 2); OH (10); and OR (5).
For each potential MCL alternative or concentration of interest, a separate exceedance
probability was calculated based on available data. States within a region were only used to
estimate exceedance probabilities for arsenic concentrations higher than their censoring level.
For example, in the Western Region, only the States of Alaska, California, and Utah have
detection limits that are equal to or less than 2 Atg/L, so only these States were used to estimate
the number of systems in the region that are likely to have mean arsenic levels greater than 2
. They also contribute to the estimates for MCL alternatives of 3,5,10,15,20, and up to 50
Arizona, Nevada, and Oregon are included for all estimates at 5 A*g/L and above. The
remaining States in the Western Region, Idaho and Washington, did not have compliance
monitoring data in ACD. The regional exceedance probability distributions based on the
compliance monitoring data are presented in Table 6-2.
The convention of using only data from States with a detection limit below or equal to the
concentration of interest to estimate regional occurrence probabilities generally yields regional
exceedance percentages that decline as concentrations increase. However, this convention does
result in two anomalies in the regional probability distributions, which appear as increases in
probabilities as arsenic concentrations rise. Specifically, in Mid-Atlantic ground water, the
probability of exceeding 5 /^g/L (0.3 percent) is based on Kentucky data, whereas the probability
Public Comment Draft
6-5
May8, 2000
-------
of exceeding 10 /^g/L (1.3 percent) is based on data from both Kentucky and North Carolina.
The second anomaly was observed in surface water in the New England Region, where
exceedance probabilities rose from 8.2 percent at 3 //g/L to 9.0 percent at 5 f^gfL. At the lower
concentration, the probability is based on data from Maine, while the higher concentration is
based on data from both Maine and New Hampshire.
The data presented in Table 6-2 suggest that arsenic occurrence in ground water systems
is lowest in the Mid Atlantic and South East Regions. Also, these data indicate that intermediate
ground water arsenic levels are found in New England, Midwest, North Central, and South
Central Regions, and that the West Region tends to have higher arsenic levels than the other
Regions. These regional patterns of arsenic occurrence in ground water are generally similar to
those reported by other studies (Focazio et al., in press; Frey and Edwards, 1997), except the
other studies found that arsenic levels in New England were relatively low and comparable to
those in the Mid Atlantic and South East. Therefore, it is possible that the compliance
monitoring data for the States of Maine, New Hampshire and New Jersey may over estimate
arsenic occurrence in the New England Region. In surface water systems, the lowest arsenic
levels occurred in the Mid-Atlantic and South East. Levels appeared to increase successively in
the other Regions, from Midwest, to South Central, to New England, to West, to North Central.
6.1.4 National Exceedance Probability Distributions
The fourth step that was used to develop national occurrence estimates was to develop
estimates of national exceedance probability distributions from the regional exceedance
probability distributions. Separate exceedance probability distributions were developed for
arsenic occurrence in the ground water and surface water of the United States. National
exceedance probability distributions were developed as the weighted sum of the exceedance
probability distributions derived for each Region, which were presented in Table 6-2. As such,
when the United States is represented by data from 7 Regions, the National distribution was
calculated as:
7) / (N
R1
Where:
is the probability that a PWS system in the United States will exceed arsenic
concentration x;
n>x,R) > n»uj2 to n>x.R?are me number of purchased and non-purchased water CWS in
Regions 1 to 7 that are predicted to have mean arsenic levels greater than x; and
NRI, NRJ to NR7 are the total number of purchased and non-purchased water CWS in
Regions 1 to 7.
Public Comment Draft
6-6
May 8, 2000
-------
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Public Comm
-------
For each potential MCL alternative or concentration of interest, a separate exceedance
probability was calculated based on available data. Exceedance probabilities from all Regions
contributed to the estimation of each of these National exceedance probability distribution
estimates at each concentration, with the exception of the Mid Atlantic Region. Because regional
exceedance probability distributions were not estimated for the Mid Atlantic Region at
concentrations of 2 or 3 jug/L, no data was available for this Region to support the development
of National estimates at these concentrations.
The national arsenic occurrence exceedance probability distributions for ground water
and surface water developed by weighting the State and regional point estimates are shown in
Table 6-3, together with national arsenic occurrence probability distributions based on a log-
normal fit to the weighted national point estimates. These exceedance probability distributions
were developed using data from CWS systems. At similar concentrations, the log-normal fit
closely matches the weighted national point estimate exceedance probabilities. The log-normal
fit was developed to estimate exceedance probabilities down to concentrations of 0.5 and 1.0
£ig/L, and the resulting log-normal distributions were used in OGWDW's cost model for the
RIA. For the simulations performed in the cost model, the continuous distributions that represent
a range of concentrations more accurately represent the potential range of arsenic concentrations
in drinking water than do selected point estimates at specific concentrations of interest. In
addition, the log-normal distribution offers computational efficiencies.
6.1.5 Number of Systems Exceeding Alternative MCLs
The estimated number of systems exceeding alternative MCL levels was calculated by
multiplying the total number of systems in the United States with the probability that a system
would exceed a specific MCL alternative. Separate estimates were developed for ground water
and surface water CWS, and for ground water and surface water NTNCWS. The total number of
systems in each category was derived from recent SDWIS data. It should be noted that the
national right-tailed ROS exceedance probability distributions for ground water and surface
water systems were derived from analysis of CWS systems, and these probability distributions
were applied to estimate arsenic occurrence in both CWS and NTNCWS. As discussed in
Chapter 5, initial analyses suggested that arsenic occurrence is similar in CWS and NTNCWS
systems. Therefore, separate exceedance probability distributions were not estimated for CWS
and NTNCWS systems.
The following section presents estimates of numbers of ground water and surface water
systems within specific size categories that may have mean arsenic levels in excess of specified
MCL alternatives. These estimates are based on the national right-tailed ROS exceedance
probability distributions for ground water and surface water, multiplied by the total number of
systems in the nation in each size category. Analyses presented in Chapter 5 indicated that there
are not meaningful or consistent differences in arsenic occurrence from size stratum to size
stratum. Therefore, for each source water type, the same national right-tailed ROS exceedance
probability distribution was applied for each size stratum.
Public Comment Draft
6-8
May 8, 2000
-------
6.2 ARSENIC NATIONAL OCCURRENCE ESTIMATES RESULTS
The techniques described above were applied to develop estimates of the proportions of
ground water and surface water systems with system mean concentrations above potential
regulatory levels. These estimates are presented separately for community water supply systems
and for non-transient, non-community water supply systems.
6.2.1 Community Water Supply Systems
Tables 6-4 and 6-5 present the projected arsenic occurrence levels in community water
supply systems with ground water and surface water sources, respectively. The data for the
probability distributions are derived exclusively from the ACD compliance monitoring database,
and the total number of systems is based on SDWIS data.
Under these estimates, 1 1,952 ground water CWS systems are estimated to have mean arsenic
levels that exceed 2 ftg/L. The estimated number of exceeding systems decreases rapidly at
higher potential MCL alternatives. For example, 5,3 19 systems are predicted to have mean
arsenic levels greater than 5 ^g/L, whereas 2,384 systems are predicted to have mean arsenic
levels greater than 10 /^g/L. While 902 systems are predicted to have mean arsenic
concentrations above 20 ,ug/L, 195 systems are estimated to have average arsenic levels greater
than 50 (Ug/L. It is worth noting that the number of systems which are predicted to exceed the
current MCL of 50 Mg/L is significantly higher than number of systems which SDWIS indicates
actually violate the MCL (from January, 1996 to March, 1999, 15 ground water CWSs have
violated the MCL).
In the United States, there are fewer surface water CWS than ground water CWS, and the
exceedance probabilities for surface water systems decrease more quickly as arsenic
concentrations rise than do the exceedance probabilities for ground water systems. As a result,
fewer surface water systems have mean arsenic levels above specific concentrations of interest
than ground water systems at corresponding arsenic concentrations. Under these estimates, 1,069
surface water CWS systems are predicted to have arsenic concentrations above 2 jwg/L, and 312
surface water CWS systems are predicted to have mean arsenic concentrations greater than 5
jUg/L. A total of 82 surface water CWS systems are predicted to have mean arsenic
concentrations that exceed 10 /ug/L, and 28 are predicted to have mean arsenic concentrations
that exceed 20 /ug/L. Seven surface water CWS systems are predicted to have arsenic
concentrations above 50
6.2.2 Non-Transient, Non-Community Water Supply Systems
Tables 6-6 and 6-7 present the projected arsenic occurrence levels in non-transient, non-
community water supply systems with ground water and surface water sources, respectively. The
data for the probability distributions are derived exclusively from the community water systems
in the ACD compliance monitoring database, as discussed in Section 6.1.5, and the total number
of systems is based on SDWIS data.
Public Comment Draft 6 - 9 May 8, 2000
-------
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There are fewer NTNCWS in the United States than CWS, and therefore the numbers of
NTNCWS systems predicted to exceed specific levels are lower than the numbers of CWS that
would exceed similar levels. These projections indicate that 5,261 ground water NTNCWS have
system mean levels above 2 ,ag/L; 2,341 have system mean levels above 5 Mg/L; and 1,049 have
system mean levels above 10 ,ug/L. While 397 GW NTNCWS are predicted to have mean
arsenic levels above 20 Aig/L, 86 are expected to have arsenic levels in excess of the current
standard, 50 /^g/L. As there are few surface water NTNCWS in the United States, 70 systems are
predicted to exceed 2 /ug/L; 6 are predicted to exceed lO^g/L; and 1 are predicted to exceed 20
,ug/L. No surface water NTNCWS facilities are predicted to have system mean levels above 50
6.3 COMPARISON OF ACD, NAOS, WADE MILLER, AND USGS OCCURRENCE ESTIMATES
In addition to the occurrence results presented above, two additional studies have recently
developed national occurrence estimates for arsenic in drinking water, the NAOS study (Frey and
Edwards, 1997), and the USGS study of arsenic occurrence in ground water (Focazio et al., in
press). The databases that were developed for these studies, and the survey methodologies that
were applied, are described in Section 4.2 of this report. Significantly, each of these occurrence
estimates was developed in a slightly different manner. The estimates presented above are based
on compliance monitoring data from more than 19,000 systems in 25 states while the NAOS
occurrence estimates are based on a stratified random sampling from representative groups
defined by source type, system size, and geographic location from 517 samples from
approximately 500 systems. The USGS analysis is based on ground water arsenic exceedance
estimates for each county. Figure 6-1 presents a comparison of arsenic occurrences estimates
from this study for ground water and surface water systems with the NAOS study and the
exceedance probabilities estimated in 1992 (Wade Miller, 1992) at concentrations of 2, 5 and 10
. The USGS occurrence estimates for ground water are omitted from Figure 6-1.
Figure 6-1
Comparison of Arsenic Exceedance Probabilities, GW and SW Systems
10
Arsenic Concentration (ug/L)
ACD
NAOS
Wade Miller '92
Public Comment Draft
6-15
May 8,2000
-------
The exceedance probabilities presented in Figure 6-1 are for both surface water and
ground water systems. At a concentration of 2 ^g/L, the estimate based on ACD (24.05 percent)
is higher than both the NAOS estimate (21.7 percent) and the Wade Miller 1992 estimate (17.3
percent). At 5 ,ug/L, the ACD and NAOS predicted exceedance probabilities are relatively
similar (10.33 and 11.5 percent, respectively). These two estimates are also relatively similar at
10 Mg/L (4.52 and 4.5 percent for ACD and NAOS, respectively). At all arsenic concentrations
of interest, the Wade Millerl992 estimates are lower than those based on ACD and NAOS. The
Wade Miller estimates relied upon NIRS data for ground water, and 1978 CWSS, NOMS and
RWS data for surface water. These data were highly censored, and had relatively high reporting
limits (generally 5 ,ug/L). Therefore, these estimates are probably less accurate than the ACD
and NAOS estimates, particularly at concentrations of 2 and 5
Table 6-8
Comparison of ACD, NAOS, and USGS Ground Water Arsenic Occurrence Estimates
Percent of Systems Estimated to Exceed Arsenic Concentrations Og/L):
JStndv
in
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Note: NAOS-Sm includes systems serving s 10,000 people, NAOS-Lg includes systems serving
> 10,000 people. NR: Not reported.
While Figure 6-1 presents a comparison of national exceedance probabilities for ground
water and surface water systems, Table 6-8 presents a comparison of national exceedance
probabilities for ground water systems. On the national level, at arsenic concentrations ranging
from 2 to 10 jug/L, these estimates agree closely. In addition, the ACD and the USGS estimates
are reasonably similar at concentrations of 20 and 50 jUg/L, although the USGS estimates are
slightly higher than the ACD estimates at these concentrations. The fact that these three studies,
each of used different data sets and different methods for calculating exceedance estimates, agree
relatively well, indicates that the arsenic occurrence estimates presented in Section 6.2 are
reasonably representative at the national level.
This comparison of exceedance probabilities suggests that arsenic occurrence projections based
on compliance monitoring data are relatively close to other recently developed projections
through the range of this comparison.
Public Comment Draft
6-16
May 8, 2000
-------
Figures 6-2 and 6-3 present comparisons of regional percent exceedances for ground
water systems at concentrations of 5 ,ug/L and 20 /^g/L, respectively. In all regions except New
England (Region 1) the ACD exceedances probabilities at 5 ng/L are lower than the USGS
exceedance probabilities. Also, the ACD estimates at 5 Aig/L are lower than NAOS estimates in
all regions except New England (Region 1) and North Central (Region 6). The three studies
agree reasonably well regarding exceedance probabilities in the Midwest (Region 4) and South
Central (Region 5) Regions at 5 vg/L. All three studies indicated that the lowest exceedance
probabilities occurred in the South East Region (Region 3), and that the highest exceedance
percentages at a concentration of 5 t-igfL occur in the West Region (Region 7). A similar
comparison is presented in Figure 6-3 for regional exceedance probabilities at the concentration
of 20 Aig/L . These probabilities are significantly lower than at 5 A*g/L. The results of the three
studies appear to agree less well at 20 A*g/L than at 5 yug/L. As at 5 pig/L, the South East Region
(Region 3) shows low exceedances probabilities at 20 t*g/L, and the West Region (Region 7)
shows higher exceedance probabilities than most of the other regions at 20 Atg/L.' The highest
exceedance probabilities at 20 /^g/L were indicated to be in the North Central Region (Region 6)
by USGS, but the other studies did not support this finding.
For surface water systems, USGS did not develop exceedance estimates, and NAOS
estimated that surface water systems in only two regions would have arsenic concentrations
above 5 ng/L in finished surface water. These Regions include the South Central, where 7
percent of systems were predicted to exceed 5 /-ig/L, and the North Central, where 12 percent of
systems were predicted to exceed concentrations of 5 and 20 Aig/L- Surface water exceedance
estimates based on ACD are presented in Table 6-2. Based on these estimates, some systems
would have arsenic concentration above 5 ^.g/L in six Regions (exceedance probabilities in
parentheses): New England (9 percent); Mid Atlantic (0.1 percent); Midwest (1.2 percent); South
Central (1 percent); North Central (3.8 percent), and West (7.4 percent). ACD-based estimates
indicate some systems will exceed arsenic concentrations of 20 yg/L in New England (0.4
percent); Midwest (0.1 percent); North Central (0.1 percent); and the West (1.1 percent).
Public Comment Draft 6-17 May 8, 2000
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i
o
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6.4 UNCERTAINTY ANALYSIS
6.4.1 Purpose of Uncertainty Analysis
An uncertainty analysis was conducted to determine the potential amount of error in the
exceedance probability estimates that are presented in Section 6.2. The ROS method that was
used to estimate system means has a potential drawback, in that it does not allow the calculation
of confidence intervals in a straightforward manner. Therefore, to determine 95-percent
confidence intervals, it was necessary to perform a statistical simulation to quantify the potential
sources of uncertainty. Three sources of uncertainty were identified and simulated: 1) sampling
variability, both within and between systems; 2) the fill-in of censored observations in the
estimation of system means; and 3) fitting of log-normal distributions to populations of system
means within each State. The combined effect of these uncertainties was modeled through a
simulation, and the results of this simulation were used to establish confidence intervals for the
arsenic occurrence estimates.
6.4.2 Uncertainty Analysis Methodology
Briefly summarized, the uncertainty simulation first simulated a population of systems,
then a mean for each system, and then estimated State exceedance probabilities based on the
simulated population of system means. Sets of exceedance probabilities obtained from many
repetitions of the simulation were then used to estimate non-parametric confidence intervals for
the concentrations of interest. In other words, bootstrap confidence intervals (Davison and
Hinkley, 1997) were computed for the estimation procedure described in Section 6.1. To
evaluate the influence of the log-normal model used to fill in censored observations, the entire
simulation was then repeated twice: the first time, a uniform distribution was used to fill in
censored observations, and the second time, a Weibull distribution was used. Also, hi order to
evaluate the influence of the right-tailed ROS method used to estimate the State exceedance
probabilities, the probabilities were computed using both ROS and an empirical method.
As mentioned above, the first step in the uncertainty analysis was to simulate a population
of systems. Systems were sampled with replacement from the list of systems in each State. The
second step was to simulate a system mean for each selected system. In this step, the combined
set of detected and non-detected concentrations in each system was re-sampled, such that if the
system had d detected and c non-detected concentrations, a random sample of size d + c was
selected at random with replacement from the d + c observations. When two or more distinct
detected concentrations were drawn, the log-space mean and variance were estimated by the ROS
method described in Appendix A; non-detects were filled in by drawing from a truncated log-
normal distribution with the given log-space mean and variance, such that the filled-m values fell
in the range from zero to the reporting level; and a real-space mean was computed for the system.
When all detects were equal or there was only one detect, each non-detect was filled in by
drawing from a uniform distribution from zero to the reporting level, and the real space system
mean was calculated. When there were no detects, the system was treated as a non-detect at the
modal reporting level.
Public Comment Draft
6-20
May 8, 2000
-------
Using the above procedure, a population of system means was generated for each State.
From these populations, national exceedance probability distributions were then computed
exactly as before: State exceedance probability distributions were estimated by the right-tailed
ROS method, at concentrations of 2,3, 5,10,15,20,25,30,40, and 50^g/L; State distributions
were combined into regional distributions; and regional distributions were combined into a
national distribution, as described in Sections 6.1.2-6.1.4.
The entire procedure above was repeated 1,000 times, in order to generate 1,000
simulated national exceedance probability distributions. At each concentration (e.g., at 10^g/L),
the 1,000 exceedance probability estimates were then sorted in increasing order, and the interval
from the mean of the 25th- and 26th-iargest estimates to the mean of the 975th- and 976th-largest
estimates was taken as a nonparametric 95% confidence interval for the true exceedance
probability at that concentration.
The confidence intervals described above quantify the uncertainty in the exceedance
probability estimates due to the sampling variability of detected arsenic concentrations. They do
not include uncertainty due to the use of the log-normal distribution to fill in the censored
observations. In order to evaluate this additional uncertainty, the confidence intervals were
recomputed twice, using two alternative distributions to fill in the censored observations: the
two-parameter Weibull (a long-tailed) distribution and the uniform (a flat) distribution. In the
first repeat of the simulation, censored values that were previously filled in by drawing from a
log-normal distribution were instead drawn from a uniform distribution on the interval from zero
to the reporting level. In this case it was not necessary to fit the distribution. In the second
repeat of the simulation, censored values were replaced by draws from a truncated Weibull
distribution, with parameters estimated by a censored maximum likelihood algorithm in SAS. In
systems with only one detect or all detects equal, the maximum likelihood algorithm could not be
meaningfully applied, so non-detects were drawn from a uniform distribution. From each of the
uniform and Weibull simulations, 1,000 replicates were independently generated and confidence
intervals were computed as above.
Another source of uncertainty in the probability estimates is the use of the right-tailed
ROS method, with its log-normal assumption, to estimate State probability distributions.
In order to evaluate this uncertainty, exceedance probabilities and their confidence intervals were
computed using both the right-tailed ROS method, as described above, and an empirical method.
The empirical method estimates exceedance probabilities as the empirical fraction of detected
concentrations above each concentration level. Empirical estimates are computed only at
concentrations of 10 /xg/L or higher, since the highest censoring limit in the ACD is 10 /ig/L, so
that all non-detects may be unambiguously counted as less than the concentrations of interest.
Public Comment Draft 6-21 May 8. 2000
-------
6.4.3 Uncertainty Analysis Results
This discussion focuses on the national ground water and surface water uncertainty
analysis results. Uncertainty analyses were conducted for log-normal, uniform and Weibull
distributions, using both right-tailed ROS and empirical methods. The results of the right tailed
ROS uncertainty analysis for the national exceedance probabilities using a log-normal
distribution are found in Table 6-3. The 95 percent confidence intervals are not centered directly
on the point estimates for the national ground water and surface water exceedance probabilities.
Rather, the point estimates are located closer to the lower limits of the 95 percent confidence
intervals. In a comparison of the uncertainty analysis results from the three distributions, some
variation is evident among the distributions. In particular, the percentages for the log-normal
distributions are slightly higher than for the uniform or Weibull distribution at most
concentrations. While there are some differences among the confidence intervals for the log-
normal, Weibuli and uniform distributions, these differences are minor. One exception is the
surface water CIs at 5 ng/L. Overall, these differences suggest that the analysis results are not
particularly sensitive to the method used for fill-in. Figures 6-4 and 6-5 depict plots of the 95
percent confidence intervals from the three distributions for ground water and surface water at
selected concentrations. Furthermore, while the widths of the CIs from the different distributions
are relatively similar, some minor variations are seen. For example, for the ground water
analysis, both the log-normal and Weibull CIs are wider than the uniform distribution CI. For
surface water, at low concentrations, the CI widths from the log normal distribution fall between
those for the Weibull and uniform CIs, but at higher concentrations are greater than the CI widths
of the Weibull and uniform distributions. All the CI widths for both surface water and ground
water narrow with increasing concentration. The 95 percent confidence intervals from the right-
tailed ROS log-normal distribution analysis were used to calculate the confidence intervals
presented in Tables 6-4,6-5, 6-6, and 6-7.
A comparison of the uncertainty analysis results for ground water and surface water
reveals different widths of the 95 percent confidence intervals. The national surface water
confidence intervals are significantly wider than the intervals for ground water exceedance
probabilities. For example, the CI at 5 jig/L for ground water is [11.74,13.04], while the CI for
surface water at 5 jig/L is [1.8,9.66]. The increased width is due to the smaller amount of data
available from surface water systems and the increased level of censoring of the surface water
data. Consequently, the results of the surface water uncertainty analysis are more sensitive to the
analytical methods employed.
In this uncertainty analysis, both a right-tailed ROS and an empirical analysis were
conducted for all distribution types. The results of the two methods differ. The empirical
percentages are lower than those for the right-tailed ROS analyses, for both surface water and
ground water. For ground water, the 95 percent confidence intervals from the empirical analysis
are wider than those from the right-tailed ROS analysis, for all distribution types. However, for
surface water, the confidence intervals for the right-tailed ROS method are wider than those from
the empirical analysis. In the log normal distribution, for both methods, the CIs become
increasingly narrow at higher concentrations. The log-normal results of these two analyses for
ground water and surface water at selected concentrations are presented in Figures 6-6 and 6-7.
Public Comment Draft 6-22 . May 8, 2000
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7. Intra-system Variability
This Chapter presents the results of analyses of infra-system variability that were
conducted using subsets of the ACD data. Section 7.1 defines the purpose of the intra-system
variability analysis. Section 7.2 provides an overview of the data that were available for these
analyses. Section 7.3 presents the methods by which the intra-system variability analyses were
conducted, and the results of each analysis. Section 7.4 briefly summarizes the results of the
intra-system variability analyses.
7.1 PURPOSE OF ANALYSES
The purpose of the intra-system analysis is to facilitate prediction of the number of
points-of-entry or POEs that will be affected by various MCL alternatives. Compliance with the
arsenic standard is measured at the point-of-entry to the distribution system, and individual
systems can have multiple points-of-entry. Thus, one system may need to install one, two, three,
or more treatment systems or blend its water sources, depending upon its configuration and POE
mean arsenic levels. If arsenic levels in all POEs in a system are below regulatory limits, it will
not need to install any treatment technologies for arsenic. Thus, arsenic levels in POE drive
compliance costs and risk reduction benefits more directly than do system mean arsenic levels.
The ideal analysis would be a survey designed to estimate arsenic levels in POE
throughout the United States. However, data are not currently available to support development
of such estimates: SDWIS does not catalogue information at the POE level; the earlier arsenic
occurrence studies focused on arsenic concentrations in systems rather than POE; and only a
third of the data sets in ACD link sample results to POE identification numbers. So the ideal
analysis is currently infeasible.
Since the ideal is infeasible, a reasonable and feasible alternative is to quantify a
relationship between POE means and system means where the data are suitable, and to use this
relationship, to estimate, from a population of system means, the number of POE means that are
likely to exceed specific regulatory alternatives. The analyses discussed in this Chapter were
designed to quantify the relationship between system means and POE means. This relationship
was quantified as an estimated coefficient of variation (CV), or relative standard deviation. The
CV values that were calculated under these analyses are being applied under that work
assignment in a RIA to estimate the number of POE that may exceed MCL alternatives under a
separate work assignment. Under this work assignment, the distribution of system mean arsenic
concentrations, the probability distributions of POE for systems of different sizes, and the CV of
the relationship between the system mean arsenic concentration and the POE arsenic
concentration means are used to estimate the number of POE in the United States that may
exceed specific arsenic concentrations.
Public Comment Draft 1 - 1 May 8, 2000
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7.2 AVAILABLE DATA
To evaluate the relationship between POE mean arsenic levels and system mean arsenic
levels, it is necessary to have data sets that include distinct POE identifiers that associate each
sample with the POE where it was collected. As indicated in Chapter 4, a total of eight States
provided data sets that included suitable POE identifiers, including:
• Arkansas;
* Alabama;
California;
• Indiana;
• Illinois;
• New Mexico;
• Oklahoma; and
Utah.
From these States, a subset of systems with at least 5 POE was identified. It included 159
ground water systems, which were represented by 3,035 samples, and 44 surface water systems
represented by 1,703 samples. These data were further subsetted to include only those systems
that had at least three POE with some detected concentrations, and were such that the uncensored
POE means were not all equal. This subset included 98 ground water and 29 surface water
systems from six States. There were no systems from Alabama or Arkansas which met these
criteria, primarily because the arsenic concentrations in these States appear to be relatively low,
and the available data were highly censored. Table 7-1 summarizes the data that was available to
support the intra-system analyses. It should be noted that there is no reason to expect that system
mean arsenic concentrations are associated with the number of POE in a system.
Table 7-1
Summary of Data in Intra system Analyses
State
California
Illinois
Indiana
New Mexico
Source Water Type
GW
sw
GW
SW
GW
SW
GW
SW
Number of Systems
22
19
5
1
1
0
37
2
Public Comment Draft
7-2
May 8.2000
-------
Table 7-1 (Continued)
Summary of Data in Intra system Analyses
State
Oklahoma
Utah
Total for All States
Source Water Type
GW
SW
GW
SW
GW
SW
Number of Systems
16
2
17
5
98
29
The criteria for including systems in the intra-system analyses were applied to all of the
States with POE identifiers. We chose the criterion of systems with five or more POE to more
accurately estimates of each system's true CV; the CV estimates of systems with fewer POE may
have been less accurate. However, if we set this criterion much higher, for example, if we had
only included systems with 10 or more POE, we would have significantly reduced the number of
systems available for analysis, and the geographic area represented by those systems. By
choosing to include a larger pool of systems, our goal was to be able to produce more accurate
estimates of average system CV that would be nationally representative. The criteria of three or
more detected, non-equal POE means was established so that there would be enough data points
for ROS to produce reasonably reliable results. This criteria also helped to make it more likely
that measured system CV's would reflect variability in true arsenic concentrations from POE to
POE in the system, by excluding highly censored systems. We were concerned that, for highly
censored systems, CV estimates might reflect conventions for handling non-detects rather than
actual intra-system variability. Estimates of intra-system variability that were developed using
these data are described in the following sections of this report.
7.3 ANALYTICAL METHODS AND RESULTS
Four methods were applied to estimate intra-system variability based on the CV of the
POE means. These include: an empirical method that produced an estimate of the CV for an
average system; a log-normal model; a model based on regression of the system CV against the
system mean; and two versions of a variance function model in which the log-variance (the
variance of the logarithm of a POE mean) is a function of the mean. These analyses and results
are described in the following sections.
73.1 Empirical Average Coefficient of Variation
In this analysis, a coefficient of variation was calculated for each system, and these
individual CV estimates were averaged to develop an empirical estimate of the national system
CV average. These analyses were relatively straightforward; first, a mean arsenic concentration
Public Comment Draft
7-3
May 8, 2000
-------
was calculated for each POE in each system. Second, system mean and CV were calculated for
each system. Third, average CV levels across all systems were estimated. The same procedure
was followed for both surface water and ground water systems.
Mean arsenic concentrations for each POE were calculated using either substitution or the
adapted ROS. The method that was applied depended on the number of arsenic positive sample
results in the data set for the particular POE. The intent of using multiple methods was to
develop the most accurate estimate of each POE mean, given the available data. As a result,
POE means were calculated as follows:
• No detects: The POE mean was labeled as non-detect at the most commonly occurring
reporting limit. These POE were later used to calculate system means by adapted ROS.
• 1-4 detects: 1A the reporting level was substituted for non-detects, and the POE mean
was calculated.
• All detects equal: ¥2 the reporting level was substituted for non-detects, and the POE
mean was calculated.
• 5 or more detects: Adapted ROS was used to calculate the POE mean as follows: detects
were plotted as in Helsel and Conn (1988); non-detects were uniformly plotted from zero
to the estimated probability, from the fitted log-normal model, at which Y is less than or
equal to the censoring level.
Once POE means were estimated for each POE, system means were estimated using
adapted ROS. Adapted ROS, when used to calculate system means, was applied to slightly
different data than when adapted ROS was used to calculate POE means. For the calculation of
system means, adapted ROS was used when there were 3 or 4 detected POE means. The system
mean, system variance, and CV were all computed from the distribution of the POE means, and
not from the individual samples, because the distribution of the POE means reflects spatial
variability between POE, and not temporal and analytic variability at the individual POE. It
should also be noted that the system means used for these analyses may differ from the system
means used in the occurrence estimates, which were averages over samples and thus include
temporal and spatial variability.
Table 7-2 shows the ranges of individual system CV for each State and source water type,
and Table 7-3 presents geometric and arithmetic mean CV determined through this empirical
approach. The CV ranges for each State are rather similar, particularly for ground water systems,
where the ranges are based on larger numbers of systems. Moreover, the range of CV for all
ground water systems is similar to the range of CV for all surface water systems. Table 7-3
shows that both the arithmetic and geometric mean CV for ground water and surface water are
similar. When ground water and surface water systems are pooled, the arithmetic mean CV is
64.1 (95 percent confidence intervals: 57.2 to 70.8) and the geometric mean CV is 52.4 (95
percent confidence intervals: 46.4 to 59.2). These confidence intervals were developed using a
bootstrap approach.
Public Comment Draft
7-4
May 8. 2000
-------
Table 7-2
Summary of Data for System Coefficients of Variation
State
California
Illinois
Indiana
New Mexico
Oklahoma
Utah
Total for All States
Source Water Type
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
GW
SW
Number of Systems
22
19
5
1
1
0
37
2
16
2
17
5
98
29
CV (%) Range
11.81-125.012
13.568-169.954
57.403 - 154.464
18.145
92.612
-
5.817-156.610
56.827 - 77.382
16.563-111.385
108.787-124.878
10.36-196.051
31.342-112.355
5.817 - 196.051
13.568 - 169.954
Table 7-3
Mean CV based on Empirical Analyses
System Type
Ground water
Surface water
GW & SW Combined
95%CIforGW&SW
Number of Systems
98
29
127
127
Arithmetic Mean
62.9
68.4
64.1
57.2 - 70.8
Geometric Mean
51.1
56.8
52.4
46.4 - 59.2
Abbreviations: CV: coefficient of variation; Cl: confidence interval; GW: ground water; and SW: surface water.
Note: As of May 2000, the estimates in this table are being revised in response to an external peer review. EPA
expects that in the final version of this report, the coefficient of variation estimates will decrease by 10-40%. Other
changes are likely due to corrections in the ACD.
Public Comment Draft
7-5
May 8. 2000
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In addition to calculating the empirical average CV, we performed several analyses to test
the relationship of the CV to the number of samples collected from an individual system, and to
test if the CV is associated with the system arsenic level. Figure 7-1 is a scatterplot of system
C V against the average number of samples per POE for the 98 ground water systems. Figure 7-2
is a similar scatterplot for the 29 surface water systems. This analysis was preformed to test if the
CV is sensitive to the precision of the POE means. As the average number of samples per POE
increases, the precision of the POE means is expected to increase. However, no correlation was
observed between system CV and mean number of samples per POE.
The second analysis evaluated the relationship between system mean arsenic
concentration and system CV. In the scatterplot shown in Figure 7-3 mean arsenic concentration
is plotted on the X-axis and system CV is plotted on the Y-axis. This plot shows that most of the
127 systems have mean arsenic concentrations that range from greater than 0 to 10 jug/L. In this
concentration range, both the highest and the lowest CV occur. At higher concentrations, fewer
systems are represented. However, even at higher mean concentrations, the system CV are
widely scattered. Regression analysis indicated that there is little correlation between the system
mean arsenic concentration and the system CV (p = 0.13). Figure 7-4 and Figure 7-5 present
similar analyses that were performed for the ground water systems and the surface water systems,
respectively. These figures show similar scattering of results, and also indicate that system mean
arsenic level is not significantly correlated with system CV.
7.3.2 Pooled Log-Space Variance
The second approach, that was used to estimate the CV for intra-system variability in
arsenic concentrations relies on a pooled log-normal model. The log-normal model assumes that
the log of the POE mean is normally distributed with a log-mean that depends on the system, but
a constant log-variance for all systems.18 If the log-variance is constant, then the log-normal
model implies that the CV (hi real space) is also constant. Pooling the data from GW and SW
systems, which assumes that the log-variance is also independent of source type, gives a log-
variance estimate of 0.78, and hence a CV of 108.4 percent with a 95 percent confidence interval
of 102.1 to 115.5 percent. In the log-normal model, the CV is related to the log-variance by the
equation:
CV(POEMeari) = Vexp(cr2) - 1 x 100%
18 Suppose X(iJ) is the mean for POE i in system j, and that Y(i^) = log (X(y) ) is normally distributed with mean
s(/) and variance s2 (so X is log-normally distributed). From the properties of the log-normal distribution (see
Johnson N. L-, and Kotz, S., "Continuous Univariate Distributions", Wiley), it follows that E(X) = exp(s(/)) sqrt(w)
and SD(X) = exp(s(/)) sqrt(w) sqrt(w-l), where w = exp(s2). Therefore CV(X) = SD(X)/E(X)*100% = sqrt(w-l) *
100 %. Since the right hand side of the CV equation depends only on w, and hence only on s, the CV must be the
same for all systems/
Public Comment Draft 1 - 8 May 8, 2000
-------
200-
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40-
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Mean Concentration (ug/L) for PWSID
System
Regression
FIGURE 7-3. Scatterplot of Coefficient of Variation
versus Mean Concentration per FVVSID (both-CWS OnJy)
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Notably, the estimated CV from the pooled log-normal model is approximately twice the
empirically derived CV discussed in section 7.3.1. There are several potential explanations for
this difference. First, it is possible that the POE means are not log-normally distributed. Second,
it is possible that the CV is not constant. Third, the empirically derived CV may be biased low.
To test if the POE means are log-normally distributed, log-normal probability plots were
prepared for each system with at least 9 POE. An example plot is presented in Figure 7-6, and all
of the plots for the 36 systems which met this criteria are included in Appendix B-4. In these
plots, each POE mean is plotted with a 99 percent confidence interval that was generated using a
procedure that simulated the distribution of a log-normal probability plot, as described in
Davison and Hinkley (1997).19 When the POE mean lies within the 99 percent confidence
interval, the fit is consistent with a log-normal distribution. These plots suggest that in general,
the distributions are consistent with a log-normal distribution.
Normal probability plots of the POE means, each standardized by the system's estimated
mean and pooled standard deviation, were generated for both ground water and surface water
systems combined, and for each type of system separately. These plots are presented in Figures
7-7, 7-8, and 7-9, respectively. These plots are similar to the log-normal probability plots
discussed above, but the POE means have been standardized to normal quantiles (based on
system mean) and plotted against these normal quantiles. In addition, 95-percent confidence
intervals are plotted for the log-POE means. These confidence intervals were generated using a
procedure that simulated the distribution of the log-normal probability plot. When the log-POE
means fall outside of the confidence interval, it suggests that the data are not consistent with a
log-normal distribution. In all three plots, the log-POE means fall outside of the confidence
envelope between the -1 and 0 normal distribution quantiles. This suggests that the distribution
of POE means may not be consistent with a log-normal distribution. Alternatively, the
assumption of a constant log-variance may be incorrect.
Simulations were run to gauge whether or not the empirically derived CV was biased low.
First, POE means for 10,000 systems were simulated, assuming that the POE means are log-
normally distributed with a log-variance of 0.78. The true CV of this distribution is 108 percent.
Five POE means were simulated for each of the 10,000 simulated systems, and CV was
calculated for each of the 10,000 simulated systems. However, the estimated CV based on the
data simulated from this distribution is 76 percent. Thus, the arithmetic mean CV estimate from
the simulated data set is rather similar to albeit somewhat higher than the empirically derived
CV, which had an arithmetic mean of 64.1 percent. Moreover, the true CV of this distribution
equals the CV estimated by the pooled log-normal model. The results of this simulation suggest
" To estimate a confidence interval for the coefficient of variation, the well known 95 % confidence interval for s2
was used: SSE/CHISQ(0.975) to SSE/CHISQ(0.025), where CHISQ(a) is the lOOa % percentile of a chi-square
distribution with n degrees of freedom, SSE is the error sum of squares for the Y values about their means s(/), and
n is the number of X's minus the number of systems. Using the coefficient of variation equation, the confidence
interval for the coefficient of variation is thus from SQRT(exp( SSE/CHISQ(0.975)) - 1) * 100 to SQRT(exp(
SSE/CHISQ(0.025)) -1) * 100.
Public Comment Draft 7-13 May 8, 2000
-------
that the empirical CV may underestimate the true CV. It is possible that, because of the
relatively small number of POE in each system, the empirical method underestimates the true
CV.
7.3.3 Regression Model Coefficient of Variation
To investigate the constancy of the CV, we fitted an empirical regression model where
the CV varies linearly with the system mean. As shown in Figures 7-3 to 7-5 above, this
regression model fitted poorly (p-value = 0.013 for both system types combined) and also
showed very low dependence of the CV on the mean (a small slope) for ground water systems
and for both system types combined. For surface water systems, a larger slope was observed,
although it is based on a limited data set of 29 systems. Thus, the regression model gives very
similar results to the empirical models with a constant CV.
7.3.4 Variance Function Models Where the Coefficient of Variation Depends on the Mean
Two variance function models were evaluated in which the CV varies as a function of the
system mean. In the first, CV is a function of" the log-space mean squared. In the second, the CV
is a function of the raw mean.
The first variance function model assumes that the log of the POE mean is normally
distributed with a log-mean that depends on the system, and a log-variance that is a constant plus
a multiple of the squared log-mean. Thus we have Log(POE mean) = ~ N(/4sys), s2 + k /^(sys)2)
which implies that CV = v [exp(s2 + k Ai(sys)2) -1] x 100%. The fitted values (by maximum
likelihood estimation) for the SW and GW combined analyses were s2(both) = 0.63, k(both) =
0.08. In order to apply this model, the CV needs to be expressed as a function of the raw system
mean, m, the expected POE mean. The above CV equation gives the CV in terms of the log-
mean. The log-mean can be expressed as a function of the raw mean by solving the quadratic
equation log m = /u(sys) + 0.5(s2 + k A^(sys)2), m > 0.002.
The second variance function model assumes that the log of the POE mean is normally
distributed with a log-mean that depends on the system, and a log-variance that is a constant plus
a multiple of the raw system mean, m. Thus we have Log(POE mean) ~ N(,u(sys), v2 + dm)
which implies that CV Wfexp^ + dm) -1] x 100%. The fitted values (by maximum likelihood
estimation) for the SW and GW combined analyses were v2(both) = 0.62, d(both) = 0.025.
When these models were applied in a plausible situation, as discussed in Section 7.3.5,
the two variance function models produced results that were similar to each other, and to the log-
normal model discussed in section 7.3.2. Thus, while there may be some evidence (e.g. the
pooled log-normal probability plots) that the CV may not be constant, these alternative statistical
models that allow for variation of the CV did not show a very strong dependence of the CV on
the mean, except at extremely low concentrations. These results are discussed further below.
Public Comment Draft
7-14
May 8, 2000
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7.3.5 Estimating the Probability POE Mean Exceeds 2 /zg/L
We compared the various methods for estimating the CV by examining their ability to
predict how often POE means exceed 2 jUg/L, the typical censoring level, for systems with mean
1 A*g/L. First, we identified the subset of systems with overall means of approximately 1 ,ug/L
(±20 percent) and at least one POE mean greater than or equal to 2 ^g/L- We found of the 45
systems which have system means in the range of 0.8 to 1 .2 ^g/L, three had a POE mean greater
than or equal to 2 ^.g/L. Thus, it was determined that the empirical probability that a system with
a mean of about 1 /ug/L would have a POE mean equal to or above 2 /^g/L is 0.067. Next we
computed the probability of an outcome greater than 2 ^g/L from a log-normal distribution with
a mean of 1 #g/L and each of the CV estimates described above. The results are shown in Table
7-4. The simple arithmetic mean CV estimate gave an exceedance probability closest to the
empirical value of 0.067. However, this finding is not surprising, because the simple arithmetic
mean CV and the empirical value are derived from the same value.
Table 7-4 Probability of an observation > 2 ^g/L from a log-
normal distribution with mean 1 //g/L and various CVs
Method
Simple arithmetic mean
Simple geometric mean
Pooled log-space variance
Log-space variance regression 1
Log-space variance regression 2
CV (%)
64.1
52.4
108.4
94.5
95.2
P(log-norma) > 2)
0.070
0.049
0.110
0.103
0.103
Public Comment Draft
7-15
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7.4 SUMMARY OF INTRA-SYSTEM ANALYSES
Four different methods were implemented to estimate a CV level, possibly depending on
the system mean, that would appropriately characterize variability in mean arsenic concentrations
from POE to POE within a system. The average CV estimates generated by these methods
ranged from 52 percent (geometric mean empirical method) to 108 percent (log-normal method).
As discussed in Sections 7.3.2, both methods appear to have some potential limitations. There is
simulation evidence that the empirical method underestimates the true CV, and, with regard to
the log-normal model, it appears that the distribution of POE means might not be consistent with
a log-normal distribution. As a result of the range of CV estimates that were generated, and the
potential limitations of each of the estimates, two CV estimates could be considered and
evaluated in the RIA. The first is the empirically derived arithmetic mean CV, 64 percent, which
represents a plausible estimate of an average CV. The second is the log-normal model CV, 108
percent, which may represent an upper bound of an average CV. This range of CV values should
suitably represent the potential average CV for distributions of POE mean arsenic concentrations
in public water supply systems.
Public Comment Draft 7-19 May 8, 2000
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-------
8. Temporal Variability
8.1 Purpose of Analysis
The purpose of the temporal variability analysis is to examine the variability of arsenic
concentrations over time in a source. This information may be used in the Regulatory Impact
Analysis to determine the probability that a single arsenic sample or the average arsenic level in a
given source would exceed regulatory levels and to estimate national annual monitoring costs.
8.2 Available Data and Results
There were insufficient data in the ACD to analyze the temporal variability of arsenic
concentrations. However, USGS had data from 353 wells with 10 or more arsenic analyses
collected over different time periods. USGS examined its raw water arsenic data to assess the
variability of arsenic levels over time and to determine whether there are temporal trends
(Focazio, et al., in press). These wells were used for various purposes, such as public supply,
research, agriculture, industry, and domestic supply, and encompassed non-potable and potable
water quality. USGS conducted a regression analysis of arsenic concentration and time for each
well and found that most of the wells had little or no temporal trend (low r-squared values when
arsenic concentrations were regressed with time). Arsenic levels for most of the wells probably
do not consistently increase or decrease over tune. In addition, USGS examined the relationship
of well depth and temporal variability by analyzing the relationship between standard deviation
and well depth for wells with mean arsenic concentrations less or equal to 10 t^g/L. They found
no relationship (Figure 8-1).
To determine the extent of the temporal variability, EPA analyzed the CVs for the mean
arsenic level in the wells. 116 wells had a CV and standard deviation of zero. Most of these
wells consistently had arsenic concentrations below the detection limit of 1 Aig/L. EPA examined
the CVs for the other wells in relation to the mean arsenic level and found a relatively constant
CV on the log-normal scale (Figure 8-2) The geometric mean of the CVs, excluding the CVs that
are zero, is 0.388 or 38.8%. The range of the non-zero CVs is 5.5% to 236.3% and the mode is
27.6%. Focazio, et al. (in press) listed several factors that may contribute to this variability,
including natural variability in geochemistry or source of contamination, sampling technique, and
changes in pumping over time.
Public Comment Draft 8 -1 May 8, 2000
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9. References
Agency for Toxic Substances and Disease Registry, 1998. Draft Toxicological Profile for
Arsenic. Prepared for the U.S. Department of Health and Human Services, ATSDR, by the
Research Triangle Institute. August, 1998.
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Available on the Internet at http://www.atsdr.cdc.gov/. ATSDR, Research Triangle Park, NC.
November, 1997.
Azcue, J. M. and Nriagu, J. O. 1994. Arsenic: Historical Perspectives. In: Arsenic in the
Environment, Part I: Cycling and Characterization, Edited by J. O. Nriagu. John Wiley and
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Berger, B.J., and A.H. Fairlamb. 1994. High-performance liquid chromatographic method for the
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Budavari, S., O'Neil, M. J., Smith, A., and Heckelman, P. E. 1989. Merck Index, ed. Merck &
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Clifford, D. and Zhang, Z. 1994. Arsenic Chemistry and Speciation. Paper presented at the
American Water Works Association Annual Conference. New York, NY. June 19-23,1994.
Clifford, D. 1986. Removing dissolved inorganic contaminants from water. Environ. Sci.
TechoL, 20: 1072-1080.
Cohen, A. C. 1991. Truncated and Censored Samples: Theory and Applications, New York:
Marcel Dekker.
Cullen, W. R., and Reimer, K. J. 1989. Arsenic speciation in the environment. Chem. Rev., 89:
713-764.
Davenport, J. R. and Peryea, F. J. 1991. Phosphate fertilizers influence leaching of lead and
arsenic in a soil contaminated with lead and arsenic in a soil contaminated with lead arsenate.
Water, Air and Soil Pollution. 57/58: 101-110.
Davison, A. C. and Hinkley, D. V. 1997. Bootstrap methods and their application. Cambridge
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Fergusson, J. 1990. The Heavy Elements: Chemistry, Environmental Impact and Health Effects.
Oxford: Pergarnon Press, 1990.
Focazio, M., Welch, A., Watkins, S., Helsel, D., and Horn, M., in press. A Retrospective
Analysis of the Occurrence of Arsenic in Ground Water Resources of the United States and
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Frey, M .M. and Edwards, M. A. 1997. Surveying arsenic occurrence. J. A WWA, 89: 105-117.
Fuhrer, G. J., Cain, D. J., McKenzie, S. W., Rinella, J. F., Crawford, J. K., Skach, K. A., and
Hornberger, M. L, and Gannett, M. W. 1996. Surface-Water-Quality Assessment of the Yakima
River Basin in Washington: Spatial and Temporal Distribution of Trace Elements in Water,
Sediment, and Aquatic Biota, 1987. U. S. Geological Survey Open-File Report 95-440,190 p.
Gulledge, J. H., and O'Connor, J. T. 1973. Removal of Arsenic (V) from water by adsorption on
aluminum and ferric hydroxides. J. A WWA., 65: 548-552.
Helsel, D. R., and Cohn, T. A. 1988. "Estimation of Descriptive Statistics for Multiply Censored
Water Quality Data." Wat. Resour. Res., 24, pp. 1997-2004.
Hess, R. E. and Blanchar, R. W. 1977. Dissolution of arsenic from waterlogged and aerated soil.
SoilScL Soc. Am. J., 41(5): 861-865.
Hinkle, S. R., and Polette, D. J. 1999. Arsenic in Ground Water of the Willamette Basin,
Oregon. U.S. Geological Survey Water-Resources Investigations Report 98^4205,32 p.
Irgolic, K. J. 1994. Determination of total arsenic and arsenic compounds in drinking water, pp.
51-60 in Arsenic: Exposure and Health, W.R. Chappell, C.O. Abemathy, and C.R. Cothera, eds.
Northwood, U.K.: Science and Technology Letters.
Isaac, R. A., Wilkenson, S. R., and Stuedemann, J. A. 1978. Analysis and fate of arsenic in
broiler litter applied to coastal Bermuda grass and Kentucky-31 tall fescue. In : D.C. Adriano
and I.L. Brisbin, Jr. (eds.), Proc. Symp. On Environmental Chemistry and Cycling Processes,
U.S. Dept. of Energy, pp. 207-220.
Jekel, M. R. 1994. Removal of Arsenic in Drinking Water Treatment. Chapter 6 in Nriagu, J.O.,
Ed., Arsenic in the Environment Part I: Cycling and Characterization. New York: John Wiley &
Sons, Inc. pp 119-132.
Jordan, D., McClelland, M., Kendig, A., and Frans, R. 1997. Monosodium methanearsonate
influence on broadleaf weed control with selected postemergence-directed cotton herbicides. J.
Cotton Sci., 1:72-75.
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May 8, 2000
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Kennedy Jenks Consultants. 1996. Final Report. Cost of Compliance with Potential Arsenic
MCLs. Prepared for the Association of California Water Authorities. November 27,1996.
Kirk-Othmer Encyclopedia of Chemical Technology, 1992. 4th Edition, Volume 3. New York,
New York, John Wiley and Sons, Inc.
Konefes, J. L. and M. K. McGee. 1996. Old cemeteries, arsenic, and health safety. Cult. Resour.
Mgmt. 19(10): 15-18.
Loebenstein, J. R. 1994. The Materials Flow of Arsenic in the United States. U.S. Department of
the Interior, Bureau of Mines, pp 1-12.
Longtin, J. P. 1988. Occurrence of Radon, Radium, and Uranium in Groundwater. J. A WWA.,
80(7):84.
Maclean, K. S., and Langille, W. M. 1981. Arsenic in orchard and potato soils and plant tissue.
Plant Soil 61(3): 413^18.
Marvinney, R. G., Loiselle, M. C., Hopeck, J. T., Braley, D., and Krueger, J. A. 1994. Arsenic in
Maine Ground Water: An Example From Buxton, Maine. 1994 Focus Conference on Eastern
Regional Ground Water Issues, pp. 701-714.
Mok, W. M., and Wai, C. M. 1994. Mobilization of arsenic in contaminated river waters. In:
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Morrison, 1975. Distribution of Arsenic from Poultry Litter in Broiler Chickens, Soils, and
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Ogden, P. R. 1990. Arsenic behavior in soil in and ground water at a Superfund site. In:
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Onishi, Y. 1978. Arsenic. In Wedephol, K. H., ed, Handbook of Geochemistry. Berlin. II/3:
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Regulations. Federal Register, December 24, 1975,59,556.
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49.
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Appendices
Public Comment Draft May 8,2000
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Appendix A
Adapted Regression on Order Statistics Methodology
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The fitting method used to fit log-normal distributions and to substitute for censored
values (i.e., values below the instrument reporting limit) was an adaptation and revision of the
Regression on Order Statistics (ROS) method as applied in Helsel and Conn (1988).20 The
difference between this adapted ROS, and the ROS developed by Helsel and Conn, is described
in the last paragraph of this appendix. The object is to estimate means and variances for systems
where large portions of the data sampled (samples or POE, depending on the data on which the
adapted ROS is used) are known to be below some value (e.g. a reporting limit or detection
limit). This is carried out by imposing a broad ordering on the data and plotting the complement
of an empirical cumulative distribution on log coordinates so that standard regression techniques
can be applied to the graph.
The adapted ROS method can be described as follows. Suppose that there are m different
reporting limits for the censored values, R, R2 R3 ... Rn,, arranged in increasing order, and also
set R<, equal to 0 and Rm+, equal to ~. Suppose there are A$ uncensored values less than R, A,
uncensored values less than R2 but greater than or equal to R, A2 uncensored values less than R3
but greater than or equal to R2 .... and, in general, Aj uncensored values less than R^, but greater
than or equal to Rj. Also, suppose there are Bj censored or uncensored values below the j*
reporting limit, Rj, i.e., either a detected concentration less than Rj or a non-detect with a
reporting limit less than or equal to Rj. If the j+lth reporting limit is exceeded, then obviously the
j* reporting limit is also exceeded. If the j-f 1th reporting limit is not exceeded, then an estimate of
the probability of exceeding the j* reporting limit is Aj divided by Aj -f Bj This estimate is
obtained by considering that Aj + Bj values are known to be below the j+l* reporting limit, of
which Aj are uncensored values between the two limits and Bj are uncensored or censored values
known to be below the lower limit, Rj. (The censored values with reporting limit R^, cannot be
used for this estimate because it is unknown whether those values would have been above or
below Rj had an instrument with this reporting limit been used instead.). This gives the empirical
formula:
Probability of exceeding the j* reporting limit
PJ = pj+1 + [Aj / (Aj + Bj.)] (1 - pj+1). Equation A. J
This equation is solved iteratively, starting with pm+l = 0 and letting j - m, m - 1, m - 2,...
The probability plotting positions (pp) for the Aj uncensored values that are less than Rj+1
but greater than or equal to Rj are uniformly spread over the probability range 1 - PJ to 1 - pj+1.
More precisely, the i* highest of these uncensored values is plotted at probability
pp(i) = (1 - Pj) + (PJ - pj+1) i / (Aj +1). Equation A.2
To fit the log-normal distribution, a simple linear regression line is fitted to the
logarithms of arsenic concentrations (y axis) versus the normal quantiles, defined as G(pp(i)), (x
20 Helsel, D. R., and Cohn, T. A. 1988. "Estimation of Descriptive Statistics for Multiply Censored Water Quality
Data." Water Resources Research, 24, 1997-2004..
Public Comment Draft May 8. 2000
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axis), where G is the inverse of the standard normal cumulative distribution function. The
intercept and slope are the estimated mean, fi, and standard deviation, s, of the log
concentrations.
The ROS method can also be used to substitute values for the censored data. The original
ROS method in Helsel and Conn (1988) chooses plotting points for censored data at reporting
limit Rj evenly spread out on the interval from 0 to 1 - pj3 which is a data-based, non-parametric
estimate of the probability of not exceeding the reporting limit. Applying this method to the
arsenic data led to some inconsistencies since estimated censored values can exceed the reporting
limit. The revised method used for this project avoided this problem by choosing plotting points
for censored data evenly spread out on the interval from 0 to the parametrically estimated
probability of not exceeding the reporting limit, computed from the fitted log-normal
distribution. Thus the probability plotting position for the 1th highest of the Cj censored values
with reporting limit Rj is
pp(i) = F {(log Rj -u)/s } i / (Cj +1), Equation A3
where F is the standard normal cumulative distribution function. The substituted arsenic
concentration for that censored value is therefore exp{n + s G [pp(i)] }, which will always be
positive and less than the reporting limit, Rj.
Given a large data set with analytical results that follow a log-normal distribution, the
original ROS and the adapted ROS should yield similar results. However, with smaller data sets,
the original ROS may yield inconsistent results, in that it predicts that some censored values will
exceed the reporting limit. In these cases, the original ROS should slightly overestimate the true
distributional parameters. The adaptation of ROS applied in these analyses should correct this
bias, and should yield better estimates of the distributional parameters than the original ROS.
Thus, the adapted ROS should behave as well as, or better than, the original ROS when applied
to the arsenic occurrence data.
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Appendix B
Analysis Results
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Appendix B-l
Box Plots
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FIR1JRE 1. Boxplofs nf Systfim M«ans by System SI'ZB for
Community Water Systems in State AK
Number of Systems Indicated Below Boxplot
U.S. EPA Headquarters Library
Mail code 3201
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Washington DC 20460
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FIGURE 2. Bnxnlnts of System Mfians by Systurn Size, for
Community Water Systems in State AL
Number of Systems Indicated Below Boxplot
-------
100.000
1
e 10.000-
_o
"2
1
B 1.000
o
U
u
"i
J: 0.100
§
1
S 0.010
en
co
0.001
100.000
i
e 10.000
1
1 1.000-
u
| 0.100
CO
1
E 0.010
CD
0.001-
Ground Water (GW) Systems
••
*
38 113 68 101 47 10 2.
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
Surface Water (SVf) Systems
1
6 4 3 24 20 13 4
25-100 101-500
501-1000 1001-3300 3301-10000
System Size
10001-SOOOO 50001+
FTRTTRE 3. Bnxplnts nf System Means hy System Sixe fnr
Community Water Systems in State AR
Number of Systems Indicated Below Boxplot
-------
100.000
i
c 10.000
g
1
c 1.000-
u
0>
Jj 0.100-
e
CO
i
E 0.010
I
0.001-
1
Ground vtater [GWJ Syste
I 1
1 1
as
*
|
B
*
56 83 45 50 29 21 2
25-100 101-500 501-1000 1D01-3300 3301-10000 10001-50000
Svstem Size
30001 +
100.000
^r 10.000
1
| 1.000
a
e 0.100
ra
s
E 0.010
|,
0.001
Surface Water (SW) Systems
1 fi
B i » T B - ?
2 8 5 5 3 18
25-100 101-500 501-1000 1001-3300 3301-10000 10001 -SOOOO
System Size
50001+
4. Boxplots of Systftin MBSTIS hy System Size for
Community Water Systems in State AZ
Number of Systems Indicated Below Boxplot
-------
100.000-
1
c 10.000-
o
1
e 1.000-
o
U
_o
"5
55
Jj 0.100-
c
§
2
E 0.010
3
CO
CO
0.001
-
; * ;
: Ground V ater [GW] Systehis .
1 |
1 1 i
|
1 *
1 *
1 1 » * ;
»
^ * *
*
177 139 73 114 109 149 59
i i i i j i i
25-100 T01 -500 SOI -1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
100.OOO
I
"^ 10.000
_o
13
a
1 1.000-
Q
"E
Q
| 0.100-
i
E 0.010-
B
co"
0.001-
Surface Water (SW) Systems
* i "
T * I
*
19 27 21 46 38 31 44
25-100
501-1000 1001-3300 3301-10000 10001-50000
System Size
50001 +
FTGtJRE S. Bnxnlrrfs of System Mfians hy System Size fnr
Community Water Systems in State CA
Number of Systems Indicated Below Boxplot
-------
100.000
' V
"^ 10.000
.£
1
o
c 1.000
6
u
1
Jj 0.100-
i
o
£ 0.010^
CO
0.001-
100.000
i
e 10.000
o
1
c
1 1.000
a
_u
o
J2 0.100
C
i
£ 0.010
s
CA
CO
0.001-
^
I « Ground vfater (GW) Systems
t .
*
1 '
*
1 *
* * .
* ^ 4
104 266 162 156 78 43 2
i i t i * i i
25-100 101 -SCO 501-1000 1001-3300 3301-10000 10001 -5OOOO 50001 +
System Size
Surface Water (SW) Systems
.
I
* * f 9 t ? ?
• ,
5 4 11 27 29 26 8
1 1 I 1 1 1 1
25 - 100 101 - 500 501 - 1000 1001 - 3300 3301 - 10000 10001 - 50000 50001 4-
System Size
FTOirRE R. Bnxplots of System Means by System SIZR for
Community Water Systems in State IL
Number of Systems Indicated Below Boxplot
-------
I
c
o
c
o
"5
a
o
I
w1
100.000
10.000
1.000
0.100
0.010-
0.001
177
Ground Water (GWJ Systems
150
30
25-100 101-500 501-1000 1001-3300 3 3 01-1000 J 10001-50000 50001 +
Svstem Size
100.000
T 10.000
_0
enic Conccntrat
s-1
o
S
43 o.ioo-
cs
o
S 0.010:
VD
0.001-
Surface Water (SW) Systems
- . 1 1 ,
2 5 7 17 13 6
25-100 101-500 501-1000 1001-3300 3301-10000
System Size
10001-50000 50001+
FIO1IRE 7. BnxnJnts of System Moans hy Sysffim Sizft fnr
Community Water Systems in State IN
Number of Systems Indicated Behnv Boxplot
-------
100.00O-
i
c 10.000-
1
1
c 1.000 -.
o
.e for
Community Water Systems in State KS
Number of Systems Indicated Below Boxplot
-------
100.000
1
^ 10.000
.0
1
1 1.000-
o
u
Jj 0.100-
I
E 0.010-
o
J
0.001
100.000
^T 10.000-
.5
1
1 1.000
o
CJ
1
^ 0.100-
g
0)
S 0.010-
I
0.001-
Ground Water [GWJ Systems
i fi i i
j * I ! T 0
26 29 5 20 10 5 1
i i i i i i i
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
Surface Water (SW] Systems
. » 1 i 1 } s
1 4 7 40 50 46 3
25-100 101-500 SOI -1000 10O1-3300 3301-10000 10001-50000 50001+
System Size
FFGTIRK 9. Bmcnlnts of Svst*>m Mftans hv Svstem Sizn for
» j * »
Community Water Systems in State KY
Number of Systems Indicated Below Boxplot
-------
100.00O-
1
e 10.000
_o
1
I 1.000
a
js 0.100-
c
u
S 0.010:
f :
w1
0.001-
Ground Water (GW) Systems
I
n
IT f B
'
55 30 9 12 3 1
25-100 101-500
501-1000 W01-3300
Svstem Size
3301-1UOOO 10001 -50000 50001 +
100.000-
f
"^ 10.000-
%o
1
m
1 1.000-
o
"S
Jj 0.100:
c
ra
i
g 0.010-
_oj
0.001
Surface Water (SW] Systems
1 2 13 5 8
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001+
System Size
F1CURF. 10. Boxnlnts of SvstRm Mftans hv Svstftrn Si7.e for
A -• J J
Community Water Systems in State ME
Number of Systems Indicated Below Boxplot
-------
100.000-
1
c 10.000
1
Is
c 1-000
o
u
u
1
| 0.100
1
E 0.010
|
0.001
1
•
:
Ground V
i
•
ater (GW) Syste
•
as
«
1 A
n
y
; ;
*
232 237 58 79 30-10 1
s
B
u
"E
o
S
100.000-
10.000
1.000-
0.100
0.010
0.001
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
Svstem Size
Surface Water (SW) Systems
11
15
101-500 SOI-1000 1001-3300 3301-10000 10001-SOOOO
System Size
FIGURE 11. Rnxnlnts of System Means hy System Size for
Community Water Systems in State MI
Number of Systems Indicated Below Boxplot
-------
11
_o
I
1
a
0}
J!
a
o
100.000
10.000
1.000
0.100
E 0.010
o.ooi-
142
100.000
^
3
2
1
S
10.000-
1.000
0.100
o.oio
0.001
Ground Vfater (GW) Systems
288
138
162
55
49
25-100 101-500 S01-100U 1001-33(10 3 301-1000 Q 10Q01-5000O 500OI-f
Svstem Size
Surface Water (SW) Systems
25-100 10I-SOO 501-1000 10O1-3300 3301-10000 10001-50000 30001 -I-
System Size
FIGURE 12. Boxplnts of System Mftans hy Systftm Si7ft For
Community Water Systems in State MN
Number of Systems Indicated Below Boxplot
-------
1
o
CJ>
o
U
a
a>
100.000
10.000
1.000
0.100
E 0.010-
0.001
Ground Water (GW) Systems
312 249
25-100 101 -500
69
91
45
19
501-1000 1001-3300 3301-10000 10001-SOOOO
Svstem Size
50001 +
>
e
o
I
g
I
5
100.000
10.000
LOGO
0.100:
0.010
o.ooi
Surface Water (SW) Systems
14
25-100 101-500
10
32
20
501-1000 1001-3300 3301-10000
System Size
10001-50000 90001 +
FTOTJRE 13. BoxnloJs of .System Means hy System $iw> fnr
Community Water Systems in State MO
Number of Systems Indicated Below Boxplot
-------
100.000-
1
^T 10.000-
JO
1
§ 1.000
o
u
_o
u
ja 0.100-
C3
O
S 0.010
CO
0.001
100.000
1
P 10.000
1
c 1.000
o
u
cj
1
ja 0.100
e
CO
2
g 0.010-
•s
OT
0.001
Ground Water (GW) Systems
: .- i
I
1 p
1 «
. ' 1
1
288 175 33 31 7-2
i i j i i ii
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
Surface Water (SW) Systems
1 i Q
1 4 i 1 i 1
1 1 1 1 S | y
8 10 8 15 9 42
25-100 101-500 501-1000 1001-3300 3301-10000
System Size
10001-SOOOO SCOOl-t-
FTOIIRK 14. Br»xplots nf SystftTn Means hy Syst«m Sizft for
Community Water Systems in State MT
Number of Systems Indicated Below Boxplot
-------
S
a
_o
CJ
CO
I
100.000
10.000
1.000
0.100
0.010
0.001
Ground Water (GWJ Systems
848 654
25-100 ltf-500
110
92
45
24
Stn-IOOU 1001-3300 3301-1UUOO 10001-50000
Svstem Size
1
.2
IS
u
100.000
10.000
1.000-
g
ja 0.100
c
C3
g 0.010
0.001
• 25
Surface Water (SW) Systems
18
25-100 101-SOO
34
41
45
SOI-1000 1001-3300 3301-10000
System Size
16
10001-50000 50001 +
FIGURE 15. Brtxplots of System Means hy System Size for
Community Water Systems in State NC
Number of Systems Indicated Below Boxplot
-------
100.000
J*
1>
7T 10.000-
.2
S
e
1 1.000:
^o :
"5
o
J3 0.100:
s :
S
2
g 0.010-
1 :
0.001-
,
Ground Water {GW] Systems
fl f] 9 fl
*
•
.
•
*
37 86 33 44 5 3
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
100.000:
1
"I" 10.000-
& :
K 1.000:
u
'e
^ 0.100-
c :
03
03
g 0.010:
I
60
0.001
Surface Water (SW) Systems
81
^
1 "
335251
25-100 101-500 SO1-1000 1001-3300 3301-10000
System Size
10001-50000 50001-I-
FICtlRR 1fi. Rnxplftts of System MRSTIS hy Syst«m Si7« for
Community Water Systems in State ND
Number of Systems Indicated Below Boxplot
-------
100.000:
I
c 10.000:
.2 ,
£ '•
•*"*
1 1.000-
u ;
™
09
Js 0.100-
i
E 0.010
03
J
0.001-
t
Ground Water (GWJ Systetas
1
ill
9 f ? a
*
*
276 175 21 25 6 3
25-100
101-500 SOI-1000 1001-3300 3301-100UO 10001-50000 S0001 +
Svstem Size
100.000
1
c 10.000
o
"5
J3
C
I 1.000-
J 0.100'
c
§
S
g 0.010:
I
0.001-
Surface Water (SW) Systems
i i
Q u) H A p -
H T T
3 4 14 7 8 2
101-500 501-1000 1001-3300 3301-WOOD 10001-50000 50001+
System Size
FTRIiTRE 17. BoxploJs nf System Means hy System Size for
Community Water Systems in State NH
Number of Systems Indicated Below Boxplot
-------
100.000-
1
c 10.000
_o
1
"a
0}
G 1.000'
a
u
~E
O)
J 0.100-
"••
s
S
2
£ 0.010-
S
0.001-
Ground Water [GWJ Systems
* *
* 4
j i :
\ I \
1111
T T T J
IIT T
' 1 * 1
t ! ! *
* «
*
99 108 38 64 65 61 4
25-100 101-500
501-1000 1001-3300 3301-10000 10001-SOOOO
Svstem Size
30001 +
f
I
100.000
10.000
£ 1.000
a
.H
0.100
0.010
0.001
Surface Water (SW) Systems
11
25-100 101-500
12
501-1000 1001-3300 3301-10000 10001-50000 500O1 +
System Size
FIRIJRK 1fl. Bnxplnts of System Means hy System SJ7R for
Community Water Systems in State NJ
Number of Systems Indicated Below Boxplot
-------
100.000
i
s
c 10.000
_o
1
"S
c 1.000-
o
a
1
Jj 0.100-
1
s o.oio-
Crt*
0.001-
100.000
1
c 10.000
1
H
1 1.000-
"E
| 0.100:
=3
i
g 0.010-
•i
0.001-
_ * » •
Ground Water (GWJ Systeftis
*
? *
*
155 222 54 48 22 15 2
i i i i > i.i
25-100 101-500 501-1000 1001-3300 3301-10000 10001-5OOOO 50001 +
System Size
Surface Water (SW) Systems
1 1
H fl
4 1 B V ft ft
| 9 D 0 B
4637441
1 i 1 I II]
25-100 101*500 501-1000 1001-3300 3301-10000 10001—50000 50001+
System Size
FinilRE 19. Bnxplots of Systfini MRHTIR hy System Size fnr
Community Water Systems in State NM
Number of Systems Indicated Below Boxplot
-------
100.000-
I
c 10.000-
e
1
e 1-000
o
w
"5
CD
£ 0.100
c
S
s
6 0.010
I
0.001
.
1
H
H
V
1
*
S
Ground V ater (GW) Syste: as *
I D I
.
*
84 73 20 28 14 1
1 1 I F f 1 1
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
100.000:
i
^r 10.000-
o
•s
a
43
C
C 1.000:
.| 0.100:
§
O
S
g 0.010:
eo !
0.001-
e
Surface Water (SW] Systems
n
A
]
j
H I 11
.
4666342
25-100 101 -SOB 501-1000 1001-3300 33 01- 1000 D IDOOl-SOOOO 50001+
System Size
FIGURE 20. Bnxplnts of System MRSHS hy Systfttn Sizes fnr
Community Water Systems in State NV
Number of Systems Indicated Below Boxplot
-------
I
§
u
I
ca
o
I
100.000
10.000
1-000
0.100
0.010
0.001-
J Ground Water (GW) Systeyis
213 289
25-100 101-500
108
165
68
47
501-1000 1001-3300 3301-10000 10001-50000
Svstem Size
100.000
f
T 10.000
I
I 1.000-
o
u
.a
a
J 0.100
e
to
a?
s
S 0.010-
I
0.001
Surface Water (SW) Systems
+ * * i »
1 2 10 25 33 43 16
25-100 1C! -500
S01-1000 1001-3300 3301-10000 10001-50000
System Size
soooi+
F1OTJRK 21. Boxplots of System Means hy Systftrn S)7ft fr»r
Community Water Systems in State OH
Number of Systems Indicated Below Boxplot
-------
100.000-
1
e 10.000-
£
c 1.000-
o
CJ
_o
C
o
Js 0.100
a
01
£ 0.010-
co
CO*
0.001-
100.000
I
^T 10.000
_o
1
I 1.000
CJ
i
4j o.ioo
CO
s
6 0.010
0
I
0.001
1
' • Ground Water (GW) Systems . 1
1 * M
j 1 1
! '
* »
83 176 66 102 21 13
25-100 101-500 501-1000 1001-3300 3301-10000 10001-50000 50001 +
System Size
Surface Water (SW) Systems
!• •
t t 1 1
T T T i
, J
15 34 22 74 43 20 8
25-100 101-500 501-1000 1001-3300 3301-10000
System Size
10001-50000 30001 +
FIGURE 22. Boxplnts of Sysletn Mftans hy SystRm Si7« for
Community Water Systems in State OK
Number of Systems Indicated Below Boxplot
-------
100.000-
1
c 10.000
_o
a
I
S 1.000-
o
SJ
j-3 0.100-
I
E 0.010-
1
CO
0.001
100.000
i
7T 10.000
o
'S
| LOGO-
'S
|t 0.100
s
1
g 0.010-
f
CO
0.001
* • Ground Water (GW) Systems
t
\ III!
i :
*•
120 114 28 40 17-13 2
i i i i i i i
25-100 101-500 SO1-100U 1001-3300 3301-10000 10001 -5OOOO 50001 +
System Size
Surface Water (SW) Systems
A A , i i
t - »
I ! ' T
9 26 17 38 18 20 4
25-100 101-500 SOI-1000 1001-3300 3301-10000 1OOO1-SOOOO 50001+
System Size
FIGURK 23. Rnxplnts of System Means hy System Size for
Community Water Systems in State OR
Number of Systems indicated Below Boxplot
-------
£
3
O
U
.a
c
100.000
10.000
i-ooo
J2 0.100
0.010
0.001
684
Ground Water (G\V) Syste is
982
376
567
251
60
25-100 101-500 501-1000 1001-3300 3301-1UOQO 100O1-5OOOO 50001 +
Svstem Size
100.000
f
"^ 10.000
.s
1
O9
C 1.000
Q
_u
09
^ 0.100-
« !
i
E 0.010-
—
0.001-
Surface Water (SW) Systems
s
!l
: T
1 1 P
(if?
28 39 26 77 62 60 27
25-100 101-500
501-1000 1001-3300 3301-10000
System Size
10001-50000 50001+
FIGURE 24. Boxnlnts of System Means hy .System Six* for
Community Water Systems in State TX
Number of Systems Indicated Below Boxplot
-------
100.000
I
c 10.000
1
1 1.000-
3
"5
Jj 0.100-
1
S 0.010-
s3
m
0.001-
100.000
i
^T 10.000
JO
1
I 1.000
u
1
|j 0.100
c
S 0.010-
f
0.001
1
Ground Water (GW) Systems
Illi"
I
•
7 35 14 36 18 10 1
Jill 1 II
25 -100 101 - 500 501 - 10OO 1001 - 3300 3301 - 10000 10001 - 50000 50001 +
System Size
*
Surface Water [SW] Systems
i
| i I a
U I
1536432
25-100 101-500
501-1000 1001-3300 3301-10000 10001 -SOOOO
System Size
5O001 +
25. Bnxplots of SystRm Means hy System Si?:B for
Community Water Systems in State UT
Number of Systems Indicated Below Boxplot
-------
This page intentionally left blank.
-------
Appendix B-2
Log-Normal Probability Plots of System Means
-------
This page intentionally left blank.
-------
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This page intentionally left blank.
-------
Appendix C
Summaries of Pre-1980 Data Sets
-------
This page intentionally left blank.
-------
Arsenic Occurrence in Public Water Supplies
Reported by the 1969 Community Water Supply Survey
Ground Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
366
148
SI
69
9
673
Number
of Detects
20
2
7
2
2
33
Percent
Nondetects
95%
99%
91%
97%
78%
95%
Minimum
(Mg/L)
15.0
30.0
7.5
15.0
5.0
5.0
Maximum
(Mg/L)
64.0
100.0
30.0
30.0
10.0
100.0
Surface Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
31
38
15
14
8
106
Number
of Detects
1
4
1
3
0
9
Percent
Nondetects
97%
89%
93%
79%
0%
92%
Minimum
O^g/L)
30.0
30.0
30.0
15.0
NA
15.0
Maximum
(Mg/L)
30.0
30.0
30.0
30.0
NA
30.0
NA: Not applicable, no positive detections were reported.
Public Comment Draft
May 8, 2000
-------
Arsenic Occurrence in Public Water Supplies
Reported by the 1978 Community Water Supply Survey
Ground Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
120
85
34
20
0
259
Number
of Detects
22
16
5
6
NA
49
Percent
Nondetects
82%
81%
15%
70%
NA
82%
Minimum
(Mg/L)
2.5
3.2
3.5
3.1
NA
2.5
Maximum
O'g/L)
28.0
17.0
17.3
8.2
NA
28.0
Surface Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
17
36
21
20
0
94
Number
of Detects
0
1
0
2
NA
3
Percent
Nondetects
100%
97%
100%
90%
NA
92%
Minimum
«L)
NA
2.5
NA
4.4
NA
2.5
Maximum
(Mg/L)
NA
2.5
NA
10.7
NA
10.7
NA: Not applicable.
Public Comment Draft
May 8.2000
-------
Arsenic Occurrence in Public Water Supplies
Reported by the Rural Water Survey
Ground Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
18
38
8
5
2
71
Number
of Detects
8
12
2
1
0
23
Percent
Nondetects
56%
68%
25%
80%
0%
68%
Minimum
C*g/L)
5.0
2.0
3.0
8.0 '
NA
2.0
Maximum
(Mg/L)
82.0
40.0
6.0
8.0
NA
82.0
Surface Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
0
3
7
4
7
21
Number
of Detects
0
0
0
1
1
2
Percent
Nondetects
NA
100%
100%
75%
86%
92%
Minimum
0*g/L)
NA
NA
NA
3.0
5.0
3.0
Maximum
G*g/L)
NA
NA
NA
3.0
5.0
5.0
NA: Not applicable, no positive detections were reported.
Public Comment Draft
May 8,2000
-------
Arsenic Occurrence in Public Water Supplies
Reported by the National Organics Monitoring Survey
Ground Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
0
0
0
3
12
15
Number
of Detects
NA
NA
NA
2
4
6
Percent
Nondetects
NA
NA
NA
67%
33%
60%
Minimum
fcg/L)
NA
NA
NA
7.0
5.0
5.0
Maximum
teg/L)
NA
NA
NA
10.0
18.0
18.0
Surface Water Supplies
Population Served
25-500
501-3,301
3,301-10,000
10,001-100,000
> 100,000
Total
Number
of
Samples
1
0
3
17
65
86
Number
of Detects
0
NA
0
6
13
19
Percent
Nondetects
100%
NA
100%
65%
80%
78%
Minimum
WL)
NA
NA
NA
5.0
5.0
5.0
Maximum
(Mg/L)
NA
NA
NA
20.0
17.0
20.0
NA: Not applicable, no positive detections were reported.
Public Comment Draft
May 8, 2000
-------
Appendix D
Database Specifications and Data Conditioning
-------
Appendix D-l
Individual State Database Specifications
Public Comment Draft
MayS. 2000
-------
ARSENIC DATA CONDITIONING NOTES FOR OCCURRENCE AND
EXPOSURE DATABASES (AOED, GRAND.CPT, and INTRA.CPT)
DATA CONVENTIONS:
All individual records are identified by the sampling point id number (e.g., S3519) or result id (e.g.,
R3519) these numbers were uniquely assigned to every record received for tracking purposes.
LIST OF DATABASES:
N1RS, NAOS, Alabama, Alaska, Arizona, Arkansas, California, Illinois, Indiana, Kansas, Kentucky, Maine,
Michigan, Minnesota, Missouri, Montana, Nevada, New Hampshire, New Jersey, New Mexico, North Carolina,
North Dakota, Ohio, Oklahoma, Oregon, Utah, and Texas.
NIRS (National Inorganics and Radionuclides Survey)
Data received from EPA.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Data given (12/18/97).
No data given (12/18/97).
Data obtained and transferred from the NIRS2 database. Two missing PWSIDs were
obtained from SDWIS. Some PWSIDs beginning with '04' are tribal systems. Set Ocala
PWSID to NIRSO (4/2/98).
No data given (12/18/97). Data obtained from documentation (Arsenic Occurrence:
USEPA Seeks Clearer Picture, 1994) provided by the EPA WAM (2/4/98).
No data given. (12/18/97) Data obtained from documentation (Arsenic Occurrence:
USEPA Seeks Clearer Picture, 1994) provided by the EPA WAM (2/4/98).
Used city name (12/18/97) in this field is PWS name found in SDWIS (3/4/98).
Population numbers for four quarters and an annual average were given. The average
was used (12/18/97).
ISSI generated for database purposes (2/2/98).
Obtained from documentation (Occurrence Assessment for Arsenic in Public Drinking
Water Supplies, September, 1992).
Assumed (2/4/98).
Data given (12/18/97).
Data given (12/18/97).
Obtained from documentation (Occurrence Assessment for Arsenic in Public Drinking
Water Supplies, September, 1992) provided by the EPA WAM.
Conditioning Notes:
• Tribal systems are included in this data set.
• Date Range: 9/84-10/86.
• Used NIRS data set combined with NIRS2 PWSIDs to obtain missing data elements.
NAOS (National Arsenic Occurrence Survey)
Data received from EPA
State: Data given (12/18/97).
County: No data given (12/18/97).
PWSID: No PWSIDs were given (12/18/97). Added "NAOS"& sample id (4/3/98).
TypeofWSS: No data given (12/18/97).
Source Type: Data converted from a numeric format to proper database code.
PWSName: ' No data given (12/18/97).
Population: Data given (12/18/97).
-------
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
ISSI generated for database purposes (2/2/98).
Obtained from documentation (National Compliance Assessment and Costs for the
Regulation of Arsenic in Drinking Water, January, 1997) provided by the EPA WAM
(2/4/98).
Data given (12/18/97).
No data given (12/18/97).
Data given (12/18/97).
Data given (12/18/97).
Assigned based on reporting limit. Values greater than the reporting limit qualified as
Detects.
Conditioning Note:
• Large CWS systems represented.
Alabama
Data received from Ed Thomas at EPA, originally from Tom DeLoach, AL
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
ISSI generated for database purposes.
Provided in database.
Provided in database.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Non-detects indicated with "0" result. Conducted a frequency analysis of detects, and
found that the lowest commonly occurring value was 1 ppb. Assumed RL = 1 ppb.
Provided in database.
Alaska
Data received from SRA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "AK" to all records (12/20/97).
No data given (12/20/97).
Data given but added "AK2" to given sysid (12/20/97).
Data obtained from SDWIS (12/20/97).
Data given but had to be modified in the following way: S, A,Y, P="SW" C, W,
G="GW" (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98).
Data provided by the data contact (12/20/97).
Assumed (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data provided by the data contact.
Data given (12720/97).
-------
Conditioning Notes:
* Date range: 12/66-^4/97.
• Deleted records S37091 and S37225 (detects reported at zero)
Arizona
Data received from Linda Bragg, Az. Dept. of Env. Quality
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "AZ" to all records.
Data given.
Added "AZ04" before the given system ID.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data given.
Data obtained from SDWIS.
ISSI generated for database purposes (5/27/98)
No data given.
No data given.
Data given.
Data given.
Data provided by data contact. Data prior to 1988 were sent with no indication of
positive or negative detection. The data (2,450 records) were moved to the Obsolete data
file. The data provided had "=" sign, which mean the values, were confirmed as positive
results. For PWS AZ0413154 (10/26/97), the arsenic value reported was >25 ppb.
No data given.
Conditioning Notes:
• Data were given in four separate files-PWS information before 1/1/93; PWS information after 1/1/93; results
before 1/1/93; and result after 1/1/93. These files were conditioned to create properly formatted spreadsheets.
Several clues indicated that dates were only given for the first sample taken on each day. In addition, PWSIDs
and PWS Names were only given for the first sample for each PWS. To create a properly formatted
spreadsheet, the dates, PWSIDs and PWS Names were copied to their correct results/records.
• Date range: 01/88^/98
• Deleted S150047, S150049, and S151149 (zero results reported for detections). (FM 8/3/98)
Arkansas
Data received from Tom Poeten, EPA Region 6
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
ISSI generated for database purposes.
Provided in database.
Database provided abbreviated PWSID numbers, which were converted to complete
PWSID numbers in accordance with directions from Tom Poeten.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
-------
California
Data received from SRA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
and verified with CA contacts.
Added "CA" to all records.
Data given (12/20/97).
Data given (12/20/97).
Data obtained from SDWIS.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data obtained from SDWIS.
ISSI generated for database purposes (2/2/98).
No data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data obtained from the data contact. (3/5/98)
Data given as "<" in the "XMOD" column (3/5/98)
Conditioning Notes:
• Date range: 11/1/90 -5/22/97.
• Records prior to November 1,1990 were deleted (5/29/98). The deleted records are located in the Obsolete file.
• Originally, the raw data from califom.dbf was used. After a close analysis and conversations with
California representatives, it was determined that arsenic .dbf was more representative of the California arsenic
data, and the California data were re-analyzed.
• Reporting Limit Information was not available for records prior to 1990, so 12687 records were removed from
IAOED, representing 1120 PWS and all systems sizes. 19 PWS with less than 25 people served, 57 PWS with
25-100 people served, 121 PWS with 100-500 people served, 124 PWS with 500-1000 people served, 253 PWS
with 1000-3300 people served, 209 PWS with 3300-10000 people served, 228 PWS with 10000-50000 people
served, 58 PWS with 50000-100000 people served, 49 PWS with 100000-1 million people served, and 2 PWS
with greater than 1 million people served.
• Records labeled unknown were deleted from the database.
Illinois
Data received from Ed Thomas EPA, Mike Crumly, IL
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
ISSI generated for database purposes.
Provided in database.
Provided in database.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
-------
Indiana
Data received from Al Lao
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
', Phil Zellinger, State of Indiana
ISSI generated for database purposes.
Provided in database.
Provided in database.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Method detection numbers provided for later results. Phil Zellinger (IN) provided
associated detection limits for each method number. Earlier results (pre 1996) reported
as positive at the reporting level. For these early samples, could not discriminate between
detects and non-detects, so samples collected before 1996 were omitted from the
database.
Provided in database.
Kansas
Data received from Bob Bostrom, Kansas Dept. of Health and Environment
State:
County:
PWSID:
TypeofWSS:
Source Type:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "KS" to all records (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given but had to be modified in the following way: NTN, TNC = "NTNCWS", and
CAP, CFF, CIN, CMH, CMU, CPM, CPV, CRW, CSC, CSI, CWD, CWS, CWW,
MHP = "CWS" (12/20/97)
Data given but had to be modified in the following way: G, W - "GW" S, P = "SW"
(12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98)
Data provided by the data contact.
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data provided by data contact.
Assigned based on reporting limit.
Conditioning Notes:
* Records with sampling point IDs S22843, S23461, S25050, and S25996 were deleted because they had no
PWSIDs.
• Date range: 1/91-12/97
Kentucky
Data received from EPA in the earlier "11 States" data set
State: Data given (12/21 /97).
County: No data given (12/21/97).
-------
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population;
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given in 5-, 6-, 7- digit format. (12/21/97) used SDWIS to obtain correct PWSIDs.
Some PWSID were still invalid, the data from MI, CA, ID are invalid (3/19/98).
No data given from raw table (12/21/97). Stakeholders information provided this data
{all data=CWS) [4/2/98]. Linked with SDWIS to determine TWSS for active systems.
Provided converted values to IAOED standards (12/21/97).
Used city name, does not correlate with SDWIS system name
Data given (12/21/97).
ISSI generated for database purposes (2/2/98).
EPA WAM provided data (4/2/98).
Assumed (4/2/98).
No Data given.
Data given (12/20/97).
Data obtained from state contacts. (3/20/98)
Data given (12720/97).
Conditioning Notes:
• No dates provided for Kentucky, hov/ever, data was collected in 1993-1994.
• The data contained in this data set was already modified and combined by EPA.
Maine
Data received from EPA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
in the earlier "11 States" data set
Data given (12/21/97).
No data given (12/21/97).
Data given in 5-, 6-, 7- digit format. (12/21/97) used SDWIS to obtain correct PWSIDs.
Some PWSID were still invalid, the data from MI, CA, ID are invalid (3/19/98).
No data given from raw table (12/21/97). Stakeholders information provided this data
(all data=CWS) [4/2/98]. Linked with SDWIS to determine TWSS for active systems.
Provided converted values to IAOED standards (12/21/97).
Used city nam?, does not correlate with SDWIS system name
Data given (12/21/97).
ISSI generated for database purposes (2/2/98).
EPA WAM provided data (4/2/98).
Assumed (4/2/98).
No Data given.
Data given (12/20/97).
Data obtained from state contacts. (3/20/98)
Data given (12/20/97).
Conditioning Note:
• The data contained in this data set was already modified and combined by EPA.
Michigan
Data received from Mark Breithart, MI DEQ.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Added "MI" to all records (02/08/98).
Data given (3/3/98).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98).
-------
Sampling Point Type:
Sample Type:
Sample Dale:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given (for details, see Michigan documentation).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20//97).
Conditioning Notes:
* Date range: 1/83-1/98.
• Data was provided in multiple data sets; one for each year of the study. Data included multiple sources, see
detailed conditioning sheet for more information.
• Deleted 102 records because no dates were provided.
• Deleted 194 records because no PWSIDs were provided,
Minnesota
Data received from Dick Clark and Karla Peterson.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given but had to be modified in the following way: G = "GW" W = "GW" and S =
"SW" (12/20/97).
No data given (12/20/97).
Data given (12/20/97)
ISSI generated for database purposes (2/2/98)
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97). If the reporting limit was not provided or reported as "N/A" and
if the reporting code was "< x-value", the reporting limit was assumed that to be the "x-
value." If the reporting limit was NA and no "< (positive result)" reported, then a
reasonable assumption was made based on review of data for similar counties, PWSID,
collection date, and point of contact. From June 10,1993, the reporting limit was 1.0 ppb
but prior to this period, the reporting limits were 1.0 and 5.0 ppb.
Assigned based on the reporting limit.
Conditioning Note:
• Date range: 12/92-12/97
Missouri
Data received from Darrell Osterhoudt, MO Dept of Health
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Added "MO" to all records.
Data obtained from SDWIS.
Data given (12/20/97).
Data obtained from SDWIS.
Data provided but verified with SDWIS.
Data given (12/20/97).
Data obtained from SDWIS.
ISSI generated for database purposes (2/2/98).
Data determined from file titles (12/20/97).
-------
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Data provided by data contact.
Assigned based on the reporting limit.
Conditioning Notes:
• Used ars_unc2.dbf (combination of ms_raw and ms_finished)
• Date range: 1/95-9/97
• Data contact indicated that only positive results were reported.
Montana
Data received from EPA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
in the earlier "11 States" data set
Data given (12/21/97).
No data given (12/21/97).
Data given in 5-, 6-, 7- digit format. (12/21/97) used SDWIS to obtain correct PWSIDs.
Some PWSID were still invalid, the data from MI, CA, ID are invalid (3/19/98).
No data given from raw table (12/21/97). Stakeholders information provided this data
(all data=CWS) [4/2/98]. Linked with SDWIS to determine TWSS for active systems.
Provided converted values to IAOED standards (12/21/97).
Used city name, does not correlate with SDWIS system name
Data given (12/21/97).
ISSI generated for database purposes (2/2/98).
EPA WAM provided data (4/2/98).
Assumed (4/2/98).
No Data given.
Data given (12/20/97).
Data obtained from state contacts. (3/20/98)
Data given (12/20/97).
Conditioning Note:
• The data contained in this data set was already modified and combined by EPA.
Nevada
Data received from SRA.
State:
County:
PWSID:
Type ofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "NV" to all records.
Data given (12/20/97).
Data given but added "NV" for given sysid (12/20/97).
Data obtained from SDWIS.
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98)
Data provided by the data contact.
Data provided by the data contact.
Data given (12/20/97).
Data given (12/20/97).
Data provided by the data contact.
Assigned based on the reporting limit.
-------
Conditioning Note:
• Date range: 2/91-8/97.
New Hampshire
Data received from EPA in the earlier "11 States" data set
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given (12/21/97).
No data given (12/21/97).
Data given in 5-, 6-, 7- digit format. (12/21/97) used SDWIS to obtain correct PWSIDs.
Some PWSID were still invalid, the data from MI, CA, ID are invalid (3/19/98).
No data given from raw table (12/21/97). Stakeholders information provided this data
(all data=CWS) {4/2/98]. Linked with SDWIS to determine TWSS for active systems.
Provided converted values to IAOED standards (12/21/97).
Used city name, does not correlate with SDWIS system name
Data given (12/21/97).
ISSI generated for database purposes (2/2/98).
EPA WAM provided data (4/2/98).
Assumed (4/2/98).
No Data given.
Data given (12/20/97).
Data obtained from state contacts. (3/20/98)
Data given (12/20/97).
Conditioning Note:
• The data contained in this data set was already modified and combined by EPA.
New Jersey
Data received from SRA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
and verified with NJ contacts.
Added "NJ" to all records.
No data given.
Data given but added "NJ" to given sysid (12/20/97).
Data obtained from SDWIS.
Data given but had to be modified in the following way: G, W="GW" P, S, U="SW"
(12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98).
Data provided by the data contact.
Data provided by the data contact.
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
Assigned based on the reporting limit.
Conditioning Note:
• Date range: 1/93-^/97
-------
New Mexico
State:
County:
PWSID:
TypeofWSS:
Source Type:
t PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "NM" to all records.
No data given.
Data given.
Data obtained from SDWIS.
Data obtained from SDWIS,
Data given.
Data given.
ISSI generated for database purposes
Data provided by the data contact.
Data provided by the data contact.
Data given.
Data given.
Data provided by the data contact.
Assigned based on the reporting limit.
North Carolina
Data received from SRA and verified with NC contacts.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "NC" 10 all records.
County codes provided (12/20/97). Used SDWIS to fill in.
Data given but added "NC" to given sysid (12/20/97).
Data given but had to be modified in the following way: C="CWS" N="TN" P="NTNC"
R="Recreation" (12/20/97).
Data given but had to be modified in the following way: G="GW" P="SW" S="SW"
W="GW" Y="GW" (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98).
Data provided by the data contact.
Data provided by the data contact.
Data given (12/20/97).
Data given (12/20/97).
Were never resolved.
Assigned based on the reporting limit.
Conditioning Note:
• Date range: 4/79-4/97.
North Dakota
Data received from SRA and verified with ND contacts.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Added "ND" to all records.
No data given (12/20/97).
Data given but added "ND" to given sysid (12/20/97).
Data obtained from SDWIS.
Data given (12/20/97).
Data given (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98).
Data provided by the data contact.
Assumption made the data contact.
-------
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data given (12/20/97).
Data given (12/20/97).
Data provided by the data contact.
Data given (12/20/97).
Conditioning Note:
• Date range: 1/93-10/96.
Ohio
Data received from EPA
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
in the earlier "11 States" data set
Data given (12/21/97).
No data given (12/21/97).
Data given in 5-, 6-, 1- digit format. (12/21/97) used SDWIS to obtain correct PWSIDs.
Some PWSID were still invalid, the data from MI, CA, ID are invalid (3/19/98).
No data given from raw table (12/21/97). Stakeholders information provided this data
(all data=CWS) [4/2/98]. Linked with SDWIS to determine TWSS for active systems.
Provided converted values to IAOED standards (12/21/97).
Used city name, does not correlate with SDWIS system name
Data given (12/21/97).
ISSI generated for database purposes (2/2/98).
EPA WAM provided data (4/2/98).
Assumed (4/2/98).
No Data given.
Data given (12/20/97).
Data obtained from state contacts. (3/20/98)
Data given (12/20/97).
Conditioning Note:
• The data contained in this data set was already modified and combined by EPA.
Oklahoma
Data received from Tom
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Poeten, EPA Region 6.
ISSI generated for database purposes.
Provided in database.
Database provided abbreviated PWSID numbers, which were converted to complete
PWSID numbers in accordance with directions from Tom Poeten.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Conditioning Note:
* Deleted 4 samples with unusual dates (years reported at 05)
-------
Oregon
Data received from Ed Thomas EPA, verified with Patrick Meyer, OR
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
ISSI generated for database purposes.
Provided in database.
Provided in database.
Data obtained from SDWIS.
Data obtained from SDWIS.
Provided in database.
Data obtained from SDWIS.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Provided in database.
Not provided in earlier samples (ND = 0). Assumed to be 5 in 1993, and 1 in 1994 and
1995 based on frequency analysis. 1994 and later samples also included an increasing
number of samples with reporting limits.
Provided in database.
Texas
Data received from SRA and verified with TX contacts.
State:
County:
PWSID:
TypeofWSS:
Source Type:
PWSName:
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Added "TX" to all records.
No data given (12/20/97).
Data give but added "TX" to given sysids.
Data obtained from SDWIS.
Data given but had to modified in the following way: S, P="SW"; G, W="GW"; Y -
blank...(SDWIS).
Data give (12/20/97).
Data given (12/20/97).
ISSI generated for database purposes (2/2/98)
Data provided by the data contact.
Data provided by the data contact.
No data given.
Data given (12/20/97).
Data provided by the data contact.
Based on the reporting limit
Conditioning Notes:
• Date range: 3/92-12/96.
• Changed "0" values to non-detects at the reporting limit; there were 51 of these cases. Because the reporting
limit was determined by the date, and some records had no date, they had to be removed, there were 13 of
them.
Utah
Data received from Larry Scanlon UT Dept of Env. Quality
State:
County:
PWSID:
Type ofWSS:
Source Type:
Purchased:
PWSName:
Added "UT" to all records.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data obtained from SDWIS.
Data given (12/20/97 and 3/99).
-------
Population:
Sampling Point ID:
Sampling Point Type:
Sample Type:
Sample Date:
EPA Analytical Value:
Reporting Limit:
Detection:
Data obtained from SDWIS.
ISSI generated for database purposes (2/2/98)
Data given (12/20/97 and 3/99).
Data given (12/20/97 and 3/99).
Data given (12/20/97 and 3/99).
Data given (12/20/97 and 3/99).
Data given. Used less than values as reporting limits.
Less than values were classified as nondetects.
Conditioning Notes:
* Used data from nciver2.xls file.
• Data from utahas96.wq2 seems to be the data from two of the SRA Utah raw files (utahpv and utahpw).
• Data updated and additional samples added with results received from Larry Scanlon (via EPA WAM) in March
1999.
• Date range: 1/78-3/99.
• Deleted S5921 (zero result reported for detection).
-------
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-------
Appendix D-2
ACD Database Specifications
-------
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-------
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Appendix D-3
Data Conditioning Process
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DATABASE DEVELOPMENT AND DATA CONDITIONING
Database Design
A two step process was used to identify the database design that is necessary for
developing arsenic occurrence and exposure projections. First, data elements were identified by
analyzing similar data models used for arsenic occurrence and exposure projections. Second, a
database was designed to support estimation of arsenic occurrence and exposure, to
accommodate the data elements identified in step one, and to maximize functionality.
Data Element Identification
ISSI identified data elements for AOED from USEPA's emerging National Contaminant
Occurrence Database (NCOD) and SAIC's previous arsenic occurrence project. The data
elements chosen for inclusion are the minimum elements required to estimate arsenic occurrence
and exposure, as well as additional elements chosen to support the Analysis Plan and the Arsenic
Occurrence and Exposure Report. Four different categories of data related to arsenic occurrence
and exposure were established to identify data elements with common characteristics, including
Location Information, PWS Information, Sample Information, and Result Information. The
Location Information category contains information on the USEPA Region, State, and County.
The PWS Information category contains the name, address, PWSID #, purchase category, type of
water system supply (Community Water System (CWS) or Non-Transient Non-Community
Water System (NT NCWS)), population, PWS latitude and longitude, and source type (ground
water or surface water). The Sample Information category table contains sampling point type
(finished or raw), sample ID, sample latitude and longitude, sample type (total or dissolved), and
sample collection date. Finally, the Contaminant Information category contains the contaminant,
USEPA analytical value, unit of measure, reporting limit, detection, and speciation.
Database Structure
The Arsenic Occurrence and Exposure Database (AOED) was designed to support
development of estimates of arsenic occurrence and exposure. The four categories (location
information, PWS information, sample information, and result information) summarized above
correspond to tables in the relational database. The tables are related in a one to many
relationship, starting with the location table and ending with the contaminant table. Data
elements identified for each category correspond to a column in the related table.
In addition to the data elements identified above, three source tables were included to
identify the source of each data point. For example, for data elements obtained from the NIRS
data set, the corresponding data elements in the source tables were populated with the code 'NR'.
Each of the source tables is related to its parent data table in a one-to-one relationship.
Public Comment Draft
May8, 2000
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Database Review Process
The database review process evaluated the potential value of the data for use in projecting
the national occurrence and exposure estimates for arsenic. Raw data sets (RDSs) that are
suitable for use in the arsenic projections were identified as a result of this process. The database
review process took place in three stages.
The first stage was to identify and evaluate the characteristics of the RDSs. An
interaction analysis was conducted, where the data elements in each RDS was compared with the
data elements specified in the AOED requirements. An interaction analysis identifies the
relationship between data elements in two distinct data sets. Several other analyses were
conducted to identify the critical data elements within each RDS and to evaluate the RDS data
quality.
The second stage of the data review process was conditioning. The results of the
interaction analysis were used to guide the population of a temporary database with suitable RDS
data formatted to fit the specifications of AOED.
The final stage was data gathering and data set decision. Data gathering consisted of
identification of data gaps and populating them with acceptable data. Data set decision involved
the evaluation of a data set to determine if a sufficient amount of information was present to
support AOED. Data sets that did not contain sufficient information to support arsenic
occurrence and exposure estimates were removed from the development process.
Raw Data Set Analyses
The RDSs consisted of a wide variety of data elements and formats. RDS analyses were
conducted to identify:
1) data quality and format,
2) critical data elements within each RDS, and
3) the relationship between the RDS and AOED data elements
The first type of analysis examined the characteristics of the data elements. These
characteristics varied by data element and data source. For example, for the Source Type data
element, one RDS might use 'G' to define a groundwater source, where another RDS might use
'GW,* while a third might use a numerical code such as 3.
The second type of analysis identified the critical data elements within each RDS. These
critical data elements were necessary to project arsenic occurrence and exposure. The critical
data elements identified were Source Type, Type of Water System Supply, PWS ID#, Reporting
Limit, Sample Collection Date, and Detection. Analyses also included a report on population
distribution within each of the RDS, the spatial and temporal coverage provided by the RDS, and
miscellaneous other analyses. These analyses provided a preliminary overview of the data
Public Comment Draft
May 8. 2000
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quality and representativeness. The results of these analyses were used to present interim status
reports to USEPA, identify data gaps, and provide a basis for the project direction.
The third type of analysis identified the relationship between the data elements present in
the RDS and those specified for AOED. Four types of relationships were identified between
RDS and AOED data elements. In the first type of relationship, a data element found in the RDS
has a definition which matches the definition of the AOED data element. This is known as a
correlated element. The second type of relationship is a calculated value, which occurs when one
or more data elements in the RDS can be manipulated to calculate the value of the AOED data
element. The third relationship is a logical inference, where the value for the AOED data
element can be logically inferred from background information about the RDS. The final
relationship, no correlation, exists when no data element in the RDS correlates with the AOED
data element.
Data Conditioning
4-
The data from the RDS were transferred to spreadsheets to facilitate further analyses.
Each spreadsheet was associated with one RDS and contained the data elements for AOED.
These spreadsheets were used to compile all of the raw data into a single uniform format that,
made it easy to perform data manipulation. Collectively, these "data sheets" comprised IAOED
(Intermediate Arsenic Occurrence and Exposure Database). IAOED was a necessary step to
properly format the data for input into AOED, the relational database.
IAOED played an important role in the identification of data gaps and RDS quality
concerns, and supported initial data analyses, the interaction analysis, and data conditioning.
Data conditioning involved using the results of the interaction analysis to guide the transfer of
data element values into IAOED according to the conditioning steps indicated below:
Case 1. Correlated Elements - The data element values were copied directly from the
RDS to IAOED. No modifications were necessary to the data, as the data element definitions
matched closely. A data element was found in the RDS whose definitions correlate closely with
the definition of the AOED data element. The value of the RDS data element can be used
directly as the value of the AOED data element. For example, the AOED data element Sample
Collection Date conforms directly to the SRA-Texas data element SAMP_DATE.
Case 2. Calculated Value - The data element values for IAOED were determined, using
an algorithm, from the RDS value to the properly formatted AOED value. To illustrate, the
AOED data element Sampling Point Type can be derived by converting the data element
ANALYTNAME from the Missouri RDS according to the following algorithm:
If ANALYTNAME = "Arsenic, Total", then code as "T"
If ANALYTNAME - "Arsenic, Dissolved", then code as"D"
If ANALYTNAME = " ", then leave field blank.
Public Comment Draft
May8, 2000
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Case 3. Logical Inference from RDS Characteristics - The data element value for IAOED
was inferred from the background information known about the RDS source. In most cases, the
same value was used for all records in a single IAOED data sheet. For example the AOED data
element USEPA Region is not present in the SRA-Alaska, but Alaskan water supplies all occur
in USEPA Region 10, so the value for USEPA Region can be populated with "10".
Case 4. No correlation - The data element value for IAOED was not populated with data
from the RDS. Efforts were made to contact the source of the data to pursue possible values for
the data element.
IAOED data conditioning involved population of the source data tables with information
to track the source of each data point, and documentation of the conditioning process to track
quality assurance and quality control (QA/QC) issues. Data conditioning also involved
generation of artificial Loc ID#, Sampling Point ID#, and Result ID# if these numbers were not
provided in the State databases in anticipation of the transfer of data from the loosely structured
"data sheet" format of IAOED to the controlled relational structure of AOED. For the eight
States with intra-system data, Sampling Point ID or Loc ID were provided so and we could track
samples that were collected from individual POE.
Data Gathering and Decision
After preliminary data conditioning, and identification of data gaps, efforts were made to
fill in the data gaps with accurate data. Representatives from the agency or organization which
provided the RDSs were contacted to obtain the missing information. For several of the RDSs,
information could not be obtained for critical data elements. These RDSs should not be used in
the arsenic occurrence projections due to insufficient information, rounding of results,
duplication of results in another data set or unknown manipulation of data.
Public Comment Draft
May 8. 2000
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