2006 Drinking Water 

 Data Reliability 

 Analysis and Action 

 Plan 

 For State Reported Public 

 Water System Data In the 

 EPA Safe Drinking Water 

 Information System / 

 Federal Version 

 (SDWIS/FED)

 

 

 

Office of Water (4606M) 

EPA 816-R-07-010

www.epa.gov/safewater 

March 2008 Printed on Recycled Paper
i

2006 Drinking Water Data Quality Analysis and Action Plan

Executive Summary

 

Safeguarding our nation’s drinking water by developing effective and appropriate policy 

decisions and conducting program oversight depends on data of known and documented quality. 

The Safe Drinking Water Information System/Federal Version (SDWIS/FED) is the 

Environmental Protection Agency’s (EPA) principal database for the national drinking water 

program. It contains data on public water systems (PWS) provided by the states to EPA. It is 

primarily used for management of state and EPA programs and for informing the public about 

the compliance status of their drinking water systems, and indirectly, the safety of their drinking 

water. EPA uses the information in SDWIS/FED for various analyses to support programmatic 

decisions and identify trends, problems, and opportunities for improvement of the states’ rule 

implementation as well as program oversight. Consequently, the utility of SDWIS/FED 

information for these purposes highly depends on the quality of the data it contains. 

EPA routinely evaluates state programs by conducting Data Verification (DV) audits, which 

evaluate state compliance decisions and reporting to SDWIS/FED. EPA prepares triennial 

summary evaluations based on the DV. This document presents results of EPA’s third triennial 

review of data quality in SDWIS/FED and includes an evaluation of the data collected from 2002 

through 2004. For the 38 states evaluated, we found that: 

• Ninety-four percent of health-based violation data in SDWIS/FED were accurate. 

• Approximately 81% of the maximum contamination level (MCL) and surface water 

treatment technology (SWTR TT) violations were reported to SDWIS/FED. 

• Including lead and copper treatment technology (LCR TT) violations, about 62% of the 

health-based violations (MCL and Treatment Technology violations) were reported to 

SDWIS/FED, where only 8% of LCR TT violations were reported to SDWIS/FED. 

• Only approximately 30% of the monitoring and reporting (M/R) violations were reported 

to SDWIS/FED. 

• The primary reason for non-reporting was due to compliance determination errors rather 

than data flow errors. 

• Further, 60% of the health-based violations1

 were reported on time and approximately 

30% of the monitoring and reporting violations were reported on time to SDWIS/FED in 

2004. 

 

Background

SDWIS/FED contains data about PWS facilities, violations (e.g., exceptions and exceedances) of 

Federal drinking water regulations adopted by the states, and enforcement actions taken by the 

state. The regulations include health-based drinking water quality standards, performance of 

 

1 The health-based violations in this reference do not include lead and copper treatment technology violations 

because they have open-ended compliance period end dates. 
ii

treatment techniques and/or process requirements. The focus of this report is on two types of 

violations: (1) health-based violations (i.e., exceedance of maximum contaminant level or nonperformance of a treatment technique or process), and (2) monitoring and reporting violations 

(i.e., a water system did not monitor, did not report monitoring results, or were late in reporting 

results to the state.) 

States manage their own processes and databases differently to document public water system 

capabilities and their program management decisions concerning violations (or noncompliance), 

and to record corrective actions undertaken. State data indicate that violations occur 

infrequently at most public water systems (PWS). Violation data that states report to EPA 

(SDWIS/FED) reflect only those major and minor noncompliance results that may lead to 

adverse public health outcomes. Violations represent a small fraction of all the determinations 

states make which demonstrates the safety of the nation’s water supply. 

 

The first triennial review of data quality evaluated data for the period 1996-1998. That 

assessment, which resulted in a detailed data analysis report in 2000, produced an action plan 

under which states and EPA worked together to improve data quality. The plan resulted in 

actions that included training state staff, streamlining reporting to SDWIS/FED, making 

SDWIS/FED error reporting and correction more user-friendly, improving DVs, following up 

with Regions after DVs, and encouraging states to notify water systems of sampling schedules 

annually. Similarly, the second triennial review of data quality analyzed the data from the period 

1999-2001 and findings were presented in the 2003 report. The recommended action plan in the 

2003 report included: 

• Development of state-specific compliance determination and quality improvements plans 

necessary to remedy the major problem areas, 

• Conducting and improving data quality analysis and report results, 

• Implementation of the OGWDW information strategic plan and SDWIS/FED 

modernization, 

• Development of an automated monitoring requirement and sampling schedule tracking 

system by the states, and evaluation of timeliness of violations and potential violation 

non-reporting. 

 

This Review

Between 2002 and 2004, EPA conducted DV audits in 38 states and reviewed data on drinking 

water system information, violations, and enforcement actions. See Table ES-1 for the list of DV 

States. EPA evaluated 2,658 PWSs, of which 43% were Community Water Systems (CWS). See 

Table ES-2 for the distribution of systems by system type and the size of population served. The 

violations addressed by the DVs are shown in the Appendix B. The period of review by rule was 

generally the two most recently scheduled monitoring periods for each water system and 

applicable rule. For the Total Coliform Rule (TCR) and the Surface Water Treatment Rule 

(SWTR TT), the most recent four quarters were evaluated. 
iii

Table ES-1: States Subject to Data Verifications from 2002-2004

Region States Region States 

1 CT, MA, RI, VT 6 AR, NM, OK, TX 

2 NJ, VI 7 IA, MO 

3 MD, PA, VA, WV 8 CO, SD, UT, WY 

4 AL, FL, KY, MS, NC(’02), 

NC(’04) SC, TN 

9 AZ, CA, R9 Tribes 

5 IL, MI, MN, OH 10 AK, ID, WA 

Table ES-2: Number of Systems included in Data Verifications 

by System Type and Size

 System Type 

System Size CWS NTNCWS TNCWS Total 

Very Small (500 or fewer) 572 637 696 1,905 

Small (501-3,300) 277 123 36 436 

Medium (3,301-10,000) 119 9 6 134 

Large (10,001-100,000) 135 4 0 139 

Very Large (>100,000) 44 0 0 44 

Total 1,147 738 773 2,658 

Summary of Results 

For the MCL/SWTR TT violations, 81% of the data were reported to SDWIS/FED, Figure ES-3 

summarizes the data quality estimates by violation type. Of the non-reported violations, 74% 

were due to compliance determination (CD) errors, where the states did not issue a violation 

when a violation had occurred. Twenty-six percent of the non-reported violations were due to 

data flow (DF) errors. Figure ES-4 summarizes the percentage of errors contributed from nonreporting by violation type. Approximately 94% of the data in SDWIS/FED were accurate. The 

overall data quality (DQ) of the MCL/SWTR TT violations was 77%. This means that 77% of 

the noncompliance determinations on MCL/SWTR TT were correctly reported in SDWIS/FED. 
iv

Figure ES-3: Data Quality Estimates by Violation Type 

81.33%

94.12%

77.21%

61.69%

94.30%

59.18%

29.02%

88.35%

27.08%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Completeness Accuracy DQ

MCL/SWTR TT Violations Health-Based Violations M/R Violations

Figure ES-4: Percentages of Error Contribution to Non-Reporting of Violations 

73.84%

84.45%

92.03%

7.97%

26.16%

15.55%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MCL/SWTR TT Violations Health-Based Violations M/R Violations

CD Error DF Error

CD: Compliance Determination 

DF: Data Flow
 

For the health-based violations including LCR TT violations, 62% of the data were reported to 

SDWIS/FED. Of the non-reported violations, 84% were due to CD errors. Approximately 94% 

of the health-based violations data in SDWIS/FED were accurate in SDWIS/FED. The overall 

data quality of the health-based violations was 59%, i.e., approximately, 59% of the 

noncompliance determinations on health-based standards were correctly reported in 

SDWIS/FED. 

Only 29% of the monitoring and reporting violations were reported to SDWIS/FED. Ninety-two 

percent of the non-reported violations were due to CD errors. Approximately 89% of the 

monitoring and reporting violations data in SDWIS/FED were accurate. The overall data quality 

of the M/R violations was 27%, i.e., 27% of the noncompliance determinations on M/R were 

correctly reported to SDWIS/FED. 

Data Reliability Improvement Action Plan

Appendix A is a joint plan of EPA and the Association of State Drinking Water Administrators 

to achieve a goal of 90 percent complete and accurate data for health-based violations, as well as 

improving the quality of monitoring and reporting violations and inventory data. Progress 

toward accomplishment of this goal will be measured annually and assessed in 2009. 
ii

Acknowledgements 

The following people contributed to this analysis and the preparation of this report: 

Project Lead Drinking Water Protection Division 

 Office of Ground Water and Drinking Water 

Chuck Job, Branch Chief, Infrastructure Branch 

 

 Leslie Cronkhite, Associate Branch Chief, Infrastructure Branch 

Principal Author Jade Freeman, Ph. D., Statistician, Infrastructure Branch 

Contributing Author Lee Kyle, IT Specialist, Infrastructure Branch 

Peer-review of the statistical methodology in this report has been provided by: 

Anthony Fristachi, Exposure Analyst 

 National Center for Environmental Assessment 

 U.S. EPA Office of Research and Development 

 

Tony R. Olsen, Ph. D., Environmental Statistician 

 Western Ecology Division 

Health and Environmental Effects Research Laboratory 

U.S. EPA Office of Research and Development 

Arthur H. Grube, Ph.D., Senior Economist 

 U.S. EPA Office of Pesticide Programs 

 

A. Richard Bolstein, Ph. D., Chairman (retired) 

Department of Applied & Engineering Statistics 

George Mason University 

John Gaughan, Ph. D., Associate Professor 

Epidemiology & Biostatistics 

Temple University School of Medicine 

Matthias Schonlau, Ph. D., Head Statistician 

Statistical Consulting Service 

 The Rand Corporation 

Collaboration on the Data Reliability Improvement Plan - Association of State Drinking 

Water Administrators
 

Table of Contents

1. Introduction..............................................................................................................................1 

 1.1 Previous Activities .......................................................................................................2 

 1.2 Regulatory Context ......................................................................................................2 

 1.3 Changes in 2006 Analysis Method ................................................................................3 

2. Overview of Data Verification .................................................................................................3 

3. Statistical Sample Design of Data Verification and Analytical Method ..................................6 

 3.1 Selection of States..........................................................................................................6 

 3.2 Selection of Systems with State.....................................................................................7 

 3.2.1 Sample Frame ....................................................................................................7 

 3.2.2 Sample Design Data Verification ......................................................................7 

 3.2.3 Sampling Procedure and Data Collection Activities .........................................8 

 3.3 Analytical Method: Weighting and Estimation .............................................................9 

4. Results from the Analysis of Data Verification.....................................................................12 

 4.1 Analysis of Inventory Data ..........................................................................................12 

 4.2 Analysis of Violation Data ..........................................................................................13 

 4.2.1 Results from 2002-2004 Data Verifications ...................................................19 

 4.2.2 Results from 1999-2001 Data Verifications ..................................................23 

 4.2. Data Quality Estimates from 1999-2001 and 2002-2004 ..................................25 

 4.3 Analysis of Enforcement Data.....................................................................................31 

5. Analysis of Timeliness of Violation Reporting in SDWIS/FED............................................31 

6. Conclusion ..............................................................................................................................34 

7. Data Reliability Improvement Action Plan ............................................................................35 

8. Future Analysis of Data Reliability ........................................................................................35 

Appendix A: 2006 Data Reliability Improvement Action Plan ...................................................37 

Appendix B: Violations Addressed by Data Verification (DV)...................................................45 

Appendix C: Definition of Public Notification (PN) Tier ...........................................................47 
1

2006 Drinking Water Data Quality Assessment and Action Plan 

1. Introduction 

The Safe Drinking Water Information System/Federal Version (SDWIS/FED) is the 

Environmental Protection Agency’s (EPA) principal database for the national drinking water 

program. Its two major uses are (1) to help manage state and EPA programs and (2) to inform 

the public about the compliance status of public water systems (PWSs) and, indirectly, the safety 

of drinking water. The Federal government uses SDWIS/FED data for program management for 

90 contaminants (as of 2005) regulated in drinking water at approximately 158,000 PWSs in 56 

state and territorial programs and on Indian lands. Data received by EPA from states in 

SDWIS/FED includes a limited set of water system descriptive information, e.g., system type, 

population served, number of service connections, water source type), data on PWSs’ violations 

of regulatory standards and process requirements, and information on state enforcement actions. 

These data, which EPA uses to assess compliance with the Safe Drinking Water Act (SDWA) 

and its implementing regulations, represent the only data states are currently required to report to 

EPA relative to drinking water safety. SDWIS/FED data can be accessed from the EPA web site 

at www.epa.gov/safewater. 

The utility of SDWIS/FED data for program management and public communication is highly 

dependent on the quality of data housed by the system. To assess this quality, EPA routinely 

conducts data verification (DV) audits in states and develops a summary evaluation every three 

years called Drinking Water Data Quality Assessment. DV auditors evaluate compliance data in 

state databases and hard copy files, monitoring plans, and other compliance information 

submitted by PWSs. The auditors also examine sanitary surveys, correspondence between the 

state and the water system, compliance determination decisions, and enforcement actions taken 

by the state. Based on this information, the auditors confirm whether all required information 

was submitted to and evaluated correctly by the state and whether required reporting elements 

were submitted to SDWIS/FED. 

This report includes (1) a description of the methodology used; (2) analyses of the data from the 

2002 to 2004 Data Verifications, the most recent triennial evaluation period; and (3) analysis of 

the timeliness of reporting in SDWIS/FED. The report also describes a plan to address 

continued improvement in drinking water compliance data reported by states. This report is not 

intended for evaluating states’ performance. This report is a tool to identify the gap between the 

states’ violation data and SDWIS/FED and to provide a benchmark for the collaborative efforts 

between the states and EPA to bridge the gap and improve the data quality in SDWIS/FED.

1.1 Previous Activities 

 

In 1998, EPA launched a major effort to assess the quality of the drinking water data contained 

within SDWIS/FED to respond to concerns regarding incorrect violations in the database. EPA 
2

enlisted the help of its stakeholders in designing the review, analyzing the results for data 

collected between 1996 and 1998, and recommending actions to improve drinking water data 

quality. The first Data Reliability Analysis of SDWIS/FED was published in October 2000. 

Findings of the first Data Reliability Analysis, which indicated that data quality needed 

improvement, were later updated by the second triennial assessment in 2003 (which included 

data collected between 1999 and 2001). Together, these assessments included comprehensive 

recommendations for EPA and state primacy agencies on quality improvements. The reports 

identified near-term actions that had already been taken or were still needed to improve data 

quality more immediately. To implement the recommendations, the states and EPA have 

conducted numerous activities and projects to improve data quality. Activities undertaken have 

included a) providing training for states; b) streamlining reporting to SDWIS/FED; c) making 

SDWIS error reporting correction more user-friendly; d) improving data verifications; e) 

following up with Regions on findings after data verifications; and f) encouraging states to 

annually notify water systems of sampling schedules. 

The Office of Ground Water and Drinking Water’s (OGWDW) response to the data reliability 

issues identified in the 2003 report included a commitment to conduct analyses which would 

provide periodic data quality estimates (DQEs), and provide input into program activities and 

priorities necessary to improve the quality and reliability of the data. Part of that commitment 

was to publish the results of these analyses every three years. 

1.2 Regulatory Context 

States make a large number of determinations regarding public water systems’ compliance with 

drinking water regulations and violations of these regulations are a small fraction of these 

determinations. Since violations represent a small fraction of all the determinations states make, 

this result indicates the general safety of the nation’s drinking water supply. For example, an 

analysis of nitrate maximum contaminant level compliance data for Oklahoma from 2004, 

showed only 3% of determinations resulted in violations. 

The data considered for evaluating quality, particularly accuracy and completeness, consist of 

the violations of health-based standards and monitoring and reporting requirements. These data 

are important for two reasons: (1) States and EPA program management relies on them to 

identify priorities and (2) states and EPA use them to inform the public about the safety of its 

drinking water. For federal program reporting purposes under the Government Performance 

Results Act (GPRA), violation data have become a major focus. EPA’s 2006-2011 strategic plan 

specifies a clean and safe water goal of “90% of the population served by community water 

systems (CWS) meeting all health-based standards and treatments by 2011.” A CWS which 

meets all health-based standards and treatments does not have a violation of the federal 

regulations for maximum contaminant levels (MCL) or treatment techniques. Due to the 

importance and emphasis on violation data, this data quality evaluation methodology addresses 

whether states correctly identify and report the violations that should have been reported to EPA 

according to state primacy agreements pursuant to Federal regulations. 
3

1.3 Changes in 2006 Analytical Method

In this analysis of 2002 to 2004 DV data, EPA uses a different method for evaluating the data 

quality as described below. 

! In the previous report, the DQEs were calculated without considering the sample design 

of DVs, i.e., the selection process by which the systems are included in the sample. In 

this assessment, the DQEs are calculated using statistical sample design-based unbiased 

estimation. The sample design and the estimation method for calculating sample 

statistics are described in detail in Section 3. 

! The completeness measure of the violation data quality in the 2003 report represented the 

proportion of accurate data in SDWIS/FED out of all violation data that should be 

reported to SDWIS/FED. However, in this 2006 analysis, EPA redefined completeness 

of SDWIS/FED based on any violation data reported to SDWIS/FED regardless of the 

accuracy. 

! Because of the changes in the estimation method described above and non-random 

selection of states for DV audits, the results from this analysis will not be compared to 

those from the 2000 or 2003 assessments. 

The statistical methodology for the analysis of DV data and the results are described in Sections 

3 and 4. The additional analysis of the timeliness of reporting in SDWIS/FED is presented in 

Section 5. 

2. Overview of Data Verification

EPA's OGWDW routinely conducts DV audits, which evaluate the management of state drinking 

water programs. During the DVs, EPA examines state compliance decisions, data on the system 

compliance and violations in the state files, and the data required to be reported to SDWIS/FED. 

During the DVs, EPA reviews data submitted by PWSs, state files and databases, and 

SDWIS/FED, and compiles the results on the discrepancies among the data. States have several 

opportunities to respond to findings while DV personnel are on site, and provide additional 

clarifying information if available. States also review the DV draft report before the final report 

is produced, and their comments are incorporated into the report. EPA responds to every state 

comment, to explain in detail whether or not the state’s additional information changed the 

finding. 

Until 2004, states were selected for DVs considering a number of factors; for example, the states 

that had not been audited for a long period of time were selected for DVs. Also, in order to 

minimize the burden on EPA Regions and states, OGWDW tried to maintain an even distribution 

of DV states across the regions2. Further, resource constraints have affected the selection of 

 

2 EPA is divided into 10 regional offices, each of which is responsible for several states and territories. 
4

certain states since it is more costly to conduct DVs in some states than others. Between 2002 

and 2004, EPA conducted DV audits in 38 states and reviewed data on drinking water system 

information, violations, and enforcement actions (Table 2-1). State files for a total of 2,658 

PWSs were evaluated, of which 43% were community water systems (Table 2-2). The 

regulations addressed by the DVs and the compliance period reviewed for each regulation are 

shown in Table 2-3. 

Table 2-1: States Subject to Data Verifications from 2002-2004

Region States Region States 

1 CT, MA, RI, VT 6 AR, NM, OK, TX 

2 NJ, VI 7 IA, MO 

3 MD, PA, VA, WV 8 CO, SD, UT, WY 

4 AL, FL, KY, MS, NC 

(’02), NC (’04), SC, TN 

9 AZ, CA, R9 Tribes 

5 IL, MI, MN, OH 10 AK, ID, WA 

Table 2-2: Number of Systems included in Data Verifications by Type and Size

System Type3

System Size CWS NTNCWS TNCWS Total 

Very Small (500 or fewer) 572 637 696 1,905 

Small (501-3,300) 277 123 36 436 

Medium (3,301-10,000) 119 9 6 134 

Large (10,001-100,000) 135 4 0 139 

Very Large (>100,000) 44 0 0 44 

Total 1,147 738 773 2,658 

 

3 Community water systems (CWSs) have at least 15 service connections or serve 25 or more of the same population 

year-round. Nontransient noncommunity water systems (NTNCWSs) regularly serve at least 25 of the same persons 

over 6 months per year. Transient noncommunity water systems (TNCWSs) provide water where people remain for 

periods less than 6 months. 
5

Table 2-3: Period of Compliance for Rules Reviewed During 2002-2004 Data Verifications 

Rule4 Compliance Period Reviewed 

Inventory 

Consumer Confidence Report (CCR) 

Most Recent 

Total Coliform Rule (TCR), 

Surface Water Treatment Rule (SWTR), 

Total Trihalomethanes (TTHMs) 

Most Recent 12-Month Period Available in 

SDWIS/FED 

 

Nitrates Most Recent Two Calendar Years 

Phase II/V excluding nitrates 1999-2001 

Lead and Copper Rule (LCR), 

Interim Radionuclides Regulation 

Most Recent Two Samples 

Enforcement 

Public Notification 

Time Period Related to Violation 

The review evaluated recent monitoring history to confirm that systems monitored according to 

the required frequency. For many rules, the review evaluated one year of information (Surface 

Water Treatment Rule, Total Trihalomethanes, Total Coliform Rule, and Consumer Confidence 

Report). The two most recent monitoring periods or review cycles were reviewed for some rules 

(interim radionuclides, Lead and Copper Rule, sanitary surveys). In other instances, the review 

covered a defined period, such as the most recent 3-year monitoring period for the Standard 

Monitoring Framework outlined in the Phase II/V Rule5 

3. Statistical Sample Design of Data Verifications and Analytical Methods 

3.1 Selection of States

As mentioned in Section 2, the states are selected for DVs by considering the date of their last 

verification, resource constraints, and burden on EPA Regions and states. This selection 

 

4 CWSs were reviewed for inventory and each of the rules listed in this table. NTNCWSs are not subject to CCR, 

TTHM monitoring, or the interim radionuclide regulation. TNCWSs are not subject to the requirements for CCR, 

SWTR, TTHM, Phase II/V Rule, or interim radionuclide regulation. 

5 The Standardized Monitoring Framework synchronizes the monitoring schedules for the Phase II/V regulation for 

chemicals and the interim radionuclides rule across defined 3-year monitoring periods and 9-year monitoring cycles.
6

procedure is a non-probability sampling method. Because of the subjective nature of the 

selection process, non-probability samples add uncertainty when the sample is used to represent 

the population as a whole. The accuracy and precision of statements about the population can 

only be determined by subjective judgment. The selection procedure does not provide rules or 

methods for inferring sample results to the population, and such inferences are not valid because 

of bias in the selection process. 

When non-probability sampling is used, the results only pertain to the sample itself, and should 

not be used to make quantitative statements about any population including the population from 

which the sample was selected. Since the DV states were selected by a non-probability sampling 

method, the results from the analysis only pertain to the DV states audited between 2002 and 

2004. Therefore, it is not appropriate to make quantitative statements or inferences about the 

entire nation from the selected states or comparisons with sampled state data quality results from 

the previous years. 

3.2 Selection of Systems within States

The DVs involve the evaluation of the states’ compliance decisions and the agreement between 

the data in the state files and SDWIS/Fed. Since neither time nor resources allow a complete 

census of consistencies between SDWIS/Fed and state records, EPA uses a statistically random 

sample of systems that is drawn from the total number of systems in the state. EPA uses the 

results from the probability sample of systems within each state to estimate DV compliance 

results for each state. The probability sample is designed to provide estimates with acceptable 

precision while minimizing the burden on Regions and states imposed by visits from auditors. 

EPA plans to further reduce burden on Regions and States through use of electronic data 

comparison. 

3.2.1 Sample Frame 

A sample frame is a list of all members of a population (in this case, the PWSs), from which a 

random sample of members will be drawn. In other words, the sample frame identifies the 

population elements from which the sample is chosen. The population elements listed on the 

frame are called the sampling units. Often these are groups or clusters of units rather than 

individual units. For each state, EPA developed a sample frame (i.e., a list of the current 

inventory of PWSs in the state) using SDWIS/FED, from which a random sample of PWSs was 

selected according to the sample design. 

3.2.2 Sample Design of Data Verification 

The unit of analysis is the recorded action taken by systems, not the systems themselves. The 

sample design for DVs is a stratified random cluster sample. In stratified sampling, the 

population is divided into non-overlapping subpopulations called strata and a random sample is 

taken from each stratum. Stratification increases the precision of the estimates when the 

population is divided into subpopulations with similar characteristics within each stratum. In 

cluster sampling, groups, or "clusters," of units in the population are formed and a random 
7

sample of the clusters is selected. In other words, within a particular stratum, rather than 

selecting individual units, clusters of units are selected. 

In the analysis of DV data, systems are grouped into three strata according to the system type 

(CWS, TNCWS, and NTNCWS) within each state. In the first stage of the sampling process, 

systems are randomly selected within each stratum. In the second stage, each action taken by the 

system is recorded. In other words, the system represents a cluster of actions. A few examples 

of these actions are: 

! System inventory information that must be reported to SDWIS/FED, 

! Violations of federal regulations (states also may report violations of state regulations), 

! Enforcement actions taken when violations occur. 

3.2.3 Sampling Procedure and Data Collection Activity 

Once the current state inventory is retrieved from SDWIS/FED, the number of systems is 

counted by size category (see Table 2-2 for size categories). The sample size for each system 

type within a state is calculated based on the acceptable precision level for the estimates within 

margin of error in most states of plus or minus five percent, with a confidence level of 90 or 95 

percent6. As discussed in section 3.2.2, the sample design is a stratified random cluster sample. 

The required sample size is given by: 

( )(deffnn ) = hh′

where nh is the size of the sample (number of systems) required for stratum h (specific state and 

system type) if a simple random sample is drawn. deff is the design effect of the clustering and 

is assumed to be greater than 1.0. nh is given by 

( )

( )

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − − +

−

=

1 1 1

1

2

2

2

2

h

hh

n

h

h

nh

h

B

ppZ

N

M

B

ppZ

n

α

α

, 

 

where nh= Number of systems required for the sample in stratum h, 

Nh = Total number of systems in the state in stratum h, 

 

6 For the three DVs that were conducted during the last quarter of 2004, (TX, VA, and IL), the confidence level for 

CWSs was 95 percent and the margin of error was plus or minus seven percent. For NTNCWSs and TNCWSs, the 

confidence level was 90 percent and the margin of error was plus or minus seven percent. 
8

Mh = Average number of actions in each system in stratum h, 

Bh = Acceptable precision level (margin of error) for stratum h, 

Zα = The abscissa of the normal curve that corresponds to the confidence level, 

and 

ph = Proportion of discrepancy in violation data between DV results and SDWIS/FED in 

stratum h (estimated from the previous assessment.)

The design effect deff depends on the proportion of actions and decisions reviewed in the DV 

that are consistent with the data in SDWIS/FED. This proportion is unknown before the DV; 

therefore, the design effect is unknown. Lacking estimates of the design effect, the DV draws a 

simple random sample within each stratum7

. Because it excludes the design effect, this sample 

may not be large enough to meet the precision targets. 

The sample size is calculated in an Excel spreadsheet. Samples are drawn from the frame 

according to the random numbers generated in an Excel spreadsheet produced by EPA. Using 

the Excel spreadsheet random number generator, a random sample of systems is developed for 

each stratum. Then, the DV auditors collect data from the state files for each sampled system on 

PWS inventory, violations, and enforcement. 

3.3 Analytical Method: Weighting and Estimation 

In this analysis, sample weights are applied to the data to adjust for the unequal probability of 

selection of systems, i.e., the differences in the likelihood of some systems appearing in the 

sample. Weights, based on the probability of selection, allow unbiased representation of the 

population from an unequal probability sample. 

In the 2002-2004 DV data analysis, EPA estimated proportions related to consistency and 

accuracy among state files, the state database, and SDWIS/FED for inventory information, 

violation data, and enforcement actions. A few examples of such proportions are the proportion 

of inventory data that are consistent between SDWIS/FED and the state file, the proportion of 

violation data that are reported to SDWIS/FED, and the proportion of enforcement data that are 

consistent between SDWIS/FED and the state file. In this report, these proportions are presented 

as percentages after being multiplied by 100. The proportion Pˆ is estimated by 

 

∑∑

∑∑∑

= =

=== = N

h

n

hh

H

h

n m

hh

h

h h

Wm

IW

P

1 1

111 ˆ

α

α

α β

αβ

α

 , 

 

7 Future DVs can estimate deff using data from previous DVs and can incorporate the design effect into the sample 

size calculation. 
9

 

where the sample weight 

h

h

h

n

N W = , 

 

= total number of clusters (systems) in stratum (system type) h, h=1,...,H, Nh

 

= number of sampled clusters in stratum h, nh

= number of data elements (reviewed actions) from cluster α in stratum h, mhα

I hαβ = 0 or 1 indicator for βth data element from the system α and Stratum h, 

corresponding to a specific characteristic. 

A simple illustration of the calculation procedure is presented here. Suppose there are three strata 

(H=3), namely CWS, NTNCWS, and TNCWS in State A. Also, suppose that the total number of 

systems in each stratum (system type in State A) is 6, 9, and 15 (N1=6, N2=9, and N3=15), 

respectively. Further, the number of sampled systems is 3 for each stratum (n1=n2=n3=3) and 

there are three violations reviewed for accuracy from each sampled system ( =3 for h=1,2,3 

and α=1,2,3). Let be 1 if the violation was accurately reported to SDWIS/FED or 0 if the 

violation was incorrectly reported. Suppose the compiled data are as shown in Table 3-1. The 

proportion of the violations with a discrepancy is estimated by the ratio of the sum of Wh

and the sum of Wh,, which, in this case, is 55/90=0.6111 or 61.11%. 

mhα

hαβ I

hα

hαβ I

m

Sampling errors are also estimated for the proportion estimates. Sampling errors are measures of 

the extent to which the values estimated from the sample (proportions in this analysis) differ 

from the values that would be obtained from the entire population. Since there are inherent 

differences among the members of any population, and data are not collected for the whole 

population, the exact values of these differences for a particular sample are unknown. 

To estimate the sampling errors, Taylor series expansion method is applied. The Taylor series 

expansion method is widely used to obtain robust variance estimators for complex survey data 

with stratified, cluster sampling with unequal probabilities of selection. The Taylor series 

obtains an approximation to a non-linear function. The Taylor series expansion method is 

applied to the variance of the proportion estimate as 

∑=

=

H

h

h PraVPraV

1

)ˆ ) ˆ ( ˆ ˆ ( , where ∑( )

=

− −

− = h n

hh

h

hhh

h ee

n

Nnn PraV

1

2

1

)/1( )ˆ ˆ ( α

α , 

∑∑

∑

= =

=

−

= N

h

n

hh

m

hh

h h

h

Wm

PIW

e

1 1

1

)ˆ (

α

α

β

αβ

α

αβ

 , 
10

and 

h

n

h

h

n

e

e

h

∑= = α 1

α

. With the sampling error, the margin of error based on a 95 percent confidence 

interval is calculated as )ˆ ˆ ( 025.0, PraVtdf , where is the percentile of the t distribution with 

df number of degrees of freedom, which is the number of clusters minus the number of strata. 

df 025.0, t

mhα

Table 3-1: Example of Proportion Estimation Procedure

Stratum 

index h 

Total 

Number 

of 

Systems 

Nh

Number 

of 

sampled 

systems 

nh

Weight 

Wh

System 

Index 

α

Total 

number 

of 

Violations

αWm hh

Violation 

Index 

β

Violation 

correctly 

reported? 

Yes=1; 

No=0 

hαβ I

hh αβ IW

1 1 2 

2 0 0 

1 3 6 

3 0 0 

1 1 2 

2 0 0 

2 3 6 

3 1 2 

1 1 2 

2 1 2 

1 

State A 

CWS 

6 3 2 

3 3 6 

3 0 0 

1 1 3 

2 0 0 

1 3 9 

3 1 3 

1 1 3 

2 1 3 

2 3 9 

3 0 0 

1 0 0 

2 0 0 

2 

State A 

NTNCWS 

9 3 3 

3 3 9 

3 1 3 

1 0 0 

2 1 5 

1 3 15 

3 1 5 

1 1 5 

2 0 0 

2 3 15 

3 1 5 

1 1 5 

2 0 0 

3 

State A 

TNCWS 

15 3 5 

3 3 15 

3 1 5 

Σ 90 55 

In Section 4, various types of proportions of consistent, reported, and accurate data in 

SDWIS/FED are calculated. These proportion estimates represent the data quality measures of 

inventory, violation, and enforcement data in SDWIS/FED based on the DVs. 
11

4. Results from the Analysis of Data Verifications 

This section presents various proportion estimates for inventory, violation, and enforcement data. 

Also, the margins of error are calculated for each point estimate. The margin of error is based on 

a 95% confidence interval, which includes the true proportion with 95% confidence. All 

calculations were performed using SAS®. 

4.1 Analysis of Inventory Data

States are required to report eight inventory data elements to SDWIS/FED for Grant Eligibility. 

These elements are 1) public water system identification number (PWS ID; 2) system status 3) 

water system type; 4) primary source water type; 5) population served; 6) number of service 

connections; 7) administrative contact address; and 8) water system name. The records for 

population or service connections are considered to be consistent when there is less than 10% 

difference between the two records. Because the inventory data are analyzed at the system level, 

the estimation approach can be based on a stratified random sampling. Then, the proportion of 

systems for which the inventory elements were reported to SDWIS/FED without discrepancies 

and its sampling error are estimated in Section 3.3 only at the cluster (system) level (or β=1). 

Inventory data quality of each data element is displayed in Table 4-1. The overall data quality of 

the eight inventory (water system identification) parameters assessed was 87%. In other words, 

87% of systems from DV states between 2002 and 2004 had consistent data for all eight 

inventory data elements between their state files and SDWIS/FED database, or 13% of systems 

had at least one data element reported with a discrepancy. The highest discrepancy rate was for 

the administrative contact address element. 
12

Table 4-1: Percent PWSs reported Grant Eligibility Inventory Data to SDWIS/FED 

without Discrepancy 

Reported Data without Discrepancy by Individual Data Element 

PWS ID System 

status 

(active or 

inactive) 

Water 

system 

type 

Primary 

source 

type 

Population 

served 

# service 

connection

s

Admin. 

contact 

address 

PWS 

name 

Reported All 

Inventory 

Data Element 

Data without 

Discrepancy 

99.83% 

(+/-0.20%)

97.26% 

(+/-1.15%)

98.21% 

(+/-0.72%)

99.31% 

(+/-0.35%)

97.11% 

(+/-0.71%)

96.22% 

(+/-0.93%)

95.97% 

(+/-1.32%)

99.87% 

(+/-0.09%)

87.4% 

(+/- 1.94%)

4.2 Analysis of Violation Data 

Federal regulations specify the outcomes which states must report to EPA that result in 

noncompliance (violation) with (a) health-based drinking water quality maximum contaminant 

levels (MCL) and related requirements for their attainment; (b) specified monitoring and 

reporting (M/R) requirements necessary to determine whether sampling, testing and treatment 

process checking occurred as stipulated in Federal regulations; and (c) health-based treatment

techniques (TT) and associated water system management processes for contaminants for which 

it is not technologically or economically feasible to set an MCL. 

 

Violation data are evaluated by comparing the following: 1) EPA’s evaluation of the state’s 

compliance decision on the violations; 2) the assigned violations in the state files; and 3) the 

violations reported to SDWIS/FED. All the findings from these comparisons can be grouped into 

one of the categories as shown in Table 4-2. The total number of violations identified during the 

2002-2004 DV is summarized below: 

• Out of 198 TCR MCL violations, 163 violations were reported to SDWIS/FED. 

• Out of 48 other (non-TCR) MCL violations, 21 violations were reported to 

SDWIS/FED. 

• Out of 41 SWTR TT violations, 35 violations were reported to SDWIS/FED. 

• Out of 176 LCR TT violations, 5 violations were reported to SDWIS/FED. 

• Out of 5,069 M/R violations, 1,589 violations were reported to SDWIS/FED. 

The following measures of data quality of violation data in SDWIS/FED are evaluated: 

! Completeness of SDWIS/FED describes how many violations that are required to be 

reported are being reported to SDWIS/FED, expressed as a percentage. This quantity is 

estimated based on the violations found by EPA and reported to SDWIS/FED (EPA=Yes 

and SDWIS/FED=Yes;1, 4 from Table 4-2) out of all violations found by EPA 

(EPA=Yes;1, 2, 3, 4 from Table 4-2). 

! Non-reporting rate in SDWIS/FED describes how many violations that are required to 
13

 

be reported are not being reported to SDWIS/FED, expressed as a percentage. This 

percentage is the complement of the Completeness estimate, i.e., 100%-Completeness. 

! Compliance Determination (CD) error rate in the non-reported violations describes 

how many non-reported violation data are the result of errors in states’ compliance 

determination (i.e., a violation was not reported because the state did not identify it as a 

violation), expressed as a percentage. This quantity is estimated based on the violations 

found by EPA, but not reported to SDWIS/FED and where the assigned violation in the 

state file does not agree with EPA (EPA=Yes and SDWIS/FED=No and EPA≠state File; 

 3 from Table 4-2) out of all violations found by EPA and not reported to SDWIS/FED 

(EPA=Yes and SDWIS/FED=No; 2 and 3 from Table 4-2).

! Data Flow (DF) error rate in the non-reported data describes how many non-reported 

violation data are as a result of reporting problems from state to SDWIS/FED, expressed 

as a percentage. This quantity is estimated based on the violations found by EPA, but not 

reported to SDWIS/FED and where the assigned violation in the state file confirmed by 

EPA (EPA=Yes, State File=Yes, and SDWIS/FED=No; 2a and 2b from Table 4-2) out of 

all violations found by EPA and not reported to SDWIS/FED (EPA=Yes and 

SDWIS/FED=No; 2 and 3 from Table 4-2).

! Accuracy of the data in SDWIS/FED describes how much of the violation data in 

SDWIS/FED are correct, expressed as a percentage. This quantity is estimated based on 

the violations found by EPA that agree with those reported to SDWIS/FED (EPA= 

SDWIS/FED; 1a, 1d, and 4a from Table 4-2) out of all violations reported to 

SDWIS/FED (SDWIS/FED=Yes; 1, 4, 5, 6 from Table 4-2).

! Compliance Determination (CD) error rate in SDWIS/FED describes how much of the 

violations data in SDWIS/FED are incorrect violations types as a result of errors in the 

state=s compliance determination, expressed as a percentage. This quantity is estimated 

based on the violations found by EPA that disagree with those reported to SDWIS/FED, 

but which are missing in the state file (State File=No and EPA≠SDWIS/FED from Table 

4-2) or the violations found by EPA that disagree with those found by the state, which 

were then reported to SDWIS/FED as found by the state (EPA≠State File= SDWIS/FED; 

1c and 4b8 from Table 4-2) out of all violations reported to SDWIS/FED 

(SDWIS/FED=Yes; 1, 4, 5, 6 from Table 4-2).

! Data Flow (DF) error rate in SDWIS/FED describes how many of the reported 

violations data are incorrect violations types due to reporting problems from the state to 

SDWIS/FED, expressed as a percentage. This quantity is estimated based on the 

violations found in the state files and confirmed by EPA, but which disagree with those 

reported to SDWIS/FED (EPA=State≠SDWIS/FED from Table 4-2) or the violations 

found by EPA that disagree with those found by the state, which were then reported to 

8 If DV auditors determined it to be a CD error 
14

 

SDWIS/FED (EPA≠State File= SDWIS/FED;1b, 1e, and 4b9 from Table 4-2) out of all 

violations reported to SDWIS/FED (SDWIS/FED=Yes; 1, 4, 5, 6 from Table 4-2).

! False Positive rate of the violation data in SDWIS/FED describes how much of the 

reported violation data in SDWIS are, in fact, false violations, expressed as a percentage. 

This quantity is estimated based on the violations not confirmed by EPA but reported to 

SDWIS/FED (EPA=No and SDWIS/FED=Yes; 5 and 6 from Table 4-2) out of all 

violations reported to SDWIS/FED (SDWIS/FED=Yes; 1, 4, 5, 6 from Table 4-2).

! Overall Data Quality Estimate in SDWIS/FED measures how many noncompliance 

determinations are correctly reported in SDWIS/FED among all noncompliance 

determinations (that are either violations or false-positive violations). This quantity is 

estimated based on the violations confirmed by EPA and correctly reported to 

SDWIS/FED (EPA=SDWIS/FED; 1a, 1d, and 4a from Table 4-2) out of all violations 

found by EPA or in the state files and SDWIS/FED (EPA=Yes or State File=Yes or 

SDWIS/FED=Yes; 1-6 from Table 4-2). When the false positive rate is 0%, this measure 

is the product of Completeness and Accuracy.

Since the DV states were not randomly selected, the states were treated as a fixed stratification 

variable for this analysis. During the DVs, there were systems that did not have any violations in 

the sample and did not require any reporting to SDWIS/FED. Thus, the actual number of sample 

systems used for the calculations was less than the number of sampled systems for the DVs. 

Furthermore, sub-domain analysis by rules or system types resulted in single-cluster strata and/or 

single observation in some clusters. A single-cluster stratum does not contribute in the 

calculation of variance estimates, which may underestimate the sampling errors. Therefore, the 

strata were combined within each EPA region except for overall data quality estimations, where 

the strata were combined within each DV state. 

9 If DV auditors determined it to be a DF error. 
Table 4-2: Violation data Comparison Categorization 

15

Was a violation found? Found By DV Auditors 

Found In state File 

Reported to SDWIS 

Were the assigned violations in agreement? 

Example Description 

1a. DV Auditors=State File=SDWIS 

A TCR violation 3100-211 was found 

in the state file, confirmed by DV auditors, and correctly reported to SDWIS/FED. 

No discrepan

1b. DV Auditors =State File

≠SDWIS 

A TCR violation record 3100-21 as found in state file and confirmed by DV auditors; the violation was incorrectly reported to SDWIS/FED as 3100-222. 

Data Flow er

1c. DV Auditors 

≠State 

File=SDWIS 

A TCR violation record 3100-22 was found in state file and reported to SDWIS/FED as 3100-22 when the violation should have been 3100-21. 

Compliance 

1d. DV Auditors =SDWIS

≠State File 

A TCR violation 3100-21 was reported to SDWIS/FED and confirmed by DV auditors but the state issued 3100-22 in the file. 

No discrepan

Yes Yes Yes 

1e. DV Auditors 

≠State 

File

≠SDWIS 

A TCR violation record 3100-22 found in state file when it should have been 3100-21 according to DV auditors, while the violation was incorrectly reported to SDWIS/FED as 3100-233.

Compliance by state and between state SDWIS/FED

Yes Yes No 

2a. DV Auditors =State File 

A TCR violation 3100-21 was found in the state file and confirmed by DV auditors, but not reported to SDWIS/FED. 

Non-reportin

1 Acute TCR MCL violation. 2 Monthly TCR MCL violation. 3 Routine Major TCR Monitoring Violation 
Table 4-2: Violation data Comparison Categorization 

16

Was a violation found? Found By DV Auditors 

Found In state File 

Reported to SDWIS 

Were the assigned violations in agreement? 

Example Description 

2b. DV Auditors 

≠State File 

A TCR violation 3100-22 was issued in the state file when it should have been 3100-21, and the violation was not reported to SDWIS/FED. 

Non-reportindeterminatioData Flow erstate file and 

Yes No No 3. N/A There should have been a TCR 

violation 3100-21, but the state did not issue a violation and did not report to SDWIS/FED. 

Non-reportindetermination 

4a. DV Auditors =SDWIS/FED 

There should have been a TCR violation 3100-21 issued in the state file, but the notice of violation (NOV) was not found in the state file, even though the violation as correctly reported to SDWIS/FED. 

Yes No Yes No discrepan

4b. DV Auditors ≠SDWIS/FED 

There should have been a TCR violation 3100-21 issued in the state file, but NOV was not found in the state file, while the violation as incorrectly reported to SDWIS/FED as 3100-22. 

Compliance by state and/between state SDWIS/FED

5a. State File=SDWIS/FED A TCR violation 3100-21 was issued in 

the state file and reported to SDWIS/FED, but it should not have been a violation. 

No Yes Yes False positiv

5b. State File

≠SDWIS/FED 

A TCR violation 3100-21 was found in the state file, but the DV Auditors concluded that there should not have been a violation in the first place. In addition, the state reported a different TCR violation type (3100-22) to SDWIS FED. 

False positiv
Table 4-2: Violation data Comparison Categorization 

17

Was a violation found? Found By DV Auditors 

Found In state File 

Reported to SDWIS 

Were the assigned violations in agreement? 

Example Description 

No No Yes 6. N/A 

A TCR violation 3100 was reported to SDWIS/FED, but DV Auditors concluded that there should not have been a violation in the first place. In addition, no evidence of a violation was found in the state files because the state rescinded a violation but has not removed it if from SDWIS/FED. 

False positiv
18

4.2.1 Results from 2002-2004 Data Verifications 

The proportion estimates and the sampling errors for the violation DQE by violation types are 

presented in Table 4-3. Eighty-one percent of the MCL and SWTR TT violations were reported 

to SDWIS/FED. Seventy-four percent of the non-reported violations were due to compliance 

determination errors and 26% were due to data flow errors. The reported violations in 

SDWIS/FED were accurate at 94%. Overall, the DQE of the violation data was 77%. This means 

that 77% of the noncompliance determinations on MCL/ SWTR TT standards were correctly 

reported in SDWIS/FED. 

Considering all health-based violations (MCL and TT violations, which include Lead and 

Copper TT), 62 percent of the violations were reported to SDWIS/FED. This means that 38% of 

the violations were not reported. Eighty-four (84) percent of the non-reported violations were 

due to compliance determination errors and 16% were due to data flow errors. The reported 

violations in SDWIS/FED were accurate at 94%. Overall, the DQE of the health-based violation 

data was 59%. This means that 59% of the noncompliance determinations on all health-based 

standards were correctly reported in SDWIS/FED. 

The quality of the health-based violations data was much lower than the MCL/SWTR TT data 

because of the quality of data associated with the Lead and Copper Rule. The data quality of the 

LCR TT violations was the lowest at 7.6%. For example, we found that out of 176 LCR TT 

violations, only 5 were reported to SDWIS/FED. The non-reporting was also mainly because of 

compliance determination errors. Specifically, 161 out of 171 violations were not recognized as 

violations when the violations had occurred. 

Twenty-nine percent of the M/R violations were reported to SDWIS/FED and 71% of the 

violations were not reported. Ninety-two percent of the non-reported violations were due to 

compliance determination errors and 8% were due to data flow errors. The reported M/R 

violations in SDWIS/FED were accurate at 88%. Overall, the DQE of the M/R violation data 

was 27%, i.e., 27% of the noncompliance determinations on M/R were correctly reported in 

SDWIS/FED. 

Table 4-3: Data Quality Estimates (DQE) by Violation Type 
19

TCR MCL OTHER MCL TOTAL MCL SWTR TT 

% COMPLETENESS OF 

SDWIS/FED 

83.29% 

(+/-9.66%) 

48.94% 

(+/-27.05%) 

78.42% 

(+/-9.39%) 

94.89% (+/-8.03%) 

%NON-REPORTING ON 

SDWIS/FED 

16.71% 

(+/-9.66%) 

51.06% 

(+/-27.05%) 

21.58% 

(+/-9.39%) 

5.11% (+/-8.03%) 

% CD 

ERROR ON 

NONREPORTED 

DATA

% DF ERROR 

ON NONREPORTED 

DATA

83.40% 

(+/-

15.07%) 

16.60% 

(+/-

15.07%) 

56.89% 

(+/-

43.12%) 

43.11% 

(+/-

43.12%) 

73.84% 

(+/-

16.98%) 

26.16% 

(+/-

16.98%) 

73.87% 

 (+/-

43.38%) 

26.87% 

(+/-

43.38%) 

%ACCURACY OF DATA IN 

SDWIS/FED 

96.65% 

(+/-2.62%) 

79.22% 

(+/-20.56%) 

94.91% 

(+/-3.00%) 

91.07% 

(+/-12.14%) 

%CD ERROR WITH DATA 

IN SDWIS/FED 0% 5.73% 

(+/-8.48%) 

0.57% 

(+/-0.80%) 3.98%(+/-7.26) 

%DF ERROR WITH DATA 

IN SDWIS/FED 0% 0% 0% 0% 

%FALSE POSITIVE DATA 

IN SDWIS/FED 

2.26% 

(+/-2.44%) 

15.05% 

(+/-17.96%) 

4.52% 

(+/-2.90%) 

4.95% 

(+/-8.38%) 

OVERALL DATA QUALITY 80.95% 

(+/-9.47%) 

42.00% 

(+/-26.67%) 

75.16% 

(+/-9.26%) 

86.63% 

(+/-15.79%) 

MCL/SWTR TT LCR TT Health-Based 

Violations M/R 

% COMPLETENESS OF 

SDWIS/FED 

81.33% 

(+/-8.86%) 

7.6% 

(+/-7.52%) 

61.69% 

(+/-10.26%) 

29.02% 

(+/-3.47%) 

%NON-REPORTING ON 

SDWIS/FED 

18.67% 

(+/-8.86%) 

92.40% 

(+/-7.52%) 

38.31% 

(+/-10.26%) 

70.98% 

(+/-3.47%) 

% CD 

ERROR ON 

NONREPORTED 

DATA

%DF ERROR 

ON NONREPORTED 

DATA

73.84% 

(+/-

16.30%) 

26.16% 

(+/-

16.30%) 

91.76% 

 (+/- 

11.80) 

8.24% 

(+/- 

11.80) 

84.45% 

(+/-

10.35%) 

15.55% 

(+/-

10.35%) 

92.03% 

(+/-1.75%) 

7.97% 

(+/-1.75%) 

%ACCURACY OF DATA IN 

SDWIS/FED 

94.12% 

(+/-3.27%) 

100% 94.30% 

(+/-3.16%) 

88.35% 

(+/-2.87%) 

%CD ERROR WITH DATA 

IN SDWIS/FED 

1.27% 

(+/-1.44%) 0% 1.24% 

(+/-1.39%) 

3.18% 

(+/-1.54%) 

%DF ERROR WITH DATA 

IN SDWIS/FED 

0% 0% 0% 0.99% 

(+/-0.53%) 

%FALSE POSITIVE DATA 

IN SDWIS/FED 

4.60% 

(+/-2.90%) 

0% 2.79% 

(+/-2.38%) 

7.48% 

(+/-2.31%) 

OVERALL DATA QUALITY 77.21% 

(+/-8.99%) 

7.6% 

(+/-7.52%) 

59.18% 

(+/-10.10%) 

27.08% 

(+/-3.37%) 

*CD=Compliance determination 

*DF=Data Flow 

Note: TCR MCL + Other MCL = Total MCL + SWTR TT = MCL/SWTR TT + LCR TT = Health-Based Violations. M/R = 

monitoring and reporting violations. 
20

In general, the majority of non-reported data were due to compliance determination errors, i.e., 

the states did not issue violations when violations had occurred. The violations had not been 

recognized, not recorded by states as violations, and consequently, not reported to SDWIS/FED. 

 We need to further examine the cause of such compliance determination errors. These errors 

may be due to late reporting or rule interpretation discrepancies. Eliminating these errors will 

significantly increase the completeness of the data in SDWIS/FED. For example, 84% of the 

non-reported health-based based violations were due to compliance determination errors. If these 

errors did not occur, the completeness of health-based violations in SDWIS/FED would be at 

94% (62%+38%×84%). Similarly, the completeness of M/R violations would also be at 94% 

(29%+71%×92%). 

The violation data are further evaluated by system type in Tables 4-4a-c. The DQEs of 

MCL/SWTR TT violations were not significantly different among the different system types. 

Likewise, the DQEs of health-based violations were not significantly different between CWSs 

and NTNCWSs. (The DQE of health-based violations for TNCWSs was not calculated since 

LCR TT data were not collected for TNCWSs.) 

Table 4-4a: MCL/SWTR TT Violations Data Quality Estimates (DQE) 

 by Public Water System Type 

CWS NTNCWS TNCWS 

% COMPLETENESS OF SDWIS/FED 78.87% 

(+/-10.59%) 

83.07% 

(+/-12.32%) 

83.25% 

(+/-15.53%) 

%NON-REPORTING IN SDWIS/FED 

21.13% 

(+/-10.59%) 

16.93% 

(+/-12.32%) 

16.75% 

(+/-15.53%) 

%CD ERROR ON 

NON-REPORTED 

DATA

%DF ERROR ON 

NON-REPORTED 

DATA

61.24% 

(+/-25.44%) 

38.76% 

(+/-25.44%) 

49.15% 

(+/-35.34%) 

50.85% 

(+/-35.34%) 

96.21% 

(+/-6.14%) 

3.79% 

(+/-6.14%) 

%ACCURACY OF DATA IN 

SDWIS/FED 

93.15% 

(+/-4.35%) 

83.42% 

(+/-13.55%) 

97.37% 

(+/-3.68%) 

%CD ERROR WITH DATA IN 

SDWIS/FED 1.06% (+/-1.47%) 6.62% 

(+/-10.75%) 

0.29% (+/-0.60%) 

%DF ERROR WITH DATA IN 

SDWIS/FED 0% 0% 0% 

%FALSE POSITIVE DATA IN 

SDWIS/FED 

5.80% 

(+/-4.13%) 

9.96% 

(+/-11.71%) 

2.34% 

(+/-3.60%) 

OVERALL DATA QUALITY 74.37% 

(+/-10.73%) 

70.49% 

(+/-13.61%) 

81.38% 

(+/-15.51%) 

*CD=Compliance determination 

*DF=Data Flow 
21

Table 4-4b: Health-Based Violations Data Quality Estimates (DQE) 

by Public Water System Type 

CWS NTNCWS 

% COMPLETENESS OF SDWIS/FED 53.39% 

(+/-12.17%)

40.86% 

(+/-15.19%)

%NON-REPORTING IN SDWIS/FED

 

46.61% 

(+/-12.17%)

 59.14% 

(+/-15.19%)

%CD ERROR ON 

NON-REPORTED 

DATA

%DF ERROR ON 

NON-REPORTED 

DATA 

78.45% 

(+/-16.17%)

21.55% 

(+/-16.17%)

93.57% 

(+/-10.15%)

6.57% 

(+/-10.15%)

%ACCURACY OF DATA IN 

SDWIS/FED 

93.47% 

(+/-4.15%)

84.95% 

(+/-12.58%)

%CD ERROR WITH DATA IN 

SDWIS/FED 1.01% (+/-1.40%) 6.01% 

(+/-9.86%)

%DF ERROR WITH DATA IN 

SDWIS/FED 0% 0%

%FALSE POSITIVE DATA IN 

SDWIS/FED 

5.52% 

(+/-3.94%)

9.04% 

(+/-10.76%)

OVERALL DATA QUALITY 51.22% 

(+/-11.75%)

36.67% 

(+/-13.02%)

*CD=Compliance determination 

 *DF=Data Flow 

 

Table 4-4c: M/R Violations Data Quality Estimates (DQE) 

by Public Water System Type 

 *CD=Compliance determination 

CWS NTNCWS TNCWS 

% COMPLETENESS OF SDWIS/FED 20.26% (+/-3.42%) 22.65% (+/-5.26%) 45.89% (+/-5.87%) 

%NON-REPORTING IN SDWIS/FED 79.74% (+/-3.42%) 77.35% (+/-5.26%) 54.11% (+/-5.87%) 

%CD ERROR ON 

NON-REPORTED 

DATA 

%DF ERROR ON 

NON-REPORTED 

DATA 

91.62% 

(+/-2.36) 

8.38% 

(+/-2.36) 

87.05% 

 (+/-5.07%) 

12.95% 

(+/-5.07%) 

96.87% 

(+/-2.31) 

3.13% 

(+/-2.31) 

%ACCURACY OF DATA IN 

SDWIS/FED 82.10% (+/-4.96%) 81.39% (+/-7.93%) 94.81% (+/-2.71%) 

%CD ERROR WITH DATA IN 

SDWIS/FED 5.25% (+/-2.84%) 4.10% (+/-2.7%) 1.83% (+/-1.99%) 

%DF ERROR WITH DATA IN 

SDWIS/FED 0.73% (+/-0.74%) 1.45% (+/-2.15%) 0.60% (+/- 0.59%) 

%FALSE POSITIVE DATA IN 

SDWIS/FED 11.92% (+/-4.42%) 13.05% (+/-7.88%) 2.75% (+/-1.77%) 

OVERALL DATA QUALITY 18.38% (+/-3.12) 20.51% (+/-4.77%) 44.17% (+/-5.79%) 

 *DF=Data Flow 

 
22

EPA has public notification (PN) requirements to ensure that the public is notified of violations 

in a timely manner. The PN requirements define three tiers of notification that are based on the 

public health significance of the violation, with tier 1 being the most significant (See Appendix 

C for the definition of PN tiers). The DQEs are also calculated by PN tier groups of violations in 

Table 4-5. Two-thirds of PN tier 1 violations were reported to SDWIS/FED. There were no 

significant differences in DQEs between PN tier 1 and PN tier 2. The DQEs for PN tier 3, which 

mostly consisted of M/R violations, were significantly lower than those for PN tier 1 and PN tier 

2. Less than two-thirds of PN tier 2 violations were reported to SDWIS/FED and only 30% of 

PN tier 3 violations were reported to SDWIS/FED. In all PN tier groups, the data in 

SDWIS/FED were highly accurate. The overall data quality does not reflect false-positive 

violations in SDWIS/FED since they can not be categorized into a PN tier. 

Table 4-5: Data Quality (DQ) by PN Tier 

PN Tier 1 PN Tier 2 PN Tier 3 

% COMPLETENESS OF SDWIS/FED 66.97% 

(+/-22.37%) 62.40% (+/-10.50%) 30.58% 

(+/-4.15%) 

%NON-REPORTING in SDWIS/FED 

33.03% 

(+/-22.37%) 37.60% (+/-10.50%) 69.42% 

(+/-4.15%) 

%CD ERROR ON 

NON-REPORTED 

DATA

%DF ERROR 

ON NONREPORTED 

DATA

32.77% 

(+/-23.93%) 

67.23% 

(+/-23.93%) 

87.21% (+/-

9.04%) 

12.79% (+/-

9.04%) 

91.44% 

(+/-

1.87%) 

8.56% 

(+/-1.87%) 

%ACCURACY OF DATA IN 

SDWIS/FED 

100% 98.65% (+/-1.52%) 95.46% (+/-1.82) 

%CD ERROR WITH DATA IN 

SDWIS/FED 

0% 1.35% (+/-1.52 %) 3.46%(+/-1.68) 

%DF ERROR WITH DATA IN 

SDWIS/FED 

0% 0% 1.08%(+/-0.58) 

OVERALL DATA QUALITY 66.97% 

(+/-22.37%) 61.56% (+/-10.59%) 29.19% 

(+/-4.13%) 

 *CD=Compliance determination 

 *DF=Data Flow 

4.2.2 Results from 1999-2001 Data Verifications 

This section presents DQEs from 1999 to 2001 data verification audits recalculated using the 

current statistical methodology described in Section 3.3. The states subjected to the DV audits 

during 1999-2001 are shown in Table 4-6. In the calculation, the DV results from Region 2 were 

not included since the state DV reports were not finalized for those states during the period of 

this analysis. The DQEs are included in Tables 4-7a and b. Because these estimates were 

computed based on a different set of DV states in a different data quality assessment time frame 

and with a different statistical sample design, it is not scientifically valid to make a national 

inference by comparing the results between Table 4.3a-b. However, the DQEs from those states 

that had repeated DV audits during both assessment periods are calculated and compared in the 

following section. 
23

Table 4-6: States Subject to Data Verifications from 1999-2001 

Region States Region States 

1 MA, ME, NH 6 AR, LA, NM, TX 

2 NY, PR 7 KS, MO, NE 

3 VA, PA, DE 8 MT, ND, UT 

4 FL, GA, KY, MS, NC, 

SC, TN 

9 HI, NV 

5 IL, IN, OH, WI 10 AK, ID, OR 

Table 4-7b shows that 69% of the MCL and SWTR TT violations were reported to SDWIS/FED. 

Seventy-nine percent of the non-reported violations were due to compliance determination errors 

and 21% were due to data flow errors. The reported violations in SDWIS/FED were accurate at 

91%. Overall, the DQE of the violation data was 64%. This means that 64% of the 

noncompliance determinations on MCL/ SWTR TT standards were correctly reported in 

SDWIS/FED. 

Table 4-7a: 1999-2001 Data Quality Estimates (DQE) for MCL and SWTR TT 

TCR MCL OTHER MCL TOTAL MCL 

% COMPLETENESS OF 

SDWIS/FED 

76.71% 

(+/-9.98%) 

63.33% 

(+/-26.99%) 

74.81% 

(+/-9.34%) 

%NON-REPORTING ON 

SDWIS/FED 

23.29% 

(+/-9.98%) 

36.67% 

(+/-26.99%) 

25.19% 

(+/-9.34%) 

%CD ERROR 

ON NONREPORTED 

DATA

%DF 

ERROR ON 

NONREPORTED 

DATA

70.69% 

(+/-18.58%) 

29.31% 

(+/-18.58%) 

68.55% 

(+/-31.68%) 

31.45% 

(+/-31.68%) 

70.25% 

(+/-16.15%) 

29.75% 

(+/-16.15%) 

%ACCURACY OF DATA IN 

SDWIS/FED 

91.71% 

(+/-2.62%) 

63.99% 

(+/-46.70%) 

88.54% 

(+/-8.87%) 

%CD ERROR WITH DATA 

IN SDWIS/FED 

1.61% 

(+/-2.96%) 

36.01% 

(+/-46.70%) 

5.54% 

(+/-7.82%) 

%DF ERROR WITH DATA 

IN SDWIS/FED 0.64% 

(+/-1.26) 0% 

0.56% 

(+/-7.82%) 

%FALSE POSITIVE DATA 

IN SDWIS/FED 

6.05% 

(+/-5.24%) 

0% 

(+/-17.96%) 

5.36% 

(+/-4.66%) 

OVERALL DATA QUALITY 71.35% 

(+/-10.16%) 

40.52% 

(+/-28.53%) 

67.14% 

(+/-10.27%) 

 *CD=Compliance determination 

 *DF=Data Flow 
24

Table 4-7b: 1999-2001 Data Quality Estimates (DQE) for MCL/ SWTR TT and MR 

SWTR TT MCL/SWTR TT MR 

% COMPLETENESS OF 

SDWIS/FED 

54.54% 

(+/-11.79) 

69.39% 

(+/-9.59%) 

34.86% 

(+/-4.59%) 

%NON-REPORTING ON 

SDWIS/FED 

45.46% 

(+/-11.75) 

30.61% 

(+/-9.59%) 

65.14% 

(+/-4.59%) 

% CD 

ERROR ON 

NONREPORTED 

DATA

% DF 

ERROR ON 

NONREPORTED 

DATA

92.43% 

(+/-13.88) 7.57% 

(+/-13.88)

79.05% 

(+/-14.44%) 

20.95% 

(+/-14.44%) 

92.26% 

(+/-2.64%) 

7.74% 

(+/-2.64%) 

%ACCURACY OF DATA 

IN SDWIS/FED 

100% 90.84% 

(+/-7.38%) 

91.85% 

(+/-2.89%) 

%CD ERROR WITH DATA 

IN SDWIS/FED 0% 4.43% 

(+/-6.34%) 

1.08% 

(+/-0.93%) 

%DF ERROR WITH DATA 

IN SDWIS/FED 0% 0.45% 

(+/-0.9%) 

0.20% 

(+/-0.25%) 

%FALSE POSITIVE DATA 

IN SDWIS/FED 

0% 4.28% 

(+/-3.8%) 

 6.87% 

(+/-2.58%) 

OVERALL DATA 

QUALITY 

54.54% 

(+/-11.75%) 

63.88% 

(+/-9.12%) 

33.51% 

(+/-4.5%) 

 *CD=Compliance determination 

 *DF=Data Flow 

Thirty-five percent of the M/R violations were reported to SDWIS/FED and 65% of the 

violations were not reported. Ninety-two percent of the non-reported violations were due to 

compliance determination errors and 8% were due to data flow errors. The reported M/R 

violations in SDWIS/FED were accurate at 92%. Overall, the DQE of the M/R violation data 

was 33%, i.e., 33% of the noncompliance determinations on M/R were correctly reported in 

SDWIS/FED. 

4.2.3 Data Quality Estimates from 1999-2001 and 2002-2004 

In order to evaluate the progress of the data quality improvement, the DQEs from the states 

where the DV audits were conducted during the data quality assessment period 1999-2001 and 

again during 2002-2004 were calculated for the purpose of comparison. The states with repeated 

DV audits for both assessment periods can be identified from Table 2-1 and Table 4-6 and are 

listed in Table 4-8. Since the LCR was not reviewed during the 1999-2001 DVs, the data from 

the LCR were excluded from 2002-2004 DV results for this evaluation 

The DQEs from these 18 states are presented in Tables 4.9 a and b, which include point 

estimates as well as the lower and upper bounds for 95% confidence intervals. In order to 

determine any significant differences (increase or decrease) in the DQEs, the two confidence 

intervals, defined by the lower and upper bounds as the end points of the interval, for the two 

DQEs should not overlap. Sixty-seven percent of MCL/SWTR TT violations with a 95% 

confidence interval (55%, 79%) were reported to SDWIS/FED during 1999-2001. Similarly, 

80% of MCL/SWTR TT violations with a 95% confidence interval (68%, 92%) were reported to 

SDWIS/FED during 2002-2004. Since the confidence intervals overlap, there was no statistically 
25

significant increase in the reporting of violations for these 18 states from 1999-2001 to 2002-

2003. The overall data quality of MCL/SWTR TT violations was 64% with a 95% confidence 

interval (52%, 75%) during 1999-2001 and 75% with a 95% confidence interval (64%, 87%) 

during 2002-2004. Based on the confidence intervals, there was no statistically significant 

increase in the overall data quality of MCL/SWTR TT violations for these 18 states from 1999-

2001 to 2002-2003. 

On the other hand, approximately, 60% of SWTR TT violations with a 95% confidence interval 

(44%, 76%) were reported to SDWIS/FED during 1999-2001. During 2002-2004, 93% of SWTR 

TT violations with a 95% confidence interval (81%, 100%) were reported to SDWIS/FED. Since 

the confidence intervals do not overlap, there was a statistically significant increase in the 

reporting of violations for these 18 states from 1999-2001 to 2002-2003. However, the accuracy 

of SWTR TT has decreased significantly from 100% to 78%. The overall data quality of SWTR 

TT violations was 60% with a 95% confidence interval (44%, 73%) during 1999-2001 and 74% 

with a 95% confidence interval (54%, 94%) during 2002-2004. Therefore, there was no 

statistically significant increase in the overall data quality of SWTR TT violations for these 18 

states from 1999-2001 to 2002-2003. 

In general, all the confidence intervals from the two periods overlap for all DQEs, except for 

SWTR TT violations completeness DQE. Therefore, there were no statistically significant 

increases or decreases in the DQEs for these states from 1999-2001 to 2002-2003 assessment. 

Table 4-8: States Subject to Data Verifications during 1999-2001 and 2002-2004 

Region States Region States 

1 MA 6 AR, NM, TX 

2 7 KS, NE 

3 VA, PA 8 UT 

4 FL, KY, MS, NC, SC, TN 9 

5 IL, OH 10 AK, ID 
26

Table 4-9a: Data Quality Estimates (DQE) for MCL

from MA, VA, PA, FL, KY, MS, NC, SC, TN, IL, OH, AR, NM, TX, MO, UT, AK, ID 

During 1999-2001 and 2002-2004 

TCR MCL Other MCL Tot

Year 1999-2001 2002-2004 1999-2001 2002-2004 1999-2001 

POINT 

ESTIMATE 70.13% 82.17% 74.57% 60.74% 70.63% 

LOWER 

BOUND 56.22% 67.46% 48.25% 30.84% 57.87% 

% COMPLETENESS OF 

SDWIS/FED 

UPPER 

BOUND 84.03% 96.88% 100% 90.64% 83.39% 

POINT 

ESTIMATE 29.87% 17.83% 25.43% 39.26% 29.37% 

LOWER 

BOUND 15.97% 3.12% 0% 9.36% 16.61% 

%NON-REPORTING ON 

SDWIS/FED 

UPPER 

BOUND 43.78% 32.54% 51.75% 69.16% 42.13% 

POINT 

ESTIMATE 74.90% 25.10% 83.54% 16.46% 58.02% 41.98% 21.14% 78.86% 73.25% 26.75% 

LOWER 

BOUND 51.93% 2.13% 61.95% 0% 12.32% 0% 0% 49.66% 51.66% 5.16% 

%CD 

ERROR ON 

NONREPORTED 

DATA 

%DF 

ERROR 

ON NONREPORTED 

DATA UPPER 

BOUND 97.87% 48.07% 100% 38.05% 100% 87.68% 50.34% 100% 94.85% 48.34% 

POINT 

ESTIMATE 90.49% 96.01% 100% 82.84% 91.56% 

LOWER 

BOUND 81.28% 91.88% 100% 59.65% 83.40% 

%ACCURACY OF DATA 

IN SDWIS/FED 

UPPER 

BOUND 99.71% 100% 100% 100% 99.72% 

POINT 

ESTIMATE 3.04% 0% 0% 0% 2.70% 

LOWER 

BOUND 0% 0% 0% 0% 0% 

%CD ERROR WITH DATA 

IN SDWIS/FED 

UPPER 

BOUND 8.99% 0% 0% 0% 7.98% 

POINT 

ESTIMATE 0% 0% 0% 0% 0% 

LOWER 

BOUND 0% 0% 0% 0% 0% 

%DF ERROR WITH DATA 

IN SDWIS/FED 

UPPER 0% 0% 0% 0% 0% 
27

Table 4-9a: Data Quality Estimates (DQE) for MCL

from MA, VA, PA, FL, KY, MS, NC, SC, TN, IL, OH, AR, NM, TX, MO, UT, AK, ID 

During 1999-2001 and 2002-2004 

TCR MCL Other MCL Tot

Year 1999-2001 2002-2004 1999-2001 2002-2004 1999-2001 

BOUND 

POINT 

ESTIMATE 6.47% 3.99% 0% 17.16% 5.74% 

LOWER 

BOUND 0% 0% 0% 0% 0% 

%FALSE POSITIVE DATA 

IN SDWIS/FED 

UPPER 

BOUND 13.54% 8.12% 0% 40.35% 12.00% 

POINT 

ESTIMATE 64.71% 79.46% 74.57% 53.95% 65.78% 

LOWER 

BOUND 50.90% 65.09% 48.25% 24.49% 53.02% 

OVERALL DATA 

QUALITY 

UPPER 

BOUND 78.52% 93.82% 100% 83.42% 78.54% 

 *CD=Compliance determination  *DF=Data Flow 
28

Table 4-9b: Data Quality Estimates (DQE) for SWTR TT, MCL/SWTR TT, and MR 

from MA, VA, PA, FL, KY, MS, NC, SC, TN, IL, OH, AR, NM, TX, MO, UT, AK, ID 

During 1999-2001 and 2002-2004 

SWTR TT MCL/SWTR TT 

Year 1999-2001 2002-2004 1999-2001 2002-2004 1999-2001 

POINT 

ESTIMATE 59.95% 93.30% 66.73% 80.36% 37.74% 

LOWER 

BOUND 43.68% 80.83% 54.74% 68.10% 31.69% 

% COMPLETENESS OF SDWIS/FED 

UPPER 

BOUND 76.21% 100% 78.72% 92.62% 43.79% 

POINT 

ESTIMATE 40.05% 6.70% 33.27% 19.64% 62.26% 

LOWER 

BOUND 43.68% 0% 21.28% 7.38% 56.21% 

%NON-REPORTING ON SDWIS/FED

UPPER 

BOUND 56.32% 19.16% 45.26% 31.90% 68.31% 

POINT 

ESTIMATE 93.23% 6.75% 89.09% 10.91% 82.04% 17.96% 67.72% 32.28% 91.63% 8.37% 

LOWER 

BOUND 76.38% 0% 62.11% 0% 63.88% 0% 40.40% 4.95% 88.91% 5.66% 

%CD ERROR ON 

NON-REPORTED 

DATA 

%DF ERROR ON 

NON-REPORTED 

DATA 

UPPER 

BOUND 100% 23.62% 100% 37.89% 100% 36.12% 95.05% 59.60% 94.34% 11.09

POINT 

ESTIMATE 100% 78.78% 94.22% 92.90% 92.01% 

LOWER 

BOUND 100% 60.28% 88.26% 88.24% 88.90% 

%ACCURACY OF DATA IN SDWIS/FED 

UPPER 

BOUND 100% 97.28% 100% 97.55% 95.11% 

POINT 

ESTIMATE 0% 17.01% 1.85% 1.63% 1.44% 

LOWER 

BOUND 0% 0% 0% 0% 0.13% 

%CD ERROR WITH DATA IN 

SDWIS/FED 

UPPER 

BOUND 0% 39.27% 5.53% 4.11% 2.75% 

POINT 

ESTIMATE 0% 0% 0% 0% 0.28% 

LOWER 

BOUND 0% 0% 0% 0% 0% 

%DF ERROR WITH DATA IN 

SDWIS/FED 

UPPER 

BOUND 0% 0% 0% 0% 0.63% 
29

Table 4-9b: Data Quality Estimates (DQE) for SWTR TT, MCL/SWTR TT, and MR 

from MA, VA, PA, FL, KY, MS, NC, SC, TN, IL, OH, AR, NM, TX, MO, UT, AK, ID 

During 1999-2001 and 2002-2004 

SWTR TT MCL/SWTR TT 

Year 1999-2001 2002-2004 1999-2001 2002-2004 1999-2001 

POINT 

ESTIMATE 0% 4.21% 3.93% 5.57% 6.27% 

LOWER 

BOUND 0% 0% 0% 0.35% 3.57% 

%FALSE POSITIVE DATA IN 

SDWIS/FED 

UPPER 

BOUND 0% 10.00% 8.44% 9.58% 8.98% 

POINT 

ESTIMATE 59.95% 73.71% 63.71% 75.46% 36.14% 

LOWER 

BOUND 43.68% 53.90% 52.70% 63.60% 30.19% 

OVERALL DATA QUALITY 

UPPER 

BOUND 76.21% 93.52% 74.71% 87.33% 42.08% 

 *CD=Compliance determination  *DF=Data Flow
30

4.3 Analysis of Enforcement Data

Federal regulations indicate the conditions under which enforcement actions will be taken with a 

PWS to ensure public health protection if the system is in violation of the Federal-State drinking 

water program. States must report a subset of these actions to EPA. EPA reports these data for 

situations where EPA is the enforcement authority because the state has decided not to obtain 

approval to implement the federal program (e.g. Wyoming, the District of Columbia and on 

Indian lands). 

Enforcement data reported to SDWIS were compared to those found in the state files during the 

DV. The proportion of enforcement data in the state files that were in agreement with those 

reported to SDWIS/FED (1a, 1c, and 5a from Table 4-2) were estimated as described in Section 

3.3 and presented in Table 4-8. The overall DQE for enforcement data was 86%. 

Table 4-8: Proportion Estimates of Enforcement Data in State Files reported to 

SDWIS/FED without discrepancy 

PWS Type CWS NTNCWS TNCWS Overall 

Proportion Estimate 73.14% 

(+/-9.65%) 

76.25% 

(+/-6.88%) 

94.92% 

(+/-2.72%) 

85.97% 

(+/-3.62) 

5. Analysis of Timeliness of Violation Reporting in SDWIS/FED 

In this section, the results from the analysis of the data in SDWIS/FED are presented. This 

analysis evaluates the timeliness of violations based on compliance period end date, which 

provides a benchmark for comparison between fiscal years. Violations are due to be reported by 

the end of the following quarter after awareness or the compliance period end date. 

Timeliness is calculated as the ratio of the number of violations reported on time and the baseline 

number of violations that should be reported, i.e., 

 Timeliness = Baseline Reported Violations ofNumber 

Timeon Reported Violations ofNumber 

where on time is defined as by the end of the following quarter after the compliance period end 

date and baseline is a point in time in the future (in this case, between 4 and 7 quarters after 

violations are due to be reported). Basically, the Timeliness is the proportion of violations that 

were eventually reported to SDWIS/FED on time. 

To compute the timeliness, the violation data were extracted from archived SDWIS/FED 

databases for each of five fiscal years (2000-2004). The violations were then grouped by PWS 
31

ID, fiscal year, quarter, violation code, contaminant code, and basic PWS attributes, and the ontime and baseline violations were summed. Table 5-1 shows the database extracted for the 

analysis. The database does not include LCR or other violations with open-ended compliance 

period end dates. For these violations, the compliance period end date is open until the system 

returns to compliance. 

Table 5-1: SDWIS/FED Database Analyzed for Timeliness 

FY2000 on time: Violations with end dates between: Archive Date 

00Q1 10/1/99 and 12/31/99 4/00 

00Q2 1/1/00 and 3/3100 7/00 

00Q3 4/1/00 and 6/30/00 10/00 

00Q4 7/1/00 and 9/30/00 1/01 

FY2000 baseline: 01Q4 tables, Archived 1/02 

 

FY2001 on time: Violations with end dates between: Archive Date 

01Q1 10/1/00 and 12/31/00 4/01 

01Q2 1/1/01 and 3/31/01 7/01 

01Q3 4/1/01 and 6/30/01 10/01 

01Q4 7/1/01 and 9/30/01 1/02 

FY2001 baseline: 02Q4 tables, Archived 1/03 

 

FY2002 on time: Violations with end dates between: Archive Date 

02Q1 10/1/01 and 12/31/01 4/02 

02Q2 1/1/02 and 3/3102 7/02 

02Q3 4/1/02 and 6/30/02 10/02 

02Q4 7/1/02 and 9/30/02 1/03 

FY2002 baseline: 03Q4 tables, Archived 1/04 

 

FY2003 on time: Violations with end dates between: Archive Date 

03Q1 10/1/02 and 12/31/02 4/03 

03Q2 1/1/03 and 3/3103 7/03 

03Q3 4/1/03 and 6/30/03 10/03 

03Q4 7/1/03 and 9/30/03 1/04 

FY2003 baseline: 04Q4 tables, Archived on 1/05 

 

FY2004 on time: Violations with end dates between: Archive Date 

04Q1 10/1/03 and 12/31/03 4/04 

04Q2 1/1/04 and 3/31/04 7/04 

04Q3 4/1/04 and 6/30/04 10/04 

04Q4 7/1/04 and 9/30/04 1/05 

FY2004 baseline: 05Q4 tables, Archived on 1/06 
32

Table 5-2: Violation Reporting Timeliness to SDWIS/FED by Violation Type 

Fiscal Year 2000 2001 2002 2003 2004 

Number of Violations Reported on Time 

TCR MCL 7,738 8,114 7,977 7,902 7,421 

Other MCL 727 652 771 1,106 1,273 

SWTR TT 932 918 1,045 774 540 

Health-Based 

Violations13 9,397 9,684 9,793 9,831 9,308 

M/R 49,782 50,868 55,425 61,967 32,742 

Number of Violations Reported for Baseline 

TCR MCL 11,445 10,963 10,795 10,821 10,510 

Other MCL 1,344 1,315 1,844 2,573 3,716 

SWTR TT 1,574 1,627 1,585 1,252 932 

Health-Based 

Violations13 14,636 13,905 14,369 14,996 15,513 

M/R 93,231 111,397 121,819 106,664 104,427 

Percent Timeliness 

TCR MCL 68% 74% 74% 73% 71% 

Other MCL 54% 50% 42% 43% 34% 

SWTR TT 59% 56% 66% 62% 58% 

Health-Based 

Violations13 65% 70% 68% 66% 60% 

M/R 53% 46% 45% 58% 31% 

Table 5-2 shows the computed timeliness of the reported violations in SDWIS/FED. 

Late reporting can have an impact on the reliability of SDWIS/FED in informing the public and 

stakeholders about the quality of their drinking water. Further, it hinders our effort to assess the 

public health risk and address the violations with enforcement actions in a timely manner. In 

2004, 60% of the health-based violations were reported on time, while only 31% of the M/R 

violations were reported on time. Note that there is a 27% decline in timeliness for the M/R 

violations from 2003. 

Additional information (in the form of pivot tables) is available from EPA upon request that 

provides additional details on the timeliness in which violations are reported across several 

additional attributes. Additional findings based on this information are the following: 

! Timeliness of reported health-based violations was similar across water system types. 

! Monitoring violations for TNCWSs was highest at 58%, and lowest for NTNCWSs at 

33%. 

 

13 These heath-based violations do not include Lead and Copper Treatment Technology (LCR TT) violations 

because of they have open-ended compliance period end dates. 
33

! Timeliness was similar across quarters. 

! Timeliness generally decreased as system size decreased. 

! It was difficult to evaluate the timeliness of reported violations for new rules, because 

many of the violations in these rules have open-ended compliance period end dates. 

6. Conclusion 

For the 38 states evaluated from 2002 to 2004, most of the reported violations in SDWIS/FED 

were accurate at 90%. Approximately 81% of the MCL and SWTR TT violations were reported 

to SDWIS/FED. Sixty-two percent of the health-based violations (including LCR TT violations) 

and 39% of the monitoring and reporting violations were reported. Non-reporting was mostly 

attributable to the fact that states did not issue violations when violations had occurred. In other 

words, the violations were not recognized, not recorded by the states as violations, and 

consequently, not reported to SDWIS/FED. Eighty-four percent of non-reported health-based 

violations and 92% of non-reported M/R violations were due to compliance determination errors. 

EPA considers non-reported violations to be a serious problem that could have public health 

implications at many levels. The information and the analyses based on such incomplete data in 

SDWIS/FED compromises our ability to determine if and when we need to take action against 

non-compliant systems, to oversee and evaluate the effectiveness of state and federal programs 

and regulations, to alleviate burden on states, and to determine whether new regulations are 

needed to further protect public health. Further, our response to public inquiries and preparing 

national reports on the quality of drinking water in a thorough and complete manner will be 

severely limited. 

Some of the discrepancies between the number of violations that should have appeared in 

SDWIS/FED and those found by the DV auditors could have included differences in rule 

interpretation in light of the flexibility provided to states in implementing rules under state 

primacy agreements. The state implementation of rules must be at least as stringent as the 

Federal regulations, but can differ in substantial respects within a reasonable scope of the 

regulation. It is critical that EPA and the states continue to work together toward reducing nonreporting, reporting errors, and late reporting of violations. 

Additional findings included the DQEs of health- based violations were not significantly 

different between CWSs and NTNCWSs. The DQEs on M/R violations for TNCWSs were 

significantly higher than those for CWSs and NTNCWSs. 

Further, the DQEs from 18 states where the DV audits were conducted during the data quality 

assessment period of 1999-2001 and again during 2002-2004 were calculated for the purpose of 

comparison. For those states, 67% of MCL/SWTR TT violations with a 95% confidence interval 

(55%, 79%) were reported to SDWIS/FED during 1999-2001. Similarly, 80% of MCL/SWTR 
34

TT violations with a 95% confidence interval (68%, 92%) were reported to SDWIS/FED during 

2002-2004. Since the confidence intervals overlap, there was no statistically significant increase 

in the reporting of violations for these 18 states from 1999-2001 to 2002-2003. The overall data 

quality of MCL/SWTR TT violations was 64% with a 95% confidence interval (52%, 75%) 

during 1999-2001 and 75% with a 95% confidence interval (64%, 87%) during 2002-2004. 

Based on the confidence intervals, there was no statistically significant increase in the overall 

data quality of MCL/SWTR TT violations for these 18 states from 1999-2001 to 2002-2003. 

Finally, 60% of MCL/SWTR TT violations were reported on time and approximately 30% of the 

MR violations were reported on time to SDWIS/FED in 2004. 

7. Data Reliability Improvement Action Plan 

Based on this analysis and on the results of previous efforts, EPA, working with its state coregulators through the Association of State Drinking Water Administrators, has developed a 

Data Reliability Improvement Action Plan (“the plan”) designed to achieve a data quality goal of 

90 percent complete and accurate data for health-based violation reporting. The plan covers the 

years 2007 through 2009 and addresses improving data quality for monitoring and reporting 

violations and inventory (water systems’ facilities) data. Principally, the plan focuses on actions 

that EPA and states can take to address compliance determination issues and thereby improve 

violation data quality. Progress toward accomplishment of the data quality goal will be 

measured annually and assessed in 2009. The plan appears in Appendix A. 

8. Future Analysis of Data Reliability 

Several factors will change both the process and the results of the data verifications and the data 

quality calculation for drinking water data. In the near term, the selection of states for DVs will 

be based on probability sampling from 2005. Specifically, the selection of states for the data 

verifications from 2005 to 2007 will be based on a probability sampling method, with every state 

being selected in a 4-year time frame. This will allow the data quality to be assessed nationally 

for rolling multiple-years. In the longer term (2008 and beyond), EPA is evaluating the 

feasibility of electronic data verification (EDV), which would collect and evaluate compliance 

sample results of regulated contaminants electronically for all CWSs. EPA believes that the most 

cost-effective and complete process of evaluating data quality in the long term may be via the 

EDV process. In each state, we can evaluate the data once every one or two years through the 

compliance determination processes recorded in the SDWIS/STATE software. SDWIS/STATE 

is already designed and developed for states to manage their drinking water programs. The 

advantages to this approach are that the software already exists and all compliance 

determinations are available for evaluation. The current DV process relies on a sample of 

systems, and due to the inherently small number of large CWSs, the large CWSs are not well 

represented in the samples. The EDV will allow us to use all systems instead of relying on a 

sample from a DV. Additionally, the drinking water administrators in decentralized states can 
35

have hands-on data in one location instead of going to regional drinking water offices. All states 

using SDWIS/STATE will have the capability to calculate data quality in near real time and take 

action on issues as they arise. Furthermore, EDV will allow states and EPA to reduce and 

reallocate time and resources spent on manual data reviews while providing a more complete 

picture of program implementation and leading to the identification of opportunities for program 

improvement. 
Appendix A: 2006 Data Reliability Improvement Action Plan

Introduction 

The past two Data Reliability Improvement Action Plans have drawn attention to actions 

that can be taken to improve data quality and the usability of SDWIS/FED data. While 

they significantly focused on information system improvements and general activities 

that should improve data quality, this 2006 plan builds on current findings for more 

recent data and capabilities not previously developed that concentrate on specific factors 

that could result in real-time data quality improvements. 

The philosophy of past data reliability improvement action plans largely was built on the 

concept that we must improve the software of the information system, SDWIS/FED. 

This has largely been done, with the last remaining step to be completed in 2007 with 

SDWIS/STATE Web Release 2. This release fully web-enables SDWIS/STATE, 

reducing resources needed to implement the software by states and reducing the 

complexity of data entry with fewer data entry screens and more drop-down lists. This 

2007-2009 Data Reliability Improvement Action Plan primarily focuses on the actions of 

those responsible for determining which data will be entered and how that will occur. 

The largest challenge is ensuring that all data reflecting determinations of violations are 

entered into the SDWIS/FED, the federal data base. 

As indicated in this report, EPA found that 77 percent of all data on MCL/SWTR TT 

violations in SDWIS/FED was complete and accurate. This is not satisfactory, has been 

the focus of media attention concerning the reliability of the data used to make decisions 

about the most important public health program in the nation for safeguarding its water 

supply to its citizens, and needs to be improved. To make a larger step forward over the 

next three years (2007-2009), EPA and ASDWA in October 2006 set a data quality goal 

of 90 percent (completeness and accuracy) for future compliance reporting of healthbased violations in the federal database, SDWIS/FED. This plan is principally focused 

on achieving that goal. Based on past analyses of state-specific results, eleven states 

have achieved this level of data quality for health-based violations, indicating that it is 

achievable. The plan also addresses improving data quality of monitoring and reporting 

violations and inventory data; that is, improving the quality of all data used and 

supporting the state and national drinking water programs with the highest quality data. 

The Plan is presented in a series of issues and plan elements with assigned responsibility 

and timeframes. 

Issues 

(1) Modify Data Verification Selection Processes: EPA continues to conduct triennial 

data quality analysis and to follow up on data verification by working with states to 

address identified differences and discrepancies from federal regulations. In the 

2005-2007 timeframe, EPA implemented probability-based selection (random 

selection) of states for data verification to enhance the representativeness of the data 

36
at the national level. The data quality results for these data will provide an indication 

of the extent of achievement of the 90 percent data quality goal set by EPA and 

ASDWA. Consistent with the August 2005 recommendation of the special ASDWAEPA Data Quality Subcommittee, the quality of results in the national database, 

SDWIS/FED will be displayed by rule and significance (i.e., public notification tier). 

 

(2) Consider All Compliance Determinations in State Data Quality: In evaluating 

SDWIS/FED data quality, EPA only considers data in the national database and not 

in the state databases reflecting all compliance determinations resulting from the 

states’ position as the primary enforcement authority for the federal program. EPA 

will develop an “electronic data verification” (eDV) tool to enable states to track any 

discrepancies of their compliance determinations relative to federal regulations and 

correct these discrepancies prior to data quality calculations and allow calculation of 

data quality relative to all compliance determinations. EPA should augment its 

SDWIS/STATE software to allow states to obtain management reports on any 

discrepancies in state compliance determination in near-real time to allow for the 

possibility of improving health-based response and data quality. 

(3) Use Electronic Scheduling and Lab Reporting: Using automated monitoring 

requirements/schedule generators and incorporating electronic reporting from 

laboratories to states would improve the quality of data that states receive from water 

systems. Anecdotal information suggests that when states issue automated 

monitoring schedules to water systems, on-time monitoring and reporting by those 

systems improves. This step increases the probability that all data will be used by the 

state in determining compliance with public health drinking water standards and that 

appropriate determinations are made. Additionally, when states receive monitoring 

data electronically, data entry errors are reduced. This second step helps ensure that 

the correct data are used in the decision process for determining compliance. Water 

system or laboratory submission of data to states must comply with the Cross-Media 

Electronic Reporting Rule (CROMERR), compliance with which will need to be 

considered in any effort to facilitate electronic reporting from laboratories to states. 

(4) Consider Data Management early in Rule Development: Data management concerns 

should be considered during every phase of the rule development process, beginning 

with the initial rule concept. If this does not occur, rules with complex reporting 

requirements may emerge, overwhelming the capability of states to implement them 

and shifting valuable resources from taking actions on real health needs to reporting. 

 Data management using electronic reporting can simplify handling data but does not 

necessarily and always mean a simpler process for protecting health and should not 

be used as a “crutch” for creating complex rules instead of focusing on simpler, 

direct key health management objectives for drinking water supply protection. 

Streamlined approaches to data management in states’ business processes must be 

considered in rule development. 

(5) Improve State Capability in Compliance Determination: Data reliability, as reported 

in the Triennial Data Reliability Report, appears to have marginally improved, even 

37
38

though this is not statistically significant. State compliance determinations play an 

integral role in determining the reliability of the data on violations reported to the 

national database, SDWIS/FED. Incorrect compliance determinations, when they do 

occur, are due in part to the complexity and number of drinking water rules. The need 

for training to facilitate correct determinations is critical, especially with the 

changing nature of state staff available to implement the drinking water regulations. 

Incorrect compliance determinations are a serious matter as they may affect public 

health. 

(6) Complete SDWIS Modernization: EPA should continue implementation of the 

OGWDW Information Strategic Plan to modernize and web-enable the 

SDWIS/STATE to take advantage of newer technologies and system platforms. This 

action will save state resources by being able to enter data from anywhere in the state 

that is web-accessible and reduce data entry time with fewer screens and more drop 

down lists. State deployment of SDWIS/STATE Web Releases 1 and 2 will take 

time because of different schedules and variation of available resources among states. 

For states using SDWIS/STATE, full use of all SDWIS/STATE modules and regular 

update of inventory data will facilitate improved data quality. 

(7) Evaluate Low Timeliness of Violation Reporting: Violation reporting timeliness is 

low and not improving. Because the states have been taking steps to improve data 

quality and the calculation of data quality considers results which may be 3 to 5 years 

old in some cases, estimates of reporting timeliness may not be current. EPA should 

use the reported results from the first year of using the modernized data flow to reevaluate timeliness for each rule, as recommended by the Data Sharing Committee. 

(8) Update Out-of-date and missing Inventory Data: Key features of inventory data 

useful in examining compliance and for determining regulatory needs are not 

routinely updated and reported. For example, consecutive systems or treatment 

objectives for recent rules are inventory data that are not reported for each system to 

which they apply. As a result, EPA cannot conduct analyses of national capability to 

treat certain contaminants. Inventory data for grant eligibility are routinely reported 

for the purposes of ensuring adequate data for receiving grants.
2006 Drinking Water Data Reliability Improvement Action Plan 

Element Element 

Description 

Activity Responsibility & 

Actions 

Complet

(1) Modify Data Verification Selection Processes 

EPA will calculate data quality with data from 2005-2007 data verification from the random selection of states and display by rule and public notification tier. 

(a) EPA will calculate data quality with data from 2005-2007 from the random selection of states and display by rule and public notification tier. 

(1) EPA - Calculate national estimate of data quality for health-based violations and separately for monitoring and reporting violations and inventory data for 2005-2007 (2) EPA - Calculate state estimates of data quality for all health based compliance determinations and separately for all monitoring and reporting compliance determinations for 2005-2007 (3) EPA – Report data quality by rule and public notification tier for 2005-2007 using data verification results 

(1) Decem2008 (2) Decem2008 (3) Decem2008 

(a) EPA will develop an “electronic data verification” (eDV) tool to enable states to track any discrepancies of their compliance determinations relative to federal regulations and correct these discrepancies prior to data quality calculations and allow calculation of data quality relative to all compliance determinations. 

(1) EPA & States - Complete pilot test of eDV tool 

(1) Decem2008 

(2) Evaluate All Compliance Determinations 

Develop a tool to allow states to identify compliance determination discrepancies from federal regulations more easily. 

(b) States will agree to provide (1) EPA & States - EPA (1) July 2

39
Element Element 

Description 

Activity Responsibility & 

Actions 

Complet

contaminant occurrence and monitoring schedules data to EPA to allow the Agency to conduct electronic data verification for all rules across all water systems in a state, retrospectively, on an annual basis, but not less frequently than every three years, to allow regular assessment of data quality and to identify opportunities for state program improvement. 

request and states provide contaminant occurrence and schedules data for all water systems from at least nine states for testing eDV tool (2) EPA & States – complete data sharing agreements for contaminant occurrence and monitoring schedule data (3) EPA & States - EPA will receive state contaminant occurrence and schedules data for all water systems from all states through completion of a data sharing agreement 

(2) Decem2008 (3) Annuabeginning 2009 

(a) States will utilize automated scheduling of water system monitoring to the extent possible and report on progress in on-time monitoring and reporting by water systems at the ASDWA-EPA Data Management Users Conference. 

(1) States – Report progress on State automated scheduling of system monitoring 

(1) AnnuaMay 2007 May 2009 May 2010 

(3) Use Automated Scheduling and Electronic Lab Reporting 

States and EPA will take steps to more fully utilize automated technology to improve reporting of water system data to states. 

(b) EPA will develop an electronic tool to allow laboratories testing drinking water samples to report to states (“lab-to-state”) reporting tool, rather than submitting paper 

(1) EPA - develop “lab-tostate” reporting tool (1) March 2007 

40
Element Element 

Description 

Activity Responsibility & 

Actions 

Complet

reports on monitoring results. (c) The EPA Office of Ground Water and Drinking Water will work with the EPA Office of Environmental Information (OEI) to incorporate CROMERR requirements in the “lab-to-state” reporting tool, work toward OEI approval of the tool. 

(1) EPA - review and approval of CROMERR compliance of “lab-to-state” reporting tool 

(1) Augus2007 

(d) States not using the EPA developed “Lab-to-State” electronic reporting tool will identify and use a similar tool 

(1) States will replace paper lab reports for compliance monitoring with automated lab reporting 

(1) Ongointhrough December 2009 

(a) EPA information systems staff will participate in early rule development through preparation of issue papers on data management for each future rule and share these papers for comment with states through the ASDWA-EPA Data Management Steering Committee. 

(1) EPA & States - information systems staff participate in rule development 

(1) Ongoi

(4) Consider Data Management early in Rule Development 

Implement a process to address data management in rule development. 

(b) States will identify staff and participate in discussions of future rules to ensure that business processes are considered. 

(1) State - staff identified for participation in rules to consider state business processes 

(1) Ongoi

(c) ASDWA and EPA will work toward agreement on a mutual generic timeline for considering data management in rule 

(1) ASDWA and EPA – reach agreement on generic timeline for including data management in rule 

(1) Decem2007 

41
Element Element 

Description 

Activity Responsibility & 

Actions 

Complet

development. development 

(1) EPA HQ & States - Complete testing eDV tool 

(1) Septe2008 

(a) EPA Headquarters will develop an electronic data verification tool to allow EPA Regions to compare the results of all state compliance determinations to the violation data reported to EPA in SDWIS/FED. 

(2) EPA Regions and States – Use eDV tool to check compliance determinations and take appropriate action 

(2) Ongoinbeginning 2009; quacheck and action 

(5) Improve State Capability in Compliance Determination 

(b) EPA Regions will ensure that data reliability improvement steps are included in all agreements and work plans with States and identify specific reasons for discrepancies, including non-reporting, of state determinations with federal regulations. 

(1) EPA Regions & States - Incorporate data reliability improvement steps in stateEPA agreements and state work plans 

(1) Annua

(c) States will identify compliance determination training needs to EPA Regions. 

(1) States - identify 

compliance determination training needs 

(1) Annua

EPA Regions, to ensure that data reliability improvement (including implementation of EPA Order 5360.1.A2) is included in annual agreements with States, will work with states to identify the specific reasons for discrepancies in compliance determinations and to identify training needs among states to facilitate capability to make correct determinations. 

(d) EPA Headquarters will develop and provide capability for training on compliance determination for states 

(1) EPA HQ - Completed/revision underway for compliance determination training 

(1) Ongoi

(6) Complete SDWIS Modernization 

Complete modernization, webenablement and deployment of SDWIS/STATE Web 

(a) Development of fully webenabled SDWIS/STATE and facilitation of fuller use of software for state program management 

(1) EPA HQ – Develop SDWIS/STATE Web Release 2 (2) EPA Regions - promote full state use of 

(1) Octob2007 (2) Annua

42
Element Element 

Description 

Activity Responsibility & 

Actions 

Complet

SDWIS/STATE software through state agreements 

Release 2, facilitate fuller use of SDWIS/STATE among states choosing to use it and regular update of inventory data to improve data quality 

(b) Deployment of web-enabled SDWIS/STATE with planned fuller use of modules by states using SDWIS/STATE and update of inventory data 

(1) States - Deploy 

SDWIS/STATE Web Release 2 (2) EPA Regions and States using SDWIS/STATE - agree to steps toward fuller use of SDWIS/STATE in agreements and workplan (3) EPA HQ and States – Conduct workshop on SDWIS/STATE Web Release 2 

(1) BeginnOctober 2(2) Annua(or as appropriat(3) Summ2008 

(7) Evaluate

Low Timeliness of Violation Reporting 

Evaluate timeliness by rule with data reported to the modernized SDWIS/FED for 2006 

(a) Evaluate timeliness by rule with data reported to the modernized SDWIS/FED for 2006 

(1) Data Sharing Committee - perform timeliness analysis in 2008 once all violation data are reported and processed; make recommendation to DMSC 

(1) 2007 

(8) Update Outof-date and missing Inventory Data 

Evaluate regulatory requirements to determine the appropriate inventory reporting relating the applicability of rules to systems, set a priority on the data needed, and work with states to update the inventory data routinely reported to EPA 

(a) Evaluate regulatory requirements to determine the appropriate inventory reporting relating the applicability of rules to systems, set a priority on the data needed, and work with states to update the inventory data routinely reported to EPA 

(1) Data Sharing Committee - evaluate inventory reporting and propose a priority on data to be updated 

(1) 2008 

43
Appendix B: Violations Addressed by Data Verification (DV) 

Violation Code Violation Name 

Violation  Type Applicable rules and contaminant codes (CCodes) 

1 MCL, Single Sample MCL 2 MCL, Average MCL 3 Monitoring, Regular MR 

Monitoring, Check/Repeat/Confirmation MR 

DBP1009, 1011, 2456, 2950

TTHM pre-'02 2941/ 42/ 43/ 44, 2950

Nitrates1038, 1040, 1041

4 

5 Notification, State Other 

Other IO1005/ 120/ 24/ 35/ 36*/ 74/ 75/ 94

6 Notification, Public Other 

7 Treatment Techniques Other 8 Variance/Exemption/Other Compliance Other 

Record Keeping Other FBR 0500 IESWTR 0300

Other VOC2378/ 80, 2955/ 64/ 68/ 69/ 76/ 77/ 79/ 80/ 81/ 82/ 83/ 84/ 85/ 87/ 89/ 90/ 91/ 

9 92/ 96

10 Operations Report Other * codes 

Non-Acute MRDL MRDL DBP 0999, 1006/ 08

SOC2005/ 10/ 15/ 20/ 31/ 32/ 33/ 34/ 35/ 36/ 37/ 39/ 40/ 41/ 42/ 43*/ 44*/ 46/ 47*/ 50/ 51/ 63/ 65/ 67, 2105/ 10, 2274/ 98, 2306/ 26/ 83/ 88/ 90/ 92/ 94/ 96/ 98, 2400, 2931/ 46/ 

11 59

12 Treatment Technique No Certif. Operator TT DBP 0400

13 Acute MRDL MRDL DBP 1008

21 MCL, Acute (TCR) MCL 

22 MCL, Monthly (TCR) MCL 

23 Monitoring, Routine Major (TCR) MR 24 Monitoring, Routine Minor (TCR) MR 25 Monitoring, Repeat Major (TCR) MR 

Monitoring, Repeat Minor (TCR) MR 

TCR 3100 

26 

27 Monitoring and Reporting Stage 1 MR DBP 0400, 0999, 1006/ 08/ 09/ 11, 2456, 2920, 2950

28 Sanitary Survey (TCR) Other SS, TCR 3100

29 M&R Filter Profile/CPE Failure MR IESWTR 0300

31 Monitoring, Routine/Repeat (SWTR-Unfilt) MR 

Monitoring, Routine/Repeat (SWTR-Filter) MR 

SWTR 0200

36 

44
45

Violation Name 

Violation  Type Applicable rules and contaminant codes (CCodes) 

Violation Code 

37 Treatment Technique State Prior Approval TT DBP 040038 M&R Filter Turbidity Reporting MR 

IESWTR 0300

39 M&R (FBRR) MR 40 Treatment Technique (FBRR) TT 

FBR 0500

41 Treatment Technique (SWTR) TT 42 Failure to Filter (SWTR) TT 

SWTR 0200

43 Treatment Technique Exceeds Turb 1 NTU TT 44 Treatment Technique Exceeds Turb 0.3 NTU TT 

IESWTR 0300

46 Treatment Technique Precursor Removal TT DBP 292047 Treatment Technique Uncovered Reservoir TT IESWTR 030051 Initial Tap Sampling for Pb and Cu MR 52 Follow-up and Routine Tap Sampling MR 53 Initial Water Quality Parameter WQP M&R MR 54 

Follow-up & Routine E.P. WQP M&R (deleted) MR 

55 

Follow-up & Routine Tap WQP M&R (deleted) MR 

56 Initial, Follow-up, or Routine SOWT M&R MR 57 OCCT Study Recommendation TT 58 OCCT Installation/Demonstration TT 59 WQP Entry Point Noncompliance TT 60 WQP Entry Point Noncompliance (deleted) TT 61 SOWT Recommendation (deleted) TT 62 SOWT Installation (deleted) TT 

LCR 5000

63 MPL Noncompliance TT LCR 1022,103064 Lead Service Line Replacement (LSLR) TT 65 Public Education TT 

LCR 5000

71 CCR Complete Failure to Report Other 72 CCR INADEQUATE REPORTING Other 

CCR 7000

75 PN Violation for NPDWR Violation Other 76 Other Non-NPDWR Potential Health Risks Other 

PN 7500
Appendix C: Definition of Public Notification (PN) Tiers 

Tier 1: Violations and Other Situations Requiring Notice Within 24 Hours

1. Violation of the MCL for total coliform, when fecal coliform or E. coli are 

present in the water distribution system, or failure to test for fecal coliform or E. 

coli when any repeat sample tests positive for coliform 

2. Violation of the MCL for nitrate, nitrite, or total nitrate and nitrite; or when a 

confirmation sample is not taken within 24 hours of the system’s receipt of the 

first sample showing exceedance of the nitrate or nitrite MCL 

3. Exceedance of the nitrate MCL (10 mg/l) by non-community water systems, 

where permitted to exceed the MCL (up to 20 mg/l) by the primacy agency 

4. Violations of the MRDL for chlorine dioxide when one or more of the samples 

taken in the distribution system on the day after exceeding the MRDL at the 

entrance of the distribution system or when required samples are not taken in 

the distribution system 

5. Violation of the turbidity MCL of 5 NTU, where the primacy agency determines 

after consultation that a Tier 1 notice is required or where consultation does not 

occur in 24 hours after the system learns of violation 

6. Violation of the treatment technique requirement resulting from a single 

exceedance of the maximum allowable turbidity limit, where the primacy agency 

determines after consultation that a Tier 1 notice is required or where 

consultation does not take place in 24 hours after the system learns of violation 

7. Occurrence of a waterborne disease outbreak, as defined in 40 CFR 141.2, or 

other waterborne emergency

8. Other violations or situations with significant potential to have serious adverse 

effects on human health as a result of short term exposure, as determined by the 

primacy agency either in its regulations or on a case-by case basis 

Tier 2: Violations Requiring Notice Within 30 Days

1. All violations of the MCL, MRDL, and treatment technique requirements except 

where Tier 1 notice is required 

2. Violations of the monitoring requirements where the primacy agency determines 

that a Tier 2 public notice is required, taking into account potential health impacts 

and persistence of the violation 

46
47

3. Failure to comply with the terms and conditions of any variance or exemption in 

place 

Tier 3: Violations and Other Situations Requiring Notice Within 1 Year

1. Monitoring violations, except where Tier 1 notice is required or the primacy 

agency determines that the violation requires a Tier 2 notice 

2. Failure to comply with an established testing procedure, except where Tier 1 

notice is required or the primacy agency determines that the violation requires a 

Tier 2 notice 

3. Operation under variance granted under §1415 or exemption granted under 

§1416 of the Safe Drinking Water Act 

4. Availability of unregulated contaminant monitoring results 

5. Exceedance of the secondary maximum contaminant level for fluoride