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Welcome to the CLU-IN Internet Seminar

Unified GuidanceSponsored by: U.S. EPA Technical Support Project

Delivered: October 7, 2010, 2:00 PM - 3:00 PM, EDT (18:00-19:00 GMT)

Instructor:Mike Gansecki, U.S. EPA Region 8 (gansecki.mike@epa.gov)

Moderator:Jean Balent, U.S. EPA, Technology Innovation and Field Services Division

(balent.jean@epa.gov)

Visit the Clean Up Information Network online at www.cluin.org

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UNIFIED GUIDANCE UNIFIED GUIDANCE WEBINARWEBINAR

Statistical Analysis of Groundwater Statistical Analysis of Groundwater Monitoring Data at RCRA FacilitiesMonitoring Data at RCRA Facilities

March 2009March 2009

Website Location: Website Location: http://www.epa.gov/epawaste/hazard/correctiveahttp://www.epa.gov/epawaste/hazard/correctiveaction/resources/guidance/sitechar/gwstats/ction/resources/guidance/sitechar/gwstats/index.htmindex.htm

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Covers and

Errata Sheet

2010

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Purpose of WebinarPurpose of Webinar

• Present general layout and contents Present general layout and contents of the Unified Guidanceof the Unified Guidance

• How to use this guidanceHow to use this guidance

• Issues of interestIssues of interest

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GENERAL LAYOUTGENERAL LAYOUT

Longleat, England

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GUIDANCE LAYOUTGUIDANCE LAYOUT

MAIN TEXTMAIN TEXTPART I Introductory Information & DesignPART I Introductory Information & Design

PART II Diagnostic MethodsPART II Diagnostic Methods

PART III Detection Monitoring MethodsPART III Detection Monitoring Methods

PART IV Compliance/Corrective Action MethodsPART IV Compliance/Corrective Action Methods

APPENDICES– References, Index, APPENDICES– References, Index, Historical Issues, Statistical Details, Historical Issues, Statistical Details, Programs & TablesPrograms & Tables

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PART I INTRODUCTORY PART I INTRODUCTORY INFORMATION & DESIGNINFORMATION & DESIGN

• RCRA Regulations, Constraints & IssuesRCRA Regulations, Constraints & Issues

• Basic Statistical ConceptsBasic Statistical Concepts

• Groundwater Monitoring FrameworkGroundwater Monitoring Framework

• Developing Background DataDeveloping Background Data

• Detection Monitoring DesignDetection Monitoring Design

• Compliance/Corrective Action Monitoring Compliance/Corrective Action Monitoring DesignDesign

• Summary of MethodsSummary of Methods

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PART II DIAGNOSTIC PART II DIAGNOSTIC METHODSMETHODS

• Exploratory Data TechniquesExploratory Data Techniques

• Fitting DistributionsFitting Distributions

• Outlier AnalysesOutlier Analyses

• Equality of Variance Equality of Variance

• Spatial Variation EvaluationSpatial Variation Evaluation

• Temporal Variation AnalysisTemporal Variation Analysis

• Managing Non-Detect DataManaging Non-Detect Data

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PART III DETECTION PART III DETECTION MONITORING METHODSMONITORING METHODS

• Two-sample TestsTwo-sample Tests

• ANOVAs, Tolerance Limits & Trend ANOVAs, Tolerance Limits & Trend TestsTests

• Prediction Limit Tests and Managing Prediction Limit Tests and Managing Multiple ComparisonsMultiple Comparisons

• Control ChartsControl Charts

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PART IV COMPLIANCE PART IV COMPLIANCE MONITORING METHODSMONITORING METHODS

• Mean, Median and Upper Percentile Tests Mean, Median and Upper Percentile Tests with Fixed Health-based Standardswith Fixed Health-based Standards

• Stationary versus Trend TestsStationary versus Trend Tests• Parametric and Non-parametric OptionsParametric and Non-parametric Options• Strategies under Compliance and Strategies under Compliance and

Corrective Action TestingCorrective Action Testing• Consideration of Tests with a Background-Consideration of Tests with a Background-

type Groundwater Protection Standardtype Groundwater Protection Standard

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HOW TO USE THIS HOW TO USE THIS GUIDANCEGUIDANCE

Man-at-Desk

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USING THE UNIFIED USING THE UNIFIED GUIDANCEGUIDANCE• Design Design of a statistical monitoring system of a statistical monitoring system

versusversus routine implementationroutine implementation• FlexibilityFlexibility necessary in selecting methods necessary in selecting methods• Resolving issuesResolving issues may require coordination may require coordination

with the regulatory agencywith the regulatory agency• Later detailed methods based on early Later detailed methods based on early

concept and concept and design Chaptersdesign Chapters• Each methodEach method has background, has background,

requirements and assumptions, procedure requirements and assumptions, procedure and a worked exampleand a worked example

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The NeumannsThe Neumanns

Alfred E. Neuman, Cover of MAD #30 John von Neumann, taken in the 1940’s1414

Temporal Variation [Chapter Temporal Variation [Chapter 14]14]Rank von Neumann Ratio Test Rank von Neumann Ratio Test Background & PurposeBackground & Purpose• A non-parametric test of first-order autocorrelation; A non-parametric test of first-order autocorrelation;

an alternative to the autocorrelation functionan alternative to the autocorrelation function

• Based on idea that independent data vary in a random but Based on idea that independent data vary in a random but predictable fashionpredictable fashion

• Ranks of sequential lag-1 pairs are tested, using the sum of Ranks of sequential lag-1 pairs are tested, using the sum of squared differences in a ratiosquared differences in a ratio

• Low values of the ratio v indicative of temporal dependenceLow values of the ratio v indicative of temporal dependence

• A powerful non-parametric test even with parametric (normal or A powerful non-parametric test even with parametric (normal or skewed) data skewed) data

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Temporal Variation [Chapter Temporal Variation [Chapter 14]14]Rank von Neumann Ratio TestRank von Neumann Ratio TestRequirement & AssumptionsRequirement & Assumptions• An unresolved problem occurs when a substantial fraction An unresolved problem occurs when a substantial fraction

of tied observations occursof tied observations occurs

• Mid-ranks are used for ties, but no explicit adjustment has Mid-ranks are used for ties, but no explicit adjustment has been developedbeen developed

• Test may not be appropriate with a large fraction of non-Test may not be appropriate with a large fraction of non-detect data; most non-parametric tests may not work welldetect data; most non-parametric tests may not work well

• Many other non-parametric tests are also available in the Many other non-parametric tests are also available in the statistical literature, particularly with normally distributed statistical literature, particularly with normally distributed residuals following trend removalresiduals following trend removal

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Temporal Variation [Chapter Temporal Variation [Chapter 14]14]Rank von Neumann Ratio Rank von Neumann Ratio ProcedureProcedure

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Rank von Neumann Example Rank von Neumann Example 14-414-4

Arsenic DataArsenic Data

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Rank von Neumann Ex.14-4 Rank von Neumann Ex.14-4 SolutionSolution

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DIAGNOSTIC TESTING DIAGNOSTIC TESTING Preliminary Data Plots [Chapter 9]Preliminary Data Plots [Chapter 9]

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Additional Diagnostic Additional Diagnostic InformationInformation• Data PlotsData Plots [Chapter 9] – [Chapter 9] – Indicate no likely outliers; data Indicate no likely outliers; data

are roughly normal, symmetric and stationary with no are roughly normal, symmetric and stationary with no obvious unequal variance across time (to be tested)obvious unequal variance across time (to be tested)

• Correlation Coefficient Normality Test Correlation Coefficient Normality Test [Section 10.6][Section 10.6]r = .99; p[r] > .1 r = .99; p[r] > .1 Accept NormalityAccept Normality

• Equality of Variance Equality of Variance [Chapter 11] - [Chapter 11] - see analyses belowsee analyses below

• Outlier Tests Outlier Tests [Chapter 12]- [Chapter 12]- not necessarynot necessary

• Spatial Variation Spatial Variation [Chapter 13]–[Chapter 13]–spatial variation not spatial variation not relevant for single variable data setsrelevant for single variable data sets

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Additional Diagnostic Additional Diagnostic InformationInformation• Von Neumann Ratio Test Von Neumann Ratio Test [Section 14.2.4][Section 14.2.4]

ν = 1.67 ν = 1.67 No first-order autocorrelationNo first-order autocorrelation

• Pearson Correlation of Arsenic vs. TimePearson Correlation of Arsenic vs. Time[p.3-12][p.3-12]; ; r = .09 r = .09 No apparent linear trendNo apparent linear trend

• One-Way ANOVA Test for Quarterly Differences One-Way ANOVA Test for Quarterly Differences [Section 14.2.2];F = 1.7, p(F) = .22[Section 14.2.2];F = 1.7, p(F) = .22Secondary ANOVA test for equal variance F = .41; p(F) Secondary ANOVA test for equal variance F = .41; p(F) =.748=.748No significant quarterly mean differences and equal No significant quarterly mean differences and equal variance across quartersvariance across quarters

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Additional Diagnostic Additional Diagnostic InformationInformation• One-Way ANOVA Test for Annual Differences One-Way ANOVA Test for Annual Differences [Chapter 14];[Chapter 14];

F = 1.96; p(F) = .175F = 1.96; p(F) = .175Secondary ANOVA test for equal variance F = 1.11; p(F) =.385Secondary ANOVA test for equal variance F = 1.11; p(F) =.385

No significant annual mean differences and equal variance No significant annual mean differences and equal variance across yearsacross years

• Non-Detect DataNon-Detect Data [Chapter 15]– [Chapter 15]– all quantitative data; evaluation all quantitative data; evaluation not needednot needed

ConclusionsConclusions

• Arsenic data are satisfactorily independent temporally, Arsenic data are satisfactorily independent temporally, random, normally distributed, stationary and of equal random, normally distributed, stationary and of equal variancevariance

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ISSUESISSUES

The Thinker, Musee Rodin in Paris2424

ISSUES OF INTERESTISSUES OF INTEREST

• RCRA REGULATORY STATISTICAL ISSUESRCRA REGULATORY STATISTICAL ISSUES• DEVELOPING AND UPDATING BACKGROUND DATADEVELOPING AND UPDATING BACKGROUND DATA• USE OF SYSTEM-WIDE FALSE POSITIVE AND POWER USE OF SYSTEM-WIDE FALSE POSITIVE AND POWER

CONTROL DESIGNCONTROL DESIGN• APPROPRIATE HYPOTHESES, METHODS, APPROPRIATE HYPOTHESES, METHODS,

STATISTICAL PARAMETERS, FALSE POSITIVE AND STATISTICAL PARAMETERS, FALSE POSITIVE AND POWER LEVELS FOR COMPLIANCE MONITORING POWER LEVELS FOR COMPLIANCE MONITORING FIXED GWPS TESTINGFIXED GWPS TESTING

• CHOICES OF PARAMETRIC AND NON-PARAMETRIC CHOICES OF PARAMETRIC AND NON-PARAMETRIC DISTRIBUTIONSDISTRIBUTIONS

• USE OF OTHER STATISTICAL METHODS AND USE OF OTHER STATISTICAL METHODS AND SOFTWARE, e.g., ProUCL®SOFTWARE, e.g., ProUCL®

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RCRA REGULATORY STATISTICAL RCRA REGULATORY STATISTICAL ISSUESISSUES

• Four-successive sample requirements Four-successive sample requirements and independent Sampling Dataand independent Sampling Data

• Interim Status Indicator Testing Interim Status Indicator Testing RequirementsRequirements

• 1 & 5% Regulatory Testing 1 & 5% Regulatory Testing RequirementsRequirements

• Use of ANOVA and Tolerance IntervalsUse of ANOVA and Tolerance Intervals

• April 2006 Regulatory ModificationsApril 2006 Regulatory Modifications

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DEVELOPING AND UPDATING DEVELOPING AND UPDATING BACKGROUND DATABACKGROUND DATA

• In development (e.g., permit or plan), In development (e.g., permit or plan), consider all historical sample dataconsider all historical sample data

• Larger background sample sizes Larger background sample sizes preferablepreferable

• Consider all diagnostic conditions, Consider all diagnostic conditions, especially outliersespecially outliers

• For updating, need 4-8 samples not For updating, need 4-8 samples not significantly different (use a Student-t or significantly different (use a Student-t or Mann-Whitney test to check)Mann-Whitney test to check)

• Special considerations for repeat test dataSpecial considerations for repeat test data

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USE OF SYSTEM-WIDE FALSE POSITIVE USE OF SYSTEM-WIDE FALSE POSITIVE AND POWER CONTROL DESIGNAND POWER CONTROL DESIGN

• Applied to detection monitoring system design for Applied to detection monitoring system design for comparisons to background (or for two-sample comparisons to background (or for two-sample background-type GWPS compliance tests)background-type GWPS compliance tests)

• Uses a design 10% annual Sitewide False Positive Uses a design 10% annual Sitewide False Positive Rate [SWFPR] apportioned to the number of Rate [SWFPR] apportioned to the number of annual statistical tests (number of wells, relevant annual statistical tests (number of wells, relevant constituents and annual test frequency)constituents and annual test frequency)

• Reference power curves based on a 1-of-1 Reference power curves based on a 1-of-1 prediction limit test 1, 2, or 4 times per yearprediction limit test 1, 2, or 4 times per year

• Fully developed for prediction limits, but Fully developed for prediction limits, but applicable to tolerance interval and control chart applicable to tolerance interval and control chart detection tests detection tests

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HYPOTHESES, METHODS, STATISTICAL HYPOTHESES, METHODS, STATISTICAL PARAMETERS, FALSE POSITIVE AND PARAMETERS, FALSE POSITIVE AND POWER LEVELS FOR COMPLIANCE POWER LEVELS FOR COMPLIANCE MONITORING FIXED GWPS TESTINGMONITORING FIXED GWPS TESTING

• The guidance identifies testing hypotheses The guidance identifies testing hypotheses based on RCRA regulations; these may differ based on RCRA regulations; these may differ in other State and regulatory programsin other State and regulatory programs

• Choices of statistical parameter to compare Choices of statistical parameter to compare to a GWPS variableto a GWPS variable

• Regulatory agency can determine Regulatory agency can determine significance and power criteria, perhaps on a significance and power criteria, perhaps on a program levelprogram level

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CHOICES OF PARAMETRIC AND NON-CHOICES OF PARAMETRIC AND NON-PARAMETRIC DISTRIBUTIONSPARAMETRIC DISTRIBUTIONS

• Under detection monitoring development, Under detection monitoring development, distribution choices are primarily determined distribution choices are primarily determined by data patternsby data patterns

• Different choices can result in a single Different choices can result in a single systemsystem

• In compliance and corrective action In compliance and corrective action monitoring, the regulatory agency may monitoring, the regulatory agency may determine which parametric distribution is determine which parametric distribution is appropriate in light of how a GWPS should be appropriate in light of how a GWPS should be interpretedinterpreted

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USE OF OTHER STATISTICAL METHODS USE OF OTHER STATISTICAL METHODS AND SOFTWARE, e.g., ProUCL®AND SOFTWARE, e.g., ProUCL®

• The Unified Guidance provides a The Unified Guidance provides a reasonable suite of methods, but by no reasonable suite of methods, but by no means exhaustivemeans exhaustive

• Statistical literature references to other Statistical literature references to other possible tests are providedpossible tests are provided

• The guidance suggests use of R-script and The guidance suggests use of R-script and ProUCL for certain applications. Many ProUCL for certain applications. Many other commercial and proprietary software other commercial and proprietary software may be available.may be available.

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Lewis Hine photo, Power House Mechanic

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