Conducting a Demonstration of Method Applicability …：進(jìn)行了示范法的適用性…

DemystifyingtheDemonstrationofMethodApplicability(DMA)CLU-INStudiosWebSeminarJuly28,2021StephenDymentUSEPATechnologyInnovationFieldServicesDivision1Howto............Askquestions“?〞buttononCLU-INpageControlslidesaspresentationproceedsmanuallyadvanceslidesReviewarchivedsessions

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2DMATechnicalBulletinNewpublicationofBrownfieldTechnicalSupportCenter(BTSC)

15pagetechnicalbulletindescribingDMAcomponents,benefits,considerationsCommonproducts,dataevaluation3shortcasestudiesAudience-technicalteammembersandstakeholders3DMAHistoryConceptfoundedinSW-846,performancebasedmeasurement(PBMS)initiativeInitialsite-specificperformanceevaluationAnalyticalanddirectsensingmethodsSampledesign,samplecollectiontechniques,samplepreparationstrategiesUsedtoselectinformationsourcesforfieldandoff-siteGoalistoestablishthatproposedtechnologiesandstrategiescanprovideinformationappropriatetomeetprojectdecisioncriteria“IthinkthatinthediscussionofnaturalproblemsweoughttobeginnotwiththeScriptures,butwithexperiments,anddemonstrations.〞GalileoGalilei

4WhyDoYouNeedaDMA?Triadusuallyinvolvesreal-timemeasurementstodriveDWSGreatestsourcesofuncertaintyusuallysampleheterogeneityandspatialvariabilityRelationshipswithestablishedlaboratorymethodsoftenrequiredtomakedefensibledecisionsProvidesaninitiallookatCSMassumptions5IsaDMAAlwaysAppropriate?No........SIguidanceindicates<20samplesSomeactivitieswithlimitedscopeorresourcesProjectswithadequateresourcestoemployestablishedmobileorfixedlabmethodsatsufficientdensity*6IsaDMAAlwaysAppropriate?

Continued...TheBrownfield’sperception“aproperty,redevelopment,orreusewhichmaybecomplicatedbythepresenceorpotentialpresenceofahazardoussubstance,pollutant,orcontaminant〞Underscorestheneedforhigherdensityinformation,collaborativedatasetsFacilitatesstakeholdercommunicationandpublicpresentations7What’sInvolved?ThereisnotemplateforDMAs!Format,timing,documentationetc.dependheavilyonsitespecifics,existinginformation,intendeddatausePerformedearlyinprogramGobeyondsimpletechnologyevaluationtooptimizefullscaleSampledesign,decisionandunitdesignationsSampleprep,throughput,otherlogisticsDatamanagementissuesDocumentation8KnowYourDecision-

ACautionaryTaleSmallArmsFiringRangeSoilGrainSize(StdSieveMeshSize)PbConc.infractionbyAA(mg/kg)Greaterthan3/8”(0.375”)10Between3/8and4-mesh”50Between4-and10-mesh108Between10-and50-mesh165Between50-and200-mesh836Lessthan200-mesh1,970Totals927(wt-averaged)9WhattoLookFor….Effectiveness-Doesitworkasadvertised?QA/QCissuesAreDLsandRLsforsitematricessufficient?Whatistheexpectedvariability?Precision?Bias,falsepositives/falsenegatives?Howdoessamplesupporteffectresults?DevelopinitialrelationshipsofcollaborativedatasetsthatprovideframeworkofpreliminaryQCprogramMatrixissues?Docollaborativedatasetsleadtothesamedecision?Assessingalternativestrategiesascontingencies10MoreBenefitsAugmentplanneddatacollectionandCSMdevelopmentactivitiesTestdrivecommunicationanddatamanagementschemes,decisionSupportTools(DSTs)SamplingandstatisticaltoolsVisualizationtools,datamanagementtoolsDeveloprelationshipsbetweenvisualobservationsanddirectsensingtoolsFlexibilitytochangetacticsbasedonDMAratherthanfullimplementationEstablishinitialdecisionlogicforDWSEvaluateexistingcontractmechanismsOptimizesequencing,staffing,loadbalance,unitizingcosts11TypicalDMAProducts-

SummaryStatistics12TypicalDMAProductsParametric-Linearregressions13WhatisaRegressionLine?14HeteroscedasticityisaFactofLifeforEnvironmentalDataSets15AppropriateRegressionAnalysisBasedonpairedanalyticalresults,ideallyfromsamesub-samplePairedresultsfocusonconcentrationrangespertinenttodecision-makingNon-detectsareremovedfromdatasetBestregressionresultsobtainedwhenpairsarebalancedatoppositeendsofrangeofinterestNoevidenceofinexplicable“outliers〞NosignsofcorrelatedresidualsHighR2values(closeto1)Constantresidualvariance(homoscedastic)isnicebutunrealistic16Example:XRFandLeadFulldataset:WonderfulR2UnbalanceddataCorrelatedresidualsApparentlypoorcalibrationTrimmeddataset:BalanceddataCorrelationgonefromresidualsExcellentcalibrationR2dropssignificantly17Smallscalevariabilitycanimpactdataqualitymorethantheanalyticalmethod18Smallscalevariabilitycanimpactdataqualitymorethantheanalyticalmethod19TypicalDMAProductsNon-parametrictechniques-Rangesorbins

20ComparabilityADirtyWord?VaguereferencesinQAPPsPARCCHowdoyoumeasurecomparability?OuruseofthetermThefrequencywithwhichresultsfromdifferenttechniquesagreewithrespecttoadeclaredreferencepoint21WeightofEvidencevs.CollaborativeDataSetsTheTriadperspectiveWeightofEvidenceCombininginformationfromvarioussourcesintoaholisticpicture,advancingtheCSMCollaborativedataUsing2ormoreanalyticalmethodstomeasurethesamecompound,analyte,surrogate,orclassUsingestablishedrelationships,onemethodcanbeusedtoinformtheuserwhenanalysisbyanotheriswarrantedorbeneficial22TypicalDMAProductsUncertaintyevaluations-Example:NavyUncertaintyCalculator23NavyUncertaintyCalculatorContinued24TypicalDMAProductsQCprogramworksheets25TranslatingDMAResultsDevelop....FieldbasedactionlevelsSOPsQA/QCprogramsStatisticalsamplingdesignDynamicworkstrategiesDecisionrules,decisionlogicdiagramsContingenciesBasedoncostandperformance26FieldBasedActionLevelsActionlevelsforfieldanalyticsordirectsensingtoolsthattriggeractionCollectionofcollaborativedataStepouts,additionalsamplingoranalysis,wellplacement,etc.RemedyimplementationRemovalConfirmationofclean(sometimesrequired)271FalseNegativeError=5%

3FalsePositiveErrors=7.7%59TotalpairsTruePositive19PairsTrueNegative36PairsFieldBasedActionLevels2859Totalpairs10FalsePositiveErrors=26%TruePositive20PairsTrueNegative29Pairs0FalseNegativeError=0%

TypicalDMAProducts293FalsePositiveErrors=7.7%59TotalpairsTruePositive19Pairs0FalseNegativeError=0%

TrueNegative26Pairs11SamplesforICP3WayDecisionStructureWithRegionofUncertaintyTypicalDMAProducts30ExampleCorrelationsBetweenLIFResponseandFreeProductPresenceoffreeproductunlikelyPresenceoffreeproductlikelyPresenceoffreeproductunlikelyPresenceoffreeproductlikelyFreeProductAt>50%RelativeFluorescenceforGasolineFreeProductAt>75%RelativeFluorescenceforOil31OptimizeSOPsandQCProgramsXRFCounttimes,bags/cups/in-situ,sampleprepFrequencyofblanks,SRMs,spikesFluorescence-LIF,FFD,ex-situDrillingplatforms,fluorescencesignaturesFrequencyandresponsethresholdsforcollectionofcollaborativesamplesImmuno-assay/bio-assayUseofcomposites,MIS,extractvolumeFrequencyofblanks,spikes,collaborativesamples32DMADatatoQCprogramQCprogramworksheets33OptimizeStatistical

SamplingDesignCharacterizeorverifyclean-upStatisticalconfidencedesiredHowclosetoeachotherarethetruemeanandALHowmuchvariabilityispresentinsoilconcentrations34StatisticalSamplingDesign35DecisionLogic36XRFSimpleDecisionRuleExampleBaggedsamples,measurementsthroughbagNeeddecisionruleformeasurementnumbersforeachbagActionlevel:25ppm3baggedsamplesmeasuredsystematicallyacrossbag10timeseachAverageconcentrations:19,22,and32ppm30measurementstotal37Example(cont.)SimpleDecisionRule:if1stmeasurementlessthan10ppm,stop,noactionlevelproblemsif1stmeasurementgreaterthan50ppm,stop,actionlevelproblemsif1stmeasurementbetween10and50ppm,takeanotherthreemeasurementsfrombaggedsample38Contingencies39EPATIFSD

DMALessonsLearnedLinearregression-canbehelpfulormisleadingHeterogeneity-largescale,smallscale,andwithinsampleDon’texpectcollaborativedatatocompareanybetterthan2labsoreventhesamelabFocusondecisionqualityStructurevendorcontractstoincludesomeDMAprinciplesParticularinstruments≠technologygeneralizations40DMAExampleWenatcheeTreeFruit:///download/char/treefruit/wtfrec.pdfTreefruittestplotcontaminatedwithOC,OP,andotherpesticidecompoundsDMAsupportedintegratedcharacterization,removal,segregationDMAlookedatIAtestkitsandfixedla