11

Deana M. Crumbling, M.S.Deana M. Crumbling, M.S.Technology Innovation OfficeTechnology Innovation Office

U.S. Environmental Protection AgencyU.S. Environmental Protection Agencyandand

Kira P. Lynch, M.S.Kira P. Lynch, M.S.Innovative Technology AdvocateInnovative Technology Advocate

Seattle District, US Army Corps of EngineersSeattle District, US Army Corps of Engineers

The Triad Approach to Better Cleanup The Triad Approach to Better Cleanup Projects: Illustrated with the Projects: Illustrated with the

Tree Fruit Case StudyTree Fruit Case Study

22

Seminar OutlineSeminar Outline

Overview of the Triad Approach Overview of the Triad Approach

Managing uncertainty means documenting “Why’s”Managing uncertainty means documenting “Why’s”

Updating the environmental data quality model Updating the environmental data quality model

Suggested terminology for communicating data Suggested terminology for communicating data

quality conceptsquality concepts

Illustrating the Triad with the Tree Fruit Case StudyIllustrating the Triad with the Tree Fruit Case Study

33

The Triad PartnershipThe Triad Partnership EPA’s TIO, USACE’s ITA Program, Argonne EPA’s TIO, USACE’s ITA Program, Argonne

National Lab, ITRCNational Lab, ITRC

Purpose of the Triad ApproachPurpose of the Triad Approach – Provide framework to integrate new & established Provide framework to integrate new & established

characterization and remediation technologies w/ smart characterization and remediation technologies w/ smart work strategies to achieve “better” cleanupswork strategies to achieve “better” cleanups

– ““Better” means documenting thatBetter” means documenting that» Uncertainties in project decisions are identified & managedUncertainties in project decisions are identified & managed

» Intolerable decision errors are avoidedIntolerable decision errors are avoided

» Decisions are scientifically defensibleDecisions are scientifically defensible

» Yet, lower project costs improve returns on public & private Yet, lower project costs improve returns on public & private economic investment (vital to successful site reuse)economic investment (vital to successful site reuse)

44

The Triad ApproachThe Triad Approach

Systematic Project

Planning

Dynamic Work Plan Strategy

Real-time Measurement Technologies

Synthesizes practitioner experience, Synthesizes practitioner experience, successes, and lessons-learned into an successes, and lessons-learned into an

institutional frameworkinstitutional framework

55

The Triad MessageThe Triad Message

Theme for the Triad ApproachTheme for the Triad Approach = Explicitly identify and = Explicitly identify and manage uncertaintiesmanage uncertainties that could lead to decision errors that could lead to decision errors

An often ignored (tools not available before!) source of An often ignored (tools not available before!) source of decision error is thedecision error is the

******sampling representativeness of data***sampling representativeness of data***

Field analytical methods and Field analytical methods and in situin situ detection of detection of subsurface contamination subsurface contamination nownow permit permit cost-effectivecost-effective management of sample representativenessmanagement of sample representativeness

Need to adapt routine practices to include mechanisms Need to adapt routine practices to include mechanisms that explicitly manage representativenessthat explicitly manage representativeness

66

Using the Triad approach requires Using the Triad approach requires

Systematic Project Systematic Project PlanningPlanning..

Systematic project Systematic project planningplanning

means always being able to explain WHY!!means always being able to explain WHY!!Systematic project Systematic project

planningplanning means never having to say,means never having to say,

““Because that’s the way we’ve always done it.”Because that’s the way we’ve always done it.”

77

Key Features of Triad ProjectsKey Features of Triad Projects Project-specific systematicProject-specific systematic planningplanning

Multidisciplinary team required (“allied env. professionals”)Multidisciplinary team required (“allied env. professionals”) Community stakeholders involvedCommunity stakeholders involved Focus on desired site outcome (“end goals”)Focus on desired site outcome (“end goals”) Identify decisions & manage decision uncertaintiesIdentify decisions & manage decision uncertainties Create opportunities for real-time decision-making (Create opportunities for real-time decision-making (dynamic work dynamic work plans plans using a decision tree) to save significant time and $$using a decision tree) to save significant time and $$

Work planning documents Work planning documents (critical to uncertainty mgt)(critical to uncertainty mgt) Clearly explain the “Clearly explain the “Why’sWhy’s” -- document the logical reasons” -- document the logical reasons for all proposed activities for all proposed activities “ “Why’s” tie directly to desired project outcomesWhy’s” tie directly to desired project outcomes

Project reports Project reports (critical to accountability)(critical to accountability) Document performance & outcome of completed activitiesDocument performance & outcome of completed activities

88

Key Features of Triad Projects Key Features of Triad Projects (Continued)(Continued)

Data Generation StrategiesData Generation Strategies FlexibilityFlexibility and and expertiseexpertise to mix, match, and modify sampling & to mix, match, and modify sampling &

analysis methods according to actual decision-making needsanalysis methods according to actual decision-making needs Exploit new tools (especially Exploit new tools (especially field measurementsfield measurements) able to) able to

manage data uncertainty (especially sample representativeness)manage data uncertainty (especially sample representativeness) provide provide real-timereal-time turn-around as needed to support real-time turn-around as needed to support real-time

decision-making (a dynamic work plan)decision-making (a dynamic work plan)

Project-specific Conceptual Site Model Project-specific Conceptual Site Model (CSM)(CSM) Organize what is known about the site Organize what is known about the site Help identify decision uncertainties and data gaps Help identify decision uncertainties and data gaps Evolve in real-time as feasible (dynamic work plan strategy)Evolve in real-time as feasible (dynamic work plan strategy) Serve as communication toolServe as communication tool

99

Updating the Updating the

Environmental Data Quality Model Environmental Data Quality Model

to Manage to Manage

Data UncertaintiesData Uncertainties

1010

Oversimplified Data Quality ModelOversimplified Data Quality Model

ScreeningMethods

ScreeningData

UncertainDecisions

“Definitive”Methods

“Definitive”Data

CertainDecisions

Methods = Data = Decisions

This Model Fails to Distinguish: This Model Fails to Distinguish: Analytical MethodsAnalytical Methods from from Data Data fromfrom Decisions Decisions

1111

Inaccurate First Generation AssumptionsInaccurate First Generation Assumptions Contaminant concentrations and behaviors are nearly Contaminant concentrations and behaviors are nearly

uniform across scales of environmental decision-making uniform across scales of environmental decision-making Impacts of spatial variability can be ignored & results from Impacts of spatial variability can be ignored & results from

tiny samples can be extrapolated to represent large matrix tiny samples can be extrapolated to represent large matrix volumesvolumes

““Data quality” depends on analytical methodsData quality” depends on analytical methods

Using regulator-approved methods ensures “definitive data” Using regulator-approved methods ensures “definitive data” QC checks that use ideal matrices are representative of QC checks that use ideal matrices are representative of

method performance for real-world samplesmethod performance for real-world samples

Laboratory QA is substitutable for project QALaboratory QA is substitutable for project QA One-size-fits-all methods eliminate the need for analytical One-size-fits-all methods eliminate the need for analytical

chemistry expertisechemistry expertise

1212

The Foundation of a Better The Foundation of a Better Data Quality ModelData Quality Model

Data Quality = Should be assessed according to Data Quality = Should be assessed according to the ability of data to provide information that the ability of data to provide information that meets user needsmeets user needs

Users need to make correct decisionsUsers need to make correct decisions

Therefore, data quality is a function of data’s…Therefore, data quality is a function of data’s…

– Ability to Ability to representrepresent the “true state” (of the decision unit) the “true state” (of the decision unit) in the context of the decision the data user wants to makein the context of the decision the data user wants to make

1313

Non-Non-Representative Representative

Sample(s)Sample(s)

Perfect Perfect Analytical Analytical ChemistryChemistry

++

““BAD” DATABAD” DATA

Distinguish: Distinguish: Analytical QualityAnalytical Quality from from Data QualityData Quality

Data Quality: More than Just AnalysisData Quality: More than Just Analysis

1414

Representative Data - Key to Sound ScienceRepresentative Data - Key to Sound Science

Using Using good sciencegood science in the cleanup of contaminated sites means thatin the cleanup of contaminated sites means thatthethe scale scale of data generation and interpretationof data generation and interpretation

must closely “match”must closely “match” (i.e., represent) (i.e., represent) thethe scale scale of of project decisions being based on that data.project decisions being based on that data.

““Sound science” also means that Sound science” also means that uncertainty uncertainty must be must be

acknowledged and managed since an acknowledged and managed since an exactexact match is not match is not usually feasible for complex, heterogeneous systems.usually feasible for complex, heterogeneous systems.

What types of things must be considered when What types of things must be considered when developing a representative data set?developing a representative data set?

1515

The concept of “representativeness” must The concept of “representativeness” must be grounded in the decision contextbe grounded in the decision context

Different decisions require different representativeness. For example:Different decisions require different representativeness. For example:– A data set A data set representative ofrepresentative of a risk assessment decision usually needs to a risk assessment decision usually needs to

estimate the average concentration over a fairly large decision unit (called estimate the average concentration over a fairly large decision unit (called an “exposure unit”) an “exposure unit”)

– A data set A data set representative ofrepresentative of a cost-effective remedial design must provide a cost-effective remedial design must provide information about concentration extremes at a scale specific to the remedial information about concentration extremes at a scale specific to the remedial option considered. Remedial scales are nearly always different from risk option considered. Remedial scales are nearly always different from risk assessment scales. assessment scales.

It is impossible to specify a one-size-fits-all data set that could be It is impossible to specify a one-size-fits-all data set that could be representative of all potential site decisions! representative of all potential site decisions!

Therefore, the first step of ensuring data quality is to clearly Therefore, the first step of ensuring data quality is to clearly understand to what decisions the data will be applied.understand to what decisions the data will be applied.

1616

The Data Quality “Chain”The Data Quality “Chain”

Sampling Rep.Sampling Rep. Analytical Rep.Analytical Rep.

Sample Support

DECISIONGoal Making

DECISION

1717

Sample Support: Size Matters!Sample Support: Size Matters!

Typical regulatory and field practices assume that the

size/volume of a sample has no effect on analytical results for contaminant concentrations.

That assumption doesn’t hold true when environmental

heterogeneity exists; sample volume can determine

the analytical result!

The Nugget Effect

Although there is the same contaminant mass in the captured

nuggets, different volumes of cleaner matrix will produce

different sample concentrations after sample homogenization.

Sample Prep

17

1818

3 Design Options for a Monitoring Well

Sample Support: Important to the Sample Support: Important to the Representativeness of Groundwater SamplingRepresentativeness of Groundwater Sampling

Preferential migration pathway

Option A

Option B

Option C

Well screen

18

1919

#1#1 #2#2 #3#3

The decision driving sample collection: Assess contamination resulting from

atmospheric deposition

Sample Support: Critical to RepresentativenessSample Support: Critical to Representativeness

““Sample support” includes Sample support” includes spatial orientationspatial orientation

Given that the dark surface layer is the soil layer impacted by atmospheric deposition relevant to this project:

Which sample support (white areas #1, #2, or #3, each homogenized before analysis) provides a sample that is representative of atmospheric deposition for this site?

Surface layer of interest

19

2020

The Data Quality “Chain”The Data Quality “Chain”

Sampling Rep.Sampling Rep. Analytical Rep.Analytical Rep.

Sample Support

Sampling Design

DECISIONGoal Making

DECISION

2121

Sample Location ~ 95%

Analytical (between methods) ~ 5%Analytical (between methods) ~ 5%

39,800 On-site41,400 Lab

500 On-site416 Lab

164 On-site136 Lab

27,800 On-site42,800 Lab

24,400 On-site27,700 Lab

1,280 On-site1,220 Lab

1

27

6 3

45

331 On-site 286 Lab

Can Your Sampling Design Detect the Impact of Can Your Sampling Design Detect the Impact of Spatial Heterogeneity?Spatial Heterogeneity?

2222

The Data Quality “Chain”The Data Quality “Chain”

Sampling Rep.Sampling Rep. Analytical Rep.Analytical Rep.

Sample Support

Sampling Design

Sample Preservation

Sub-Sampling

Sample Preparation Method(s)

Determinative Method(s)

DECISIONGoal

Result Reporting

Making

DECISION

Extract Cleanup

Method(s)

All links in the Data Quality chain must be intact for data to be representative of the decision!

e.g., Method 8270 = GC-MS for SVOCs

2323

Summing UncertaintiesSumming Uncertainties

Uncertainties add according to (aUncertainties add according to (a22 + b + b22 = c = c22))

Ex. 33 X

Total UncertaintyAnalytical Uncertainty

Sampling Uncertainty

Ex. 1

Ex. 21/3 X

Ex. 1

Ex. 3Ex. 2

2424

Partitioning Data UncertaintyPartitioning Data Uncertainty

Std Dev Std Dev Sampling Sampling : Std Dev Std Dev Analytical Analytical = SampSamp:AnalAnal RatioRatio

Example using a Brownfields project data set Example using a Brownfields project data set (scrap yard site with contaminated soil)(scrap yard site with contaminated soil)

(Total variability determined from entire data set. LCS data used to estimate (Total variability determined from entire data set. LCS data used to estimate analytical variability. Sampling variability calculated by subtraction.)analytical variability. Sampling variability calculated by subtraction.)

PbPb (high spatial variability):(high spatial variability): 32553255 : 33 = 10851085 : : 11AsAs (natural background present):(natural background present): 22.422.4 : 77 = 33 : : 11

A 3:1 ratio for sampling-to-analytical Std Dev = 90% of A 3:1 ratio for sampling-to-analytical Std Dev = 90% of statistical variance due to non-method considerationsstatistical variance due to non-method considerations

A 1000:1 ratio for sampling-to-analytical Std Dev = 99.999% of A 1000:1 ratio for sampling-to-analytical Std Dev = 99.999% of statistical variance due to non-method considerationsstatistical variance due to non-method considerations

2525

NEW for Project Management!! NEW for Project Management!! Representativeness Now Affordable!Representativeness Now Affordable!

Cheaper analytical technologies permit Cheaper analytical technologies permit increased sampling densitiesincreased sampling densities..

Real-time measurements support Real-time measurements support real-time real-time decision-making decision-making to drive down project costs.to drive down project costs.

– Rapid feedback for course correction Rapid feedback for course correction smarter smarter samplingsampling

– Real-time identification and management of Real-time identification and management of decision and data uncertaintiesdecision and data uncertainties

– New software packages available for statistical & New software packages available for statistical & geostatistical analysis & decision supportgeostatistical analysis & decision support

Costly “definitive” analytical methods

Cheaper (screening?) analytical methods

High spatial densityLow DL + analyte specificity

Manages analytical uncertainty= analytical representativeness = analytical quality

“Definitive” analytical qualityScreening sampling quality

Manages sampling uncertainty= sampling representativeness = sampling quality

“Definitive” sampling qualityScreening analytical quality

The Strengths & Limitations of MethodsThe Strengths & Limitations of Methods

2626

Costly definitive analytical methods

Cheap (screening?) analytical methods

High spatial density Low DL + analyte specificity

Manages sampling uncertainty

Manages analytical uncertainty

Collaborative data sets complement each other so that all sources of data uncertainty important to the decision are managed

Data Quality for Heterogeneous MatricesData Quality for Heterogeneous Matrices

Collaborative Data Sets

2727

2828

Managing Sample RepresentativenessManaging Sample Representativeness

¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢

¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢

¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢

$ $ $

$ $ $

To This

Ex 1Ex 2Ex 3

Fixed Lab Analytical

UncertaintySampling Uncertainty

Ex 1

Sampling Uncertainty Controlled through Increased Sampling Density

Field Analytical

Data Ex 2Decreased Sampling Variability

after Removal of Hotspots

Fixed Lab Data Ex 3

Remedy: remove hot spots

2828

From This

$ $ $

$ $ $

¢ ¢ ¢ ¢ ¢ ¢ ¢ ¢

¢ ¢¢

2929

Examples of Terminology to Examples of Terminology to

Anchor Data Quality Anchor Data Quality

Concepts in Concepts in

Uncertainty ManagementUncertainty Management

3030

Misleading TerminologyMisleading Terminology

Field Screening

Misleading because…Misleading because…• Not all methods run in the field are screening methods!Not all methods run in the field are screening methods!

• Not all data produced in the field are screening quality data!Not all data produced in the field are screening quality data!• Fixed labs using definitive analytical methods may produce screening Fixed labs using definitive analytical methods may produce screening

quality data!quality data!• Screening methods can (and should) be used more often in fixed labs Screening methods can (and should) be used more often in fixed labs

to better manage sampling uncertainty and improve analytical to better manage sampling uncertainty and improve analytical performance of traditional methods.performance of traditional methods.

3131

Proposed Clarification of TermsProposed Clarification of TermsData QualityData Quality

Collaborative data setsCollaborative data sets = = distinctly different data sets (i.e., distinctly different data sets (i.e., produced by different methods that might not be statistically comparable) produced by different methods that might not be statistically comparable) used in concert with each other to co-manage sampling and analytical used in concert with each other to co-manage sampling and analytical uncertainties to an acceptable level. Usually this is the most cost-uncertainties to an acceptable level. Usually this is the most cost-effective way to generate decision quality data.effective way to generate decision quality data.

Decision quality data*Decision quality data* = = Effective data*Effective data* = = data shown to be data shown to be effective for decision-making (see extended definition, slide 32)effective for decision-making (see extended definition, slide 32)

Screening quality data*Screening quality data* = = some useful information is provided; some useful information is provided; but too much uncertainty present to support decision-making if used but too much uncertainty present to support decision-making if used alone.alone. [Note: Applies to [Note: Applies to bothboth excessive analytical or sampling excessive analytical or sampling uncertainties. Applies to data produced by definitive analytical uncertainties. Applies to data produced by definitive analytical methods if the sampling representativeness is not known.]methods if the sampling representativeness is not known.]

* Includes sampling uncertainty. Nature of the analytical method irrelevant.* Includes sampling uncertainty. Nature of the analytical method irrelevant.

3232

““Effective Data”Effective Data” “Decision Quality Data” “Decision Quality Data”

Data ofData of known qualityknown quality

that can be logically demonstrated to bethat can be logically demonstrated to be effective for making the specified decisioneffective for making the specified decision

because both thebecause both the sampling and analytical uncertaintiessampling and analytical uncertainties

are managed to the degree necessary to meet clearlyare managed to the degree necessary to meet clearly defined (and stated) decision confidence goalsdefined (and stated) decision confidence goals

3333

Case Study: Wenatchee Tree Fruit SiteCase Study: Wenatchee Tree Fruit Site

Pesticide IA kits guide dynamic work plan: remove Pesticide IA kits guide dynamic work plan: remove and segregate contaminated soil for disposaland segregate contaminated soil for disposal

230 230 IA analysesIA analyses (w/ thorough QC) (w/ thorough QC) ++ 29 29 fixed-labfixed-lab samples for 33 samples for 33 analytesanalytesManaged Managed sampling uncertaintysampling uncertainty: :

achieved very high confidence achieved very high confidence that all contamination above that all contamination above action levels was located and action levels was located and removedremoved

Managed Managed field analyticalfield analytical uncertaintyuncertainty as additional QC on as additional QC on critical samples: confirmed & critical samples: confirmed & perfected field kit action levels)perfected field kit action levels)

Clean closure data setClean closure data set– 33 fixed lab samples for analyte-specific pesticide analysis 33 fixed lab samples for analyte-specific pesticide analysis – Demonstrate Demonstrate fullfull compliance with compliance with allall regulatory requirements for regulatory requirements for allall 33 pesticide analytes to >95% statistical confidence33 pesticide analytes to >95% statistical confidence the first timethe first time!!

Projected cost: ~$1.2M; Actual: $589K (Save ~ 50%)Projected cost: ~$1.2M; Actual: $589K (Save ~ 50%) Field work completed: <4 months; single mobilizationField work completed: <4 months; single mobilization

http://cluin.org/char1_edu.cfm#site_charhttp://cluin.org/char1_edu.cfm#site_char

3434

Wenatchee Tree Fruit Case Study: Soil Removal Wenatchee Tree Fruit Case Study: Soil Removal Using Field Analytical and a Dynamic Work PlanUsing Field Analytical and a Dynamic Work Plan

34

3535

Wenatchee Tree Fruit Project Wenatchee Tree Fruit Project OverviewOverview

Action required to achieve clean closure Action required to achieve clean closure – 390 tons of soil removed (56 tons incinerated; 334 390 tons of soil removed (56 tons incinerated; 334

tons landfilled)tons landfilled) Total costTotal cost

– Projected: ~$1.2M; Actual: $589KProjected: ~$1.2M; Actual: $589K– Savings: ~50%Savings: ~50%

Total field timeTotal field time– Single mobilization: <4 months from start of field Single mobilization: <4 months from start of field

work until project completionwork until project completion Outcome: Happy client, regulator, stakeholdersOutcome: Happy client, regulator, stakeholders

3636

Systematic PlanningSystematic Planning

3737

Coordinate/Assemble TeamsCoordinate/Assemble Teams

Who’s Who?: Coordinate with client, Who’s Who?: Coordinate with client, regulators and stakeholdersregulators and stakeholders

Planning Team: client, State, stakeholder, Planning Team: client, State, stakeholder, and USACE staff and USACE staff

Technical/Field Team: USACE staff, prime Technical/Field Team: USACE staff, prime contractor staff, and subcontractor staffcontractor staff, and subcontractor staff

Community outreach found little additional Community outreach found little additional interestinterest

3838

First Step: Identify DecisionsFirst Step: Identify Decisions

Problem: Pesticide contamination of vadose Problem: Pesticide contamination of vadose soilsoil

Decisions to be made:Decisions to be made:

– Locate and remove contaminationLocate and remove contamination– Remaining soil meet WA state cleanup stdsRemaining soil meet WA state cleanup stds– Manage excavated material for disposalManage excavated material for disposal

» incineration incineration » landfillinglandfilling

3939

Desired Decision ConfidenceDesired Decision Confidence

Detect contaminationDetect contamination– Grid size set to detect a 5 ft. x 10 ft. elliptical Grid size set to detect a 5 ft. x 10 ft. elliptical

hotspothotspot Remove contamination so that remaining soil Remove contamination so that remaining soil

meets stringent WA state regulatory cleanup meets stringent WA state regulatory cleanup standards:standards:– for 33 individual pesticide analytesfor 33 individual pesticide analytes– to a 95% statistical confidenceto a 95% statistical confidence

4040

State the Decision Goals State the Decision Goals (the Data Quality Objectives)(the Data Quality Objectives)

Provide results of sufficient analytical quality to Provide results of sufficient analytical quality to – guide soil removal, guide soil removal, – segregate and classify wastes for final disposal, andsegregate and classify wastes for final disposal, and

– confirm compliance with the required regulatory confirm compliance with the required regulatory closure decision confidence.closure decision confidence.

Provide turnaround times for data that can support Provide turnaround times for data that can support real-time decision-making in the field.real-time decision-making in the field.

Provide sufficient sampling density to detect a 5X10 Provide sufficient sampling density to detect a 5X10 ft. hotspot.ft. hotspot.

Field Analytical

Field Analytical

Field Analytical

Field Analytical

Fixed Lab

Fixed Lab

4141

Managing Sampling UncertaintyManaging Sampling Uncertainty

Understanding how contamination

occurred

4242

Site Grid with Probable Locations Site Grid with Probable Locations of Buried Bagsof Buried Bags

Row C

Row B

Row A

FR2/3

FR4/5

Col 1

Col 2

Col 3

Col 4

Col 5

Col 6

Col 7

Col 8

Col 9

Final Remediation Boundary

Original Remediation Boundary

NorthDrawing not to scale

X-Y Coordinate Origin

4343

Optimize the Data Collection DesignOptimize the Data Collection Design

Use a Dynamic Work PlanUse a Dynamic Work Plan Use immunoassay (IA) field kits for on-site Use immunoassay (IA) field kits for on-site

analysis to guide DWPanalysis to guide DWP Perform pre-field work pilot study toPerform pre-field work pilot study to

– Assess IA kit suitabilityAssess IA kit suitability– Estimate field/IA kit decision/action levelsEstimate field/IA kit decision/action levels– Evaluate Geoprobe performanceEvaluate Geoprobe performance– Prepare SOPs and contingency plansPrepare SOPs and contingency plans

Use fixed lab analyses to generate site closure Use fixed lab analyses to generate site closure confirmation data setsconfirmation data sets

4444

Systematic Planning: Analytical Systematic Planning: Analytical Optimize On-Site MethodsOptimize On-Site Methods

Pre-field work pilot study:Pre-field work pilot study: Compared IA to analyte-specific analyses Compared IA to analyte-specific analyses

– Understand cross-reactivity behavior of IA kitsUnderstand cross-reactivity behavior of IA kits– Establish initial field decision/action levels: Establish initial field decision/action levels:

» 5 ppm for sum DDT; 0.086 ppm for sum cyclodienes5 ppm for sum DDT; 0.086 ppm for sum cyclodienes

Project-specific SOPs established (PBMS) to Project-specific SOPs established (PBMS) to improve project performance and save labor costsimprove project performance and save labor costs– Adjusted range of calibration standardsAdjusted range of calibration standards– Increased the volume of the extraction solventIncreased the volume of the extraction solvent– Used a different solvent for the cyclodiene kitUsed a different solvent for the cyclodiene kit

4545

Immunoassay KitsImmunoassay Kits

One kit for Cyclodiene FamilyOne kit for DDT Family

4545

4646

Field Lab Endrin Regression Analysis (R2 = .60)

Field Lab DDT, ppm

Field Lab Endrin, ppm

Data ComparabilityData Comparability

46

4747

QA/QC for IA KitsQA/QC for IA Kits

3-point calibration & CCV w/ each batch (12 3-point calibration & CCV w/ each batch (12 samples)samples)

Reagent blankReagent blank Matrix duplicate (intra-laboratory sample split)Matrix duplicate (intra-laboratory sample split) LCS [prepared from a purchased (soil) PE LCS [prepared from a purchased (soil) PE

sample]sample] Split sample confirmation analysis (by fixed lab Split sample confirmation analysis (by fixed lab

analysis) for samples representing critical decision analysis) for samples representing critical decision pointspoints– Excavation boundariesExcavation boundaries– Clean closure data set for regulatory complianceClean closure data set for regulatory compliance

4848

Systematic Planning: Analytical Systematic Planning: Analytical Optimize Off-site MethodsOptimize Off-site Methods

Organophosphorus (OP) pesticides:Organophosphorus (OP) pesticides:– SW-846 Method 8141 (GC/NPD) was changed to SW-846 Method 8141 (GC/NPD) was changed to

SW-846 Method 8270 (GC/MS)SW-846 Method 8270 (GC/MS) Carbamates by GC:Carbamates by GC:

– A blend of EPA Water Method 632 and SW-846 A blend of EPA Water Method 632 and SW-846 Method 8141 (GC/NPD) was usedMethod 8141 (GC/NPD) was used

Paraquat in soil by spectrophotometry:Paraquat in soil by spectrophotometry:– An industry developed method was usedAn industry developed method was used

Certain fixed lab methods for pesticides were Certain fixed lab methods for pesticides were optimized using PBMS principles:optimized using PBMS principles:

4949

Site Grid Showing DP Core Sampling Site Grid Showing DP Core Sampling LocationsLocations

Row C

Row B

Row A

FR2/3

FR4/5

Col 1

Col 2

Col 3

Col 4

Col 5

Col 6

Col 7

Col 8

Col 9

Final Remediation Boundary

Original Remediation Boundary

NorthDrawing not to scale

X-Y Coordinate Origin

Site Characterization Sample

5050

Dynamic Work Plan Decision Matrix for Dynamic Work Plan Decision Matrix for Characterization (Geoprobe Core) TestingCharacterization (Geoprobe Core) Testing

Scenario # 6 to 12"12 to24"

24 to36"

36 to48"

48 to60"

60 to72" Action

1 No n/a n/a n/a n/a n/a Confirmation Sampling2 Yes No n/a n/a n/a n/a Find contamination in 0-12" sample, field sample 12-24" sample.

Find no contamination in 12-24" sample above MTCA: Remove 0-12" of soil.Confirmation Sampling. No Further Action.

3 Yes Yes No n/a n/a n/a Find contamination in 0-12" sample, field sample 12-24" sample.Find contamination in 12-24" sample, field sample 24-36" soil sample.Find no contamination in 24-36" sample above MTCA: Remove 0-24" of soil.Confirmation Sampling. No Further Action.

4 Yes Yes Yes No n/a n/a Find contamination in 0-12" sample, field sample 12-24" sample.Find contamination in 12-24" sample, field sample 24-36" soil sample.Find contamination in 24-36" sample, field sample 36-48" soil sample.Find no contamination in 36-48" sample above MTCA: Remove 0-36" of soil.Confirmation Sampling. No Further Action.

5 Yes Yes Yes Yes No n/a Find contamination in 0-12" sample, field sample 12-24" sample.Find contamination in 12-24" sample, field sample 24-36" soil sample.Find contamination in 24-36" sample, field sample 36-48" soil sample.Find contamination in 36-48" sample, field sample 48-60" soil sample.Find no contamination in 48-60" sample above MTCA: Remove 0-48" of soil.Confirmation Sampling. No Further Action.

6 Yes Yes Yes Yes Yes No Find contamination in 0-12" sample, field sample 12-24" sample.Find contamination in 12-24" sample, field sample 24-36" soil sample.Find contamination in 24-36" sample, field sample 36-48" soil sample.Find contamination in 36-48" sample, field sample 48-60" soil sample.Find contamination in 48-60" sample, field sample 60-72" soil sample.Find no contamination in 48-60" sample above MTCA: Remove 0-60" of soil.Confirmation Sampling. No Further Action.

0-12”

50

5151

Dynamic Work Plan for Removing Dynamic Work Plan for Removing Contaminated SoilContaminated Soil

IA results on DP core samples used to develop IA results on DP core samples used to develop backhoe excavation profilebackhoe excavation profile– Profile correctness later confirmed by fixed lab resultsProfile correctness later confirmed by fixed lab results

After backhoe excavation complete, floor analyzed After backhoe excavation complete, floor analyzed by IAby IA– If IA results > field action level, more soil removed by If IA results > field action level, more soil removed by

hand.hand. New floor tested by IA.New floor tested by IA.

– When IA results < field action level, sample for fixed lab When IA results < field action level, sample for fixed lab analysis collected (for clean closure data set)analysis collected (for clean closure data set)

5252

Implementing the Work PlanImplementing the Work Plan

5353

Buried Bag Removal ActivitiesBuried Bag Removal Activities

5353

5454DP (Geoprobe) SamplingDP (Geoprobe) Sampling

5555

Dividing DP Core into 1-ft LiftsDividing DP Core into 1-ft Lifts

5555

5656

Homogenization of Samples Homogenization of Samples

5656

5757

Collecting a Clean Closure SampleCollecting a Clean Closure Sample

5757

5858 Final Remediation BoundaryNorthDrawing not to scale

Top number is feet bgs planned for excavation and the bottom is feet bgs actually excavated

Original Remediation BoundaryX-Y Coordinate Origin

Col 1

Col 2

Col 3

Col 4

Col 5

Col 6

Col 7

Col 8

Col 9

22/1

1/ 55/

11/ 4

4/ 22.5/ 2

2/ 44/

11/

11/

11/

11/

55/ 1

4.5/ 44/ 2

2/22/ 4

4/ 22.5/

44/2

2/22/

44/

55/ 1

1/

Row B

Row C

Row A

FR2/3

FR4/5

22.5/

22.5/

11/

CSM: Planned & Actual Excavation DepthsCSM: Planned & Actual Excavation Depths

5959

Unexpected Contamination? Unexpected Contamination? No Problem!No Problem!

After floor excavation completed, State regulator After floor excavation completed, State regulator asked for sidewall testing (beyond scope of asked for sidewall testing (beyond scope of original work plan)original work plan)

Unexpected contamination found in some pit Unexpected contamination found in some pit sidewalls (outside of original site boundaries)sidewalls (outside of original site boundaries)

IA guided sidewall delineation and excavationIA guided sidewall delineation and excavation– IA results accepted as sole data to establish clean IA results accepted as sole data to establish clean

closure for 80% of the sidewall areaclosure for 80% of the sidewall area

6060 Final Remediation BoundaryNorthDrawing not to scale

Top number is feet bgs planned for excavation and the bottom is feet bgs actually excavated

Original Remediation BoundaryX-Y Coordinate Origin

Col 1

Col 2

Col 3

Col 4

Col 5

Col 6

Col 7

Col 8

Col 9

22/1

1/ 55/

11/ 4

4/ 22.5/ 2

2/ 44/

11/

11/

11/

11/

55/ 1

4.5/ 44/ 2

2/22/ 4

4/ 22.5/

44/2

2/22/

44/

55/ 1

1/

Row B

Row C

Row A

FR2/3

FR4/5

22.5/

22.5/

11/

Final CSM: Lateral and Vertical RemovalsFinal CSM: Lateral and Vertical Removals

6161

Decision: Locate and remove contaminated soilDecision: Locate and remove contaminated soil– 230 IA analyses; 29 fixed lab samples as confirmatory QC230 IA analyses; 29 fixed lab samples as confirmatory QC– Outcome: Very high degree of certainty that all significant Outcome: Very high degree of certainty that all significant

contamination located and removedcontamination located and removed

Decision: Demonstrate clean closureDecision: Demonstrate clean closure– 33 fixed lab samples for analyte-specific pesticide analysis 33 fixed lab samples for analyte-specific pesticide analysis – Outcome: Demonstrated full compliance with all regulatory Outcome: Demonstrated full compliance with all regulatory

requirements for all 33 analytes to a 95% (or better) confidencerequirements for all 33 analytes to a 95% (or better) confidence– 16 IA for sidewalls (kit decision level accepted as “clean”)16 IA for sidewalls (kit decision level accepted as “clean”)

Decision: Dispose of soil (RCRA Subtitle CDecision: Dispose of soil (RCRA Subtitle C requirements)requirements)– Fixed lab analyses (TCLP OC pesticides, total OP and OC Fixed lab analyses (TCLP OC pesticides, total OP and OC

pesticides, TCLP metals) demonstrate compliancepesticides, TCLP metals) demonstrate compliance

Data Effective for Making DecisionsData Effective for Making Decisions

6262

Wenatchee Tree Fruit Project Wenatchee Tree Fruit Project Triad Approach SuccessesTriad Approach Successes

Systematic planning focused on end-use of dataSystematic planning focused on end-use of data State regulator focused on project State regulator focused on project

outcome/performance, permitting flexibility to outcome/performance, permitting flexibility to maximize innovation and resource savings. maximize innovation and resource savings.

Demonstration of method applicability (a pilot study) Demonstration of method applicability (a pilot study) determined:determined:– appropriate field sampling & measurement toolsappropriate field sampling & measurement tools– project-specific field action levels for decisionsproject-specific field action levels for decisions– project-specific SOPs and QC for field and fixed lab project-specific SOPs and QC for field and fixed lab

analysesanalyses

6363

Wenatchee Tree Fruit Project Wenatchee Tree Fruit Project Triad Successes (continued)Triad Successes (continued)

IA analysis increased the number, density, and IA analysis increased the number, density, and information value of samples.information value of samples.

CSM refined/matured in the field; specific sampling CSM refined/matured in the field; specific sampling strategies selected to match specific decisionsstrategies selected to match specific decisions

Final Cost: Site characterized (incl. analytical PBMS), Final Cost: Site characterized (incl. analytical PBMS), remediated, closed (very high confidence), restored, remediated, closed (very high confidence), restored, and waste disposed for and waste disposed for < ½ projected cost< ½ projected cost

– Waste disposal: Waste disposal: 56 tons incinerated; 334 tons landfilled56 tons incinerated; 334 tons landfilled

Time: Time: < 4 months of field work< 4 months of field work

6464

SummarySummary Theme for Triad approach: Manage overall project Theme for Triad approach: Manage overall project

uncertainties to match desired project outcomeuncertainties to match desired project outcome Sampling: increase sampling density and representativeness Sampling: increase sampling density and representativeness

of sampling design by field analytics and dynamic work plansof sampling design by field analytics and dynamic work plans

Analytical: PBMS principlesAnalytical: PBMS principles

Avoid ambiguous goals or terminologyAvoid ambiguous goals or terminology

Call out unspoken assumptionsCall out unspoken assumptions

Promote the analytical service provider as a partner Promote the analytical service provider as a partner bringing vital expertise to project planning and bringing vital expertise to project planning and implementationimplementation

6565

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