quantitative ltmo methods

Quantitative LTMO Methods:Overview and Air Force Investment

Philip Hunter, P.G.Philip Hunter, P.G.AF Center for Environmental ExcellenceAF Center for Environmental Excellence(AFCEE)(AFCEE)

USEPA Technical Support ProjectUSEPA Technical Support ProjectSan Antonio, Texas San Antonio, Texas October, 2005October, 2005

11

OverviewOverview

LTMO OverviewLTMO OverviewToolsToolsCase StudiesCase StudiesNeedsNeedsTrendsTrends

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LTMOLTMOWhat is the Opportunity?What is the Opportunity?

LTMO case studies demonstrate redundancy in LTMO case studies demonstrate redundancy in well networkswell networksTypical LTM sampling effort can be reduced by Typical LTM sampling effort can be reduced by 20% 20% –– 40%40%LTMO focuses on essential data and accepts LTMO focuses on essential data and accepts tolerable uncertainty in environmental decisiontolerable uncertainty in environmental decision--makingmakingHelps to improve & simplify LTM programsHelps to improve & simplify LTM programs

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LTMO ToolsLTMO ToolsWhat Do They Do?What Do They Do?

Identify essential sampling locationsIdentify essential sampling locationsDetermine an optimal sampling frequencyDetermine an optimal sampling frequencyAssess relative importance of individual wellsAssess relative importance of individual wellsBut, there is But, there is nono purely objectivepurely objective solution or solution or answeranswer

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RequirementsRequirements

Electronic dataElectronic dataConceptual site modelConceptual site modelData sufficiency; sample size, # eventsData sufficiency; sample size, # eventsDescription of current monitoring programDescription of current monitoring programWell construction & coordinatesWell construction & coordinatesCleanup goals & regulatory limits Cleanup goals & regulatory limits

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LTMO “Big Picture”LTMO “Big Picture”Roadmap to Site ClosureRoadmap to Site Closure

Characterization Remediation

LTMInitial Design

LTMOptimized

LTMComplete

MW SamplingNetwork

Review5 Yr

Exit Strategy-DQOs Met-Goals Achieved

Site Closure

AdjustValidate

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LTMO Major ComponentsLTMO Major ComponentsGeneral ProcessGeneral Process

QualitativeAnalysis

OptimizedNetwork

Data Mgmt•Legacy Data•Current Data

DecisionFramework

RegulatoryBuy-in

CostAnalysis

NetworkReduction

NetworkExpansion

ToolInventoryReporting

SpatialAnalysis

TemporalAnalysis

Validation &5 Yr Review

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LTMO InvolvesLTMO InvolvesTemporal ComparisonsTemporal Comparisons

93 94 95 96 97 98 99YEAR

0

100

200

300

TCE

(ug/

L )

93 94 95 96 97 98 99YEAR

0

100

200

300

TCE

(ug/

L )“Nice to have”

All DataSamples = 240

“Essential”90% ReductionSamples = 27

Tolerable UncertaintyWithout Loss of Information

Redundancy

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Quantitative LTMO Involves Spatial Comparisons

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Frame 001⏐ 7 Jun2004⏐eafb.tce.t1.cut6.map-XYWell Reduction 40%All Wells

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What’s Out There?What’s Out There?

1010

Air Force LTMO MethodsAir Force LTMO Methods

-GTS-MAROS-Parsons 3-Tiered

1111

AFCEE Optimization ToolsAFCEE Optimization Tools

GTS

Algorithm

MAROS

Software

Optimization Tools

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GTS Optimization AlgorithmGTS Optimization Algorithm(translated to software, 2005)(translated to software, 2005)

Preprocess Data &

Construct Indicators (A)

Identify Temporal

Redundancy

Compute Composite Temporal

Variogram (B)

Perform Data Thinning (E)

Re-compute Slope & Assess

Accuracy (F)

Slope Still Inbounds &

Sign Unchanged?

Y

N

Determine Variogram Sill

(C)

Increase Thinning %

Eliminate Wells With All NDs or

With < 8-10 Distinct Sampling

Dates

XConstruct Temporal

Variogram

Iteratively Thin Selected Wells

Adjust Global Sampling Frequency

Estimate Slope & Confidence

Bounds for Each Remaining Well

(D)

Initialize Thinning %

Using C= 5% or 10%

Adjust Individual Well Sampling Frequencies

Finalize Degree of Temporal Redundancy

X2

X1

Set sampling intervals for

eliminated wells using global

sampling frequency from temporal variogram (X1)

Global sampling frequency should be applied to all wells except for those adjusted individually (X2)

P1

Individual well sampling

frequencies should take precedence

over global frequency unless

operationally impractical

Monitoring and sampling wells on

individually determined

schedules could add undue operational

costs. If so, just apply global sampling

frequency to all wells

Include on-going review every 3-5

yrs; Re-do analyses using

more recent data

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GTS SoftwareGTS SoftwareBasic FunctionalityBasic Functionality

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Geostatistical TemporalGeostatistical Temporal--SpatialSpatial(GTS) Algorithm(GTS) Algorithm

Design emphasizes decisionDesign emphasizes decision--logic frameworklogic framework“Plug“Plug--in” architecturein” architectureUses geostatistical and trend optimization Uses geostatistical and trend optimization methods that are semimethods that are semi--objectiveobjective–– VariogramVariogram = spatial correlation measure= spatial correlation measure–– KrigingKriging = spatial interpolation = spatial = spatial interpolation = spatial

regressionregression–– LocallyLocally--Weighted Quadratic RegressionWeighted Quadratic Regression (LWQR)(LWQR)

Software now availableSoftware now available

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GTS TemporalGTS Temporal AnalysisAnalysis

Flexible strategies for optimizing sampling Flexible strategies for optimizing sampling frequenciesfrequencies–– Individual well analysis; Individual well analysis; “iterative “iterative

thinning”thinning”–– Temporal variogramTemporal variogram for well groups & for well groups &

broad areasbroad areas

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Iterative ThinningIterative Thinning

Individual well analysisIndividual well analysis–– Estimate baseline trendEstimate baseline trend–– Randomly “weed out” data pointsRandomly “weed out” data points–– ReRe--estimate trendestimate trend–– Assess significant departure from baselineAssess significant departure from baseline

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Iterative ThinningIterative ThinningRequirementsRequirements

At least 8 sampling events per wellAt least 8 sampling events per wellNDs set to common imputed valueNDs set to common imputed valueComplex trends, seasonal patterns OKComplex trends, seasonal patterns OK–– LWQR fits nonLWQR fits non--linear trendslinear trends

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GTS Spatial AnalysisGTS Spatial Analysis

Locally weighted quadratic regression Locally weighted quadratic regression (LWQR)(LWQR)replaces Kriging algorithmreplaces Kriging algorithmLWQR LWQR BenefitsBenefits–– Smoothing technique, not an interpolatorSmoothing technique, not an interpolator–– Robust; does not assume or require a spatial Robust; does not assume or require a spatial

covariance model (variogram)covariance model (variogram)–– Can estimate complex seasonal trends and nonlinear Can estimate complex seasonal trends and nonlinear

data data –– Handles multiple values in time and spaceHandles multiple values in time and space–– A less complex and flexible alternative for software A less complex and flexible alternative for software

developmentdevelopment

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GTS Spatial AnalysisGTS Spatial AnalysisRequirementsRequirements

At least 20At least 20--30 regularly30 regularly--monitored wellsmonitored wells–– Irregular sampling schedules OKIrregular sampling schedules OK

Best COCs have:Best COCs have:–– Higher detection frequenciesHigher detection frequencies–– Greater spatial spread & intensityGreater spatial spread & intensity

Good to have 2Good to have 2--3 years of most recent monitoring 3 years of most recent monitoring data at each welldata at each well

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Fairchild

Ellsworth

F.E. WarrenOffutt

Nellis

Randolph

Dyess

Plattsburgh

McGuireBeale

McClellan Travis

Vandenberg Edwards

Los Angeles

Wright-Patterson

Grissom

K.I. Sawyer

PeaseHanscom

Westover

Kelly

Little Rock

AF Plant 4

AF Plant PJKS

Newark

Battle-creek

Shaw

AF Plant 6

Hickam8 Locations

Patrick

Kirtland

Cannon

Davis-Monthan

Keesler

Bolling/Andrews

Mt Home

Camp Pendleton (USMC)

Ft Drum (USA)

Cape Canaveral

Pope

Griffiss

Havre AFS

Grand Forks

Lowry

George

NortonWilliams

Carswell

Bergstrom

Maxwell/Gunter

ColumbusMyrtle Beach

MacDill

LangleyScott

Chanute

Wurtsmith

Madison ANG

Eaker

Rickenbacker

Kanehoe Bay(USN)

Castle

Luke

Ft Bliss

Lackland

Altus

Peterson

Homestead

Ft Carson

Loring

Roslyn

Gentile

England

Mather

Onizuka

Ontario ANGB

Percol

MMRAF Plant 59Niagra Falls

Sunset Golf CrsDoverHQ AFMC

AF Plant 85

Seymour Johnson

Charleston

Arnold

Avon Park

Robins

Ft Rucker MoodyEglinHurlburtTyndall

Barksdale

JohnsonSpace CenterBrooks

Camp Stanley

NAS Ft WorthGoodfellow

Reese

WhitemanMcConnell

Tinker

Sioux

Minot

Cavelier

DuluthMcChord

AF Plant 78Hill

AF Plant PJKS

AF Academy

Lamar

MarchSuperior Valley

AF Plant 44

Laughlin

AF Plant 3

AF Plant 42

Holloman

O’Hare

Richards-Gebaur

Malstrom

GTS Project LocationsGTS Project Locations

as of 4/8/98

Vance AFB

EielsonGalena

Elmendorf

Clear

611th CEOS15 Locations

Arctic Surplus

King Salmon

Kodiak Island(USCG)Eareckson Johnston

Atoll

GoldStone DSCC

Kingsley Field(ANG)

Buckley

Schriever

DDHU

Cheyenne MountainDDJCTracy

Jet Propulsion Lab

White Sands

HulmanFieldANGB

Glen ResearchCenter

DDSP

DSCR

DDMTMarshalSpace Center

Gulfport

Stennis FieldSpace Center

Langley Research Center

Goddard Space Flight Center

Wallops Flight Facility

Hanford

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Hydrogeological TerrainsHydrogeological Terrains

Homogenous sands, glacial outwash; “Sandbox”Homogenous sands, glacial outwash; “Sandbox”Compact glacial tills, overlying bedrockCompact glacial tills, overlying bedrockCarbonate rocks, fractured limestoneCarbonate rocks, fractured limestoneFractured media; igneous, metamorphic Fractured media; igneous, metamorphic Unconsolidated alluvial depositsUnconsolidated alluvial depositsWeathered & transition zones; saproliteWeathered & transition zones; saprolite

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Contaminant HydrogeologyContaminant Hydrogeology

Single plumes and basewide studiesSingle plumes and basewide studiesOUs; commingled plumes, multiple sourcesOUs; commingled plumes, multiple sourcesGroundwater management areas (5 zones)Groundwater management areas (5 zones)Multiple horizonsMultiple horizonsWell networks; 30 Well networks; 30 –– 1200 wells1200 wellsShallow water table and confined aquifersShallow water table and confined aquifers

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ContaminantsContaminants

Chlorinated solvents; TCE, daughter productsChlorinated solvents; TCE, daughter productsBTEXBTEXEmerging COCs; 1,4 DioxaneEmerging COCs; 1,4 DioxaneMetalsMetalsRadiological waste, uraniumRadiological waste, uranium

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GTS Case Study ResultsGTS Case Study Results

Potential Savings at AF InstallationsPotential Savings at AF Installations

Edwards Loring Pease

Original FrequencyOriginal Frequency Annual Quarterly Annual

Optimized IntervalOptimized Interval Every 7 Qtrs Every 2-3 Qtrs Every 8 Qtrs

Redundant WellsRedundant Wells 20-34% 20-30% 10-36%

Cost ReductionCost Reduction 54-62% 33-39% 49-52%

Annual Cost Annual Cost SavingsSavings

$230 K-$270 K $300 K- $360 K $85 K- $90 K

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Tinker AFB Case StudyTinker AFB Case Study

GTS“Independent”/Quantitative

Resident Experts“Judgemental/Qualitative”

1145 Wells550 MWs 523

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Tinker AFB Case StudyTinker AFB Case StudyAdditional Redundant Wells Identified Additional Redundant Wells Identified

Judgmental approach targeted “obviously” redundant wellsJudgmental approach targeted “obviously” redundant wellsGTS addressed GTS addressed all all wells with sufficient datawells with sufficient dataRedundant Wells; about 50% agreement for common wells Redundant Wells; about 50% agreement for common wells analyzed by both approachesanalyzed by both approachesTinker staff found 90% of wells analyzed to be redundantTinker staff found 90% of wells analyzed to be redundantGTS found 38% of wells analyzed to be redundantGTS found 38% of wells analyzed to be redundantTinker to use both studies; marry results with internal documentTinker to use both studies; marry results with internal document

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GTS Results, Tinker AFBGTS Results, Tinker AFB

Potential Savings, Baseline Costs = $1600K/yrPotential Savings, Baseline Costs = $1600K/yr

Tinker

Original FrequencyOriginal Frequency Quarterly - Annual

Optimized IntervalOptimized Interval Every 5 Qtrs

Redundant WellsRedundant Wells 38%

Cost ReductionCost Reduction 59-61%

Annual Cost SavingsAnnual Cost Savings $950 K-$995 K

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GTS Map ComparisonGTS Map ComparisonTCE Upper Zone, Tinker AFBTCE Upper Zone, Tinker AFB

Optimized Map (38% less Wells)Base Map (All Wells)

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GTS CostGTS Cost--Accuracy Tradeoff CurvesAccuracy Tradeoff CurvesPixelPixel--byby--Pixel Comparison of Entire InstallationPixel Comparison of Entire InstallationBaseline Contamination vs Optimized NetworkBaseline Contamination vs Optimized Network

3030

Algorithm & Software DevelopmentAlgorithm & Software Development

Algorithms, 2 versionsAlgorithms, 2 versionsSoftware, GTS version 1.0Software, GTS version 1.02D and 3D studies2D and 3D studiesSmoothing algorithm replaced KrigingSmoothing algorithm replaced KrigingCurrent investment; about $550K over 6 yrsCurrent investment; about $550K over 6 yrs

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GTS Software Enhancements & GTS Software Enhancements & NeedsNeeds

Capability to handle > 200 wellsCapability to handle > 200 wellsEnable exploratory toolsEnable exploratory toolsGenetic search algorithm to improve map comparisonsGenetic search algorithm to improve map comparisonsImprove computational efficiencyImprove computational efficiencyEnhance graphics & reportingEnhance graphics & reporting3D analysis3D analysisTraining & supportTraining & support

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TrendsTrends

Synergy of quantitative and qualitativeSynergy of quantitative and qualitativeMarriage of independent and internal studiesMarriage of independent and internal studiesUse of genetic search algorithmsUse of genetic search algorithmsContractors requesting training and supportContractors requesting training and supportAF customers requesting support per RPO initiativesAF customers requesting support per RPO initiativesEPA training is effective in promoting technologyEPA training is effective in promoting technologyIncreased contractor & regulator interest in toolsIncreased contractor & regulator interest in tools

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SummarySummary

Improving LTM programs & supporting environmental Improving LTM programs & supporting environmental decisions are key goalsdecisions are key goalsA variety of LTMO tools are availableA variety of LTMO tools are availableMany factors determine choice of tools for specific Many factors determine choice of tools for specific applicationapplicationSimpleSimple--toto--complex case studies have matured complex case studies have matured technologytechnologyEnhancements to existing tools are neededEnhancements to existing tools are neededAdditional training and technical support is neededAdditional training and technical support is needed

3434

InformationInformation

GTS (Vers 0.6) and MAROS (Vers 2.0) software downloadsGTS (Vers 0.6) and MAROS (Vers 2.0) software downloadshttp://www.afcee.brooks.af.mil/products/rpo/default.asphttp://www.afcee.brooks.af.mil/products/rpo/default.aspGuidance & example reportsGuidance & example reports

http://www.afcee.brooks.af.mil/products/rpo/default.asp

3535

ThanksThanks

Philip Hunter, P.G.AFCEE/BCEBrooks City-Base, TexasTel 210 [email protected]

quantitative ltmo methods

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