Geostatistical Modeling as a Geostatistical Modeling as a Quality Management Tool to Quality Management Tool to

Address Uncertainty in Decision-Address Uncertainty in Decision-making for Large Scale Sediment making for Large Scale Sediment

Assessment and Remediation Assessment and Remediation ProjectsProjects

Judith A. SchofieldJudith A. Schofield11, Pierre Goovaerts, Justin Telech, Ken Miller, and , Pierre Goovaerts, Justin Telech, Ken Miller, and Molly Middlebrook AmosMolly Middlebrook Amos

Computer Sciences CorporationComputer Sciences Corporation

Louis BlumeLouis BlumeU.S. EPA Great Lakes National Program OfficeU.S. EPA Great Lakes National Program Office

U.S. EPA’s 28th Annual Conference on Managing Environmental Quality Systems U.S. EPA’s 28th Annual Conference on Managing Environmental Quality Systems May 14, 2009May 14, 2009

11PresenterPresenter

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AcknowledgmentsAcknowledgments

• Diana MallyDiana Mally

• David Wethington David Wethington (now with USACE)(now with USACE)

• Marc TuchmanMarc Tuchman

From U.S. EPA’s Great Lakes National Program From U.S. EPA’s Great Lakes National Program Office, we acknowledge the following project Office, we acknowledge the following project leads and contributors:leads and contributors:

And from the Michigan Department of Environmental Quality:

•Michael Alexander

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GeostatisticsGeostatistics

• Set of statistical techniques used in Set of statistical techniques used in the analysis of georeferenced datathe analysis of georeferenced data

• Increasingly popular in part due to Increasingly popular in part due to the availability of geographic the availability of geographic information systems (GIS) softwareinformation systems (GIS) software

• Powerful tool when used in Powerful tool when used in combination with GIScombination with GIS

How can Geostatistics be How can Geostatistics be used as a Quality used as a Quality Management Tool?Management Tool?• Pools data to get the best representation of the sitePools data to get the best representation of the site

• Uncertainty can be quantifiedUncertainty can be quantified

• Supports generation of cost effective sampling Supports generation of cost effective sampling designsdesigns

• Large quantities of data can be more easily Large quantities of data can be more easily visualizedvisualized

• Decisions are defensible, transparent, well-Decisions are defensible, transparent, well-documented, and reproducibledocumented, and reproducible

• Facilitates informed cleanup decisions and effective Facilitates informed cleanup decisions and effective use of remedial resourcesuse of remedial resources

44

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Geostatistics in Sediment Geostatistics in Sediment Assessment and RemediationAssessment and Remediation

• Describe extent and nature of Describe extent and nature of contaminationcontamination

• Identify data gapsIdentify data gaps

• Generate statistical sampling designsGenerate statistical sampling designs

• Calculate sediment volumesCalculate sediment volumes

• Develop remedial designDevelop remedial design

• Evaluate achievement of cleanup goalsEvaluate achievement of cleanup goals

• Communicate conditions to stakeholdersCommunicate conditions to stakeholders

Sediment Assessment and Sediment Assessment and Remediation Projects using Remediation Projects using GeostatisticsGeostatistics• Fox River, WIFox River, WI• Hudson River, NYHudson River, NY• Minnesota Slip, Duluth Harbor, MN Minnesota Slip, Duluth Harbor, MN • East Fork Poplar Creek, TNEast Fork Poplar Creek, TN• Great Lakes Legacy ActGreat Lakes Legacy Act

– Black Lagoon, MIBlack Lagoon, MI– Hog Island, WIHog Island, WI– Ruddiman Creek, MIRuddiman Creek, MI– Division Street Outfall, MIDivision Street Outfall, MI– St. Louis River, WISt. Louis River, WI– Ashtabula River, OHAshtabula River, OH– Trenton Channel, MITrenton Channel, MI– Buffalo River, NYBuffalo River, NY– Lincoln Park, WILincoln Park, WI

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Trenton Channel of the Trenton Channel of the Detroit RiverDetroit River• The Detroit River is one of 42 Areas of Concern The Detroit River is one of 42 Areas of Concern

(AOCs) in the Great Lakes (AOCs) in the Great Lakes – investigations of the Upper Trenton Channel, within the investigations of the Upper Trenton Channel, within the

Detroit River AOC, have shown that sediments are Detroit River AOC, have shown that sediments are contaminated with polychlorinated biphenyls (PCBs), contaminated with polychlorinated biphenyls (PCBs), mercury and total polycyclic aromatic hydrocarbons (Total mercury and total polycyclic aromatic hydrocarbons (Total PAHs) among other contaminantsPAHs) among other contaminants

– EPA’s Great Lakes National Program Office (GLNPO) and the EPA’s Great Lakes National Program Office (GLNPO) and the Michigan Department of Environmental Quality (MDEQ) are Michigan Department of Environmental Quality (MDEQ) are evaluating the extent of sediment contamination in support evaluating the extent of sediment contamination in support of a potential Great Lakes Legacy Act cleanup project of a potential Great Lakes Legacy Act cleanup project

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88

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Trenton Channel RI/FFSTrenton Channel RI/FFS

• In 2006, GLNPO and MDEQ initiated a remedial In 2006, GLNPO and MDEQ initiated a remedial investigation and focused feasibility study (RI/FFS) investigation and focused feasibility study (RI/FFS) of the site of the site

• Sediment samples were collected and analyzed for Sediment samples were collected and analyzed for a large variety of contaminants of concern a large variety of contaminants of concern

• Initial sampling was conducted in 2006 data (Phase Initial sampling was conducted in 2006 data (Phase I of the RI/FFS)I of the RI/FFS)

• Based on review of the Phase I data, GLNPO and Based on review of the Phase I data, GLNPO and MDEQ developed a series of questions that were MDEQ developed a series of questions that were the focus of additional sampling in 2007 (Phase II the focus of additional sampling in 2007 (Phase II of the RI/FFS)of the RI/FFS)

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Sediment ConcentrationsSediment Concentrations

Table 1 Overall Descriptive Statistics of Trenton Channel

COC # Results

Mean Median Min Max Standard Deviation

RSD (%)

% Non-detects

% Exceeding TOC

Mercury (ppm)

128 2.3 0.55 0.025 85 9.7 418 23 31

Total PCBs (ppb)

128 8434 180 60 460000 47427 562 65 27

Total PAHs (ppb)

128 41104 12375 84 534600 82242 200 5 36

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COC – contaminant of concernTOC – threshold of concern

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Statistical and Geostatistical Statistical and Geostatistical AnalysisAnalysis• Exploratory Data AnalysisExploratory Data Analysis

• Statistics Statistics – Hypothesis testing using t-tests and Hypothesis testing using t-tests and

regressionregression

• Geostatistical AnalysisGeostatistical Analysis– 3D modeling3D modeling– SGeMS (Stanford Geostatistical Modeling SGeMS (Stanford Geostatistical Modeling

Software)Software)

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KrigingKriging

• Evolved in mineral exploration and Evolved in mineral exploration and mining of minerals, ores, and coals mining of minerals, ores, and coals

• In 1963, G. Matheron named In 1963, G. Matheron named kriging after Daniel Gerdhaus kriging after Daniel Gerdhaus Krige, a South African mining Krige, a South African mining engineer, who used the technique engineer, who used the technique to more accurately predict the to more accurately predict the extent of gold deposits in extent of gold deposits in unsampled areasunsampled areas

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KrigingKriging

• Method of interpolationMethod of interpolation– Optimally predicts data values by Optimally predicts data values by

using data taken at known using data taken at known locationslocations

– Creates contours or isopleths of Creates contours or isopleths of data across an areadata across an area

• Other common methods of Other common methods of interpolation include inverse interpolation include inverse distance weighting and splinedistance weighting and spline

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Geostatistical Analysis Geostatistical Analysis BasicsBasics

1.1. Overlay gridOverlay grid

2.2. Model sediment depth and create 3D Model sediment depth and create 3D grid using ordinary kriginggrid using ordinary kriging

3.3. Transform contaminant concentrationsTransform contaminant concentrations

4.4. Compute 3D variogram for each Compute 3D variogram for each contaminant and fit weighted least-contaminant and fit weighted least-square regression modelsquare regression model

5.5. Estimate contaminant concentrations Estimate contaminant concentrations for each block using kriging & for each block using kriging & surrounding observed concentrationssurrounding observed concentrations

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Note: Results are preliminary.

Min

Max

2020

Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Southeast of the Site (depth exaggerated 25 times)

Min

Max

Note: Results are preliminary

2121

Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Northwest of the Site (depth exaggerated 25 times)

Min

Max

Note: Results are preliminary

2222

Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Northeast of the Site (depth exaggerated 25 times)

Min

Max

Note: Results are preliminary

Model Evaluation and Model Evaluation and ValidationValidation

Consistency of 3D model with core data

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Model Evaluation and Model Evaluation and ValidationValidation

Consistency of 3D model with core data2424

2525

Note: Results are preliminary

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Note: Results are preliminary

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Stochastic SimulationStochastic Simulation1.1. Generate Generate equiprobable modelsequiprobable models for mercury, for mercury,

Total PCBs and Total PAH distributionsTotal PCBs and Total PAH distributions

2.2. Apply Apply dredging scenariodredging scenario to each set of 3 to each set of 3 simulationssimulations

3.3. Compute corCompute corresponding responding volume to be volume to be dredgeddredged

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Simu #1 (Hg)Simu #1 (Hg) Simu #1 (PCB)Simu #1 (PCB) Simu #1 (TPAH)Simu #1 (TPAH)

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Uncertainty about Dredging Uncertainty about Dredging VolumesVolumes

Worst caseWorst casescenarioscenario

Best caseBest casescenarioscenario

Simu Simu #1#1

Simu Simu #2#2

Simu Simu #3#3

Simu Simu #50#50

…… …

……

…

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Sediment Volume Estimates

Mean Minimum

Maximum

90,317 80,971 101,633

Note: Results are preliminary

Probability that at least one TOC is exceededProbability that at least one TOC is exceeded

3030TOC – threshold of concern

Note: Results are preliminary

Probability that at least one TOC is exceededProbability that at least one TOC is exceeded

3131TOC – threshold of concernNote: Results are preliminary

Next stepsNext steps

• Depending on next steps at the site, Depending on next steps at the site, develop sampling design that develop sampling design that addresses areas with greatest addresses areas with greatest uncertainty at threshold of concernuncertainty at threshold of concern

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GeostatisticsGeostatistics

• Pools data to get the best representation of the Pools data to get the best representation of the sitesite

• Facilitates informed cleanup decisions and Facilitates informed cleanup decisions and effective use of remedial resourceseffective use of remedial resources

• Uncertainty can be quantifiedUncertainty can be quantified

• Geostatistical analyses can support generation Geostatistical analyses can support generation of cost effective sampling designsof cost effective sampling designs

• Large quantities of data can be more easily Large quantities of data can be more easily visualizedvisualized

• Decisions are defensible, transparent, well-Decisions are defensible, transparent, well-documented, and reproducibledocumented, and reproducible3333

Lessons LearnedLessons Learned

• Follow systematic planning!Follow systematic planning!

• Clearly define decisionClearly define decision

• Develop sampling design considering specific data Develop sampling design considering specific data analysis techniquesanalysis techniques

• Communicating results is a challenge and requires Communicating results is a challenge and requires investment of project leadinvestment of project lead

• Collection of accurate representative sediment Collection of accurate representative sediment depth data is criticaldepth data is critical

• Research is needed to ground truth and refine the Research is needed to ground truth and refine the tools for sediment projectstools for sediment projects

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