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Judith A. Schofield 1 , Pierre Goovaerts, Justin Telech, Ken Miller, and Molly Middlebrook Amos

Geostatistical Modeling as a Quality Management Tool to Address Uncertainty in Decision-making for Large Scale Sediment Assessment and Remediation Projects. Judith A. Schofield 1 , Pierre Goovaerts, Justin Telech, Ken Miller, and Molly Middlebrook Amos Computer Sciences Corporation Louis Blume

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Judith A. Schofield 1 , Pierre Goovaerts, Justin Telech, Ken Miller, and Molly Middlebrook Amos

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  1. Geostatistical Modeling as a Quality Management Tool to Address Uncertainty in Decision-making for Large Scale Sediment Assessment and Remediation Projects Judith A. Schofield1, Pierre Goovaerts, Justin Telech, Ken Miller, and Molly Middlebrook Amos Computer Sciences Corporation Louis Blume U.S. EPA Great Lakes National Program Office U.S. EPA’s 28th Annual Conference on Managing Environmental Quality Systems May 14, 2009 1Presenter

  2. Acknowledgments Diana Mally David Wethington (now with USACE) Marc Tuchman From U.S. EPA’s Great Lakes National Program Office, we acknowledge the following project leads and contributors: • And from the Michigan Department of Environmental Quality: • Michael Alexander

  3. Geostatistics • Set of statistical techniques used in the analysis of georeferenced data • Increasingly popular in part due to the availability of geographic information systems (GIS) software • Powerful tool when used in combination with GIS

  4. How can Geostatistics be used as a Quality Management Tool? • Pools data to get the best representation of the site • Uncertainty can be quantified • Supports generation of cost effective sampling designs • Large quantities of data can be more easily visualized • Decisions are defensible, transparent, well-documented, and reproducible • Facilitates informed cleanup decisions and effective use of remedial resources

  5. Geostatistics in Sediment Assessment and Remediation • Describe extent and nature of contamination • Identify data gaps • Generate statistical sampling designs • Calculate sediment volumes • Develop remedial design • Evaluate achievement of cleanup goals • Communicate conditions to stakeholders

  6. Sediment Assessment and Remediation Projects using Geostatistics • Fox River, WI • Hudson River, NY • Minnesota Slip, Duluth Harbor, MN • East Fork Poplar Creek, TN • Great Lakes Legacy Act • Black Lagoon, MI • Hog Island, WI • Ruddiman Creek, MI • Division Street Outfall, MI • St. Louis River, WI • Ashtabula River, OH • Trenton Channel, MI • Buffalo River, NY • Lincoln Park, WI

  7. Trenton Channel of the Detroit River • The Detroit River is one of 42 Areas of Concern (AOCs) in the Great Lakes • investigations of the Upper Trenton Channel, within the Detroit River AOC, have shown that sediments are contaminated with polychlorinated biphenyls (PCBs), mercury and total polycyclic aromatic hydrocarbons (Total PAHs) among other contaminants • EPA’s Great Lakes National Program Office (GLNPO) and the Michigan Department of Environmental Quality (MDEQ) are evaluating the extent of sediment contamination in support of a potential Great Lakes Legacy Act cleanup project

  8. Trenton Channel RI/FFS • In 2006, GLNPO and MDEQ initiated a remedial investigation and focused feasibility study (RI/FFS) of the site • Sediment samples were collected and analyzed for a large variety of contaminants of concern • Initial sampling was conducted in 2006 data (Phase I of the RI/FFS) • Based on review of the Phase I data, GLNPO and MDEQ developed a series of questions that were the focus of additional sampling in 2007 (Phase II of the RI/FFS)

  9. Sediment Concentrations COC – contaminant of concern TOC – threshold of concern

  10. Statistical and Geostatistical Analysis • Exploratory Data Analysis • Statistics • Hypothesis testing using t-tests and regression • Geostatistical Analysis • 3D modeling • SGeMS (Stanford Geostatistical Modeling Software)

  11. Kriging • Evolved in mineral exploration and mining of minerals, ores, and coals • In 1963, G. Matheron named kriging after Daniel Gerdhaus Krige, a South African mining engineer, who used the technique to more accurately predict the extent of gold deposits in unsampled areas

  12. Kriging • Method of interpolation • Optimally predicts data values by using data taken at known locations • Creates contours or isopleths of data across an area • Other common methods of interpolation include inverse distance weighting and spline

  13. Geostatistical Analysis Basics • Overlay grid • Model sediment depth and create 3D grid using ordinary kriging • Transform contaminant concentrations • Compute 3D variogram for each contaminant and fit weighted least-square regression model • Estimate contaminant concentrations for each block using kriging & surrounding observed concentrations

  14. Min Max Note: Results are preliminary.

  15. Min Max Note: Results are preliminary Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Southeast of the Site (depth exaggerated 25 times)

  16. Min Max Note: Results are preliminary Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Northwest of the Site (depth exaggerated 25 times)

  17. Min Max Note: Results are preliminary Total PAH Concentrations in Sediment at the Trenton Channel Site, View from Northeast of the Site (depth exaggerated 25 times)

  18. Consistency of 3D model with core data Model Evaluation and Validation

  19. Consistency of 3D model with core data Model Evaluation and Validation

  20. Note: Results are preliminary

  21. Note: Results are preliminary

  22. Stochastic Simulation Simu #1 (Hg) Simu #1 (PCB) Simu #1 (TPAH) 27 Generate equiprobable models for mercury, Total PCBs and Total PAH distributions Apply dredging scenario to each set of 3 simulations Compute corresponding volume to be dredged

  23. Uncertainty about Dredging Volumes Simu #1 Simu #3  …… … Simu #2 Simu #50 Best case scenario Worst case scenario

  24. Note: Results are preliminary

  25. Probability that at least one TOC is exceeded Note: Results are preliminary TOC – threshold of concern

  26. Probability that at least one TOC is exceeded Note: Results are preliminary TOC – threshold of concern

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

  28. Geostatistics • Pools data to get the best representation of the site • Facilitates informed cleanup decisions and effective use of remedial resources • Uncertainty can be quantified • Geostatistical analyses can support generation of cost effective sampling designs • Large quantities of data can be more easily visualized • Decisions are defensible, transparent, well-documented, and reproducible

  29. Lessons Learned • Follow systematic planning! • Clearly define decision • Develop sampling design considering specific data analysis techniques • Communicating results is a challenge and requires investment of project lead • Collection of accurate representative sediment depth data is critical • Research is needed to ground truth and refine the tools for sediment projects

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