Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Determining site-specific background level with geostatistics for remediation of heavy metals in neighborhood soils

1 Department of Chemistry, University at Buffalo-SUNY, Buffalo, NY, USA
2 Department of Geography, Ohio University, Athens, OH, USA

Topical Section: Pollution, Control & Remediation

The choice of a relevant, uncontaminated site for the determination of site-specific background concentrations for pollutants is critical for planning remediation of a contaminated site. The guidelines used to arrive at concentration levels vary from state to state, complicating this process. The residential neighborhood of Hickory Woods in Buffalo, NY is an area where heavy metal concentrations and spatial distributions were measured to plan remediation. A novel geostatistics based decision making framework that relies on maps generated from indicator kriging (IK) and indicator co-kriging (ICK) of samples from the contaminated site itself is shown to be a viable alternative to the traditional method of choosing a reference site for remediation planning. GIS based IK and ICK, and map based analysis are performed on lead and arsenic surface and subsurface datasets to determine site-specific background concentration levels were determined to be 50 μg/g for lead and 10 μg/g for arsenic. With these results, a remediation plan was proposed which identified regions of interest and maps were created to effectively communicate the results to the environmental agencies, residents and other interested parties.
  Figure/Table
  Supplementary
  Article Metrics

References

1. US Environmental Protection Agency. Terminology Reference System. Available from: https://iaspub.epa.gov/sor_internet/registry/termreg/searchandretrieve/termsandacronyms/search.do.

2. Wyoming Department of Environmental Quality, Establishing Site-Specific Background Metal Concentrations in Soil, 2000.

3. US Environmental Protection Agency, Establishing Background Levels, 1995.

4. US Environmental Protection Agency, Guidance for Comparing Background and Chemical Concentration Levels in Soil for CERCLA Sites. Office of Emergency and Remedial Response, Washington, DC 20460, EPA 540-R-01-003, 2002.

5. Stensvold KA, Scientific Investigations Report 2011-5202, US Department of the Interior, US Geological Survey, 2012, 1-27.

6. Vosnokis KAS, Perry E (2009) Background versus Risk-Based Screening Levels: An Examination of Arsenic Background Soil Concentrations in Seven States. Int J Soil Sediment Water 2: 2.

7. Mortefolio MJ, Derivation of Site-Specific Arsenic Background in Soil: A Case Study. Proceedings of the Annual International Conference on Soils, Sedements, Water and Energy. 2010, 12: 6.

8. Love D, Loock D, Kuzyk ZZ, et al. (2005) Development of Site-Specific Environmental Criteria from Background Data. In: Assessment and Remediation of Contaminated Sites in Arctic and Cold Climates (ARCSACC), Edmunton, Alberta.

9. New York Department of Envirionmental Conservation, Determination of Soil Cleanup Objectives and Cleanup Levels, 1994.

10. New Jersey Department of Envirionmental Protection, Soil Cleanup Criteria (Site Remediation Program), 2015.

11. New Hampshire Department of Envirionmental Services, NHDES Contaminated Sites Risk Characterization and Managment Policy, 1998.

12. California Regional Water Quality Control Board, Applicaiton of Risk-Based Screening Levels and Decision Making to Sites with Impacted Soil and Groundwater, 2001.

13. State of Colorado Department of Public Health and Environment, Hazardous Materials and Waste Management Division (2014) Arsenic Concentrations in Soil Risk Mangement Guidance for Evaluating.

14. U.S. Environmental Protection Agency (2014) Regional Screening Level (RSL) Summary Table.

15. State of Connecticut, Department of Energy and Environmental Protection (2013) Regulation of Department of Energy and Environmental Protection Concerning Remediation Standard.

16. Michigan Department of Envirionmental Quality (2013) Cleanup Criteria Requirements for Response Activity (Formerly the Part 201 Generic Cleanup Criteria and Screening Levels).

17. Minnesota Pollution Control Agency (1999) Site Remediation Section: Risk-Based Guidance for Soil-Human Health Pathway.

18. Milillo TM, Sinha G, Gardella JA (2012) Use of Geostatistics for Remediation Planning to Transcend Urban Political Boundaries. Environ Pollut 170: 52-62.    

19. Goovaerts P, Kriging versus Stochastic Simulation for Risk Analysis in Soil Contamination. In: geoENV I-Geostatistics for Environmental Applications. Springer Netherlands, 1997: 247-258.

20. Webster R, Oliver MA, Geostatistics for Environmental Scientists. John Wiley & Sons, 2007, 330.

21. Fairbanks P, 'Legitimate questions' on toxics and illness. Buffalo News, p. B1, 25 March 2001.

22. Fairbanks P, Taking Action. Buffalo News, p. B1, 8 September 2001.

23. Fairbanks P, Herbeck D (2002) LTV Steel to Help Pay for $16 Million Cleanup. Buffalo News, 16 July 2002.

24. Groll M (2002) Toxic Neglect: City Had Early Warnings of Contamination. Buffalo News, p. A1, 11 Feburary 2002.

25. Gardella JA, Milillo TM, Sinha G, et al. (2007) Linking Community-Service Learning and Envirionmental Analytical Chemistry. Analytical Chemistry 79: 811-818.

26. Gardella JA, Milillo TM, Sinha G, et al. (2009) Linking Advanced Public Service Learning And Community Participation With Environmental Analytical Chemistry: Lessons From Case Studies In Western New York. In: Redlawsk D, Rice T (Eds.), Service Learning with Government Partners. San Francisco, CA: John Wiley & Sons, 98-112.

27. Verstraete S, Van Meirvenne M (2008) A Multi-Stage Sampling Strategy for the Delineation of Soil Pollution in a Contaminated Brownfield. Environ Pollut 154: 184-191.    

28. Environmental Science Research Institute (ESRI). Available from: http://www.esri.com.

29. United States Census Bureau (2000) 108th Congressional District Census TIGER Line File. Available from: ftp://ftp2.census.gov/geo/tiger/tgrcd10.

30. Brewer C, Harrower M. ColorBrewer 2.0. Available from: http://colorbrewer2.org.

31. Goovaerts P (2009) AUTO-IK: A 2D Indicator Kriging Program for the Automated Non-Parametric Modeling of Local Uncertainty in Earth Sciences. Comput Geosci-UK 35: 1255-1270.    

32. Lin YP, Chu HJ, Wu CF, et al. (2011) Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques. J Environ Res Public Health 8: 75-88.

33. Antunes I, Albuquerque MTD (2013) Using Indicator Kriging for the Evaluation of Arsenic Potential Contamination in an Abandoned Mining Area (Portugal). Sci Total Environ 442: 545-552.

34. Goovaerts P (1994) Comparative Performance of Indicator Algorithms for Modeling Conditional Probability Distribution Functions. Mathamatical Geology 26: 389-411.    

35. Johnston K, Ver Hoef JM, Krivoruchko K, et al. Using ArcGIS Geostatistical Analyst. Redlands: ESRI, 2001.

36. Isaaks EH, Srivastava RM, An Introduction to Applied Geostatistics, New York: Oxford University Press, 1989, 592 p.

Copyright Info: © 2017, Gaurav Sinha, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved