Adaptive spatial sampling of contaminated soil

Cox, Jr, L A

doi:10.1111/j.1539-6924.1999.tb01127.x

Title: Adaptive spatial sampling of contaminated soil

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Abstract

Suppose that a residential neighborhood may have been contaminated by a nearby abandoned hazardous waste site. The suspected contamination consists of elevated soil concentrations o chemicals that are also found in the absence of site-related contamination. How should a risk manager decide which residential properties to sample and which ones to clean? This paper introduces an adaptive spatial sampling approach which uses initial observations to guide subsequent search. Unlike some recent model-based spatial data analysis methods, it does not require any specific statistical model for the spatial distribution of hazards, but instead constructs an increasingly accurate nonparametric approximation to it as sampling proceeds. Possible cost-effective sampling and cleanup decision rules are described by decision parameters such as the number of randomly selected locations used to initialize the process, the number of highest-concentration locations searched around, the number of samples taken at each location, a stopping rule, and a remediation action threshold. These decision parameters are optimized by simulating the performance of each decision rule. The simulation is performed using the data collected so far to impute multiple probably values of unknown soil concentration distributions during each simulation run.

Authors:: Cox, Jr, L A

Publication Date:: Wed Dec 01 00:00:00 EST 1999

Research Org.:: Cox Associates, Denver, CO (US)

OSTI Identifier:: 20006166

Resource Type:: Journal Article

Journal Name:: Risk Analysis

Additional Journal Information:: Journal Volume: 19; Journal Issue: 6; Other Information: PBD: Dec 1999; Journal ID: ISSN 0272-4332

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES; SOILS; SAMPLING; REMEDIAL ACTION; HAZARDOUS MATERIALS; SPATIAL DISTRIBUTION

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                    Cox, Jr, L A. Adaptive spatial sampling of contaminated soil.  United States: N. p., 1999. 
        Web.  doi:10.1111/j.1539-6924.1999.tb01127.x.

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                    Cox, Jr, L A. Adaptive spatial sampling of contaminated soil.  United States.  https://doi.org/10.1111/j.1539-6924.1999.tb01127.x

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                    Cox, Jr, L A. 1999.  
        "Adaptive spatial sampling of contaminated soil".  United States.  https://doi.org/10.1111/j.1539-6924.1999.tb01127.x.

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@article{osti_20006166,

  title        = {Adaptive spatial sampling of contaminated soil},

  author       = {Cox, Jr, L A},

  abstractNote = {Suppose that a residential neighborhood may have been contaminated by a nearby abandoned hazardous waste site. The suspected contamination consists of elevated soil concentrations o chemicals that are also found in the absence of site-related contamination. How should a risk manager decide which residential properties to sample and which ones to clean? This paper introduces an adaptive spatial sampling approach which uses initial observations to guide subsequent search. Unlike some recent model-based spatial data analysis methods, it does not require any specific statistical model for the spatial distribution of hazards, but instead constructs an increasingly accurate nonparametric approximation to it as sampling proceeds. Possible cost-effective sampling and cleanup decision rules are described by decision parameters such as the number of randomly selected locations used to initialize the process, the number of highest-concentration locations searched around, the number of samples taken at each location, a stopping rule, and a remediation action threshold. These decision parameters are optimized by simulating the performance of each decision rule. The simulation is performed using the data collected so far to impute multiple probably values of unknown soil concentration distributions during each simulation run.},

  doi          = {10.1111/j.1539-6924.1999.tb01127.x},

  url          = {https://www.osti.gov/biblio/20006166},
  journal      = {Risk Analysis},

  issn         = {0272-4332},

  number       = 6,

  volume       = 19,

  place        = {United States},

  year         = {Wed Dec 01 00:00:00 EST 1999},

  month        = {Wed Dec 01 00:00:00 EST 1999}

}

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