Probabilistic Techniques for Backcalculating Soil Cleanup Targets

    Published: Jan 1992

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    Most public health risk assessments combine a series of average, conservative, and worst-case assumptions to derive a conservative point estimate of risk or a conservative point estimate for a cleanup target. This paper demonstrates a risk assessment methodology (probabilistic sensitivity analysis) that overcomes many of the limitations in current practices under the federal Superfund program and corresponding state programs. The methodology begins, as do many conventional methods, with the preparation of a spreadsheet to estimate soil cleanup targets. Following this key inputs are then modeled as random variables described by probability density functions (PDFs) or cumulative distribution functions (CDFs). The method then estimates PDFs or CDFs for the soil cleanup target using commercial Monte Carlo software on a desktop computer. With appropriate precautions to consider various pitfalls, we use the probabilistic method to estimate distributions for soil cleanup targets for two compounds in soils regulated by the New Jersey Department of Environmental Protection. In this manuscript, rather than advocate specific numerical results, we instead emphasize a general approach to probabilistic sensitivity analysis through visualization of results too granular to understand through tabulations.


    soil cleanup targets, Monte Carlo simulation, probabilistic sensitivity analysis

    Author Information:

    Lloyd, KJ
    Senior Associate and President, Alceon Corporation, Harvard Square Station, Cambridge, MA

    Thompson, KM
    Harvard School of Public Health, Boston, MA

    Burmaster, DE
    Senior Associate and President, Alceon Corporation, Harvard Square Station, Cambridge, MA

    Committee/Subcommittee: D18.14

    DOI: 10.1520/STP23849S

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