Spatial Data Analysis and Identification of Pollutant Sources

        Many potentially responsible parties (PRPs) in environmental remediation cases are troubled by the suspicion that their properties may not be the sources of airborne contaminants now in soils that they are required to cleanup. Until recently, it has been difficult or prohibitively expensive to test this hypothesis using realistically available sampling data and to determine fair and effective boundaries for remediation efforts. In 1995, Alceon and Cox Associates developed a new approach to analyzing the spatial distribution of soil concentrations of contaminants that helps to pinpoint the most likely locations of the source(s) and that allows statistically justified boundaries to be determined for the extent of contamination that can reasonably be attributed to a specific site. This method, based on applications of published nonparametric statistical methods combined with estimates of concentration contours, enables PRPs and regulators to design remediation programs that are both less expensive and more health-protective -- as well as more fair -- than previous approaches. The technique is based on the following principles:

        1. Test for patterns of association between distance from the site in a given direction and soil concentrations of site-associated contaminants. An entire map of the concentration field of contaminants around a site can be estimated efficiently, and boundaries and uncertainties can be calculated and displayed. The technique also supports sequential sampling strategies that make the most efficient and economical use of sample data. This step establishes statistical boundaries between background locations and locations potentially affected by the site. It also allows the discovery of probable locations of unknown sources and it automatically generates confidence bounds on the location of the boundary between background and site-affected areas.

        2. Use a worst first approach to setting remediation priorities among non-background locations. This principle is both health-protective and cost-effective. It compares favorably on all dimensions to standard approaches such as cleaning up entire clusters or sectors based on average contaminant concentrations.

        3. Stop remediation activities when exposures or risks have distributions that are acceptable compared to background distributions. While definitions of acceptable must typically be negotiated between PRPs and regional environmental regulators, the distribution matching philosophy provides a principled method for achieving fair, effective allocations of remediation effort.

        This approach has been applied in practice with promising results in a residential neighborhood in Chicago, IL.

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