Geostatistical Modeling of the Spatial Distribution of Soil Dioxins in the Vicinity of an Incinerator. 1. Theory and Application to Midland, Michigan

摘要

Deposition of pollutants around point sources of contamination, such as incinerators, can display complex spatial patterns depending on prevailing weather conditions, the local topography, and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decision-making, such as delineation of areas targeted for additional sampling or remediation. This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual variability of field samples. The approach is used to delineate areas with high levels of dioxin TEQ_(DF)-WHO_(98) (toxic equivalents) around an incinerator, accounting for 53 field data and the output of the EPA Industrial Source Complex (ISC3) dispersion model. The dispersion model explains 43.7% of the variance in the soil TEQ data, whereas the regression residuals are spatially correlated with a range of 776 m. One hundred realizations of soil TEQ values are simulated on a grid with a 50 m spacing. The benefit of stochastic simulation over spatial interpolation is 2-fold: (1) maps of simulated point TEQ values can easily be aggregated to the geography that is the most relevant for decision making (e.g., census block, ZIP codes); and (2) the uncertainty at the larger scale is simply modeled by the empirical distribution of block-averaged simulated values. Incorporating the output of the atmospheric deposition model as a spatial trend yields a more realistic prediction of the spatial distribution of TEQ values than log-normal kriging using only the field data, in particular, in sparsely sampled areas away from the incinerator. The geostatistical model provided guidance for the study design (census block-based population sampling) of the University of Michigan Dioxin Exposure Study (UMDES), focused on quantifying exposure pathways to dioxins from industrial sources, relative to background exposures.