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

Abstract

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 decisionmaking, 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 level 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 soil TEQ data, while the regression residuals are spatially correlated with a range of 776 meters. One hundred realizations of soil TEQ values are simulated on a grid with a 50 meter spacing. The benefit of stochastic simulation over spatial interpolation is twofold: 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 spatial trend yields a more realistic prediction of the spatial distribution of TEQ value than lognormal 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. *Corresponding author phone: 734-913-1098; fax: 734-913-2201; e-mail: goovaerts@biomedware.com. Corresponding author address: BioMedware Inc, 516 North State Street Ann Arbor, MI 48104 (USA). Brief 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 field variability.

Hierarchical cluster analysis of polychlorinated dioxins and furans in Michigan, USA, soils: Evaluation of industrial and background congener profiles

“…The Near Floodplain population was defined as respondents who lived in census blocks that included areas in the 100-year FEMA floodplain, but whose properties themselves were outside of the FEMA floodplain and had not, to the respondents' knowledge, been flooded by the Tittabawassee River. The Plume represents the residents in the city of Midland whose properties were likely impacted by the historical discharges from incinerators at the Dow facility, as delineated using a paired atmospheric-transport and geostatistical model [16]. The Other Midland/Saginaw population represented areas of Midland, Saginaw, and part of Bay Counties outside of the Floodplain, Near Floodplain, and Plume.…”

Geostatistical Modeling of the Spatial Distribution of Soil Dioxin in the Vicinity of an Incinerator. 2. Verification and Calibration Study

“…In a companion paper (5), the ISCST3 model served as secondary information for the geostatistical prediction of soil concentrations of toxic equivalents (TEQ) emitted from an incinerator in Midland, Michigan. In particular, wet and dry depositions were used to model the spatial trend in TEQ values, leading to the mapping of the probability of exceedence of target threshold values at the census block level.…”

Point source identification using a simple permutation test: a case study of elevated PCDD/F levels in ambient air and soil and their relation to the distance to a local municipal solid waste incinerator

“…Thus, source identification based on fingerprint analyses of soil PCDD/Fs might not be appropriate for soil samples. Because of the limitation in sample size of the current study, geostatistical modeling of the spatial distribution of dioxins requiring relatively large scale of collected sample such as that of the industrial site study in Midland, Michigan (Goovaerts et al 2008a, b) is not applicable. Dispersion modeling may also be used as an alternative for the assessment of the measured concentrations of the collected samples (Oettl et al 2003;Shih et al 2009).…”