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

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• 作者：Pierre Goovaerts ; Hoa T. Trinh ; Avery H. Demond ; Timothy Towey ; Shu-Chi Chang ; Danielle Gwinn ; Biling Hong ; Alfred Franzblau ; David Garabrant ; Brenda W. Gillespie ; James Lepkowski ; Peter Adriaens
• 刊名：Environmental Science & Technology
• 出版年：2008
• 出版时间：May 15, 2008
• 年：2008
• 卷：42
• 期：10
• 页码：3655 - 3661
• 全文大小：2795K
• 年卷期：v.42,no.10(May 15, 2008)
• ISSN：1520-5851

文摘

A key component in any investigation of cause−effect relationships between point source pollution, such as an incinerator, and human health is the availability of measurements and/or accurate models of exposure at the same scale or geography as the health data. Geostatistics allows one to simulate the spatial distribution of pollutant concentrations over various spatial supports while incorporating both field data and predictions of deterministic dispersion models. This methodology was used in a companion paper to identify the census blocks that have a high probability of exceeding a given level of dioxin TEQ (toxic equivalents) around an incinerator in Midland, MI. This geostatistical model, along with population data, provided guidance for the collection of 51 new soil data, which permits the verification of the geostatistical predictions, and calibration of the model. Each new soil measurement was compared to the set of 100 TEQ values simulated at the closest grid node. The correlation between the measured concentration and the averaged simulated value is moderate (0.44), and the actual concentrations are clearly overestimated in the vicinity of the plant property line. Nevertheless, probability intervals computed from simulated TEQ values provide an accurate model of uncertainty: the proportion of observations that fall within these intervals exceeds what is expected from the model. Simulation-based probability intervals are also narrower than the intervals derived from the global histogram of the data, which demonstrates the greater precision of the geostatistical approach. Log-normal ordinary kriging provided fairly similar estimation results for the small and well-sampled area used in this validation study; however, the model of uncertainty was not always accurate. The regression analysis and geostatistical simulation were then conducted using the combined set of 53 original and 51 new soil samples, leading to an updated model for the spatial distribution of TEQ in Midland, MI.