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Your Environment. Your Health.

Harvard School of Public Health

Superfund Research Program

Optimizing Sampling and Statistical Analysis for Hazardous Waste Site Assessment

Project Leader: Brent A. Coull
Co-Investigator: Peter Toscas (Commonwealth Scientific Industrial Research Organization (CSIRO))
Grant Number: P42ES016454
Funding Period: 2010-2015
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2010-2014)

Site assessment is a key aspect of understanding the risks of and planning remediation of hazardous waste sites. Budget constraints, site heterogeneity and uncertainties in assessing exposure complicate assessment. The ideal approach also varies according to the purpose. From a regulatory perspective, goals include general characterization of a site, estimating mean levels for risk assessment, planning remediation, and assessing compliance.

In contrast, environmental epidemiologists are interested in estimating individual exposures as accurately as possible to study associations with health outcomes. The objective of this project is to provide statistical design and analysis tools to improve the accuracy and reliability of site and exposure assessment for Superfund hazardous waste sites. The approach is based on statistical modeling, along with optimal design considerations that maximize prediction accuracy while minimizing cost and accounting for practical considerations. Building on the basic spatial kriging model, the spatial model-based approach to design and analysis is compared to existing design-based approaches that do not account for spatial correlation. Researchers are extending these to complicated real-world settings, including the use of previous targeted samples and non-detect, proxy and composite samples, which may allow a reduction in sampling costs. At the megasite scale, researchers are relating soil concentrations to biomarker levels in the Tar Creek Superfund site, and developing spatial measurement error models for relating environmental concentrations to exposure as measured by biomarkers, thus accounting for incomplete environmental sampling. Researchers are also clarifying under what circumstances a spatial model-based approach provides real benefits in practice, reducing cost and uncertainty. To make spatial-model based methods accessible to the site-assessment community, researchers are developing software tools for use by Environmental Protection Agency (EPA) and site professionals.

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