Projects

Optimal Spatial Design for Environmental Health Research

The goal of this NIEHS funded research project is to devise a general approach for collecting environmental and biological samples that incorporates optimal spatial design. Recent advances in the theory and application of spatial statistics, as well as new kinds of data management techniques and analytical approaches made possible through the development of GIS software, hold tremendous promise for ex ante enhancing study design in environmental health sciences. This research project seeks to take advantage of ex ante applications made possible by these developments with the following specific aims:

• Develop a spatially-based sampling procedure that improves upon traditional random or categorical sampling approaches.
• Incorporate optimality into the procedure with regard to information gain concerning the nature of the relationship between contaminant and explanatory variables as well as the spatial pattern in the contaminant levels.
• Develop a system for sampling sequentially and adaptively to take full advantage of the information made available through the sampling process.
• Demonstrate innovative spatial design approaches by collecting environmental samples in the field.
• Assess the importance of the spatial resolution at which analyses and sampling protocols are undertaken (i.e., small changes in location may in fact equate to large changes in exposure).

The proposed project leverages active partnerships between the Nicholas School of the Environment and Earth Sciences and Trinity College of Arts and Sciences at Duke University. It uses arsenic as a prototype contaminant for exploring how to advance these new methods. The research is concerned primarily with developing a general framework for applying these methods to optimize sampling design across a wide variety of contaminants. This research seeks to help researchers improve estimates of exposures, sample more strategically, update models more efficiently, and draw better and more meaningful links between environmental contaminants and health endpoints.

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