Spatial Analysis for Reducing Uncertainties in Human Health Risk Assessment
2001, 56 (2):
Combining spatial, temporal, and statistical methods in assessing human health risks near a potential source of environmental pollution has been a central research topic attracting researchers from various disciplines. Existing data and methodological problems of human health risk assessment, however, have contributed significantly to the uncertainties in the assessment results. An integrated spatial analytical framework that can provide strategic evaluation and reduce uncertainties in human public health risk assessment is very much needed to help reveal the underlying relationships between suspected pollution sources and human health outcomes, so that sensible and effective health and environmental policies can be devised. This paper identifies four major problems in human health risk assessment, including data availability and scale, methods of analysis, interpretations, and reactions to interpretations. Unlike other environmental analyses, human health risk assessment must require both health and demographic data, therefore, the very nature of it requires spatial analysis and GIS. In order to reduce uncertainties in the assessment results, the spatial analytical framework must be comprehensive, integrative, and interactive. A framework that has four groups of functions is proposed, including visualization and measurement, cluster detection, scale analysis, and focused exposure modeling. Using an example from a previous study on the relationship between a Superfund hazardous waste site in Louisiana and the cancer incidence statistics surrounding the site, this paper demonstrates that ambiguity and uncertainties occur in many analysis steps, confirming further the need to develop a systematic spatial analysis framework to reducing uncertainties in the public health risk assessment process.
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