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A multi-criteria geographic information system screening approach for prioritizing response activities following a chemical, biological, or radiological incident
Citation:
Blue, J., S. Arden, S. Ratick, M. Rodgers, E. Wexler, E. Rebour, T. Boe, AND W. Calfee. A multi-criteria geographic information system screening approach for prioritizing response activities following a chemical, biological, or radiological incident. Chapter 5, Contemporary Perspectives in Data Mining. Emerald Publishing Limited, Bingley , Uk, 5:101–138, (2025).
Impact/Purpose:
PRESTO was developed using a H-WOWA approach to support EPA in responding to contamination incidents, especially those involving CBR materials. PRESTO allows users to prioritize locations for initial cleanup operations after a natural disaster or release of CBR contaminants. The goal of such prioritization is to protect public health and the environment, in part by ensuring that locations where contaminants are likely to spread quickly (if not contained or remediated) are addressed first. PRESTO aggregates large quantities of relevant, often disparate, risk indicators into a single score at each location across a selected study area. The data are then readily available to support EPA in the decision-making process.
Description:
This chapter describes the development of a decision support tool, the Priority Response Environmental Screening Tool (PRESTO), designed to assist the U.S. Environmental Protection Agency (EPA), in partnership with state and local decision-makers, in prioritizing locations for initial cleanup operations aimed at reducing risks to public health and the environment after a natural disaster or chemical, biological, or radiological (CBR) incident. In the event of a natural disaster or release of CBR contaminants, for example, EPA and its partners would need to determine which locations should be prioritized to prevent the spread of contamination and mitigate long-term risks. Decision makers are often hampered by having too little or too much data, such that it is not obvious how relevant data can be incorporated into a decision-making framework. PRESTO provides a flexible and adaptable framework to address the challenges of aggregating data in a meaningful way to facilitate quickly identifying and understanding the resulting information to inform decisions. To demonstrate how PRESTO could inform disaster response planning following a hypothetical CBR release, a 10-mile radius study domain centered on Philadelphia, PA was analyzed. Nine data sets measuring a range of issues reflecting risks to human health and the environment were used for this demonstration. The demonstration showed that there were substantial differences in which locations would be prioritized for response by using data aggregation schemes available within PRESTO for determining priority locations compared to relying on more commonly used data aggregation schemes. This is because PRESTO allows data sets reflecting high risks at a given location to be emphasized, even if other data sets reflect low risks because they represent different issues. Specifically, this analysis showed that in some locations in the northwest of the study area and in center city Philadelphia, very high risks that emanate from exceedingly high population densities (so the possibility that many more people would be affected by contamination) and from large areas with impervious surfaces (which would allow rapid spread of contamination via stormwater) are captured by using PRESTO. However, simpler analyses would not prioritize any locations in the center of the city. PRESTO also makes the data sets and associated issues that drive specific prioritization decisions readily accessible, such that motivations for decisions are transparent via easily understood graphical displays.
