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An Integrated Modeling Platform for Human and Ecological Exposure and Risk in Multimedia Environmental Systems (HE2RMES)
Citation:
Babendreier, J., A. Parks, J. Brandmeyer, D. Womack, AND S. Beaulieu. An Integrated Modeling Platform for Human and Ecological Exposure and Risk in Multimedia Environmental Systems (HE2RMES). Presented at Society of Toxicologists Meeting, Durham, NC, May 08 - 11, 2012.
Impact/Purpose:
Poster for Society of Toxicologists Meeting May 2012
Description:
When considering potentially hazardous materials in the context of initial use, reuse, recycling, or ultimate disposal in the landscape, a crux of most decision will be understanding the cumulative contaminant exposure and risk of a given material to human health and well-being. This will necessarily involve study of both human and ecological receptors. Decision analysis will boil-down to where, at what load rates, and with what practices can products be sustainably managed. On reuse of wastes, for example, EPA and communities now consider alternative to "where", expanding upon teh mix of legacy waste management sources we must manage (e.g., landfills, land application, impoundments). The attendant dilemma - neither group can readily assess risk of most material management decisions faced. Our premise is that sustainable solutions must address partitioning across all media, cumulative exposure across all pathways, and be transferable in application. Modeling solutions, if useful, will also need to be readily parameterized (i.e. anywhere USA) and capable of tracking the effects of uncertain information. We demonstrate the HE2RMES modeling system, a recent effort in integration of such a modeling approach, supporting multimedia, multipathway, and multireceptor assessment. This work builds upon USEPA's core technology development under the iemTechnologies integrated environmental modeling platform. The iemTechnologies is comprised of the D4EM, FRAMES, and SuperMUSE software systems which, together, facilitate plug-n-play integration and simulation of data and models.
