Citation:

Barzyk, T., H. Huang, L. Martin, L. Tran, AND D. Catanzaro. Quantifying Exposure and Risk Disproportionality in Environmental Justice Populations. 2016 Annual International Society of Exposure Science Meeting, Utrecht, NETHERLANDS, October 09 - 13, 2016.

Impact/Purpose:

The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA

Description:

Disproportionate risk suggests a predisposition within an individual or population to be either differentially exposed or affected by a given stressor or combination of stressors, which are especially prevalent in Environmental Justice (EJ) communities. Research gaps remain in accurately quantifying disproportionate risk, including: 1) the extent to which varying stressors can be combined into a single health risk score, and 2) determining rigorous scientific methods to compare the relative risk of each stressor in relation to the others, and to develop cross-cutting solutions to address them. This work focuses on the latter. Stressors can be chemical or non-chemical in origin. A primary research topic is how stressors interact with susceptible and vulnerable populations and how that might affect health risk estimates (e.g., how dose-response functions might be impacted). While associations have been well-documented (i.e., that low-income, minority populations are highly exposed or disproportionately impacted), less has been done to determine the relative risk associated with particular stressors in order to establish priorities for exposure reduction and risk mitigation. Until quantitative techniques become available to combine varying stressors, comparative or relative risk assessment provides an opportunity to include data, expert advice, and local knowledge to make informed decisions about addressing risk. These methods have been applied to multiple case studies in EPA Regions throughout 2014-2015. The process we followed was informed by stakeholder input and real-world testing, and has been developed into a web-based software application called the Community Cumulative Assessment Tool (CCAT), a broadly designed tool appropriate to use for evaluating wide-ranging risk scenarios, yet retaining some user flexibility to meet the unique needs of each assessment. We present an overview of CCAT, along with case study results and lessons learned, to highlight the utility of comparative risk assessment (and resulting risk management actions), especially in the absence of methods that combine risk from varying stressors.

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