Topic Areas:

Manuscript Keywords:

Datasets , Indicators, Cumulative Environmental Health Impacts

Community Keywords:

Why is this useful?

Thirty California ZIP CodesTM were chosen to represent a diversity of geographic regions and community types. We selected indicators for this pilot analysis based on several criteria. Foremost was an indicator’s ability to represent a component of cumulative impact. Other criteria included adequate geographic resolution, enough variation in the indicator across the state to discern differences between communities, state-wide availability, currency of data, overall data quality, and our assessment of the ability of the indicator to be understood by the lay public. For the sake of transparency, we restricted ourselves to the use of publicly-available datasets. We also sought to minimize the number of indicators and the potential overlap among them in order to simplify the subsequent analysis and avoid
double counting.


Laura Meehan August 1,*, John B. Faust 1 , Lara Cushing 2 , Lauren Zeise 1 and George V. Alexeeff 1


Polluting facilities and hazardous sites are often concentrated in low-income communities of color already facing additional stressors to their health. The influence of socioeconomic status is not considered in traditional models of risk assessment. We describe a pilot study of a screening method that considers both pollution burden and population characteristics in assessing the potential for cumulative impacts. The goal is to identify communities that warrant further attention and to thereby provide actionable guidance to decision- and policy-makers in achieving environmental justice. The method uses indicators related to five components to develop a relative cumulative impact score for use in comparing communities: exposures, public health effects, environmental effects, sensitive populations and socioeconomic factors. Here, we describe several methodological considerations in combining disparate data sources and report on the results of sensitivity analyses meant to guide future improvements in cumulative impact assessments. We discuss criteria for the selection of appropriate indicators, correlations between them, and consider data quality and the influence of choices regarding model structure. We conclude that the results of this model are largely robust to changes in model structure.


Laura Meehan August 1,*, John B. Faust 1 , Lara Cushing 2 , Lauren Zeise 1 and George V. Alexeeff 1

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