Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

Author:

Kodros John K.12,Carter Ellison3,Oke Oluwatobi34,Wilson Ander5ORCID,Jathar Shantanu H.1ORCID,Magzamen Sheryl67ORCID

Affiliation:

1. Department of Mechanical Engineering Colorado State University Fort Collins CO USA

2. Now at Clarity Movement Berkeley CA USA

3. Department of Civil and Environmental Engineering Colorado State University Fort Collins CO USA

4. Now at Building Energy and Environment Division National Institute of Standards and Technology Gaithersburg MD USA

5. Department of Statistics Colorado State University Fort Collins CO USA

6. Department of Environmental and Radiological Health Sciences Colorado State University Fort Collins CO USA

7. Department of Epidemiology Colorado School of Public Health Colorado State University Fort Collins CO USA

Abstract

AbstractThe environmental justice literature demonstrates consistently that low‐income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K‐means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non‐Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county‐level blood lead levels, 67th percentile in county‐level NO2, 79th percentile in county‐level CO, and 78th percentile in county‐level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county‐level unemployment, 70th percentile in county‐level population rate lacking a high school diploma, 73rd percentile in county‐level poverty rate, and 28th percentile in county‐level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

Funder

U.S. Environmental Protection Agency

JPB Foundation

Publisher

American Geophysical Union (AGU)

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