Using Local Indicators of Spatial Association to Analyze the Environmental Justice Implications of Ambient Air Pollution in the United States

Abstract

This article demonstrates how local indicators of spatial association (LISA) can be applied to advance distributive environmental justice analysis of air pollution. It presents a case study that compares the spatial distributions of fine particulate matter (PM_2.5) and social vulnerability at the neighborhood (census tract) level in the continental United States, to investigate the following questions: (1) Where does high ambient PM_2.5 pollution and high social vulnerability spatially coincide? (2) What are the dominant themes and indicators of social vulnerability in these places? The bivariate local Moran’s I statistic is used to classify tracts into relevant spatial clusters for visualizing the relationship between annual average PM_2.5 concentrations and the Centers for Disease Control and Prevention’s Social Vulnerability Index. The “High–High” cluster represents tracts at the convergence of high PM_2.5 concentrations and high social vulnerability. Results indicate that High–High tracts are located mainly in densely populated urban areas and more populous states and contain almost 43 million people. Socioeconomic status and minority status/language are the dominant themes of social vulnerability for tracts in this cluster. The most prominent individual indicators that distinguish High–High tracts from others include limited English language proficiency, crowding, zero-vehicle households, minority status, no high school diploma, and below poverty level. Findings illustrate how bivariate LISA can detect priority locations where interventions can address both physical and social aspects of vulnerability to air pollution, as well as clarify which vulnerable groups should be prioritized for further investigation and mitigation.