An Index for Assessing Demographic Inequalities in Cumulative Environmental Hazards with Application to Los Angeles, California

Abstract

Researchers in environmental justice contend that lowincome communities and communities of color face greater impacts from environmental hazards. This is also of concern for policy makers. In this context, our paper has two principal objectives. First, we propose a method for creating an index capable of summarizing racial-ethnic and socioeconomic inequalities from the impact of cumulative environmental hazards. Second, we apply the index to Los Angeles County to illustrate the potential applications and complexities of its implementation. Individual environmental inequality indices are calculated based on unequal shares of environmental hazards for racial-ethnic groups and socioeconomic positions. The illustrated hazards include ambient concentrations of particulate matter, nitrogen dioxide, and estimates of cancer risk associated with modeled estimates for diesel particulate matter. The cumulative environmental hazard inequality index (CEHII) then combines individual environmental hazards, using either a multiplicative or an additive model. Significant but modest inequalities exist for both individual and cumulative environmental hazards in Los Angeles. The highest level of inequality among racial-ethnic and socioeconomic groups occurs when a multiplicative model is used to estimate cumulative hazard. The CEHII provides a generalized framework that incorporates environmental hazards and socioeconomic characteristics to assess inequalities in cumulative environmental risks.

Identifying Vulnerable Populations through an Examination of the Association Between Multipollutant Profiles and Poverty

“…The multipollutant profile approach developed will be applied to estimates of air pollution concentrations for NO 2 , PM 2.5 , diesel (road), and diesel (offroad) exposures obtained using a recently published paper. 18 Briefly, NO 2 estimates were obtained via a land-use regression. The variables used for NO 2 model selection included land-use information (e.g., commercial and industrial), road network, traffic, population distribution, physical properties, and remote sensing-derived greenness and soil brightness.…”

Air Pollution Inequality and Its Sources in SO2 and NOX Emissions among Chinese Provinces from 2006 to 2015

“…Dong et al [27] mentioned that SO 2 emission is unequally exposed in the different regions in China due to the uneven evolution of industrial activities. The NO X emission pattern is a well-known pollutant proxy used in studies because it is mostly caused by traffic and urbanization, although people in different social classes or groups are exposed to it unequally [11,12,25,[28][29][30]. Gu et al [31] found the regional disparity in NO X emission trend in China was that less-developed provinces suffer more growth rate of NO X emission than rich provinces due to the lack of strict environmental regulation to control emission in the less-developed provinces.…”

“…Despite the decrease of SO2 and NOX emissions during the period of the 12th FYP, there is a significant regional disparity in both emissions which needs more attention to improve the local air quality [28]. …”

Problem formulation for risk assessment of combined exposures to chemicals and other stressors in humans

“…For instance, an index can be created for racial/ethnic and socio-economic inequalities (Su et al 2009), or for social determinants (Salinas et al 2012). Matrices (WHO 2010) and algorithms (NEJAC 2010;Zartarian et al 2011;Hastings et al 2013) have been developed to help identify highest comparative risks.…”