Impact, efficiency, inequality, and injustice of urban air pollution: variability by emission location

Abstract

Reducing exposure to air pollution is an important goal for many local and national governments. Disparities in air pollution exposure by race, ethnicity, and socioeconomic class are well documented; reducing these disparities is another important policy target. Meeting both goals requires tools to evaluate how emission reduction options affect average exposures and exposure disparities. Here, we consider the role of emission location in implementing control strategies, and investigate the effect of two practical, space-based approaches-low-emission zones and truck rerouting-on diesel particle levels in Southern California. We employ Eulerian grid modeling to explore the impact that emission location has on four outcomes important to policymakers: total pollution exposure, exposure efficiency (i.e. exposure impact per unit emission), exposure inequality (i.e. deviations from exposure being equally distributed across the population; unequal exposure among individuals), and exposure injustice (i.e. associations between exposure and demographic attributes such as race or ethnicity; unequal exposure among groups). Our results highlight potential trade-offs (e.g. an increase in equality but reduction in justice for interventions in some locations) as well as opportunities for 'win-win' solutions (locations for which emission reductions would reduce all four target outcomes). We find that a simple, straightforward approach-reducing emissions in neighborhoods with a high proportion of minority residents-may or may not yield the strongest benefits to environmental justice; the reason is that the straightforward approach fails to account for meteorology and where pollution travels after being emitted. In short, we demonstrate an approach that can be used to identify areas in which emissions reductions would have high efficiency and would also result in disproportionately large reductions to average exposure, exposure inequality, and exposure injustice. The approach presented here could be used to design and prioritize local or national emission reduction efforts.

Characterizing disproportionality in facility-level toxic releases in US manufacturing, 1998–2012

“…Studies analyzing patterns in greenhouse gas (GHG) emissions pinpoint disproportionalities across nations, class groups, and households (Baer 2009, Kennedy et al 2014. Analyses of air pollution from vehicle emissions identify the disproportional contribution of high-emitting vehicles (Cadle et al 1997, Nguyen andMarshall 2018).…”

Environmental Injustice in Mexico City: A Spatial Quantile Approach

“…In that case, policy-makers can consider spatially targeted emission-reduction policies (e.g. truck-rerouting, low-emission zones, industry re-allocation) in some locations to produce the strongest benefits to environmental justice (Nguyen and Marshall 2018).…”

Clearing the Air: The Impacts of Ambient Air Pollution on Environmental Equity

“…Even fewer studies have used Chemical Transport Models (CTMs). The literature review to date has found only three studies that used the Comprehensive Air Quality Model with Extensions (CAMx) (Marshall, Swor, & Nguyen, 2014;Nguyen & Marshall, 2018), and the Community Multiscale Air Quality (CMAQ) modelling system (Fann et al, 2011). These studies examined how changes in emissions will change environmental inequity, and two are discussed in more detail.…”

“…Similarly, many studies use the human health risks calculated through the National-Scale Air Toxics Assessment model (Chakraborty, 2009;Chakraborty et al, 2014;Collins et al, 2015Collins et al, , 2011Grineski et al, 2014), which models certain individual air toxics using U.S. EPA's Community Multiscale Air Quality (CMAQ) model at a 12km grid resolution. Very few papers model concentrations directly using detailed Chemical Transport Models (CTMs); three exceptions are the use of the Comprehensive Air Quality Model with Extensions (CAMx) by Marshall, Swor, and Nguyen (2014) and Nguyen and Marshall (2018); as well as the use of the Community Multiscale Air Quality (CMAQ) modelling system by Fann et al (2011). The benefit of using a CTM is illustrated in these papers;…”

“…These studies require SES and pollution datasets at a refined spatial scale. Many studies rely on land-use regression models (Brunt et al, 2016;Clark et al, 2014;Rosofsky et al, 2018;Temam et al, 2017) or atmospheric dispersion models (Martenies et al, 2017;Poorfakhraei et al, 2017;Pratt et al, 2015;Tayarani et al, 2016) to provide the necessary spatial resolution, while a small number of papers model concentrations using Chemical Transport Models (CTMs) (Fann et al, 2011;Marshall et al, 2014;Nguyen & Marshall, 2018). The benefit of using a CTM is the flexibility to change parameters and model future scenarios.…”