Quantifying Impacts of Emission Reductions on Air Pollution Equity and Health Effects in New York City

This study examines inequity in exposure to fine particulate matter (PM2.5) in New York City and surrounding areas across income groups. By contrasting the sensitivities of public health and equity measures to emissions reductions on a location-by-location basis, this study offers novel yet practical suggestions to coordinate air quality management strategies that prioritize different policy endpoints.Air quality simulations were run using the USEPA’s Community Multiscale Air Quality (CMAQ) model and its adjoint version over New York City at 1 km resolution. CMAQ is used to estimate concentration surfaces for quantifying exposure to PM2.5. Second, the adjoint of CMAQ was run to estimate the sensitivity of domain-wide inequity parameters to pollution emissions, on a location-by-location basis for different sectors.Preliminary results show that lower income populations in New York City tend to be exposed to higher concentrations of PM2.5. We find that emission reductions in Brooklyn, Harlem, and the Bronx would be most beneficial for reducing domain-wide inequity, while reductions in Manhattan would result in increased inequity. We also calculate monetized health benefits per ton of primary PM emissions across the domain, with benefits estimated to be significant (as high as $10M/ton) and spatially variable across the domain. We propose a novel approach for monetizing air pollution inequity, and find that monetized benefits from inequity reductions are comparable in magnitude (up to $5M/ton) to health benefits from avoided mortality. We examine hypothetical scenarios where quantified benefits with regards to health an inequity are considered in tandem to coordinate policies that target both endpoints simultaneously. Our results demonstrate that focusing emission reductions on sources that are influential on both environmental justice and public health can yield improvements across multiple policy goals simultaneously.