ISEE-1026
Background:
In recent years, communities surrounding airports have expressed increasing concern about the potential contributions of airport air toxics emissions to health risks, and it would be valuable to be able to predict the exposure and health risk implications of emissions or emissions changes at a defined airport, both from an individual and a population perspective. In this study, we apply a high-resolution atmospheric dispersion model (AERMOD) to a sample of 33 airports across the U.S., including small and large airports in urban and rural settings in different areas of the country.
Methods:
We estimate the emission rates required at these airports to exceed a specified individual risk threshold (i.e., 10–6 for the lifetime cancer risk for the maximally exposed individual), and we additionally determine the total population risk associated with these emissions, to determine whether prioritization based on maximum individual exposure and risk would correspond with prioritization based on total population exposure and risk. We additionally develop models to explain the heterogeneity in these emission rates across airports, based on meteorological and population data. To provide a realistic characterization of pollutant fate and transport, airport emissions are modeled based on time-varying operational profiles as several vertically-layered area sources. We focus on air toxics with different chemical characteristics and varying cancer potency evidence—benzene, 1,3-butadiene, and particle-bound PAHs. We apply AERMOD and estimate incremental concentrations from airports at the centroids of census tracts or block groups within 50 km. We estimate the emissions required to exceed the individual risk threshold using previously-published inhalation unit risk factors, and we combine incremental concentrations with population estimates at the population centroids to calculate the total population exposure due to the airport emissions.
Results:
Our findings indicate that the minimum emissions threshold varies significantly across airports, predicted by covariates for local meteorology and population distributions as well as airport activity patterns, and that optimization based on individual exposure and risk thresholds will differ from optimization based on total population exposure and risk.
Discussion:
These results allow communities and other stakeholders to quickly but reasonably determine the likelihood of public health impacts of concern for airport modifications or expansions, and can be expanded to include non-cancer or criteria pollutant effects in future assessments.
