A Hybrid Modeling Framework to Estimate Traffic-Related Exposure in Three Connecticut Cities

Abstract

Traffic-related air pollution is one of the major challenges facing urban areas. As traffic-related emissions result in air pollutant concentrations that vary over spatial scales of under 100m, it is challenging to implement models to capture this behavior. In this study, we develop a hybrid modeling framework combining a regional model (CAMx) and a local scale dispersion model (RLINE) to estimate concentrations of both primary and secondary species from roadway emission sources. We use the Particulate Matter Source Apportionment Technology (PSAT) to quantify the concentrations from traffic-related emission sources. We employ RLINE to estimate pollutant distribution profiles for traffic-related emissions for typical diurnal conditions each month at a fine resolution. We use the traffic-related contributions from CAMx-PSAT alongside the profiles from RLINE to distribute the traffic-related contributions spatially and temporally. This allows us to efficiently estimate air pollutant concentrations at fine spatial (40mx40m) and hourly temporal resolution.We conduct a model evaluation of our framework for NO2 in the year 2011 using both satellite data and regression model estimates at census block resolution. We have applied this modeling framework to three cities in Connecticut (Hartford, New Haven, and Windham) and quantified human exposure to NOx, PM2.5, and elemental carbon. We also assess the health risk associated with each species for individuals of different age and genders. Finally, we quantify the environmental inequality based on income and population density. Our approach using a dispersion model is unique as it uses the mass fraction of the total dispersed pollutant at different receptor points and hence is not dependent on extensive roadway emissions data or extensive model runs. This modeling approach overcomes two major challenges facing modeling for traffic-related exposures: double counting emissions and a lack of temporal variability.