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Resolving Local-Scale Emissions for Modeling Air Quality near Roadways
Citation:
COOK, R., V. ISAKOV, J. TOUMA, W. G. BENJEY, J. THURMAN, E. KINNEE, AND D. ENSLEY. Resolving Local-Scale Emissions for Modeling Air Quality near Roadways. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, 58(3):451-461, (2007).
Impact/Purpose:
The National Exposure Research Laboratory's (NERL's) Atmospheric Modeling Division (AMD) conducts research in support of EPA’s mission to protect human health and the environment. AMD's research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation's air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways in order to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, employing a “bottom up” approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve spatial accuracy of the activity information obtained from a Travel Demand Model. In addition we present an air quality modeling application of this methodology in New Haven, CT. This application employs a hybrid modeling approach, where a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture with the modeling domain due to spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.