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“A Modeling Framework for Improved Characterization of Near-Road Air Quality at Fine Scales for Nationwide Exposure Assessment.”
Ying, S., S. Arunachalam, B. Naess, K. Talgo, A. Valencia, V. Isakov, Brad Schultz, AND T. Palma. “A Modeling Framework for Improved Characterization of Near-Road Air Quality at Fine Scales for Nationwide Exposure Assessment.”. Presented at Annual 2013 CMAS Conference, Chapel Hill, NC, October 28 - 30, 2013.
Communities at the proximity of roadways are exposed to high levels of air pollution from automobile exhaust and are under potential risk of adverse health effects. To understand the relationship between air pollution and adverse health effects, exposure and risk assessment studies are needed which require the characterization of the near-road air quality. Furthermore, to locate the hotspot of the high-risk region, a fine scale modeling output is preferred. The objective of this study is to a) Characterize near-road air quality at fine spatial resolution, and b) Explore feasibility of extending this to a national scale. RLINE, a new dispersion model used for modeling roadway emissions as line sources based upon a steady-state Gaussian formulation is used to model criteria air pollutants and mobile source air toxics (MSATs) at census block level. To prepare model-ready emission source data, we rely on county-level Motor Vehicle Emission Simulator 2010b (MOVES) outputs from the EPA. Since emissions factors from MOVES are sensitive to road / vehicle type and vehicle speed, traffic activity data as well as road-link data would be required to estimate actual emission rates. In this study, the traffic and road-link information is obtained from Federal Highway Administration’s Freight Analysis Framework 3 (FAF3) which includes both activity and roadway link definition for the entire nation. Additionally, we estimate broad regional background at these locations using a space-time ordinary kriging (STOK) approach that uses AQS measurements from the region. We first apply the modeling framework to model near-road air quality in Detroit, MI and compare outputs with results from a previous study where similar method was implemented albeit using very detailed traffic activity and road link data. We then apply this framework to the two other study areas North Carolina and Portland to characterize near-road air quality at fine spatial resolution.
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD 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 AMAD 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.
Record Details:Record Type: DOCUMENT (PRESENTATION/POSTER)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LAB
ATMOSPHERIC MODELING DIVISION
AIR-SURFACE PROCESSES MODELING BRANCH