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A Modeling Framework for Improved Characterization of Near-Road Exposure at Fine Scales
Citation:
Isakov, V., M. Breen, AND T. Palma. A Modeling Framework for Improved Characterization of Near-Road Exposure at Fine Scales. International Society of Exposure Science 2014 annual meeting, Cincinnati, OH, September 12 - 15, 2014.
Impact/Purpose:
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.
Description:
Traffic-related air pollutants could cause adverse health impact to communities near roadways. To estimate the population risk and locate "hotspots" in the near-road environment, quantifying the exposure at a fine spatial resolution is essential. A new state-of-the-art research line source dispersion model (R-LINE) provides an opportunity to improve the characterization of near-road exposure at fine scales. We modeled concentrations from on-road sources using R-LINE at census block level by using traffic activity data from the FHWA’s freight analysis framework (FAF3) in conjunction with pollutant and vehicle-specific Emissions Factors from Mobile Vehicle Emission Simulator (MOVES). An approach called the Annual Stability and Wind Clustering method (ASWIC) was used to select representative meteorological conditions for which we simulated hourly concentrations with R-LINE, which were then scaled based on weights to yield annual average concentrations. We estimated background concentrations using Spatio-Temporal Ordinary Kriging (STOK) technique that uses observations from the AQS network. The total ambient concentration was then calculated by summing up the background and on-road concentrations. We applied this framework over three regions of the U.S. - Portland, Maine, North Carolina's Piedmont region, and evaluated against data from a field study in Detroit near the I-96 highway. The difference between the modeled and observed CO concentration is within a factor of two at four monitoring sites near I-96 in Detroit, MI. The modeled concentration is approximately 25% lower than the observed data within 100 meter from the road. The concentration drops by 40 to 60% after 100 meters from the road in Portland, Maine and 20 to 40% in the North Carolina Piedmont. Background concentration dominates PM2.5 and benzene in most of the areas in the North Carolina Piedmont, but on-road source contributes more than 57% of the total within 50 meters from the interstate roadways.
URLs/Downloads:
https://www.intlexposurescience.org/Public/Annual_Meeting/PastMeetings/2014_Meeting.aspx