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Source-receptor reconciliation of fine-particulate emissions from residential wood combustion in the southeastern United States
Citation:
Napelenok, S., R. Vedantham, P. Bhave, G. Pouliot, AND R. Kwok. Source-receptor reconciliation of fine-particulate emissions from residential wood combustion in the southeastern United States. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 98:454-460, (2014).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’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. 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:
An extensive collection of speciated PM2.5 measurements including organic tracers permitted a detailed examination of the emissions from residential wood combustion (RWC) in the southeastern United States over an entire year (2007). The Community Multiscale Air Quality model-based Integrated Source Apportionment Method (CMAQ-ISAM) was used in combination with the U.S. National Emissions Inventory (NEI) to compute source contributions from ten categories of biomass combustion, including RWC. A novel application of the receptor-based statistical model, Unmix, was used to subdivide the observed concentrations of levoglucosan, a unique tracer of biomass combustion. Using the CMAQ-ISAM and Unmix models together, we find that the emission-based RWC contribution to ambient carbonaceous PM2.5 predicted by the model is approximately a factor of two lower than indicated by observations. Recommendations for improving the temporal allocation of the emissions are proposed and tested to show a potential improvement in model RWC predictions, quantified by approximately 15% less bias. Further improvements in the sector predictions could be achieved with a survey-based analysis of detailed RWC emission patterns.
