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Dynamic Model Evaluation of NOx Emissions Reductions on Ozone Concentrations in the Presence of Uncertain Emission Inventories
Citation:
NAPELENOK, S., K. FOLEY, D. KANG, T. E. PIERCE, R. MATHUR, AND S. T. RAO. Dynamic Model Evaluation of NOx Emissions Reductions on Ozone Concentrations in the Presence of Uncertain Emission Inventories. Chapter 75, Douw G. Steyn & Silvia Trini Castelli (ed.), NATO/ITM Air Pollution Modeling and its Application, XXI. Springer Netherlands, , Netherlands, Series C:447-451, (2011).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis 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:
The Community Multiscale Air Quality (CMAQ) model was evaluated for its ability to reproduce observed changes in ambient concentrations of ozone (O3) for two seasons: the summer of 2002 and the summer of 2005 covering the eastern United States. These two summer periods were distinguished by large emissions reductions stemming from controls mandated by the NOx State Implementation Plan (SIP) after 2002. CMAQ was evaluated for the robustness of its response in ambient O3 levels to changes in NOx emissions. Furthermore. uncertainties in the NOx emissions inventory were propagated through the model using a direct sensitivity approach. Considering a 50% uncertainty in the area and mobile source NOx emissions, the model was able to replicate changes in O3 concentrations between 2002 and 2005 at most observation sites.