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Constraining ozone-precursor responsiveness using ambient measurements
Citation:
Digar, A., D. Cohan, X. Xiao, K. Foley, B. Koo, AND G. Yarwood. Constraining ozone-precursor responsiveness using ambient measurements. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, 118(2):1005-1019, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′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:
This study develops probabilistic estimates of ozone (O3) sensitivities to precursoremissions by incorporating uncertainties in photochemical modeling and evaluating modelperformance based on ground-level observations of O3 and oxides of nitrogen (NOx).Uncertainties in model formulations and input parameters are jointly considered to identifyfactors that strongly influence O3 concentrations and sensitivities in the Dallas-FortWorth region in Texas. Weightings based on a Bayesian inference technique andscreenings based on model performance and statistical tests of significance are used togenerate probabilistic representation of O3 response to emissions and model inputparameters. Adjusted (observation-constrained) results favor simulations using the sixthversion of the carbon bond chemical mechanism (CB6) and scaled-up emissions of NOx,dampening the overall sensitivity of O3 to NOx and increasing the sensitivity of O3 tovolatile organic compounds in the study region. This approach of using observations toadjust and constrain model simulations can provide probabilistic representations ofpollutant responsiveness to emission controls that complement the results obtained fromdeterministic air-quality modeling.
