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Observation and Modeling of the Evolution of Texas Power Plant Plumes
Citation:
Zhou, W., D. COHAN, R. W. PINDER, J. Neuman, J. S. Holloway, J. Peischl, T. B. Ryerson, J. B. Nowak, F. Flocke, AND W. G. Zheng. Observation and Modeling of the Evolution of Texas Power Plant Plumes. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, Germany, 12(1):455-468, (2012).
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:
During the second Texas Air Quality Study 2006 (TexAQS II), a full range of pollutants was measured by aircraft in eastern Texas during successive transects of power plant plumes (PPPs). A regional photochemical model is applied to simulate the physical and chemical evolution of the plumes. The observations reveal that SO2 and NOy were rapidly removed from PPPs on a cloudy day but not on the cloud-free days, indicating efficient aqueous processing of these compounds in clouds. The model reasonably represents observed NOx oxidation and PAN formation in the plumes, but fails to capture the rapid loss of SO2 (0.37 h−1) and NOy (0.24 h−1) in some plumes on the cloudy day. Adjustments to the cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO2 loss. However, NOy in the model was insensitive to QC. These findings highlight cloud processing as a major challenge to atmospheric models. Model-based estimates of ozone production efficiency (OPE) in PPPs are 20–50% lower than observation-based estimates for the cloudy day
URLs/Downloads:
Observation and Modeling of the Evolution of Texas Power Plant Plumes (PDF, NA pp, 915 KB, about PDF)Atmospheric Chemistry & Physics
