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Atmosphere 2011, 2(3), 407-425; doi:10.3390/atmos2030407

Air Quality Response Modeling for Decision Support

1,*  and 2
1 Department of Civil and Environmental Engineering, Rice University, 6100 Main Street MS 519, Houston, TX 77005, USA 2 U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
* Author to whom correspondence should be addressed.
Received: 20 June 2011 / Revised: 10 August 2011 / Accepted: 17 August 2011 / Published: 26 August 2011
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
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Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results.
Keywords: sensitivity analysis; source apportionment; instrumented models; air quality modeling; review sensitivity analysis; source apportionment; instrumented models; air quality modeling; review
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cohan, D.S.; Napelenok, S.L. Air Quality Response Modeling for Decision Support. Atmosphere 2011, 2, 407-425.

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