Abstract
We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing meteorology and emissions loading over the contiguous United States. To this end, we spectrally decomposed the daily maximum 8-h (MDA8) ozone time-series for the period from 1990 to 2010 using the Kolmogorov-Zurbenk (KZ) filter to examine the variability in the relative strengths of the synoptic forcing (i.e., short-term variation induced by weather fluctuations) and the baseline forcing (i.e., long-term variation induced by emissions, policy, and trends) embedded in model output and observations. Using the information extracted from the synoptic and baseline forcings in ozone observations over the 21-year period, we present a new method for applying regional ozone air quality models in the regulatory setting. The new method provides the confidence limits for the 4th highest MDA8 ozone value and number of ozone exceedances for a given emission reduction scenario. This information is useful to policy-makers in deciding upon the emission control policy that can help meet and maintain the ozone National Ambient Air Quality Standard.
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References
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Acknowledgements
Although this work has been reviewed and approved for publication by the U.S. Environmental Protection Agency, it does not necessarily reflect the Agency’s views and policies. Two of the authors (MA and HL) gratefully acknowledge the support of this work by the Electric Power Research Institute (EPRI) Contract #00-10005071, 2015–2017.
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Trivikrama Rao, S., Luo, H., Astitha, M., Hogrefe, C., Mathur, R., Kumar, N. (2018). On Regional Modeling to Support Air Quality Policies. In: Mensink, C., Kallos, G. (eds) Air Pollution Modeling and its Application XXV. ITM 2016. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-57645-9_4
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DOI: https://doi.org/10.1007/978-3-319-57645-9_4
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