Predictions of UK regulated power station contributions to regional air pollution and deposition : A model comparison exercise
Chemel, C.; Sokhi, Ranjeet S.; Dore, Anthony J.; Sutton, Paul; Vincent, K.J.; Griffiths, Stephen J.; Hayman, Garry D.; Wright, Raymond D.; Baggaley, Matthew; Hallsworth, Stephen; Prain, H. Douglas; Fisher, Bernard E. A.
Citation: Chemel , C , Sokhi , R S , Dore , A J , Sutton , P , Vincent , K J , Griffiths , S J , Hayman , G D , Wright , R D , Baggaley , M , Hallsworth , S , Prain , H D & Fisher , B E A 2011 , ' Predictions of UK regulated power station contributions to regional air pollution and deposition : A model comparison exercise ' Journal of the Air and Waste Management Association , vol 61 , no. 11 , pp. 1236-1245 . DOI: 10.1080/10473289.2011.609756
Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study
Original article can be found at: http://www.tandf.co.uk/ Copyright Taylor & Francis
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