Abstract
In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution (GPD) in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants: PM10, NO2 and SO2, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators: E MA (mean absolute error), E RMS (root mean square error), I A (index of agreement) and R 2 (coefficient of determination) were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NO2 belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.
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Foundation item: Project(51178466) supported by the National Natural Science Foundation of China; Project(200545) supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China; Project(2011JQ006) supported by the Fundamental Research Funds of the Central Universities of China; Project(2008BAJ12B03) supported by the National Key Program of Scientific and Technical Supporting Programs of China
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Zhou, Sm., Deng, Qh. & Liu, Ww. Extreme air pollution events: Modeling and prediction. J. Cent. South Univ. Technol. 19, 1668–1672 (2012). https://doi.org/10.1007/s11771-012-1191-2
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DOI: https://doi.org/10.1007/s11771-012-1191-2