Journal
ATMOSPHERIC POLLUTION RESEARCH
Volume 7, Issue 2, Pages 363-369Publisher
TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
DOI: 10.1016/j.apr.2015.10.010
Keywords
APEC; Emission control; Meteorological condition; Circulation types; Artificial neural network
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Several statistical methods are performed in this study to evaluate the effect of emission control measures on particulate matter (PM) concentrations during the 2014 Asia-Pacific Economic Cooperation (APEC) Summit held in Beijing. The concentration of PM2.5 is reduced by 30%, 38% on a year-on-year and month-on-month basis during the APEC emission control period while PM10 reducing by 41% and 26% respectively. Considering from the impacts of the weather circulation types, PM2.5 and PM10 are both reduced by 34%. ANN model is considered to be appropriate with the ability of characterising non-linear phenomena. In this work an ANN model is built to predict the daily averaged PM concentrations. Comparing the observed PM concentrations and the predicted value which are forecasted by ANN model on condition that no emission control measures were carried out during the APEC, the reductions for PM2.5 and PM10 are 24%, 28% respectively. All these results suggest that strict emission control measures push down the pollution level effectively, and more observed data with long time could improve the accuracy of evaluation results. More stringent controls of regional emissions should be lasted for the continuous improvements of air quality in Beijing-Tianjin-Hebei region. Copyright (C) 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
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