Application of Statistical Distribution of PM_(10) Concentration in Air Quality Management in 5 Representative Cities of China  

作　　者：WANG Xi CHEN Ren Jie CHEN Bing Heng KAN Hai Dong 

机构地区：[1]School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University [2]Institute of Global Environment Change, Fudan University [3]Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University [4]Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Bureau

出　　处：《Biomedical and Environmental Sciences》2013年第8期638-646,共9页生物医学与环境科学（英文版）

基　　金：supported by the National Basic Research Program (973 program) of China (2011CB503802);Gong-Yi Program of China Ministry of Environmental Protection (201209008);the Program for New Century Excellent Talents in University (NCET-09-0314)

摘　　要：Objective To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard （AQS） and the reduction of particulate matter emission to meet the AQS from the statistical properties （probability density functions） of air pollutant concentration. Methods The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi＇an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the Iognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method. Results The daily PM10 average concentration in the 5 cities was fitted using the Iognormal distribution. The exceeding duration was predicted, and the estimated PMlo emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AO, S. Conclusion Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.Objective To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard （AQS） and the reduction of particulate matter emission to meet the AQS from the statistical properties （probability density functions） of air pollutant concentration. Methods The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi＇an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the Iognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method. Results The daily PM10 average concentration in the 5 cities was fitted using the Iognormal distribution. The exceeding duration was predicted, and the estimated PMlo emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AO, S. Conclusion Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.

关 键 词：Statistical distribution PM10 concentration LOGNORMAL 

分 类 号：X831[环境科学与工程—环境工程]