4.7 Article

Spatiotemporal distribution and short-term trends of particulate matter concentration over China, 2006-2010

期刊

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
卷 21, 期 16, 页码 9665-9675

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-014-2996-3

关键词

Artificial neural network (ANN); Fine particles (PM2.5); Inhalable particles (PM10); Satellite remote sensing; Spatiotemporal distribution

资金

  1. National Natural Science Foundation of China [41301380, 41371016]
  2. Research on PM2.5 Remote Sensing monitoring key technology and operational method in central-eastern China [201309011]
  3. China Postdoctoral Science Foundation [2013M542086]

向作者/读者索取更多资源

Air quality problems caused by atmospheric particulate have drawn broad public concern in the global scope. In the paper, the spatiotemporal distributions of fine particle (PM2.5) and inhalable particle (PM10) concentrations estimated with the artificial neural network (ANN) over China during 2006 to 2010 have been discussed. Most high PM10 concentration appears in Xinjiang, Qinghai, Gansu, Ningxia, Hubei, and parts of Inner Mongolia. The distribution of PM2.5 concentration is consistent with China's three gradient terrains. The seasonal variations of PM2.5 and PM10 concentrations both indicate that they are higher in north China in spring and winter, lowest in summer. In autumn, most provinces in south China appear high value. In particular, high PM2.5 concentration appears in the southeast coastal cities while high PM10 concentration prefers the central regions in south China. On this basis, seasonal Mann-Kendall test method is utilized to analyze the short-term trends. The results also show significant changes of PM2.5 and PM10 concentrations of China in the past 5 years, and most provinces present the tendency of reduction (3-5 mu g/m(3) for PM2.5 and 10-20 mu g/m(3) for PM10 per year) while a fraction of provinces appear the increasing trend of 8-16 mu g/m(3) (PM2.5) and 16-30 mu g/m(3) (PM10). Simultaneously, PM2.5 population exposure is discussed with the combination of population density-gridded data. Municipalities get much higher exposure level than other provinces. Shanghai suffers the highest population exposure to PM2.5, followed by Beijing and then Tianjin, Jiangsu province. Most provincial capitals, such as Guangzhou, Nanjing, Chengdu, and Wuhan, face much higher exposure level than other regions of their province. Moreover, the PM2.5 exposure situation is more serious in southeast than northwest regions for Beijing-Tianjin-Hebei region. Also, per capita PM2.5 concentration and population-weighted PM2.5 concentration are calculated. The former shows that the high-level regions distribute in Guangdong, Shanghai, and Tianjin, while the latter in Hebei, Chongqing, and Shandong provinces. Further studies may consider optimizing concentration estimation model and use it to discuss the effects of particulate matters on human health.

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