Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China)

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• 作者：Yong Yang ; George Christakos
• 刊名：Environmental Science & Technology
• 出版年：2015
• 出版时间：November 17, 2015
• 年：2015
• 卷：49
• 期：22
• 页码：13431-13438
• 全文大小：413K
• ISSN：1520-5851

文摘

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space鈥搕ime variability of PM_2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM_2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space鈥搕ime PM_2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM_2.5 exceeded 75, 115, 150, and 250 渭g m^鈥? varied: 43鈥?53, 13鈥?28, 4鈥?6, and 0鈥?5 days, respectively. The PM_2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM_2.5 exceeded 150 渭g m^鈥? during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM_2.5 pollution intensity and space鈥搕ime variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM_2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.