Department of Resources & Environmental Information, College of Resources & Environment, Huazhong Agricultural University , Wuhan, Hubei 430070, China.
Key Laboratory of Arable Land Conservation (Middle & Lower Reaches of Yangtse River), Ministry of Agriculture , Wuhan, Hubei 430070, China.
Environ Sci Technol. 2015 Nov 17;49(22):13431-8. doi: 10.1021/acs.est.5b03614. Epub 2015 Nov 6.
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-time variability of PM2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space-time PM2.5 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 PM2.5 exceeded 75, 115, 150, and 250 μg m(-3) varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM2.5 exceeded 150 μg m(-3) during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM2.5 pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.
中国经历了与经济快速增长密切相关的严重颗粒物（PM）污染问题。深入了解和描述时空空气污染分布是一个非常有益的领域，可以提高风险评估和环境政策的水平。本研究采用贝叶斯最大熵（BME）方法评估中国山东省 PM2.5 浓度的时空变化，并预测其分布。使用 2014 年空气质量监测站获得的每日 PM2.5 浓度。基于 BME 生成的时空 PM2.5 分布，我们进行了三种查询分析，以揭示主要分布特征。结果表明，整个研究区域污染严重（BME 地图确定了 2014 年的重度污染集群）。污染严重程度的定量描述包括污染水平和持续时间。区域 PM2.5 超过 75、115、150 和 250μg m(-3)的天数分别为 43-253、13-128、4-66 和 0-15 天。PM2.5 模式表现出从东向西逐渐增加的趋势，山东西部是一个污染严重的地区（PM2.5 在很长一段时间内超过 150μg m(-3)）。冬季的污染比其他季节严重得多。PM2.5 污染强度和时空变化的站点指标用于评估区域不确定性和风险，并根据污染物阈值进行解释。观察到的超过特定阈值的 PM2.5 浓度几乎随阈值的增加呈线性增加，而超过污染的相对概率随阈值的增加急剧下降。
