京津冀PM2.5时空分布特征及其污染风险因素

为分析京津冀及其周边区域2013年典型污染事件中PM_2.5的时空分布特征及污染风险因素,根据国家城市环境空气质量实时发布数据和京津冀地区地理国情信息监测成果,采用空间数据挖掘方法对PM_2.5污染的热点区域进行了划分；并采用地理探测器定量分析了PM_2.5污染风险因子及其影响程度. 结果表明:在选取的京津冀6个城市中,在PM_2.5污染事件统计上存在保定—廊坊—北京—天津—承德—张家口的污染顺序. PM_2.5污染在空间上呈河南省(山东省)—河北省—北京市(天津市)一线的带状分布特征,在单次污染事件中,城市间的PM_2.5污染存在空间运移关系. 空间热点探测表明,京津冀及其周边区域主要分为5个热点聚集区,其中3个高值区分布在北京市、天津市、河北省和山东省的中部,面积分别为5.31×104、10.26×104、5.04×104 km2. 在8个污染风险因子中,污染企业总数(影响力为0.97,下同)、降水量(0.93)、地形坡度(0.89)对PM_2.5污染的影响显著高于其他风险因子；其他风险因子影响力排序依次为人口数量(0.60)、降水量大于0.1 mm的降水日数(0.57)、地表覆盖类型(0.52)、年均相对湿度(0.51)、年均风速(0.33),但风险因子间相比没有显著性差异. 研究显示,京津冀地区PM_2.5污染的主要因素是污染物排放,其次,气象要素中的年降水量和自然地理环境中的地形坡度也是影响PM_2.5污染特征的重要风险因子. 

Investigation of Temporal-Spatial Characteristics and Underlying Risk Factors of PM2.5 Pollution in Beijing-Tianjin-Hebei Area

Abstract: In order to investigate the temporal-spatial characteristics of typical PM_2.5 pollution events in 2013 and the risk factors of PM_2.5 pollution in Beijing-Tianjin-Hebei and surrounding areas, real-time, published data on the national urban environmental air quality and geographic national condition monitoring results were analyzed. The spatial data mining method was used to divide the hot spot areas of PM_2.5 pollution in Beijing-Tianjin-Hebei and surrounding areas. Using the geographic detector model, the risk factors of PM_2.5 pollution and the associated influence degree were quantitatively analyzed. The results showed that the pollution in selected cities in the Beijing-Tianjin-Hebei area followed the order Langfang-Beijing-Baoding-Tianjin-Chengde-Zhangjiakou. The PM_2.5 pollution showed zonal distribution characteristics, and there was a spatial migration pattern among the cities in the Beijing-Tianjin-Hebei region during a single pollution event. The spatial hot spot detection indicated that Beijing-Tianjin-Hebei and its surrounding areas were divided into five hot spot areas, with the top three of them being distributed in Beijing, Tianjin, and Hebei-central Shandong regions, with areas of 53,0 square kilometers, 102,0 square kilometers and 50,0 square kilometers, respectively. Among the eight PM_2.5 pollution risk factors, the number of industrial companies (influence index 0.94), precipitation (0.93) and topographic slope (0.89) had a significantly higher influence on PM_2.5 pollution than other risk factors. The influence power index of the other risk factors were as follows:population (0.60), number of precipitation days (0.57), land cover (0.52), relative air humidity (0.51) and wind speed (0.33). The influence of population on PM_2.5 pollution was slightly greater than that of number of precipitation days, land cover, relative air humidity and wind speed, but with no significant differences among them. The results showed that the main factor in PM_2.5 pollution in the Beijing-Tianjin-Hebei region is pollutant emission. Secondly, the annual precipitation of meteorological elements and the terrain slope of the natural geography environment are the important risk factors that affect the PM_2.5 pollution characteristics.