摘要:
文章基于2016~2017年武汉城市圈各城市站点PM2.5逐时监测数据,主要利用空间自相关、核密度法和空间计量模型,从不同的时空尺度来分析武汉城市圈PM2.5的空间分布格局和影响因素。结果表明:在年尺度上,2016~2017年武汉城市圈PM2.5浓度整体上呈下降趋势,空间分布上呈中东部高、西南部低、局地略有突出的分布特征并表现出明显的空间集聚性,城市圈内部各城市PM2.5污染浓度差异明显,且各城市之间存在一定的空间溢出效应;从年内尺度上看,武汉城市圈PM2.5浓度总体上呈"U"字型分布,冬春季污染最为严重,秋季、夏季次之,且4个季节的PM2.5浓度值存在较强的空间自相关性,表现出不同程度的空间集聚现象;从影响因素上看,无论是自然环境要素还是社会经济要素均对城市圈PM2.5浓度变化起重要作用,按其贡献强度依次是温度>民用汽车拥有量>风速>能源消费水平>城镇化率>第二产业占比>湿度>节能环保支出,而森林覆盖率和海拔高度对PM2.5没有表现出明显的直接效应;从大气污染物本身关系上看,PM10直接作用于PM2.5的浓度变化,且起关键性的作用,CO和NO2则主要是通过PM10对PM2.5浓度间接地产生影响,而O3对PM2.5浓度影响较小且呈负相关关系。
Abstract:
This paper analyzed the spatial distribution pattern and influencing factors of PM2.5 in Wuhan Metropolitan Area from different spatial and temporal scales by using spatial autocorrelation, nuclear density method and spatial econometric model which were based on the hourly monitoring data of PM2.5 in Wuhan Metropolitan Area from 2016 to 2017. The results showed that, on the year time-scale, the concentration of PM2.5 in Wuhan Metropolitan Area showed a downward trend from 2016 to 2017. The spatial distribution of PM2.5 in Wuhan Metropolitan Area exhibited high values in the middle and east, low values in the southwest, and slightly prominent values in local area with obvious spatial agglomeration. The concentration of PM2.5 was obviously different within the urban circle, and there were certain spatial spillover effects among the cities. On the yearly time-scale, the PM2.5 concentration in Wuhan Metropolitan Area showed a U-shaped distribution, with the most serious pollution in winter and spring, followed by autumn and summer, and the PM2.5 concentration in the four seasons had strong spatial autocorrelation, showing different degrees of spatial agglomeration. From the perspective of influencing factors, natural environmental factors or social economic factors play an important role in the change of PM2.5 concentration in the metropolitan area. According to their contribution intensity the order was:temperature > civilian car ownership > wind speed > energy consumption level > urbanization rate > secondary industry proportion > humidity > energy conservation and environmental protection expenditure, while forest coverage and altitude had no obvious direct effect on PM2.5. In terms of the relationship between air pollutants, PM10 acted directly on the concentration of PM2.5 and played a key role. CO and NO2 indirectly affected the concentration of PM 2.5 through PM10, while O3 had little effect on the concentration of PM2.5 and had a negative correlation.
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