Abstract:

Through GIS spatial analysis, this study firstly conducts spatial distribution of PM_2.5 concentrations using PM_2.5 data from 35 automatic air quality monitoring stations in Beijing during the period of 2014–2016. Then population spatial processing is carried out based on DMSP/OLS nighttime light data. On this basis, the authors assess the exposure risk to PM_2.5 pollution in Beijing from four aspects: PM_2.5 concentration, the characteristic of population exposure, the population exposure intensity, and the population weighted concentration. The results show that 1) high PM_2.5 concentrations were mainly distributed in the south, while low concentrations were distributed in the north. There was a good spatial coincidence between the distribution of population exposure to PM_2.5 and population distribution, i.e. the densely populated area had high risk of population exposure to PM_2.5. 2) During 2014–2016, 100% of population exposed to high PM_2.5 yearly mean concentrations (>35 μg/m³) which exceeded the secondary level of Ambient Air Quality Standards (GB 3095–2012), and the ratio of population exposed to 24 hourly mean concentrations (>75 μg/m³) declined over a 3-year period. The share of population exposure to exceeding standard PM_2.5 concentration was much higher than those in global average level. 3) The population weighted PM_2.5 yearly average concentrations and PM_2.5 yearly average concentrations had difference, which is related with exposed population and the distribution of PM_2.5 pollution. 4) The distributions of PM_2.5 concentration and population are different, so the real impact level on health of human of PM_2.5 pollution is different from PM_2.5 concentration. Thus, taking the factor of population into account, the risk assessment of exposure to PM_2.5 pollution is more accurate.

Key words: PM_2.5, population exposure, population weighted, risk assessment

摘要：

基于2014—2016年的北京地区PM_2.5监测数据, 用空间插值法获得北京地区的PM_2.5空间分布, 并基于DMSP/OLS夜间灯光数据, 模拟得到北京地区的人口密度空间分布。在此基础上, 从PM_2.5浓度空间分布、PM_2.5污染的人口暴露特征、PM_2.5污染人口暴露强度以及人口加权PM_2.5浓度4个方面评估北京地区PM_2.5污染暴露风险。结果显示: 1) PM_2.5浓度呈现南高北低的空间分布特征, 人口暴露风险空间分布与人口密度空间分布呈现高度的一致性, 即人口密度高的区域, PM_2.5污染人口暴露风险也相对较高; 2) 2014, 2015, 2016年北京地区GB3095—2012二级年均浓度标准35 μg/m³的超标人口比例均为100%, 24小时平均浓度标准75 μg/m³的超标人口比例呈逐年显著下降趋势; 3) 2014—2016年北京市人口加权PM_2.5年均浓度值与PM_2.5年均值均存在差异, 差异度与城市暴露人口和污染情况密切相关; 4) 由于PM_2.5污染物浓度空间分布特征与人口密度空间分布特征不同, 北京市PM_2.5污染对总体人群的实际影响和健康危害与其平均浓度水平并不相同, 因此考虑人口密度空间分布特征的暴露风险评估比只考虑PM_2.5污染物浓度的暴露风险评估更准确。

关键词: PM_2.5, 人口暴露, 人口加权, 风险评估