| 中国PM2.5污染空间分布的社会经济影响因素分析 |
| 摘要点击 2992 全文点击 1141 投稿时间：2017-09-10 修订日期：2017-11-08 |
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| 中文关键词 PM2.5 社会经济 空间统计 空间自相关 空间回归 |
| 英文关键词 PM2.5 socio-economy spatial statistics spatial autocorrelation spatial regression |
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| 中文摘要 |
| 中国的细颗粒物（PM2.5）污染具有危害性强、覆盖范围大、空间分布不均匀的特点.本研究以2015年中国PM2.5监测站点数据为基础，尝试结合空间分析的方法，对PM2.5污染空间分布的社会经济影响因素进行分析.首先以省级行政区划为基本单元，选取Moran's I指数和局部自相关指数（LISA）分析PM2.5在国家尺度上的分布特征.然后利用普通最小二乘回归模型（OLS）和地理加权回归模型（GWR）分析PM2.5浓度的空间分布和各项社会经济指标的相关性.结果表明，GWR模型比OLS模型更好地揭示出PM2.5浓度分布和各项因素之间的关系.PM2.5浓度在空间分布上存在以京津冀为中心的高浓度聚集区向四周逐渐递减，在广西、四川等南部省份形成低浓度聚集区的空间分布结构.另外，森林覆盖率和人均电力消费量与PM2.5浓度显著负相关，人均私家车保有量和PM2.5浓度显著正相关，其中人均私家车保有量是对PM2.5浓度影响最大的因素. |
| 英文摘要 |
| In recent years, the PM2.5 pollution in China has become a top environmental and health concern, involving the characterization of healthy effects over a broad spatial area with uneven geographical distribution. This research aims to explore the influential factors for the PM2.5 distribution from a socio-economic perspective, based on the observations from China's 1497 monitoring sites in 2015. First, the Moran's I index and the local indicators of spatial association (LISA) were computed to outline the distribution of PM2.5 on a national scale using provincial-level divisions. Second, the correlation between the spatial distribution of PM2.5 and socio-economic factors were analyzed by ordinary least squares (OLS) and geo-weighted regression (GWR) models. The results indicated that the GWR model explained the causal relationships better. Generally, Beijing, Tianjin, and Hebei had peak levels of PM2.5, while Guangxi, Sichuan, and several other southern provinces had the lowest levels. Particularly, forest coverage rate and electricity consumption per capita were negatively correlated with the concentration of PM2.5. In this study, the vehicle ownership per capita proved to be the most significant factor that positively contributed to the concentration. |
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