汾渭平原PM2.5浓度的影响因素及空间溢出效应

基于实时监测和遥感反演数据，利用空间自相关分析和空间回归分析等方法，探讨了汾渭平原2015~2017年PM_2.5浓度时空变化规律和影响因素，揭示了各因素的空间溢出效应.结果表明：（1）2015~2017年汾渭平原PM_2.5浓度逐年上升，主要由采暖期（11月~次年3月）的快速上升引起，非采暖期（4~10月）年际变化不大.（2）PM_2.5月均浓度变化曲线呈底部宽缓的U型，采暖期PM_2.5污染明显高于非采暖期，超标天数占全年总超标天数比例由2015年的75.0%上升到2017年的83.4%.（3）2015~2017年，除铜川和三门峡外，各城市PM_2.5浓度都有不同程度的上升.咸阳至运城间的平原地区和洛阳盆地污染最严重，已形成连片的高污染区域，且区域内城乡差异小.临汾及其上游平原地区其次，但主要分布在城镇，城乡差异较大.（4）空间回归分析表明，汾渭平原PM_2.5浓度有显著的空间溢出效应.年均气温、城镇化率、能源消费指数和年均人口不仅与本地PM_2.5浓度有显著的正相关，而且会加重邻近地区PM_2.5污染.年降水量和地形起伏度则不仅与本地PM_2.5浓度有显著的负相关，而且能降低邻近地区PM_2.5浓度.风的传输作用能加重本地PM_2.5污染，植被覆盖度能消减本地PM_2.5浓度，但其间接效应都不显著.

Influence factors and spillover effect of PM2.5concentration on Fen-wei Plain

Based on data collected by real-time monitoring and remote sensing retrieval from 2015 to 2017, the paper probed into the spatial and temporal change of PM_2.5 concentration and its influence factors on Fen-wei Plain via spatial autocorrelation analysis and spatial regression analysis. The results showed that:1) The growing trend of the concentration during these three years was a result of a rapid increase during the heating period (from November to next March), while there was no significant inter-annual variation during the non-heating period (from April to October). 2) The average monthly change of PM_2.5 concentration was in a U shape, with a much higher concentration during the heating period. And days with PM_2.5 non-attainment during the heating period to the total yearly PM_2.5 polluted days increased from the 75.0% in 2015 to the 83.4% in 2017. 3)Cities on the Plain were all in an increasing trend except Tongchuan and Sanmenxia, among which plains from Xianyang to Yuncheng and Luoyang Basin were experiencing the worst PM_2.5 pollution with a subtle rural-urban difference, and, thus, formed a highly polluted area. Then it followed by Linfen and plains along side the upper reach of Fen River, which were also in a bad condition but with an evident urban-rural difference. 4) Based on spatial regression analysis, there was a significant spatial spillover effect for the PM_2.5concentration on the Plain. Driving factors including annual average temperature, urbanization rate, and energy consumption positively effected the PM_2.5 concentration, and additionally, they drove the PM_2.5 pollution of neighboring areas into a worse situation. On the contrary, annual precipitation and relief amplitude were not only negatively correlated with the concentration of PM_2.5, they also helped for a lower PM_2.5 concentration in neighboring areas. Moreover, the transmission effect by wind facilitated the PM_2.5 pollution, while vegetation coverage discourage PM_2.5 concentration, but neither of their indirect effect was significant.