| 基于随机森林模型的中国PM2.5浓度影响因素分析 |
| 摘要点击 2655 全文点击 1005 投稿时间：2019-10-18 修订日期：2019-12-04 |
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| 中文关键词 PM2.5 影响因素 区域差异 随机森林 中国 |
| 英文关键词 PM2.5 influencing factor regional variation random forest China |
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| 中文摘要 |
| 选取气溶胶光学厚度、海拔、年降水量、年均气温、年均风速、人口密度、GDP密度和NDVI作为影响因子，基于随机森林模型、特征重要性排序和偏依赖图技术，研究中国PM2.5浓度空间分布的影响因素及其区域差异.结果表明：①与多元回归、广义可加模型和BP神经网络相比，随机森林模型估算的PM2.5浓度精度最高，可用于PM2.5污染的影响因素研究.② PM2.5浓度随气溶胶光学厚度、人口密度和GDP密度的增加呈先上升后平稳的趋势，随降水、风速和NDVI的增加呈先下降后平稳的趋势，随海拔和气温的增加呈下降→上升→下降的趋势.③气溶胶光学厚度对PM2.5浓度空间分布的影响最大，可解释37.96%的PM2.5浓度空间分异；年降水量对PM2.5浓度空间分布的影响最小，解释率仅为5.75%.④影响因子与PM2.5浓度的关系存在空间异质性，同一影响因子对不同地理分区的PM2.5浓度的影响程度有所不同.气溶胶光学厚度对华南地区PM2.5浓度的空间分布影响最大，对东北地区影响最小. |
| 英文摘要 |
| In this paper, aerosol optical depth (AOD), elevation (DEM), annual precipitation (PRE), annual average temperature (TEM), annual average wind speed (WS), population density (POP), gross domestic product density (GDP), and normalized difference vegetation index (NDVI) were selected as factors influencing PM2.5 concentration. The random forest model, order of feature importance, and partial dependency plots were applied to investigate these factors and their regional differences in PM2.5 spatial pattern. The results showed that:① The random forest model was more accurate than multiple regression, generalized additive, and back propagation neural network models in estimating PM2.5 concentration, which can be applied to quantifying PM2.5 influencing factors. ② PM2.5 concentration initially increased and then remained stable with increases in AOD, POP, and GDP, and initially decreased and then stabilized with increases in PRE, WS, and NDVI. The responses of DEM and TEM to PM2.5 concentration changed from decline to ascend and then changed to decline again. ③ AOD had the largest influence on PM2.5 annual concentrations with a spatial influencing magnitude of 37.96%, whereas PRE had the least influence with a merely individual spatial influencing magnitude of 5.75%. ④ The relationships between PM2.5 pollution and influencing variables vary with geography and thus exhibit significant spatial heterogeneity. The same factor had different spatial influencing magnitudes on PM2.5 annual concentrations in seven geographical subareas. AOD had the greatest influence on PM2.5 concentration in the south of China, with the least influence in the northeast. |
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