【摘要】 基于2018年中国逐日PM_2.5数据，选用随机森林方法构建了高精度PM_2.5浓度估算模型，并在季节和区域尺度上验证了其时空适用性，进一步利用特征重要性方法系统阐释了各影响因子对PM_2.5浓度变化的重要程度，最后利用偏依赖技术探究了不同影响因素的交互作用对PM_2.5浓度变化产生的综合影响。结果表明：（1）相比于多元线性回归与极端梯度提升树模型，利用多源数据构建的随机森林模型精度最高，可准确模拟出PM_2.5的浓度，且在季节和区域尺度上也有良好的适用性；（2） PM_2.5浓度估算模型的特征重要性排序分析发现，对2018年全国日均PM_2.5浓度影响显著的因子主要是时空、大气边界层高度等全局性因素，表明大气污染防治应把握PM_2.5传输机制，强化区域联防联控；（3）偏依赖交互效应研究发现温度和相对湿度以及年积日、纬度、温度和大气边界层高度的组合对PM_2.5浓度变化会产生显著影响，说明提升空气质量要从多因子协同治理的角度出发。更多还原

【Abstract】 Based on the daily PM_2.5 data of China in 2018, a high-precision PM_2.5 concentration estimation model was constructed using random forest method, and the temporal and spatial applicability of the model was verified at seasonal and regional scales. Further, the importance of each influencing factor to the change of PM_2.5 concentration was systematically explained using the feature importance method.Finally, the comprehensive influence of the interaction of different influencing factors on PM_2.5concentration change was explored using the partial dependence technique. The results show that:（1）Compared with the multiple linear regression model and the extreme gradient ascending tree model, the random forest model based on multi-source data has the highest accuracy, which not only can accurately simulate the PM_2.5 concentration, but also has good applicability at the seasonal and regional scales.（2）According to the ranking results of model feature importance, the factors that had significant impact on the average daily PM_2.5 concentration in 2018 were mainly global factors such as space-time and atmospheric boundary layer height, which indicated that the prevention and control of air pollution should follow the PM_2.5 transmission mechanism, and regional joint prevention and control should be strengthened in air pollution prevention.（3） The partial dependent interaction effect study shows that the combination of temperature, relative humidity, annual cumulative day, latitude, temperature and atmospheric boundary layer height has a significant impact on PM_2.5 concentration change, indicating that to improve air quality should start from the perspective of multi-factor collaborative governance.更多还原