大气与环境光学学报 ›› 2023, Vol. 18 ›› Issue (3): 245-257.

• 光学遥感 • 上一篇    下一篇

中国区域PM2.5浓度估算以及影响因素解析

曹媛 1,2, 宫明艳 3, 沈非 1,2, 麻金继 1,2*, 杨光 1,2, 林锡文 1,2   

  1. 1 安徽师范大学地理与旅游学院, 安徽 芜湖 241002; 2 资源环境与地理信息工程安徽省工程技术研究中心, 安徽 芜湖 241002; 3 安徽师范大学物理与电子信息学院, 安徽 芜湖 241002
  • 收稿日期:2021-11-15 修回日期:2022-01-09 出版日期:2023-05-28 发布日期:2023-05-28
  • 通讯作者: E-mail: jinjima@ahnu.edu.cn E-mail:jinjima@ahnu.edu.cn
  • 作者简介:曹 媛 (1997- ), 女, 安徽马鞍山人, 硕士研究生, 主要从事大气环境污染方面的研究。E-mail: yuanCao@ahnu.edu.cn
  • 基金资助:
    国家自然科学基金项目 (42271372)

Estimation of PM2.5 concentration and analysis of influencing factors in China

CAO Yuan 1,2, GONG Mingyan 3, SHEN Fei 1,2, MA Jinji 1,2*, YANG Guang 1,2, LIN Xiwen 1,2   

  1. 1 School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; 2 Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241002, China; 3 School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China
  • Received:2021-11-15 Revised:2022-01-09 Published:2023-05-28 Online:2023-05-28
  • Contact: Jinji MA E-mail:jinjima@ahnu.edu.cn

摘要: 基于2018 年中国逐日PM2.5数据, 选用随机森林方法构建了高精度PM2.5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PM2.5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PM2.5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PM2.5的浓度, 且在季节和区域 尺度上也有良好的适用性; (2) PM2.5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PM2.5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PM2.5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PM2.5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。

关键词: 大气遥感, 随机森林, PM2.5, 时空关联

Abstract: Based on the daily PM2.5 data of China in 2018, a high-precision PM2.5 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 PM2.5 concentration was systematically explained using the feature importance method. Finally, the comprehensive influence of the interaction of different influencing factors on PM2.5 concentration 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentration change, indicating that to improve air quality should start from the perspective of multi-factor collaborative governance.

Key words: atmospheric remote sensing, random forest, PM2.5, spatio-temporal correlation

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