基于GF-5B卫星的随机森林模型反演京津冀地区PM_2.5

来源：中国环境科学杂志2024年第11期北京时间：

作者：张瑞杰, 周春艳, 陈辉, 周伟, 谢慧真, 陈睿智, 王中挺

单位：1. 中国环境科学研究院, 北京 100012;2. 生态环境部卫星环境应用中心, 北京 100094

摘要：This study utilizes aerosol optical depth (AOD) data derived from the domestic GF-5B DPC payload, ERA- 5meteorological data, NDVI data, DEM data, nighttime light data, and ground-based PM2.5 measurements. Based on a random forest model, an AOD-PM2.5 inversion model is developed to estimate the PM2.5 concentration in the Beijing-Tianjin-Hebei region for the year 2022. The results showed that the model's coefficient of determination (R2) for the entire year was 0.80, with a root mean square error (RMSE) of 11.43 μg/m3. For the seasonal models, the R2 values were 0.84, 0.71, 0.88, and 0.87 for spring, summer, autumn, and winter, respectively, with corresponding RMSE values of 9.50, 7.37, 9.71, and 11.32μg/m3. Both the annual and seasonal models demonstrated good simulation accuracy. The PM2.5 concentrations in the Beijing-Tianjin-Hebei region in 2022 exhibited significant seasonal variation, with the highest concentrations in winter and the lowest in summer. The estimated annual average PM2.5 concentration was 30.86μg/m3. During the summer and autumn, AOD was the primary factor influencing PM2.5 concentrations, while in spring and winter, the boundary layer height and relative humidity were the main influencing factors.

关键词：高分五号B星,随机森林模型,京津冀,气溶胶光学厚度(AOD),PM2.5

基金资助：国家重点研发计划(2022YFE0209100);国家自然科学基金资助项目(41971324)