| 基于随机森林的京津冀地区PM2.5遥感反演及变化分析 |
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| 引用本文： | 康新礼,张文豪,刘原萍,顾行发,余涛,张丽丽,徐桦昆.基于随机森林的京津冀地区PM2.5遥感反演及变化分析[J].遥感技术与应用,2022,37(2):424-435. |
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| 作者姓名： | 康新礼 张文豪 刘原萍 顾行发 余涛 张丽丽 徐桦昆 |
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| 作者单位： | 1.北华航天工业学院 遥感信息工程学院，河北 廊坊 065000;2.河北省航天遥感信息处理与应用协同创新中心，河北 廊坊 065000;3.中国科学院空天信息创新研究院 遥感卫星应用国家工程实验室，北京 100094 |
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| 基金项目： | 国家自然科学基金项目(41801255);河北省自然科学基金项目(D2020409003);河北省高等学校科学技术研究项目(ZD2021303);北华航天工业学院博士科研启动基金(BKY?2021?31);高分辨率对地观测系统重大专项(30?Y30F06?9003?20/22);民用航天预研项目(D040102);国防基础科研项目(JCKY2020908B001);国防基础科研计划(JCKY2019407D004);北华航天工业学院博士科研基金项目(BKY201703) |
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| 摘 要： | 大气细颗粒物PM2.5是影响人类生存环境和身体健康的主要大气环境污染物，研究PM2.5质量浓度季节变化的规律及空间分布特征，对于大气污染物的预防和治理有着重要的意义。利用2018~2020年MODIS卫星L2级AOD产品、MERRA-2气象数据以及地面站点PM2.5实测数据，基于改进的随机森林算法，构建AOD-PM2.5反演模型，对京津冀地区PM2.5质量浓度进行估算，并分析PM2.5质量浓度空间分布特征以及季节变化规律。结果表明：①春夏秋冬4组模型决定系数（R2）均值分别为0.78、0.66、0.83、0.83，模拟精度较高。②2018~2020年京津冀地区春夏秋冬四季PM2.5浓度呈显著的空间分布特征及季节变化规律。其中PM2.5污染最大值出现在冬季，最小值出现在夏季。③历年同季节相比，京津冀地区PM2.5污染范围和浓度数值均有所减小，2020春季和秋季PM2.5污染范围与2018年、2019年相比改善较明显。
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| 关 键 词： | PM2.5 随机森林 MODIS MERRA-2 京津冀 |
| 收稿时间： | 2021-06-07 |
PM2.5 Remote Sensing Retrieval and Change Analysis in Beijing-Tianjin-Hebei Region based on Random Forest Model |
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| Xinli Kang,Wenghao Zhang,Yuanping Liu,Xingfa Gu,Tao Yu,Lili Zhang,Huakun Xu.PM2.5 Remote Sensing Retrieval and Change Analysis in Beijing-Tianjin-Hebei Region based on Random Forest Model[J].Remote Sensing Technology and Application,2022,37(2):424-435. |
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| Authors: | Xinli Kang Wenghao Zhang Yuanping Liu Xingfa Gu Tao Yu Lili Zhang Huakun Xu |
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| Abstract: | Atmospheric fine particulate matter PM2.5 is the main atmospheric environmental pollutant that affects human living environment and health. It is of great significance to study the seasonal variation and spatial distribution characteristics of PM2.5 mass concentration for the prevention and treatment of air pollutants. In this study， the MODIS L2 AOD products， MERRA-2 meteorological data and the PM2.5 measured data from ground stations from 2018 to 2020 were used to build the AOD-PM2.5 inversion model based on the improved random forest algorithm. The PM2.5 in Beijing-Tianjin-Hebei region was estimated， and the spatial distribution characteristics and seasonal variation of PM2.5 mass concentration were analyzed. The results showed that： （1） The mean values of determination coefficients （R2） of spring， summer， autumn and winter model were 0.78， 0.66， 0.83 and 0.83， respectively. And the accuracy of simulation is higher.（2） The PM2.5 concentrations of spring， summer， autumn and winter in Beijing-Tianjin-Hebei region from 2018 to 2020 showed significant spatial distribution characteristics and seasonal variation. The maximum of PM2.5 concentrations occurred in winter and the minimum value appeared in summer. （3） Compared with the same season over the years， the PM2.5 pollution range and PM2.5 concentration in the Beijing-Tianjin-Hebei region have improved. Compared with 2018 and 2019， the PM2.5 pollution range in spring and autumn of 2020 improved significantly. |
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| Keywords: | PM2 5 Random Forest MODIS MERRA-2 Beijing-Tianjin-Hebei |
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