| 基于遥感数据估算近地面PM2.5浓度的研究进展 |
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| 引用本文： | 杨晓辉,肖登攀,王卫,柏会子,唐建昭,范丽行.基于遥感数据估算近地面PM2.5浓度的研究进展[J].环境科学研究,2022,35(1):40-50. |
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| 作者姓名： | 杨晓辉 肖登攀 王卫 柏会子 唐建昭 范丽行 |
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| 作者单位： | 1.河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 河北 石家庄 050011 |
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| 基金项目： | 国家自然科学基金项目(No.41471091)； |
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| 摘 要： | 卫星反演的气溶胶光学厚度(AOD)具有广泛的空间覆盖度和相对较高的时空分辨率. 基于AOD与PM2.5的相关关系来估算PM2.5浓度已成为监测近地面PM2.5的有效途径，其估算结果较可靠，能够为治理PM2.5污染提供数据基础和科学依据. 从反演AOD数据集和PM2.5浓度估算模型2个方面进行梳理归纳，从卫星轨道运行类型角度分析各类传感器的产品特征，并对缺失AOD的插补方法进行分类评价；对PM2.5浓度的估算模型进行比较分析，指出不同模型的优缺点和适应性. 结果表明:①各类卫星传感器均具有特定功能及优缺点，其中地球同步轨道(GEO)卫星的快速发展，使其在估算PM2.5浓度的应用上越来越广泛. ②插补后的AOD比AOD初始产品具有更连续的时空分布和更高的准确性，基于模型的多变量估算不仅可以实现数据的全面覆盖，还可以获得更好的估算精度. ③组合模型成为估算PM2.5浓度的重要方法，机器学习模型的加入能够有效提高PM2.5浓度的估算精度. 研究显示，利用AOD估算近地面PM2.5浓度不仅弥补了地面PM2.5监测的空间不连续性，更有助于解析PM2.5浓度的时空分布特征及污染来源.
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| 关 键 词： | PM2.5 气溶胶光学厚度(AOD) 插补方法 经验统计模型 遥感反演 |
| 收稿时间： | 2021-08-15 |
Research Progress of Ground-Level PM2.5 Concentration Estimation Based on Remote Sensing Data |
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| YANG Xiaohui,XIAO Dengpan,WANG Wei,BAI Huizi,TANG Jianzhao,FAN Lihang.Research Progress of Ground-Level PM2.5 Concentration Estimation Based on Remote Sensing Data[J].Research of Environmental Sciences,2022,35(1):40-50. |
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| Authors: | YANG Xiaohui XIAO Dengpan WANG Wei BAI Huizi TANG Jianzhao FAN Lihang |
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| Institution: | 1.Institute of Geographical Sciences, Hebei Academy of Sciences, Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang 050011, China2.College of Geography Science, Hebei Normal University, Shijiazhuang 050024, China3.Hebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang 050024, China |
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| Abstract: | Satellite-retrieved aerosol optical depth (AOD) has broad spatial coverage and relatively high spatio-temporal resolution. Estimating PM2.5 concentrations based on the correlation between AOD and PM2.5 has become an effective method for monitoring the ground-level PM2.5. The estimation results are reliable and can provide data and scientific basis for the treatment of PM2.5 pollution. The inversion AOD dataset and PM2.5 concentration estimation model were sorted out and summarized. The product characteristics of various sensors were analyzed from the perspective of satellite orbit operation type. Moreover, the gap-filling methods on AOD missing data were analyzed in detail. In addition, the PM2.5 concentration estimation models were compared to analyze their advantages, disadvantages and adaptability. The results suggest that: (1) Various satellite sensors have specific functions, advantages and disadvantages. The rapid development of geosynchronous orbit (GEO) satellites is widely used for estimating PM2.5 concentrations. (2) AOD after gap-filling has a more continuous spatiotemporal distribution and higher accuracy than the original AOD product. The model based on multivariate estimation can not only achieve comprehensive coverage of data, but also obtain better estimation accuracy. (3) The combined model has become an important method for estimating PM2.5 concentrations. The inclusion of machine learning models can effectively improve the accuracy of PM2.5 estimation. Overall, using AOD to estimate near-ground PM2.5 concentrations compensates for the spatial discontinuity of ground PM2.5 monitoring, and is more helpful in analyzing the spatiotemporal distribution characteristics of PM2.5 concentrations and pollution sources. |
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