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细颗粒物（Fine Particulate Matter，PM2.5）是影响空气质量和公共健康的关键因素之一。高时空分辨率的PM2.5数据是公共健康风险评估和流行病学研究的基本需求。相较于地面站点，卫星遥感技术具有连续观测、宽覆盖和低成本的优势，基于卫星气溶胶光学厚度（Aerosol Optical Depth，AOD）反演PM2.5质量浓度的方法已成为热点。本研究概述了卫星AOD产品反演PM2.5浓度的原理，介绍了用于PM2.5反演的主要卫星AOD产品及其反演精度；总结了现有的PM2.5估算方法及其优缺点，指出目前PM2.5反演研究存在的问题；提出未来PM2.5反演方向主要集中在高时空分辨率的PM2.5浓度重建、基于激光雷达数据的三维PM2.5浓度反演及PM2.5化学组分反演等方向。
Fine particulate matter (PM2.5) is a dynamic and complex mixture of particle matter with aerodynamic diameter equal or less than 2.5 μm which can seriously affect air quality and public health. High spatial and temporal resolution PM2.5 data is a basic requirement for public health risk assessment and epidemiological research. Compared with ground-based dataset, the satellite remote sensing provides continuous, wide space coverage and low cost observation, and PM2.5 mass concentrations retrieval based on satellite Aerosol Optical Depth (AOD) has become a hot topic. This paper systematically scrutinizes researches on near-surface PM2.5 concentration retrieved based on satellite AOD products. Introducing the basic method of estimating PM2.5 concentration based on satellite AOD products, and the main satellite AOD products used for PM2.5 retrieve and their accuracy are described in details, as well as the existing PM2.5 estimation methods and their pros and cons. Finally, problems existing in PM2.5 retrieve research and the development direction of PM2.5 retrieve research in the future are pointed out. Scale factor method, physical mechanism model and statistical model are all three methods that can accurately estimate the PM2.5 concentrations at different degrees in different periods, but the scale factor method and physical mechanism model are less used because of their own limits. Statistical models have been widely used and improved due to their unique descriptive ability of temporal or spatiotemporal heterogeneity and strong nonlinear description ability. However, there are three main shortages in the current PM2.5 retrieve research: 1. the non-random missing problem of satellite AOD causes the missing PM2.5 data; 2.accuracy of retrieve models, 3. chemical composition estimation of PM2.5. Therefore, in order to accurately reveal the spatial and temporal trends of near-ground PM2.5 and improve the accuracy of near-ground PM2.5 calculated from satellite AOD products, we predict several future research directions: first of all, the AOD products of the new high spatial resolution (such as FY-4 and GF-5) and high temporal resolution (HIMAWARI-8 /-9) satellites could greatly promote the research of estimating on PM2.5, which is of great significance for the reconstruction of PM2.5 concentrations with high spatial-temporal resolution. Secondly, with the development of atmospheric detection technology, satellite-based, airborne and ground-based lidar can obtain vertical distribution information and particle matter sensor carried on the UAV can achieve the vertical monitoring of PM2.5, which can be combined with optical remote sensing satellite data and ground monitoring data to achieve three-dimensional PM2.5 concentration retrieve. Finally, PM2.5 chemical component information is particularly important for analyzing the cause of pollution, exposure characteristics, and so on, its space-time change trend research is an important development direction. However, the ground PM2.5 component observation network is still imperfect, how to overcome the dependence on ground station network in satellite remote sensing estimation and achieve high-precision retrieve of chemical composition needs further study. Through this study, it is helpful to further understand the principles, advantages and disadvantages of different PM2.5 estimation methods, provide inspiration for the new development direction of near-surface PM2.5 concentrations retrieve based on satellite AOD products, and improve the accuracy and spatial-temporal resolution of near-surface PM2.5 concentrations retrieve.