细颗粒物PM_2.5（Fine Particulate Matter）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PM_2.5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AOD（Aerosol Optical Depth）反演PM_2.5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PM_2.5浓度的原理，介绍了用于PM_2.5反演的主要卫星AOD产品及其反演精度；总结了现有的PM_2.5估算方法及其优缺点，指出目前PM_2.5反演研究存在的问题；提出未来PM_2.5反演方向主要集中在高时空分辨率的PM_2.5浓度重建、基于激光雷达数据的三维PM_2.5浓度反演及PM_2.5化学组分反演等方向。比例因子法、物理机理模型和统计模型这3种方法都能在不同时期不同程度地准确估算PM_2.5浓度，代表了那个时期较为前沿的研究热点，但比例因子法和物理机理模型因其自身的局限性而应用较少，而统计模型因其独特的时间或时空异质性的可描述性和强大的非线性描述能力的优势而被广泛应用并不断改进。目前PM_2.5反演研究存在的问题主要有3种：（1）卫星AOD的非随机缺失问题造成估算的PM_2.5数据缺失；（2）反演模型的精度问题；（3）PM_2.5的化学成分估算问题。基于此，本文为了准确揭示近地面PM_2.5的时空变化趋势，提高基于卫星AOD产品的近地面PM_2.5反演研究的准确性，提出了几点未来的研究方向：首先，新型的高空间（如风云四号、高分五号）、高时间分辨率（Himawari-8/9）卫星AOD产品在PM_2.5的精细化估算研究上具有很大优势，这对于高时空分辨率的PM_2.5浓度重建具有重要意义；其次，随着大气探测技术的发展，星载、机载及地基激光雷达都能够获取垂直分布信息，搭载在无人机上的颗粒物传感器可实现PM_2.5垂直方向上的监测，将其与光学遥感卫星数据及地面监测数据结合，可实现三维的PM_2.5浓度反演；最后，PM_2.5化学组分信息对于分析污染成因、暴露特征等尤其重要，其时空变化趋势研究是一个重要的发展方向，然而，地面PM_2.5组分观测站网仍不完善，如何克服卫星遥感估算中对地面站网的依赖，实现PM_2.5化学成分的高精度反演需要进一步研究。通过本研究，有助于进一步了解不同PM_2.5估算方法的原理机制及其优缺点，为基于卫星AOD产品反演近地面PM_2.5浓度的新的发展方向提供启示，提升近地面PM_2.5浓度反演的精度及时空分辨率。

Fine particulate matter (PM_2.5) is a dynamic and complex mixture of particle matter with an aerodynamic diameter equal or less than 2.5 μm that can seriously affect the air quality and public health. High spatial and temporal resolution PM_2.5 data is a basic requirement for public health risk assessment and epidemiological research. Compared with ground-based datasets, satellite remote sensing provides continuous, wide space coverage and low-cost observation, and the PM_2.5 mass concentrations retrieval based on the satellite aerosol optical depth (AOD) has become a popular topic. This paper systematically scrutinizes the research on the near-surface PM_2.5 concentration retrieved based on satellite AOD products. The basic method of estimating the PM_2.5 concentration based on satellite AOD products is introduced, and the main satellite AOD products used for PM_2.5 retrieval and their accuracy are described in detail. The existing PM_2.5 estimation methods and their pros and cons are also discussed. Finally, the problems identified in PM_2.5 retrieval research and the development direction of PM_2.5 retrieval research are presented in the future.The scale factor method and the physical mechanism and statistical models can accurately estimate the PM_2.5 concentrations at different degrees in different periods, but the scale factor method and the physical mechanism model are less used than the statistical model because of their limitations. 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, the current PM_2.5 retrieve research has three main limitations: 1. the non-random missing problem of satellite AOD causes missing PM_2.5 data; 2. inaccuracy of retrieval models, and 3. Poor chemical composition estimation of PM_2.5. Therefore, to accurately reveal the spatial and temporal trends of near-ground PM_2.5 and improve the accuracy of the near-ground PM_2.5 calculated from satellite AOD products, we predict several future research directions. First, the AOD products of new high-spatial-resolution (such as FY-4 and GF-5) and high-temporal-resolution (HIMAWARI-8/-9) satellites could greatly promote the research on PM_2.5 estimation, which is of great significance to the reconstruction of PM_2.5 concentrations with high spatial-temporal resolutions. Second, with the development of atmospheric detection technology, satellite-based, airborne, and ground-based lidar can obtain vertical distribution information, and the particle matter sensor carried on UAVs can achieve the vertical monitoring of PM_2.5, which can be combined with optical remote sensing satellite and ground monitoring data to achieve three-dimensional PM_2.5 concentration retrieval. Finally, PM_2.5 chemical component information is particularly important for analyzing the cause of pollution and exposure characteristics, and its space–time change trend research is an important development direction. However, the ground PM_2.5 component observation network is still imperfect, and overcoming the dependence on ground station network in satellite remote sensing estimation and achieving the high-precision retrieval of chemical composition need further study.This study is helpful in further understanding the principles, advantages, and disadvantages of different PM_2.5 estimation methods, providing inspiration for the new development direction of near-surface PM_2.5 concentrations retrieval based on satellite AOD products, and improving the accuracy and spatial-temporal resolution of near-surface PM_2.5 concentrations retrieval.