Purpose of Review

PM_2.5 satellite remote sensing is the most powerful way to acquire the PM_2.5 distribution and variation at a large scale with high resolution. Thus, PM_2.5 remote sensing methods have been widely developed and applied in multiple environmentally related research areas in recent decades. Hence, the purpose of this review is to summarize these methods, required input data and main applications of PM_2.5 and its remote sensing components.

Recent Findings

In general, two-step methods have been used for estimating PM_2.5, which first retrieves the aerosol optical depth (AOD) and estimates PM_2.5 from the AOD with other supplemental data containing the temporal or spatial variation impact on PM_2.5 or data correlated with PM_2.5 variation by different AOD-PM_2.5 models. The AOD-PM_2.5 models have been developed by using different methods, including empirical-statistical models (single or combined statistical models and big data-based machine learning methods), CTM-based models and semi-empirical/physical models. Current research can provide high-resolution (e.g. daily variations at 1 km and hourly variations at ~1 km) PM_2.5 products, which have been widely used in air pollution management, health impact assessments, numerical data assimilation and climate impact analyses.

Summary

This review summarizes the current research on method development, application, achievement and remaining challenges in remote sensing of PM_2.5 and its components, which are essential for further improvement of the methods and accuracy of PM_2.5 remote sensing and are likely applicable to other PM_2.5 component remote sensing methods in the future.

中文翻译：

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卫星遥感估计PM 2.5及其组成

审查目的

PM _2.5卫星遥感技术是高分辨率，大规模获取PM _2.5分布和变化的最强大方法。因此，近几十年来，PM _2.5遥感方法得到了广泛的开发，并应用于多个与环境有关的研究领域。因此，本次审查的目的是总结PM _2.5及其遥感组件的这些方法，所需的输入数据和主要应用。

最近的发现

通常，已采用两步法估算PM _2.5，该方法首先获取气溶胶光学深度（AOD），然后从AOD估算PM _2.5，以及其他补充数据，其中包括对PM _2.5的时空变化影响或与PM _2.5相关的数据。 PM _2.5的不同AOD-PM变化_2.5车型。AOD-PM _2.5模型是通过使用不同的方法开发的，包括经验统计模型（单一或组合统计模型以及基于大数据的机器学习方法），基于CTM的模型和半经验/物理模型。当前的研究可以提供高分辨率（例如1 km处的每日变化和〜1 km处的每小时变化）PM _2.5 产品已广泛用于空气污染管理，健康影响评估，数值数据同化和气候影响分析。

概要

这篇综述总结了当前对PM _2.5及其组件的遥感方法开发，应用，成就和尚存挑战的研究，这对于进一步改进PM _2.5遥感的方法和准确性至关重要，并且很可能适用于其他PM _2.5未来的组件遥感方法。