Satellite Remote Sensing for Estimating PM 2.5 and Its Components

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.