A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges

Detailed, reliable, and continuous monitoring of aerosol optical depth (AOD) is essential for air quality management and protection of human health. The satellite-based AOD datasets have been typically used in many studies for the estimation of particulate matter (PM 2.5 and PM 10 ) concentration in the tropospheric region. The prime focus of this study is to review the past studies to analyze the performance of various satellite-based AOD datasets and models used for PM estimation. The review results suggest that every satellite sensors data have some specific capabilities as well as some drawbacks. Multi-angle imaging spectroradiometer (MISR) and visible infrared imaging radiometer suite (VIIRS) datasets showed better consistency in AOD and PM estimation in comparison to the moderate resolution imaging spectroradiometer (MODIS) datasets. In the context of PM estimation models’ accuracy, the mixed-effect model (MEM) has been extensively used and found to be more consistent in general, whereas, geographically weighted regression (GWR) model outperforms other statistical regression models in regional scale. Incorporation of land use parameters along with meteorological parameters improves the PM estimation accuracy at various spatial scale. The review suggests that in the near future, high resolution (spatial and temporal) satellite data with the improved algorithms will be highly appreciable for accurate estimation of AOD and PM.