A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges
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REVIEW PAPER
A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges Avinash Kumar Ranjan 1 & Aditya Kumar Patra 2 & A. K. Gorai 1 Received: 16 February 2020 / Revised: 16 July 2020 / Accepted: 20 July 2020 # Korean Meteorological Society and Springer Nature B.V. 2020
Abstract 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 (PM2.5 and PM10) 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. Keywords Remote sensing . Aerosol optical depth (AOD) . Particulate matter (PM) . Satellite data
Abbreviations AATSR Advanced along-track scanning radiometer ABI Advanced baseline imager AERONET AERosol RObotic NETwork AHI Advanced Himawari Imager ANN Artificial neural networks AOD Aerosol optical depth AVHRR Advanced very high-resolution radiometer
Responsible Editor: Chang-Keun Song. * A. K. Gorai [email protected]; http://orcid.org/0000-0002-2276-6870 Avinash Kumar Ranjan [email protected]; http://orcid.org/0000-0002-6406-8544 Aditya Kumar Patra [email protected]; http://orcid.org/0000-0002-4408-3421 1
Department of Mining Engineering, National Institute of Technology, Rourkela 769008, India
2
Department of Mining Engineering, Indian Institute of Technology, Kharagpur 721302, India
CALIOP CPCB CTM GAM GEOS GOCI GTWR GWR IR LR LUR MAIAC MEM MISR MLR MODIS OLS OMI PBLH
Cloud-aerosol lidar with orthogonal polarization Central pollution control board Chemical transport model Generalized additive model Geostationary operational environment satellite Geostationary ocean colour imager Geographically and temporally weighted regression Geographically weighted regression Infrared Linear regr
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