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Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

Authors :
Wonei Choi
Hyeongwoo Kang
Dongho Shin
Hanlim Lee
Source :
Remote Sensing, Vol 13, Iss 13, p 2464 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.234c2f9a34a740debdd93fc0315004b6
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13132464