Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visib...

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Published in:Remote Sensing
Main Authors: Qiaolin Zeng, Jinhua Tao, Liangfu Chen, Hao Zhu, SongYan Zhu, Yang Wang
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Q
Online Access:https://doi.org/10.3390/rs12050881
https://doaj.org/article/f30e7b75675848ca8449187508b4a580
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spelling ftdoajarticles:oai:doaj.org/article:f30e7b75675848ca8449187508b4a580 2023-05-15T13:06:48+02:00 Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth Qiaolin Zeng Jinhua Tao Liangfu Chen Hao Zhu SongYan Zhu Yang Wang 2020-03-01T00:00:00Z https://doi.org/10.3390/rs12050881 https://doaj.org/article/f30e7b75675848ca8449187508b4a580 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/5/881 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs12050881 https://doaj.org/article/f30e7b75675848ca8449187508b4a580 Remote Sensing, Vol 12, Iss 5, p 881 (2020) viirs aod pm 2.5 aerosol type mixed-effects model china Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12050881 2022-12-31T16:09:12Z Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R 2 = 0.15), whereas the correlations for smoke and urban types were better (R 2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM 2.5 concentrations in Beijing−Tianjin−Hebei (BTH), Sichuan−Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R 2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM 2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM 2.5 , and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM 2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM 2.5 was analyzed and the PM 2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM 2.5 concentrations were predominantly distributed in areas of human activity and industrial areas. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 12 5 881
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic viirs aod
pm 2.5
aerosol type
mixed-effects model
china
Science
Q
spellingShingle viirs aod
pm 2.5
aerosol type
mixed-effects model
china
Science
Q
Qiaolin Zeng
Jinhua Tao
Liangfu Chen
Hao Zhu
SongYan Zhu
Yang Wang
Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
topic_facet viirs aod
pm 2.5
aerosol type
mixed-effects model
china
Science
Q
description Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R 2 = 0.15), whereas the correlations for smoke and urban types were better (R 2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM 2.5 concentrations in Beijing−Tianjin−Hebei (BTH), Sichuan−Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R 2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM 2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM 2.5 , and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM 2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM 2.5 was analyzed and the PM 2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM 2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.
format Article in Journal/Newspaper
author Qiaolin Zeng
Jinhua Tao
Liangfu Chen
Hao Zhu
SongYan Zhu
Yang Wang
author_facet Qiaolin Zeng
Jinhua Tao
Liangfu Chen
Hao Zhu
SongYan Zhu
Yang Wang
author_sort Qiaolin Zeng
title Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
title_short Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
title_full Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
title_fullStr Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
title_full_unstemmed Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth
title_sort estimating ground-level particulate matter in five regions of china using aerosol optical depth
publisher MDPI AG
publishDate 2020
url https://doi.org/10.3390/rs12050881
https://doaj.org/article/f30e7b75675848ca8449187508b4a580
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 12, Iss 5, p 881 (2020)
op_relation https://www.mdpi.com/2072-4292/12/5/881
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs12050881
https://doaj.org/article/f30e7b75675848ca8449187508b4a580
op_doi https://doi.org/10.3390/rs12050881
container_title Remote Sensing
container_volume 12
container_issue 5
container_start_page 881
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