Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5...

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Published in:Remote Sensing
Main Authors: Wei Wang, Feiyue Mao, Lin Du, Zengxin Pan, Wei Gong, Shenghui Fang
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2017
Subjects:
Q
Online Access:https://doi.org/10.3390/rs9080858
https://doaj.org/article/767c93a9e566428d88fb7399d1e7890b
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spelling ftdoajarticles:oai:doaj.org/article:767c93a9e566428d88fb7399d1e7890b 2023-05-15T13:06:18+02:00 Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China Wei Wang Feiyue Mao Lin Du Zengxin Pan Wei Gong Shenghui Fang 2017-08-01T00:00:00Z https://doi.org/10.3390/rs9080858 https://doaj.org/article/767c93a9e566428d88fb7399d1e7890b EN eng MDPI AG https://www.mdpi.com/2072-4292/9/8/858 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9080858 https://doaj.org/article/767c93a9e566428d88fb7399d1e7890b Remote Sensing, Vol 9, Iss 8, p 858 (2017) air pollution geostationary satellite Himawari-8 hourly AOD hourly PM2.5 Science Q article 2017 ftdoajarticles https://doi.org/10.3390/rs9080858 2022-12-31T15:17:57Z Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 9 8 858
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic air pollution
geostationary satellite
Himawari-8
hourly AOD
hourly PM2.5
Science
Q
spellingShingle air pollution
geostationary satellite
Himawari-8
hourly AOD
hourly PM2.5
Science
Q
Wei Wang
Feiyue Mao
Lin Du
Zengxin Pan
Wei Gong
Shenghui Fang
Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
topic_facet air pollution
geostationary satellite
Himawari-8
hourly AOD
hourly PM2.5
Science
Q
description Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours.
format Article in Journal/Newspaper
author Wei Wang
Feiyue Mao
Lin Du
Zengxin Pan
Wei Gong
Shenghui Fang
author_facet Wei Wang
Feiyue Mao
Lin Du
Zengxin Pan
Wei Gong
Shenghui Fang
author_sort Wei Wang
title Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
title_short Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
title_full Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
title_fullStr Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
title_full_unstemmed Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
title_sort deriving hourly pm2.5 concentrations from himawari-8 aods over beijing–tianjin–hebei in china
publisher MDPI AG
publishDate 2017
url https://doi.org/10.3390/rs9080858
https://doaj.org/article/767c93a9e566428d88fb7399d1e7890b
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 9, Iss 8, p 858 (2017)
op_relation https://www.mdpi.com/2072-4292/9/8/858
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs9080858
https://doaj.org/article/767c93a9e566428d88fb7399d1e7890b
op_doi https://doi.org/10.3390/rs9080858
container_title Remote Sensing
container_volume 9
container_issue 8
container_start_page 858
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