Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm
Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM 2.5 monitoring system, the ground-based PM 2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific...
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ftdoajarticles:oai:doaj.org/article:fa132a774f5541f7a02132038791eaf4 2023-05-15T13:07:04+02:00 Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm Ying Li Yong Xue Jie Guang Lu She Cheng Fan Guili Chen 2018-11-01T00:00:00Z https://doi.org/10.3390/rs10121906 https://doaj.org/article/fa132a774f5541f7a02132038791eaf4 EN eng MDPI AG https://www.mdpi.com/2072-4292/10/12/1906 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10121906 https://doaj.org/article/fa132a774f5541f7a02132038791eaf4 Remote Sensing, Vol 10, Iss 12, p 1906 (2018) PM 2.5 AOD fine mode fraction MODIS Science Q article 2018 ftdoajarticles https://doi.org/10.3390/rs10121906 2022-12-31T11:26:53Z Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM 2.5 monitoring system, the ground-based PM 2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific Particle Swarm Extinction Mass Conversion Algorithm (SPSEMCA) using remotely sensed data is introduced. Ground-level observed PM 2.5 , planetary boundary layer height (PBLH) and relative humidity (RH) reanalyzed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and aerosol optical depth (AOD), fine-mode fraction (FMF), particle size distribution, and refractive indices from AERONET (Aerosol Robotic Network) of the Beijing area in 2015 were used to establish this algorithm, and the same datasets for 2016 were used to test the performance of the SPSEMCA. The SPSEMCA involves four steps to obtain PM 2.5 values from AOD datasets, and every step has certain advantages: (I) In the particle correction, we use η 2.5 (the extinction fraction caused by particles with a diameter less than 2.5 μm) to make an accurate assimilation of AOD 2.5 , which is contributed to by the specific particle swarm PM 2.5 . (II) In the vertical correction, we compare the performance of PBLHc retrieved by satellite Lidar CALIPSO data and PBLHe reanalysis by ECMWF. Then, PBLHc is used to make a systematic correction for PBLHe. (III) For extinction to volume conversion, the relative humidity and the FMF are used together to assimilate the AVEC (averaged volume extinction coefficient, μm 2 /μm 3 ). (IV) PM 2.5 measured by ground-based air quality stations are used as the dry mass concentration when calculating the AMV (averaged mass volume, cm 3 /g) in humidity correction, that will avoid the uncertainties derived from the estimation of the particulate matter density ρ. (V) Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km × 1 km AOD was used to retrieve high resolution PM 2.5 , and a LookUP Table-based Spectral ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 10 12 1906 |
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English |
topic |
PM 2.5 AOD fine mode fraction MODIS Science Q |
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PM 2.5 AOD fine mode fraction MODIS Science Q Ying Li Yong Xue Jie Guang Lu She Cheng Fan Guili Chen Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
topic_facet |
PM 2.5 AOD fine mode fraction MODIS Science Q |
description |
Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM 2.5 monitoring system, the ground-based PM 2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific Particle Swarm Extinction Mass Conversion Algorithm (SPSEMCA) using remotely sensed data is introduced. Ground-level observed PM 2.5 , planetary boundary layer height (PBLH) and relative humidity (RH) reanalyzed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and aerosol optical depth (AOD), fine-mode fraction (FMF), particle size distribution, and refractive indices from AERONET (Aerosol Robotic Network) of the Beijing area in 2015 were used to establish this algorithm, and the same datasets for 2016 were used to test the performance of the SPSEMCA. The SPSEMCA involves four steps to obtain PM 2.5 values from AOD datasets, and every step has certain advantages: (I) In the particle correction, we use η 2.5 (the extinction fraction caused by particles with a diameter less than 2.5 μm) to make an accurate assimilation of AOD 2.5 , which is contributed to by the specific particle swarm PM 2.5 . (II) In the vertical correction, we compare the performance of PBLHc retrieved by satellite Lidar CALIPSO data and PBLHe reanalysis by ECMWF. Then, PBLHc is used to make a systematic correction for PBLHe. (III) For extinction to volume conversion, the relative humidity and the FMF are used together to assimilate the AVEC (averaged volume extinction coefficient, μm 2 /μm 3 ). (IV) PM 2.5 measured by ground-based air quality stations are used as the dry mass concentration when calculating the AMV (averaged mass volume, cm 3 /g) in humidity correction, that will avoid the uncertainties derived from the estimation of the particulate matter density ρ. (V) Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km × 1 km AOD was used to retrieve high resolution PM 2.5 , and a LookUP Table-based Spectral ... |
format |
Article in Journal/Newspaper |
author |
Ying Li Yong Xue Jie Guang Lu She Cheng Fan Guili Chen |
author_facet |
Ying Li Yong Xue Jie Guang Lu She Cheng Fan Guili Chen |
author_sort |
Ying Li |
title |
Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
title_short |
Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
title_full |
Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
title_fullStr |
Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
title_full_unstemmed |
Ground-Level PM 2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm |
title_sort |
ground-level pm 2.5 concentration estimation from satellite data in the beijing area using a specific particle swarm extinction mass conversion algorithm |
publisher |
MDPI AG |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10121906 https://doaj.org/article/fa132a774f5541f7a02132038791eaf4 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 10, Iss 12, p 1906 (2018) |
op_relation |
https://www.mdpi.com/2072-4292/10/12/1906 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10121906 https://doaj.org/article/fa132a774f5541f7a02132038791eaf4 |
op_doi |
https://doi.org/10.3390/rs10121906 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
12 |
container_start_page |
1906 |
_version_ |
1766033972623572992 |