Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation
Abstract Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Rob...
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crioppubl:10.1088/2515-7620/accfe9 2024-06-02T07:54:21+00:00 Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation Kim, Kwang Nyun Kim, Seung Hee Park, Sang Seo Lee, Yun Gon 2023 http://dx.doi.org/10.1088/2515-7620/accfe9 https://iopscience.iop.org/article/10.1088/2515-7620/accfe9 https://iopscience.iop.org/article/10.1088/2515-7620/accfe9/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0/ https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Communications volume 5, issue 5, page 051004 ISSN 2515-7620 journal-article 2023 crioppubl https://doi.org/10.1088/2515-7620/accfe9 2024-05-07T14:02:41Z Abstract Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM 10 and PM 2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data. Article in Journal/Newspaper Aerosol Robotic Network IOP Publishing Environmental Research Communications 5 5 051004 |
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Abstract Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM 10 and PM 2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data. |
format |
Article in Journal/Newspaper |
author |
Kim, Kwang Nyun Kim, Seung Hee Park, Sang Seo Lee, Yun Gon |
spellingShingle |
Kim, Kwang Nyun Kim, Seung Hee Park, Sang Seo Lee, Yun Gon Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
author_facet |
Kim, Kwang Nyun Kim, Seung Hee Park, Sang Seo Lee, Yun Gon |
author_sort |
Kim, Kwang Nyun |
title |
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
title_short |
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
title_full |
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
title_fullStr |
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
title_full_unstemmed |
Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation |
title_sort |
feasibility analysis of aeronet lunar aod for nighttime particulate matter estimation |
publisher |
IOP Publishing |
publishDate |
2023 |
url |
http://dx.doi.org/10.1088/2515-7620/accfe9 https://iopscience.iop.org/article/10.1088/2515-7620/accfe9 https://iopscience.iop.org/article/10.1088/2515-7620/accfe9/pdf |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Environmental Research Communications volume 5, issue 5, page 051004 ISSN 2515-7620 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/2515-7620/accfe9 |
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Environmental Research Communications |
container_volume |
5 |
container_issue |
5 |
container_start_page |
051004 |
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1800754965779578880 |