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...

Full description

Bibliographic Details
Published in:Environmental Research Communications
Main Authors: Kim, Kwang Nyun, Kim, Seung Hee, Park, Sang Seo, Lee, Yun Gon
Format: Article in Journal/Newspaper
Language:unknown
Published: IOP Publishing 2023
Subjects:
Online Access: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
id crioppubl:10.1088/2515-7620/accfe9
record_format openpolar
spelling 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
institution Open Polar
collection IOP Publishing
op_collection_id crioppubl
language unknown
description 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
container_title Environmental Research Communications
container_volume 5
container_issue 5
container_start_page 051004
_version_ 1800754965779578880