Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning

Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hendrik Andersen, Jan Cermak, Roland Stirnberg, Julia Fuchs, Miae Kim, Eva Pauli
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	atmospheric aerosols covid-19 satellite remote sensing machine learning Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/0d500a20a2ef4a71a25048e30886d417
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0d500a20a2ef4a71a25048e30886d417
record_format	dspace
spelling	oai:doaj.org-article:0d500a20a2ef4a71a25048e30886d4172021-11-04T15:00:42ZAssessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning1600-088910.1080/16000889.2021.1971925https://doaj.org/article/0d500a20a2ef4a71a25048e30886d4172021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000889.2021.1971925https://doaj.org/toc/1600-0889Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.Hendrik AndersenJan CermakRoland StirnbergJulia FuchsMiae KimEva PauliTaylor & Francis Grouparticleatmospheric aerosolscovid-19satellite remote sensingmachine learningMeteorology. ClimatologyQC851-999ENTellus: Series B, Chemical and Physical Meteorology, Vol 73, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	atmospheric aerosols covid-19 satellite remote sensing machine learning Meteorology. Climatology QC851-999
spellingShingle	atmospheric aerosols covid-19 satellite remote sensing machine learning Meteorology. Climatology QC851-999 Hendrik Andersen Jan Cermak Roland Stirnberg Julia Fuchs Miae Kim Eva Pauli Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
description	Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.
format	article
author	Hendrik Andersen Jan Cermak Roland Stirnberg Julia Fuchs Miae Kim Eva Pauli
author_facet	Hendrik Andersen Jan Cermak Roland Stirnberg Julia Fuchs Miae Kim Eva Pauli
author_sort	Hendrik Andersen
title	Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
title_short	Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
title_full	Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
title_fullStr	Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
title_full_unstemmed	Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning
title_sort	assessment of covid-19 effects on satellite-observed aerosol loading over china with machine learning
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/0d500a20a2ef4a71a25048e30886d417
work_keys_str_mv	AT hendrikandersen assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning AT jancermak assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning AT rolandstirnberg assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning AT juliafuchs assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning AT miaekim assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning AT evapauli assessmentofcovid19effectsonsatelliteobservedaerosolloadingoverchinawithmachinelearning
_version_	1718444755297239040

Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning

Ejemplares similares