Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the A...

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Autores: Chen, Zhao-Yue, Méndez Turrubiates, Raúl Fernando, Petetin, Hervé, Lacima, Aleksander, Pérez García-Pando, Carlos, Ballester, Joan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/60608
Acceso en línea:http://hdl.handle.net/10230/60608
http://dx.doi.org/10.1016/j.scitotenv.2024.170593
Access Level:acceso abierto
Palabra clave:Aerosol
Aerosol Optical Depth
Particulate matter
Satellite
cAOD
fAOD
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oai_identifier_str oai:repositori.upf.edu:10230/60608
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
title Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
spellingShingle Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
Chen, Zhao-Yue
Aerosol
Aerosol Optical Depth
Particulate matter
Satellite
cAOD
fAOD
title_short Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
title_full Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
title_fullStr Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
title_full_unstemmed Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
title_sort Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
dc.creator.none.fl_str_mv Chen, Zhao-Yue
Méndez Turrubiates, Raúl Fernando
Petetin, Hervé
Lacima, Aleksander
Pérez García-Pando, Carlos
Ballester, Joan
author Chen, Zhao-Yue
author_facet Chen, Zhao-Yue
Méndez Turrubiates, Raúl Fernando
Petetin, Hervé
Lacima, Aleksander
Pérez García-Pando, Carlos
Ballester, Joan
author_role author
author2 Méndez Turrubiates, Raúl Fernando
Petetin, Hervé
Lacima, Aleksander
Pérez García-Pando, Carlos
Ballester, Joan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Aerosol
Aerosol Optical Depth
Particulate matter
Satellite
cAOD
fAOD
topic Aerosol
Aerosol Optical Depth
Particulate matter
Satellite
cAOD
fAOD
description Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/60608
http://dx.doi.org/10.1016/j.scitotenv.2024.170593
url http://hdl.handle.net/10230/60608
http://dx.doi.org/10.1016/j.scitotenv.2024.170593
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sci Total Environ. 2024 Mar 25;918:170593
info:eu-repo/grantAgreement/EC/H2020/773051
info:eu-repo/grantAgreement/ES/2PE/PRE2020-091985
info:eu-repo/grantAgreement/EC/H2020/871115
info:eu-repo/grantAgreement/ES/2PE/PID2020-113840RA-I00
info:eu-repo/grantAgreement/ES/2PE/PID2020-116324RA695
info:eu-repo/grantAgreement/EC/H2020/865564
info:eu-repo/grantAgreement/EC/HE/101069213
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
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spelling Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessmentsChen, Zhao-YueMéndez Turrubiates, Raúl FernandoPetetin, HervéLacima, AleksanderPérez García-Pando, CarlosBallester, JoanAerosolAerosol Optical DepthParticulate matterSatellitecAODfAODAerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.In this initial version of the geodatabase, the authors from ISGlobal would like to express their gratitude for the support they received from various organizations. The Spanish Ministry of Science and Innovation's “Centro de Excelencia Severo Ochoa 2019–2023” Program (CEX2018-000806-S-20-1), the Ministry of Research and Universities of the Government of Catalonia (2021 SGR 01563), and the Generalitat de Catalunya through the CERCA Program all provided support. ZC acknowledges support from the grant PRE2020-091985 funded by MCIN/AEI/10.13039/501100011033 and by European Social Fund invests in your future. CP acknowledges funding from the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC, the European Research Council (ERC) under the Horizon 2020 research and innovation program through the ERC Consolidator Grant FRAGMENT (grant agreement no. 773051), H2020 ACTRIS IMP (#871115), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550). HP has received funding from the Ministerio de Ciencia e Innovación through the MITIGATE project (grant no. PID2020-113840RA-I00 funded by MCIN/AEI/10.13039/501100011033) and the Ramon y Cajal grant (RYC2021-034511-I) and the European Union's NextGeneration EU/PRTR (PID2020-116324RA695). JB gratefully acknowledges funding from the European Union's Horizon 2020 and Horizon Europe research and innovation programs under grant agreements No 865564 (European Research Council Consolidator Grant EARLY-ADAPT) and 101069213 (European Research Council Proof-of-Concept HHS-EWS), as well as from the Spanish Ministry of Science and Innovation under grant agreement No RYC2018-025446-I (programme Ramón y Cajal).Elsevier202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/60608http://dx.doi.org/10.1016/j.scitotenv.2024.170593reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésSci Total Environ. 2024 Mar 25;918:170593info:eu-repo/grantAgreement/EC/H2020/773051info:eu-repo/grantAgreement/ES/2PE/PRE2020-091985info:eu-repo/grantAgreement/EC/H2020/871115info:eu-repo/grantAgreement/ES/2PE/PID2020-113840RA-I00info:eu-repo/grantAgreement/ES/2PE/PID2020-116324RA695info:eu-repo/grantAgreement/EC/H2020/865564info:eu-repo/grantAgreement/EC/HE/101069213© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/606082026-06-12T07:21:37Z
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