Welcome to the UPF Digital Repository

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

Show simple item record

dc.contributor.author Chen, Zhao-Yue
dc.contributor.author Méndez Turrubiates, Raúl Fernando
dc.contributor.author Petetin, Hervé
dc.contributor.author Lacima, Aleksander
dc.contributor.author Pérez García-Pando, Carlos
dc.contributor.author Ballester, Joan
dc.date.accessioned 2024-06-27T14:27:29Z
dc.date.available 2024-06-27T14:27:29Z
dc.date.issued 2024
dc.identifier.citation Chen ZY, Turrubiates RFM, Petetin H, Lacima A, Pérez García-Pando C, Ballester J. Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments. Sci Total Environ. 2024 Mar 25;918:170593. DOI: 10.1016/j.scitotenv.2024.170593
dc.identifier.issn 0048-9697
dc.identifier.uri http://hdl.handle.net/10230/60608
dc.description.abstract 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.
dc.description.sponsorship 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).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Sci Total Environ. 2024 Mar 25;918:170593
dc.rights © 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/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.scitotenv.2024.170593
dc.subject.keyword Aerosol
dc.subject.keyword Aerosol Optical Depth
dc.subject.keyword Particulate matter
dc.subject.keyword Satellite
dc.subject.keyword cAOD
dc.subject.keyword fAOD
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/773051
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S-20-1
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PRE2020-091985
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871115
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-113840RA-I00
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116324RA695
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/865564
dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101069213
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking