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dc.contributor.authorFung, Pak Lun
dc.contributor.authorSavadkoohi, Marjan
dc.contributor.authorZaidan, Martha Arbayani
dc.contributor.authorNiemi, Jarkko V.
dc.contributor.authorTimonen, Hilkka
dc.contributor.authorPandolfi, Marco
dc.contributor.authorAlastuey, Andres
dc.contributor.authorQuerol Carceller, Xavier
dc.contributor.authorHussein, Tareq
dc.contributor.authorPetaja, Tuukka
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Recursos Naturals i Medi Ambient
dc.date.accessioned2024-02-12T11:29:34Z
dc.date.available2024-02-12T11:29:34Z
dc.date.issued2024-02-01
dc.identifier.citationFung, P. [et al.]. Constructing transferable and interpretable machine learning models for black carbon concentrations. "Environment international", 1 Febrer 2024, vol. 184, núm. article 108449.
dc.identifier.issn1873-6750
dc.identifier.urihttp://hdl.handle.net/2117/401704
dc.description.abstractBlack carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73–0.85) and nitrogen dioxide (NO2, r = 0.68–0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80–0.86; mean absolute error MAE = 3.90–4.73 %) and at the urban background site in Dresden (R2 = 0.79–0.84; MAE = 4.23–4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.
dc.description.sponsorshipThis study is supported by the RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial Areas, European Union’s Horizon 2020 research and innovation program, Green Deal, European Commission, contract 101036245). RI-URBANS is implementing the ACTRIS (http://actris.eu) strategy for the development of services for improving air quality in Europe. The authors would also like to thank the support from “Agencia Estatal de Investigación” from the Spanish Ministry of Science and Innovation under the project CAIAC (PID2019-108990RB-I00), AIRPHONEMA (PID2022-142160OB-I00), and the Generalitat de Catalunya (AGAUR, SGR-447), Technology Industries of Finland Centennial Foundation to Urban Air Quality 2.0 project, Research Council of Finland Flagship funding (project number: 337549, 337552), Research Council of Finland Research Fellowship funding (project number: 355330) and European Commission via on-CO2 Forcers And Their Climate, Weather, Air Quality And Health Impacts (FOCI, project number: 101056783). P.L. Fung would like to acknowledge Artificial Intelligence for Urban Low-Emission Autonomous Traffic (AIforlessAuto) funded under the Green and Digital transition call from the Research Council of Finland (project numbers: 347197, 347198) for the support. M. Savadkoohi would like to thank the Spanish Ministry of Science and Innovation for her FPI grant (PRE-2020-095498). The authors also take the opportunity to thank Dr. Susanne Bastian from the Saxon State Office For Environment for contributing to data collection in the study. Open access funded by Helsinki University Library.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria química::Biotecnologia
dc.subject.lcshFlue gases - Purification - Equipment and supplies
dc.subject.otherBC estimation
dc.subject.otherVirtual sensors
dc.subject.otherRelative importance
dc.subject.otherNeural network
dc.subject.otherSHAP
dc.subject.otherTraffic emission
dc.titleConstructing transferable and interpretable machine learning models for black carbon concentrations
dc.typeArticle
dc.subject.lemacGasos de combustió--Depuració--Aparells i accessoris
dc.identifier.doi10.1016/j.envint.2024.108449
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0160412024000357
dc.rights.accessOpen Access
local.identifier.drac37953934
dc.description.versionPostprint (published version)
local.citation.authorFung, P.; Savadkoohi, M.; Zaidan, M.; Niemi, J.; Timonen, H.; Pandolfi, M.; Alastuey, A.; Querol , X.; Hussein, T.; Petaja, T.
local.citation.publicationNameEnvironment international
local.citation.volume184
local.citation.numberarticle 108449


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