Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona

Domínguez, Alan; Dadvand, Payam; Cirach, Marta; Gascon Merlos, Mireia, 1984-; Raimbault, Bruno; Galmés, Toni; Goméz Herrera, Laura; Persavento, Cecilia; Samuelsson, Karl; Tonne, Cathryn; Nieuwenhuijsen, Mark J.; Sunyer Deu, Jordi; Basagaña Flores, Xavier; Rivas, Ioar

Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona

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dc.contributor.author Domínguez, Alan
dc.contributor.author Dadvand, Payam
dc.contributor.author Cirach, Marta
dc.contributor.author Gascon Merlos, Mireia, 1984-
dc.contributor.author Raimbault, Bruno
dc.contributor.author Galmés, Toni
dc.contributor.author Goméz Herrera, Laura
dc.contributor.author Persavento, Cecilia
dc.contributor.author Samuelsson, Karl
dc.contributor.author Tonne, Cathryn
dc.contributor.author Nieuwenhuijsen, Mark J.
dc.contributor.author Sunyer Deu, Jordi
dc.contributor.author Basagaña Flores, Xavier
dc.contributor.author Rivas, Ioar
dc.date.accessioned 2024-11-28T08:27:16Z
dc.date.available 2024-11-28T08:27:16Z
dc.date.issued 2024
dc.description.abstract Background: Air pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects. Aim: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona. Methods: Two monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots. Results: Models showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from -0.38 to 0.92 for long-term exposure, and - 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC. Conclusions: Our models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.
dc.description.sponsorship Research described in this article was conducted under contract to the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (785994, AirNB project) and the Health Effects Institute (HEI) (4959-RFA17–1/18–1-5, FRONTIER project), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. A full list of the funding sources that supported specific parts of the project can be found at https://projectebisc.org/en/funding-sources/. We also acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S) and the Generalitat de Catalunya through the CERCA Program. Ioar Rivas received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 886121 and Ramón y Cajal fellowship (RYC2021-032781-I), funded by the MCIN/AEI/10.13039/501100011033 and the European Union «NextGenerationEU»/PRTR.
dc.format.mimetype application/pdf
dc.identifier.citation Domínguez A, Dadvand P, Cirach M, Arévalo G, Barril L, Foraster M, et al. Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona. Sci Total Environ. 2024 Dec 1;954:176632. DOI: 10.1016/j.scitotenv.2024.176632
dc.identifier.doi http://dx.doi.org/10.1016/j.scitotenv.2024.176632
dc.identifier.issn 0048-9697
dc.identifier.uri http://hdl.handle.net/10230/68849
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Sci Total Environ. 2024 Dec 1;954:176632
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/785994
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/886121
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.keyword Air pollution
dc.subject.keyword Epidemiology
dc.subject.keyword Exposure assessment
dc.subject.keyword Spatiotemporal models
dc.title Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

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Articles (Departament de Medicina i Ciències de la Vida)
Articles (Barcelona Institute for Global Health (ISGlobal) – Campus Mar)
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