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Estimating Daily PM2.5 and PM10 over Italy Using an Ensemble Model

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    Estimating Daily PM2.5 and PM10 over Italy Using an Ensemble Model
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    • Alexandra Shtein*
      Alexandra Shtein
      Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
      *E-mail: [email protected]
      More by Alexandra Shtein
      Orcidhttp://orcid.org/0000-0002-2852-4477
    • Itai Kloog
      Itai Kloog
      Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
      More by Itai Kloog
    • Joel Schwartz
      Joel Schwartz
      Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston 02115, Massachusetts, United States
      More by Joel Schwartz
    • Camillo Silibello
      Camillo Silibello
      ARIANET s.r.l., Milano 20128, Italy
      More by Camillo Silibello
      Orcidhttp://orcid.org/0000-0002-0400-6755
    • Paola Michelozzi
      Paola Michelozzi
      Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome 00147, Italy
      More by Paola Michelozzi
    • Claudio Gariazzo
      Claudio Gariazzo
      Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers’ Compensation Authority (INAIL), Monte Porzio Catone (RM) 00078, Italy
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    • Giovanni Viegi
      Giovanni Viegi
      Institute for Biomedical Research and Innovation, National Research Council, Palermo 90146, Italy
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    • Francesco Forastiere
      Francesco Forastiere
      Institute for Biomedical Research and Innovation, National Research Council, Palermo 90146, Italy
      Environmental Research Group, King’s College, London SE1 9NH, U.K.
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    • Arnon Karnieli
      Arnon Karnieli
      Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel
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    • Allan C. Just
      Allan C. Just
      Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
      More by Allan C. Just
      Orcidhttp://orcid.org/0000-0003-4312-5957
    • Massimo Stafoggia
      Massimo Stafoggia
      Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Rome 00147, Italy
      Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2020, 54, 1, 120–128
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    https://doi.org/10.1021/acs.est.9b04279
    Published November 21, 2019
    Copyright © 2019 American Chemical Society
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    Abstract

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    Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM2.5 and PM10 concentrations were produced over Italy for 2013–2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model. The filled-in MAIAC AOD together with additional spatial and temporal predictors were used as inputs in the three first models; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the four models by allowing the weights of each model to vary over space and time. The GAM ensemble model outperformed the four separate models, decreasing the cross-validated root mean squared error by 1–42%, depending on the model. The spatiotemporally resolved PM estimations produced by the suggested model can be applied in future epidemiological studies across Italy.

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.9b04279.

    • Study area map, detailed description of predictors, modelling stages flowchart, details of machine learning models and the CTM, cross-validation framework, summary statistics of PM10 and PM2.5 concentrations, map of PM2.5 monitors spread along geoclimatic zones, spatial variation in the effect of different learners on PM2.5 concentrations within the GAM model, variably in performance between different station types and different zones, distribution of the percent of relative errors for the GAM ensemble model estimations, maps of mean PM10 concentrations for 2013–2015 around metropolitan areas (PDF)

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    Cite this: Environ. Sci. Technol. 2020, 54, 1, 120–128
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    https://doi.org/10.1021/acs.est.9b04279
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