Articles producció científica> Enginyeria Mecànica

Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality

  • Identification data

    Identifier: imarina:9282205
  • Authors:

    Fabregat A
    Vernet A
    Vernet M
    Vázquez L
    Ferré JA
    Urban Climate
    10.1016/j.uclim.2022.101284
    Urban Climate. 45
  • Others:

    Author, as appears in the article.: Fabregat A; Vernet A; Vernet M; Vázquez L; Ferré JA
    Department: Enginyeria Mecànica
    URV's Author/s: Fabregat Tomàs, Alexandre / Ferré Vidal, Josep Anton / Vernet Peña, Antonio
    Keywords: Vector regression methodology Urban pollution Transportation emissions Pollutant dispersion Machine learning Low emission zone Covid-19 Air quality urban pollution transportation emissions system prediction pollution pollutant dispersion low emission zone line global burden dispersion disease covid-19 algorithms air quality
    Abstract: Classical pollutant dispersion models, based on the numerical resolution of some approximate form of the momentum, energy and chemical species conservation equations, are usually limited by incomplete descriptions of the atmospheric boundary layer hydrodynamics, partial characterizations of the emission inventories and, often, high computational costs. Using the metropolitan area of Barcelona as benchmark, the Machine Learning aproach presented here alleviates these limitations providing very accurate local predictions of key pollutant concentrations. Originating mostly from Open Data sources, time-series data on road, maritime and air traffic along with meteorological records from October 2017 to June 2021, have allowed, by means of Machine Learning techniques, to create a model capable of estimating the individual contributions of each mode of transport to worsened Air Quality. Also, when used to investigate the impact of recently implemented mitigation measures, model results predict a reduction of approximately 8 μg·m−3 for CO and NOx. In contrast, O3, PM10 and SO2 are found to be unaffected. The COVID-19 lockdown provided an accidental opportunity to improve the model's robustness and predictive capability through unusually low emission rates from transportation.
    Thematic Areas: Urban studies Química Meteorology & atmospheric sciences Interdisciplinar Geography, planning and development Geociências Environmental sciences Environmental science (miscellaneous) Engenharias i Ciencias sociales Ciências ambientais Atmospheric science Arquitetura, urbanismo e design Administração pública e de empresas, ciências contábeis e turismo
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: alexandre.fabregat@urv.cat anton.vernet@urv.cat josep.a.ferre@urv.cat
    Author identifier: 0000-0002-6032-2605 0000-0002-7028-1368 0000-0002-0831-0885
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S2212095522002024
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Urban Climate. 45
    APA: Fabregat A; Vernet A; Vernet M; Vázquez L; Ferré JA (2022). Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality. Urban Climate, 45(), -. DOI: 10.1016/j.uclim.2022.101284
    Article's DOI: 10.1016/j.uclim.2022.101284
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Atmospheric Science,Environmental Science (Miscellaneous),Environmental Sciences,Geography, Planning and Development,Meteorology & Atmospheric Sciences,Urban Studies
    Vector regression methodology
    Urban pollution
    Transportation emissions
    Pollutant dispersion
    Machine learning
    Low emission zone
    Covid-19
    Air quality
    urban pollution
    transportation emissions
    system
    prediction
    pollution
    pollutant dispersion
    low emission zone
    line
    global burden
    dispersion
    disease
    covid-19
    algorithms
    air quality
    Urban studies
    Química
    Meteorology & atmospheric sciences
    Interdisciplinar
    Geography, planning and development
    Geociências
    Environmental sciences
    Environmental science (miscellaneous)
    Engenharias i
    Ciencias sociales
    Ciências ambientais
    Atmospheric science
    Arquitetura, urbanismo e design
    Administração pública e de empresas, ciências contábeis e turismo
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