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

  • Datos identificativos

    Identificador: imarina:9282205
    Autores:
    Fabregat AVernet AVernet MVázquez LFerré JA
    Resumen:
    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.
  • Otros:

    Autor según el artículo: Fabregat A; Vernet A; Vernet M; Vázquez L; Ferré JA
    Departamento: Enginyeria Mecànica
    Autor/es de la URV: Fabregat Tomàs, Alexandre / Ferré Vidal, Josep Anton / Vázquez Vilamajó, Luis Enrique / Vernet Peña, Antonio
    Palabras clave: 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
    Resumen: 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.
    Áreas temáticas: 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
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: alexandre.fabregat@urv.cat anton.vernet@urv.cat josep.a.ferre@urv.cat lluis.vazquez@urv.cat lluis.vazquez@urv.cat
    Identificador del autor: 0000-0002-6032-2605 0000-0002-7028-1368 0000-0002-0831-0885 0000-0002-2347-5784 0000-0002-2347-5784
    Fecha de alta del registro: 2024-08-03
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S2212095522002024
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Urban Climate. 45
    Referencia de l'ítem segons les normes 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
    DOI del artículo: 10.1016/j.uclim.2022.101284
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    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
  • Documentos:

  • Cerca a google

    Search to google scholar