Autor segons l'article: Fabregat A; Vernet A; Vernet M; Vázquez L; Ferré JA
Departament: Enginyeria Mecànica
Autor/s de la URV: Fabregat Tomàs, Alexandre / Ferré Vidal, Josep Anton / Vázquez Vilamajó, Luis Enrique / Vernet Peña, Antonio
Paraules clau: 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
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: alexandre.fabregat@urv.cat anton.vernet@urv.cat josep.a.ferre@urv.cat lluis.vazquez@urv.cat lluis.vazquez@urv.cat
Identificador de l'autor: 0000-0002-6032-2605 0000-0002-7028-1368 0000-0002-0831-0885 0000-0002-2347-5784 0000-0002-2347-5784
Data d'alta del registre: 2024-08-03
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S2212095522002024
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Urban Climate. 45
Referència 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 de l'article: 10.1016/j.uclim.2022.101284
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications