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Pregled bibliografske jedinice broj: 1059877

Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon


Šimić, Iva; Lovrić, Mario; Godec, Ranka; Kröll, Mark; Bešlić, Ivan;
Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon // Environmental pollution, 263 (2020), 114587, 9 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1059877 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon

Autori
Šimić, Iva ; Lovrić, Mario ; Godec, Ranka ; Kröll, Mark ; Bešlić, Ivan ;

Izvornik
Environmental pollution (0269-7491) 263 (2020); 114587, 9

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
AdaBoost ; EC ; Lasso regression ; NO2 ; random forest regression ;

Sažetak
Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM10 and NO2, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Interdisciplinarne prirodne znanosti, Računarstvo, Javno zdravstvo i zdravstvena zaštita, Integrativna bioetika (prirodne, tehničke, biomedicina i zdravstvo, biotehničke, društvene, humanističke znanosti)



POVEZANOST RADA


Projekti:
MZOS-022-0222882-2271 - Vremensko-prostorna razdioba i porijeklo lebdećih čestica u urbanim sredinama (Šega, Krešimir, MZOS ) ( POIROT)

Ustanove:
Institut za medicinska istraživanja i medicinu rada, Zagreb,
Dječja bolnica Srebrnjak

Profili:

Avatar Url Ranka Godec (autor)

Avatar Url Ivan Bešlić (autor)

Avatar Url Mario Lovrić (autor)

Avatar Url Iva Šimić (autor)

Poveznice na cjeloviti tekst rada:

www.sciencedirect.com

Citiraj ovu publikaciju:

Šimić, Iva; Lovrić, Mario; Godec, Ranka; Kröll, Mark; Bešlić, Ivan;
Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon // Environmental pollution, 263 (2020), 114587, 9 (međunarodna recenzija, članak, znanstveni)
Šimić, I., Lovrić, M., Godec, R., Kröll, M., Bešlić, I. & (2020) Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon. Environmental pollution, 263, 114587, 9.
@article{article, year = {2020}, pages = {9}, chapter = {114587}, keywords = {AdaBoost, EC, Lasso regression, NO2, random forest regression, }, journal = {Environmental pollution}, volume = {263}, issn = {0269-7491}, title = {Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon}, keyword = {AdaBoost, EC, Lasso regression, NO2, random forest regression, }, chapternumber = {114587} }
@article{article, year = {2020}, pages = {9}, chapter = {114587}, keywords = {AdaBoost, EC, Lasso regression, NO2, random forest regression, }, journal = {Environmental pollution}, volume = {263}, issn = {0269-7491}, title = {Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon}, keyword = {AdaBoost, EC, Lasso regression, NO2, random forest regression, }, chapternumber = {114587} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Uključenost u ostale bibliografske baze podataka::


  • AGRICOLA
  • EMBASE (Excerpta Medica)
  • MEDLINE
  • Environmental Periodicals Bibliography
  • Current Contents - Agriculture, Biology & Environmental Sciences
  • Energy Information Abstracts
  • Air Pollution Control Association Journal
  • Biological and Agricultural Index
  • GeoSciTech
  • Web of Science





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