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Pregled bibliografske jedinice broj: 1191473

Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia


Lovrić, Mario; Antunović, Mario; Šunić, Iva; Vuković, Matej; Kecorius, Simonas; Kröll, Mark; Bešlić, Ivan; Šimić, Iva; Pehnec, Gordana
Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia // Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering (CSEE'22) / El Naggar, Hanny ; Barros, Joaquim (ur.).
Ottawa: International ASET Inc., 2022. ICEPTP 187, 8 doi:10.11159/iceptp22.187 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1191473 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia

Autori
Lovrić, Mario ; Antunović, Mario ; Šunić, Iva ; Vuković, Matej ; Kecorius, Simonas ; Kröll, Mark ; Bešlić, Ivan ; Šimić, Iva ; Pehnec, Gordana

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering (CSEE'22) / El Naggar, Hanny ; Barros, Joaquim - Ottawa : International ASET Inc., 2022

ISBN
978-1-927877-99-9

Skup
7th World Congress on Civil, Structural, and Environmental Engineering (CSEE 2022)

Mjesto i datum
Online, 10.04.2022. - 12.04.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
machine learning ; air quality ; corona crisis ; pm1 ; pm2.5 ; pm10 ; traffic

Sažetak
In this paper we present an approach to investigate changes in concentration of particulate matter (PM) mass concentrations during the COVID-19 lockdown. Concentrations of PM1, PM2.5 and PM10 were measured in an urban background sampling site on the north of Zagreb from 2009 to late 2020 on a 24h basis. The concentrations were fed alongside meteorological and temporal data to Random Forest (RF) models tuned by Bayesian optimization. The models’ predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. We examined three pollution periods in 2020 in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the “new normal”. We conducted an evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA). The results showed that no significant difference (p = 0.33) was observed for PM2.5 and PM10 in April 2020 - compared to the same period in 2018 and 2019. The noticeable change in PM1 was observed in the same period related to a higher normalized concentration in 2018, but no difference between 2019 and 2020. No significant changes were observed for the “new normal” as well. Our results thus lead to the assumption that a reduction in mobility during COVID-19 lockdown did not significantly affect particulate matter concentration in long- term.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Interdisciplinarne prirodne znanosti, Računarstvo



POVEZANOST RADA


Ustanove:
Institut za medicinska istraživanja i medicinu rada, Zagreb,
Institut za antropologiju

Profili:

Avatar Url Iva Šunić (autor)

Avatar Url Gordana Pehnec (autor)

Avatar Url Ivan Bešlić (autor)

Avatar Url Mario Lovrić (autor)

Avatar Url Iva Smoljo (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi avestia.com

Citiraj ovu publikaciju:

Lovrić, Mario; Antunović, Mario; Šunić, Iva; Vuković, Matej; Kecorius, Simonas; Kröll, Mark; Bešlić, Ivan; Šimić, Iva; Pehnec, Gordana
Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia // Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering (CSEE'22) / El Naggar, Hanny ; Barros, Joaquim (ur.).
Ottawa: International ASET Inc., 2022. ICEPTP 187, 8 doi:10.11159/iceptp22.187 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Lovrić, M., Antunović, M., Šunić, I., Vuković, M., Kecorius, S., Kröll, M., Bešlić, I., Šimić, I. & Pehnec, G. (2022) Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia. U: El Naggar, H. & Barros, J. (ur.)Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering (CSEE'22) doi:10.11159/iceptp22.187.
@article{article, author = {Lovri\'{c}, Mario and Antunovi\'{c}, Mario and \v{S}uni\'{c}, Iva and Vukovi\'{c}, Matej and Kecorius, Simonas and Kr\"{o}ll, Mark and Be\v{s}li\'{c}, Ivan and \v{S}imi\'{c}, Iva and Pehnec, Gordana}, year = {2022}, pages = {8}, DOI = {10.11159/iceptp22.187}, chapter = {ICEPTP 187}, keywords = {machine learning, air quality, corona crisis, pm1, pm2.5, pm10, traffic}, doi = {10.11159/iceptp22.187}, isbn = {978-1-927877-99-9}, title = {Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia}, keyword = {machine learning, air quality, corona crisis, pm1, pm2.5, pm10, traffic}, publisher = {International ASET Inc.}, publisherplace = {online}, chapternumber = {ICEPTP 187} }
@article{article, author = {Lovri\'{c}, Mario and Antunovi\'{c}, Mario and \v{S}uni\'{c}, Iva and Vukovi\'{c}, Matej and Kecorius, Simonas and Kr\"{o}ll, Mark and Be\v{s}li\'{c}, Ivan and \v{S}imi\'{c}, Iva and Pehnec, Gordana}, year = {2022}, pages = {8}, DOI = {10.11159/iceptp22.187}, chapter = {ICEPTP 187}, keywords = {machine learning, air quality, corona crisis, pm1, pm2.5, pm10, traffic}, doi = {10.11159/iceptp22.187}, isbn = {978-1-927877-99-9}, title = {Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia}, keyword = {machine learning, air quality, corona crisis, pm1, pm2.5, pm10, traffic}, publisher = {International ASET Inc.}, publisherplace = {online}, chapternumber = {ICEPTP 187} }

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