Pregled bibliografske jedinice broj: 1191473
Insignificant Changes in Particulate Matter during the COVID-19 Lockdown: A Machine Learning Study in Zagreb, Croatia
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)
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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:
Iva Šunić
(autor)
Gordana Pehnec
(autor)
Ivan Bešlić
(autor)
Mario Lovrić
(autor)
Iva Smoljo
(autor)