Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

Mario Lovrić, Mario Antunović, Iva Šunić, Matej Vuković, Simonas Kecorius, Mark Kröll, Ivan Bešlić, Ranka Godec, Gordana Pehnec, Bernhard C. Geiger, Stuart K. Grange and Iva Šimić
Additional contact information
Mario Lovrić: Know-Center, Inffeldgasse 13, 8010 Graz, Austria
Mario Antunović: Ascalia d.o.o., Ulica Trate 16, 40000 Čakovec, Croatia
Iva Šunić: Institute for Anthropological Research, Gajeva 32, 10000 Zagreb, Croatia
Matej Vuković: Pro2Future GmbH, Inffeldgasse 25F, 8010 Graz, Austria
Simonas Kecorius: Institute of Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
Mark Kröll: Know-Center, Inffeldgasse 13, 8010 Graz, Austria
Ivan Bešlić: Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Ksaverska cesta 2, 10000 Zagreb, Croatia
Ranka Godec: Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Ksaverska cesta 2, 10000 Zagreb, Croatia
Gordana Pehnec: Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Ksaverska cesta 2, 10000 Zagreb, Croatia
Bernhard C. Geiger: Know-Center, Inffeldgasse 13, 8010 Graz, Austria
Stuart K. Grange: Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dübendorf, Switzerland
Iva Šimić: Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Ksaverska cesta 2, 10000 Zagreb, Croatia

IJERPH, 2022, vol. 19, issue 11, 1-16

Abstract: In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM 1 , PM 2.5 and PM 10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) 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. Three pollution periods in 2020 were examined 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”. An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM 1 , PM 2.5 and PM 10 in April 2020—compared to the same period in 2018 and 2019. No significant changes were observed for the “new normal” as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

Keywords: random forests; LightGBM; air quality; coronavirus disease of 2019; PM 1; PM 2.5; PM 10; traffic (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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