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Lovrić, M.* ; Antunović, M.* ; Šunić, I.* ; Vukovic, M.* ; Kecorius, S. ; Kröll, M.* ; Bešlić, I.* ; Godec, R.* ; Pehnec, G.* ; Geiger, B.C.* ; Grange, S.K.* ; Šimić, I.*

Machine learning and meteorological normalization for assessment of particulate matter changes during the COVID-19 lockdown in Zagreb, Croatia.

Int. J. Environ. Res. Public Health 19:6937 (2022)

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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 PM₁, PM_2.5 and PM₁₀ 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 PM₁, PM_2.5 and PM₁₀ 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..

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Lightgbm ; Pm1 ; Pm10 ; Pm2.5 ; Air Quality ; Coronavirus Disease Of 2019 ; Random Forests ; Traffic

ISSN (print) / ISBN 1661-7827

e-ISSN 1660-4601

Journal International Journal of Environmental Research and Public Health

Quellenangaben Volume: 19, Issue: 11, Article Number: 6937

Publisher MDPI

Publishing Place Basel, Switzerland

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Epidemiology II (EPI2)

Grants Bundesamt für Umwelt
Natural Environment Research Council
University of York
FOEN
BigData Technologies