engleski

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

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.

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

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