Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution

Kavouras, I.; Kaselimi, M.; Protopapadakis, E.; Doulamis, N.

In January, 2020, a new virus, named SARS-COV-2, was identified and announced to the public;in March the World Health Organization (WHO) declared a worldwide pandemic. To reduce the transmissibility of the new virus, the local authorities, worldwide, introduced a series of measures to flatten the curve. Many of the measures included some form of lockdown and movement restrictions. This unique coordinated worldwide reaction, created an opportunity for researching the effects of low traffic in air quality. In this work we research the relation between the COVID-19 measures and the Air Quality Index (AQI), using four pollutant gases (CO, O3, NO2, SO2). Also, we used a variety of machine learning tools (DNN, DTR, K-NN, Lasso, LReg, MAdam, MGBR, RFR, Ridge) to estimate the accuracy of each method in the prediction of the concentration for each gas one week later. The results showed that after the strict COVID-19 restriction measures the concentration of each pollutant gas reduced rapidly and increased again after the relaxation of lockdown measures. Finally in cases like Australia, where the measures weren't as strict as other countries, no improvement observed. © 2021 ACM.