Data Mining Analysis on Air Pollutants During the COVID-19 Pandemic in Asuncion, Paraguay

In this work Data Mining techniques were applied to atmospheric contamination gases and meteorologic data collected during the pandemics of COVID-19 in order to extract relevant knowledge about its behaviour. These data were collected in the greater area of Asuncion, Paraguay during three time frames, before the start of pandemics, during the strict movement restriction and finally during the flexible movement restriction. Additionally, this data was correlated with daily new cases and deaths of COVID-19. Spearman correlation, association analysis and temporal statistics studies were used for obtaining significant knowledge. The conclusions showed that humid and cold conditions tend to offer better air quality and the highest levels of pollution occur during times of greatest human activity. The contamination levels correlation with the pandemic data are mainly negative. Another conclusion is that atmospheric pollution by carbon and sulfur has an anthropogenic origin, mainly due to human mobility activities and particularly the fossil fuels based automotor traffic.