In Disaster medicine and public health preparedness
OBJECTIVE : The focus of this study is to monitor the effect of lockdown on the various air pollutants due to COVID-19 pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced.
METHODS : Various machine learning techniques: Decision Trees, Linear Regression and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during lockdown period and last two years 2018 and 2019 has been presented.
RESULTS : From the experimental work, it has been observed that the pollutants Ozone and Toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are Ozone, NH3, NO2, and PM10.
CONCLUSIONS : The novel corona virus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control Ozone pollution as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.
Sethi Jasleen Kaur, Mittal Mamta
Air Pollutants, COVID-19, Decision Trees, Linear Regression, Machine Learning, Random Forest