Exploring Air Quality Dynamics and Predictive Modeling by Using Artificial Intelligence During COVID-19 Lock Down Over the Western Part of India

Abstract

The lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (>401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.