Assessing Human Impact on Air Quality with Bayesian Networks and IDW Interpolation

As the explosion of the human population happens globally, meeting the demands for livelihood should also involve considerations for sustainability. Though there are several causes of global warming, air pollution makes a tremendous contribution to it. The Air Quality Index (AQI) measures how clean or polluted the air is in specific areas based on six major pollutants such as sulphur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), carbon monoxide (CO), and particulate matter (PM2.5 and PM10). There are six levels in the AQI, as "good," "satisfactory," "moderate," "poor," and "severe," that validate the score between 0-500. The implicit factor that affects the AQI is human movement within the environment. This research work involves real-time datasets collected from the TNPCB (Tamil Nadu Pollution Control Board) regarding Madurai’s AQI at three stations collected for the year 2021(during COVID-19 period). The Bayesian network exhibits the causal relationship between human movement and the Air Quality Index through probabilistic modelling. An IDW Interpolation chart is also visualized to conceptualize the human intervention (NIL, Partial and Complete) for the AQI value obtained in 3 stations.