Evaluating the Effect of Human Activity on Air Quality using Bayesian Networks and IDW Interpolation

As the world's human population continues to grow, it's crucial to also consider sustainability when addressing the need for living standards. While global warming has multiple causes, air pollution significantly contributes to it. The Air Quality Index (AQI) is a tool that determines the air's quality in certain areas by evaluating six primary pollutants, including sulphur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), carbon monoxide (CO), and particulate matter (PM2.5 and PM10). The AQI ranges from "good" to "severe," with scores ranging from 0 to 500, indicating the level of pollution. The AQI is also influenced by human activity in the environment. This study utilizes real-time data from the TNPCB (Tamil Nadu Pollution Control Board) on Madurai's AQI at three locations over the year 2021, during the COVID-19 pandemic. The Bayesian network is used to illustrate how human movement impacts the Air Quality Index through probabilistic analysis. Additionally, an IDW Interpolation chart is presented to demonstrate the impact of human activity on the AQI levels at the three stations.