Apict:<u>A</u>ir Pollution E<u>pi</u>demiology Using Green AQI Predi<u>ct</u>ion During Winter Seasons in India

During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for <inline-formula><tex-math notation="LaTeX">$\boldsymbol {PM_{2.5}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\boldsymbol {NO_{2}}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\boldsymbol {CO}$</tex-math></inline-formula> pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the <inline-formula><tex-math notation="LaTeX">$\boldsymbol {PM_{2.5}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\boldsymbol {NO_{2}}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\boldsymbol {CO}$</tex-math></inline-formula> concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.