Accurate forecasting of critical pollutants such as NO2and PM2.5 is essential in the ongoing battle against air pollution. This paper introduces ConvTrans, a novel hybrid architecture that integrates convolutional and Transformer technologies to enhance the precision of PM2.5 and NO2predictions. Our evaluations demonstrate that ConvTrans improves PM2.5 prediction accuracy, increasing the R-squared value by 3.6%, reducing RMSE by 5.4%, MSE by 7.5%, and MAE by 17.3%. Similarly, for NO2predictions, the model shows a 4.2% increase in R-squared, along with a 5.4% reduction in RMSE, a 13.7% decrease in MSE, and a 5.6% reduction in MAE. These results highlight the model’s effectiveness in short-term forecasting, addressing key challenges in environmental management.

Air pollution presents serious threats to public health and environmental sustainability, especially when it comes to concentrations of ozone (O3). Even though they are accurate, traditional monitoring methods have spatial limitations that cause gaps in the coverage of data. In order to tackle this issue, we provide a hybrid deep learning model that predicts O3levels in different parts of India by integrating Transformer and Convolutional Neural Networks (CNN) using satellite and meteorological data. While the Transformer model uses meteorological data to identify temporal trends, the CNN component analyzes satellite pictures to extract spatial information. Multiple assessment criteria, including R2, MSE, RMSE, and MAE, show that we enhance prediction accuracy by merging both models. Our model performs better in both short- and long-term predictions than other deep learning techniques and RNN, such as CNN, GRU, and LSTM models. The results suggest that the hybrid CNN-Transformer model is a promising solution for accurate air pollution forecasting, offering critical insights for timely interventions and policy development.

Air quality is a critical factor influencing public health, with pollutants such as particulate matter (PM10, PM2.5), ground-level ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2) posing significant health risks. Understanding the relationship between these pollutants is essential for effective air quality management. In this study, we apply a comprehensive range of statistical models, including Multivariate Analysis of Variance (MANOVA), Canonical Correlation Analysis (CCA), Factor Analysis, post-hoc tests, linear regression, and other techniques to evaluate the correlation between PM2.5 and key air pollutants. By employing these methods, we aim to identify the most significant contributors to PM2.5 levels and provide insights to inform air quality control strategies. Our findings offer a robust statistical framework for predicting PM2.5 concentrations, enhancing the ability of policymakers and environmental agencies to mitigate pollution-related health risks.