AI for Cleaner Air: Predictive Modeling of PM2.5 Using Deep Learning and Traditional Time-Series Approaches

Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is quantified using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination () metrics. The comparative analysis identifies optimal predictive approaches for air quality modeling, emphasizing computational efficiency and accuracy. Additionally, CNN classification performance is evaluated using a confusion matrix, accuracy, precision, and F1-score. The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models, exhibiting lower error rates and higher values, thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting. This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.