Vision-AQ: Explainable Multi-Modal Deep Learning for Air Pollution Classification in Smart Cities

Accurate air quality prediction (AQP) is crucial for safeguarding public health and guiding smart city management. However, reliable assessment remains challenging due to complex emission patterns, meteorological variability, and chemical interactions, compounded by the limited coverage of ground-based monitoring networks. To address this gap, we propose Vision-AQ (Visual Integrated Operational Network for Air Quality), a novel multi-modal deep learning framework that classifies Air Quality Index (AQI) levels by integrating environmental imagery with pollutant data. Vision-AQ employs a dual-input neural architecture: (1) a pre-trained ResNet50 convolutional neural network (CNN) that extracts high-level features from city-scale environmental photographs in India and Nepal, capturing haze, smog, and visibility patterns, and (2) a multi-layer perceptron (MLP) that processes tabular sensor data, including PM 2.5 , PM 10 , and AQI values. The fused representations are passed to a classifier to predict six AQI categories. Trained on a comprehensive dataset, the model achieves strong predictive performance with high accuracy, precision, recall and F1-score of 99%, with 23.7 million parameters. To ensure interpretability, we use Grad-CAM visualization to highlights the model’s reliance on meaningful atmospheric features, confirming its explainability. The results demonstrate that Vision-AQ is a reliable, scalable, and cost-effective approach for localized AQI classification, offering the potential to augment conventional monitoring networks and enable more granular air quality management in urban South Asia.