Environmental Image-Based PM2.5 Prediction Using Vision Transformers

Fine particulate matter (PM2.5) is one of the most harmful atmospheric pollutants, posing significant risks to human health and environmental sustainability. Conventional PM2.5 monitoring systems rely on dense sensor networks, which are often expensive, require regular maintenance, and provide limited spatial coverage, particularly in resource-constrained regions. This study proposes an image-based PM2.5 prediction framework the proposed Vision Transformer framework achieves to estimate PM2.5 concentrations directly from environmental RGB images without depending solely on physical sensors. A dataset comprising 6,587 environmental images from eight geographic classes was synchronized with ground-truth PM2.5 measurements and processed through image resizing, normalization, illumination correction, and data augmentation. To improve model interpretability and robustness, physics-informed visual descriptors, including atmospheric transmission, sky smoothness, color ratio, contrast, entropy, and solar zenith angle, were integrated with transformer-based patch embeddings. The proposed ViT model utilizes multi-head self-attention to capture long-range spatial dependencies associated with atmospheric haze and scattering effects, which are difficult to model using conventional convolutional architectures. The performance of the proposed framework was evaluated against Support Vector Regression (SVR), Random Forest (RF), and CNN-LSTM models using regression-based metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). The results demonstrate that the proposed Vision Transformer framework achieves superior PM2.5 prediction performance, showing improved accuracy and robustness across urban and semi-urban environments and varying atmospheric conditions. The findings indicate that environmental images contain valuable visual information for reliable PM2.5 estimation, providing a low-cost, scalable, and sensor-independent solution for future air quality monitoring applications, particularly in areas with limited monitoring infrastructure.