Accurate air pollution prediction needs systems that monitor diverse environmental data, adapt to changing pollution patterns, and ensure secure real-time communication. Existing systems struggle to capture distributed sensor interactions and dynamic environmental variations effectively. To address this problem, the study proposes an Adaptive and Efficient Air Pollution Monitoring Network (AEPM-Net) incorporating Metal Oxide Semiconductor (MOS) nanosensors with an average particle size ranging from 20 to 40 nm for sensitive air-quality monitoring. The system monitors levels of Carbon Monoxide \(\:CO\) , Nitrogen Dioxide \(\:{NO}_{2}\) , Sulfur Dioxide \(\:{SO}_{2}\) , Nitric Oxide \(\:NO\) , Volatile Organic Compound \(\:VOC\) , and Particulate Matter \(\:PM\) , which includes fine particulate matter \(\:PM2.5\) and coarse particulate matter \(\:PM10\) , in different locations. The system uses SPECK encryption and additive Lightweight Homomorphic Encryption (LHE) to protect collected pollutant data, while noise filtering and modified Z-score normalization methods help to stabilize the data. The Spatio-Temporal Data Processing Module (ST-DPM) analyzes nanosensor data across time and space by capturing correlations among distributed sensors and environmental variations. The Hierarchical Attention Mechanism (HAM) prioritizes important spatio-temporal features to improve prediction accuracy. The Dynamic Adaptation Module (DAM) adjusts parameters in real time, while the Pollution Level Prediction Module (PLPM) forecasts pollutant levels with uncertainty handling. Over 90 days, the model achieved R² values of 0.973 \(\:CO\) , 0.974 \(\:NO\) , 0.975 \(\:{NO}_{2}\) , 0.978 \(\:{SO}_{2}\) , 0.971 \(\:VOC\) , 0.969 \(\:PM2.5\) , and 0.967 \(\:PM10\) . The proposed framework supports environmental sustainability by enabling early detection of hazardous pollutants, improving urban air quality monitoring, and assisting effective pollution control to protect public health.
