Accurate Air Pollution Sensing and Forecasting via Mobile Infrastructure and Hybrid CNN-LSTM

Abstract: Urban air pollution represents a significant public health challenge where traditional Continuous Ambient Air Quality Monitoring Stations (CAAQMS) provide accurate measurements but suffer from sparse spatial distribution. This research presents an integrated framework combining mobile IoT sensors with hybrid deep learning for comprehensive air quality assessment. The system deploys ESP32-based sensor modules with electrochemical gas detectors (MQ-135, MQ-7, MQ-136) and optical particulate matter sensors to capture spatially distributed measurements of PM2.5, NO2, CO, and SO2. A hybrid CNNLSTM model processes spatial patterns and temporal dependencies to calibrate sensor readings and generate Air Quality Index (AQI) forecasts. The prototype implementation demonstrates feasibility, achieving Mean Absolute Error of approximately 24 AQI units, with complete mobile deployment projected to reduce errors by 20-40% and provide city-wide coverage with over 50,000 daily measurements.

Keywords: Air Quality, Deep Learning, IoT Sensors, Forecasting, Sensor Calibration