In-situ validation of embedded physics-based calibration in low-cost particulate matter sensor for urban air quality monitoring

Low-cost particle sensors enable dense, geospatially distributed networks that enhance the spatial and temporal resolution of urban air quality monitoring. However, field interference in complex urban systems challenges the reliability of sensor data. Robust evaluation and calibration are essential to address this issue. In this study, a low-cost sensor system was deployed near standard monitoring stations from March 1 to May 30, 2024, recording PM2.5 concentration, PM10 concentration, particle counts in six different size channels, and ambient temperature and humidity. The results revealed systematic overestimation and interactions with environmental factors in the sensor data. To address these challenges, a physics-based calibration model, leveraging sensor-reported particle size information, was developed and compared with traditional empirical and machine learning models. These calibration models were embedded into the sensor system, followed by a second field campaign from June 1 to 30. While the machine learning model achieved the best performance during the first campaign (R2 > 0.90, RMSE