Improving Real-time High-Resolution Estimates of PM2.5 Concentration Fields in Urban Areas by the SmartAQ+ System with Data Fusion and Machine Learning

Monitoring PM_2.5 (mass of particles with diameter less than 2.5 μm) concentrations is challenging due to the limited number of ground-level monitoring stations and the limitations of existing modeling approaches. This study introduces SmartAQ+, a data fusion model that combines a chemical transport model-based system (SmartAQ) with low-cost sensor measurements and machine learning (ML) to enhance high-resolution PM_2.5 estimations at the present-time at a 1 × 1 km² scale. SmartAQ+ integrates real-time data from low-cost PM_2.5 sensors, weather stations, and land-use information to improve the accuracy of present-time PM_2.5 estimations at all locations in an urban area. SmartAQ+ demonstrated superior performance compared to SmartAQ that does not use real-time measurements in estimating the present-time PM_2.5, reducing the corresponding mean error, fractional bias (FBIAS) and fractional error (FERROR) by a factor of two. SmartAQ+ correctly identified 132 out of 190 PM_2.5 exceedance events of the daily limit of 25 μg m⁻³, compared to SmartAQ’s 34, while reducing false positives by a factor of 2 and missed events by a factor of 3. The performance gains depended on the availability of nearby sensors. In data sparse zones and during unusual events the model can inherit biases from the chemical transport model and can underestimate extremes. The study highlights the potential of data fusion models to address the limitations of standalone approaches, offering more precise air quality estimations in areas of a city in which there are no measurements.