AI-Driven Particulate Matter Forecasting and Spatial Estimation in the CityAirQ Urban Monitoring Network

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 μ g/m 3 ), a 4.7% reduction relative to Random Forest (7.47 μ g/m 3 ), while the Seasonal naïve (10.41 μ g/m 3 ), Persistence (11.51 μ g/m 3 ), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean R 2 of 0.873 on the classic four-station network and a median R 2 of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ . The historical-transfer Random Forest retained a sample-limited positive PM 2.5 association with the raw mobile readings ( r = 0.432 , n = 6 ), while a strict one-day-ahead online persistence predictor reduced PM 2.5 MAE from 40.58 to 20.00 μ g/m 3 on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives.