Citywide Air Quality Forecasting over Sparse Sensor Networks: Cross-Location Generalization and Deep Learning Reliability Under Missing Data

Smart city environmental monitoring depends on sparse air quality sensor networks and analytics services that remain reliable under node additions, outages, and missing streams. We propose an operational deep learning framework for citywide cross-location forecasting from a limited set of sensors, delivering low-latency, real-time concentration heatmaps at unsensed locations by combining temporal prediction with spatial regression. We formulate single-stage spatiotemporal forecasting and benchmark nine recurrent, convolutional, and multilayer architectures against classical baselines. The framework forecasts O3, NO2, PM2.5, and PM10 over horizons from 1 hour to 10 days. Using open monitoring data from Madrid (Spain) and Cali (Colombia), we evaluate generalization by holding out stations, reflecting deployment to new sensor nodes and sparse coverage regimes. We further compare missing data handling strategies and show that common imputation can substantially degrade accuracy, increasing RMSE by up to 74% in some settings. Beyond prediction, the framework provides a basis for guiding sensor network densification; confidence estimates can highlight locations where additional sensors may be most beneficial. These results provide actionable guidance for deploying AI-enabled sensing services with robust performance under realistic sensor reliability constraints while supporting real-time citywide mapping.