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Urban air pollution is a major environmental problem affecting human healthand quality of life. Monitoring stations have been established to continuouslyobtain air quality information, but they do not cover all areas. Thus, thereare numerous methods for spatially fine-grained air quality inference. Sinceexisting methods aim to infer air quality of locations only in monitoredcities, they do not assume inferring air quality in unmonitored cities. In thispaper, we first study the air quality inference in unmonitored cities. Toaccurately infer air quality in unmonitored cities, we propose a neuralnetwork-based approach AIREX. The novelty of AIREX is employing amixture-of-experts approach, which is a machine learning technique based on thedivide-and-conquer principle, to learn correlations of air quality betweenmultiple cities. To further boost the performance, it employs attentionmechanisms to compute impacts of air quality inference from the monitoredcities to the locations in the unmonitored city. We show, through experimentson a real-world air quality dataset, that AIREX achieves higher accuracy thanstate-of-the-art methods.

AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities