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Given significant air pollution problems, air quality index (AQI) monitoringhas recently received increasing attention. In this paper, we design a mobileAQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,to efficiently build fine-grained AQI maps in realtime. Specifically, we firstpropose the Gaussian plume model on basis of the neural network (GPM-NN), tophysically characterize the particle dispersion in the air. Based on GPM-NN, wepropose a battery efficient and adaptive monitoring algorithm to monitor AQI atthe selected locations and construct an accurate AQI map with the sensed data.The proposed adaptive monitoring algorithm is evaluated in two typicalscenarios, a two-dimensional open space like a roadside park, and athree-dimensional space like a courtyard inside a building. Experimentalresults demonstrate that our system can provide higher prediction accuracy ofAQI with GPM-NN than other existing models, while greatly reducing the powerconsumption with the adaptive monitoring algorithm.

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing