Air pollution introduces a major challenge for societies. Massive deployment of air quality sensing devices and data analytics for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultra-low latency analytics. This paper conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.
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