Rui Ma
ABSTRACT
Mobile sensing systems are deployed for urban air pollution monitoring to increase coverage over a city. However, the sampling irregularity brings great challenges for fine-grained pollution field recovery. To address this problem, we proposed a generative model based inference algorithm. By modeling the air pollution evolution and data sampling process separately, the temporal-spatial correlation of pollution field can be considered with irregular sampled data. We use a convolutional long-short term memory structure in the generative model and train it with the scattered observations from mobile sensing. Evaluations on synthesized data and a deployment in the city of Tianjin show that our algorithm accurately captures fine-grained PM2.5 pollution patterns and changes. The average inference error is 6.7μg/m3, which achieves 23.8% improvement over existing techniques.
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Digital Library
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