A Spatial-Temporal Model to Improve PM2.5 Inference

PM2.5 is one of the major indicators of ambient air quality which has become a focus of public attention. Urban PM2.5 can be measured by air quality monitoring stations which are costly and not sufficiently installed in a city. In this paper, we aim to infer the PM2.5 information at the place where there is no air quality monitoring station. As PM2.5 concentration varies over time and space domains, we propose a joint topic model to jointly model the spatial and temporal patterns of PM2.5. Numerical results suggest that the proposed model achieves better inference based on five related datasets compared with traditional methods.