In Taiwan, PM2.5 was conventionally monitored by a small number of 77 monitoring stations. Recently, a project using a large number of small devices, called AirBoxes, was launched in March 2016 to monitor PM2.5 concentrations. Although thousands of AirBoxes have been deployed across Taiwan to give a broader coverage, they are mostly located in big cities, and their measurements are less accurate. We propose to use a spatial statistics technique, called kriging, which gives PM2.5 level with its standard error at any location in Taiwan by incorporating spatial dependence structure of AirBox data in a statistically optimal way. This provides a smoothly varied real-time PM2.5 concentration map and its associated standard error map. In addition, we develop a novel spatio-temporal control chart that monitors anomalous measurements by utilizing neighboring AirBox information. Our method automatically adaptive to different neighboring structures at different AirBox locations without the need to specify a neighborhood range. The proposed method has abilities to detect potential emission sources, malfunctioned AirBoxes, and AirBoxes that are wrongly put indoors.