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Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information. [ABSTRACT FROM AUTHOR]

To efficiently and effectively monitor and mitigate air pollution in the urban environment, it is of paramount importance to integrate into a unified whole air pollutant concentration databases coming from different sources including the ground-based stations, mobile sensors, remote sensing, atmospheric-chemical-transport models and social media for the analysis and unraveling of the complex air pollution processes in space and time. This study constructs and implements for the first time a prototype of the fully integrated air pollution decision support system (APDSS) that put together in an integrated manner all relevant multi-scale, multi-type and multi-source data for decision-making on urban air pollution. The prototype contains the main system that handles the multi-source, multi-type and multi-scale databases, queries, visualization and data mining algorithms and the integrated modules that individually and holistically capitalize on the power of the ground-based stations, ground and aerial mobile sensors, satellite-borne remote-sensing technologies, atmospheric-chemical-transport models and social media. It renders a solid scientific foundation and system development methodology for the study of the spatiotemporal air pollution profiles crucial to the mitigation of urban air pollution. Real-life applications of the prototype are employed to illustrate the functionality of the APDSS. [ABSTRACT FROM AUTHOR]