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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]