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Long- and short-term exposure to PM_2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM_2.5 exposure during the period January 10–31, 2013. Technically, the processing of large space-time PM_2.5 datasets and the mapping of the space-time distribution of PM_2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM_2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms …