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This research proposes an Activity Pattern embedded Air Pollution Exposure Model (AP2EM), based on survey data of when, where, and how people spend their time and indoor/outdoor ratios for microenvironments. AP2EM integrates random forest and agent-based approaches to simulate the stochastic exposure to outdoor fine particulate matter (PM 2.5) along with indoor and in-vehicle PM 2.5 of outdoor origin. The R2 of the linear regression between the model's calculations and personal measurement was 0.65, which was more accurate than the commonly-used aggregated exposure (AE) model and the outdoor exposure (OE) model. The population-weighted PM 2.5 exposure estimated by the AP2EM was 36.7 μg/m3 in Jiangsu, China, during 2014–2017. The OE model overestimated exposure by 54.0%, and the AE model underestimated exposure by 6.5%. These misestimate reflect ignorance of traditional studies on effects posed from time spent indoors (~85%) and doing low respiratory rate activities (~93%), problems of biased sampling, and neglecting low probability events. The proposed AP2EM treats activity patterns of individuals as chains and uses stochastic estimates to model activity choices, providing a more comprehensive understanding of human activity and exposure characteristics. Overall, the AP2EM is applicable for other air pollutants in different regions and benefits China's air pollution control policy designs. [Display omitted] • A stochastic exposure model was developed for single- or multi-air pollution exposure estimates. • Modification effects of activity patterns and microenvironments on exposure were considered. • Activity patterns and PM 2.5 exposure characteristics in China were surveyed and analyzed. • Our model corrected the estimate biases of exposure compared with traditional methods. • Our model benefits modeling exposure of small or large groups under realistic or policy scenarios. [ABSTRACT FROM AUTHOR]