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Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM_2.5 removed natural dust (PM_{2.5_No Dust}) concentrations and the corresponding population's PM_{2.5_No Dust} exposure (PPM_2.5E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM_{2.5_No Dust}-polluted regions, whose spatial relative magnitude of PM_{2.5_NoDust} pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations …