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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 2.5 E) 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 accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]