Here, a hierarchical autoregressive spatio-temporal model under the Bayesian framework is proposed to address the simultaneous multi-site ${\rm PM}_{2.5}$ prediction. The true daily average concentration of ${\rm PM}_{2.5}$ is regarded as a potential spatio-temporal process, then the temporal correlation is described by the first-order autoregressive process and the spatial correlation is captured based on the Matérn process, which greatly improves the efficiency in dimension reduction and synchronous prediction. In addition, meteorological factors such as daily maximum temperature, relative humidity and wind speed are used as explanatory variables to improve the prediction accuracy. The combination of Bayesian method and MCMC can realize parameter estimation and prediction process due to the model's hierarchical structure. The empirical analysis of daily ${\rm PM}_{2.5}$ concentration in Beijing shows that the proposed model has good interpolation or prediction performance in both spatial and temporal dimensions.