Abstract: The atmospheric particulate pollution is serious in the Beijing-Tianjin-Hebei Region. To explore the spatial-temporal distribution of ρ(PM_2.5), based on the measured ρ(PM_2.5), MODIS aerosol optical depth (AOD), meteorological and land use data from 2013 to 2014, we developed a linear mixed effect model to make regress ρ(PM_2.5) measurements with AOD, meteorological and land use factors in Hebei Province. Then ten-fold cross validation was used to validate the accuracy of the predictions. Finally, the correction factors, which were calculated from the measured annual average ρ(PM_2.5) divided those in the models, were employed to correct biases in the predicted ρ(PM_2.5) caused by nonrandom missing of AOD. The results showed that:(1) The R² of the mixed effects model for Hebei Province was 0.85. The cross-validation R² was 0.77, the Root Mean Square Error (RMSE) and Relative Prediction Error (RPE) were 18.28 μg/m³ and 28.68%, respectively. (2) The correction factors of 2013-2014 ranged from 1.24 to 2.05. The corrected annual ρ(PM_2.5) of Hebei Province was 89.84 μg/m³, which was close to the monitoring data. (3) For the spatial pattern, the PM_2.5 concentrations in the plains were higher than those in the mountainous areas, the ρ(PM_2.5) in the southwestern plain were higher than those in the northeastern part. (4) The ρ(PM_2.5) had positive correlation with the AOD, temperature, relative humidity and negative correlation with the wind speed and atmospheric visibility. All these results suggested that the mixed effect model allows one to assess the spatial-temporal distribution of the ρ(PM_2.5) reliably; it can also predict the spatial-temporal pattern of the ρ(PM_2.5) in non-measured regions. Proper combination of forecasting factors and model correction factor are able to help improve the prediction accuracy of the model.