A hindcast experiment is conducted for the heavy air pollution event in Beijing that occurred during 16-21 December 2016 and the sensitive area for target observation that can help to improve initialization are explored using the Weather Research Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). To address the uncertainty of meteorological initial field, the sensitive variables and areas for the prediction of PM2.5 concentration in Beijing are identified by adopting four sets of initial analysis fields. The results show that when considering the initial uncertainties of wind, temperature, and specific humidity, their reductions in Heilongjiang region can most significantly decrease the forecast error of the PM2.5 concentration in Beijing. Furthermore, it is found that the improvement of the accuracy of initial wind fields, especially that in Heilongjiang region, decreases the forecast error of the PM2.5 concentration in Beijing to a great extent, and the decrease can be up to more than 40% in southwestern Beijing. Therefore, increasing more meteorological (especially wind) observations in Heilongjiang region and assimilating these observations into the initial field of the WRF model will significantly improve the quality of the initial meteorological condition and thus greatly reduce the PM2.5 forecast error of the air pollution event in Beijing. The model forecast skill will be greatly improved. It is concluded that the wind component in the initial field represents the physical variable and Heilongjiang region is the sensitive area for target observation associated with the forecast of the heavy air pollution event in Beijing that is selected for the present study.