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Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM 2.5 observations on the model chemical initial field PM 2.5 and visibility forecasts were investigated. The results show that assimilation of PM 2.5 reduces the uncertainty in the initial PM 2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM 2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µ g m -3 , and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM 2.5 improves PM 2.5 and visibility forecasts throughout the time window of 24 h. The PM 2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM 2.5 further contributes to the improvement of the visibility forecast. When the PM 2.5 increment is negative, it corresponds to an increase in visibility, and when the PM 2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM 2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast. [ABSTRACT FROM AUTHOR]