In this paper, the four-dimensional variational (4D-Var) technique is applied to assimilate aircraft measurements during the Transport and Chemical Evolution over the Pacific (TRACE-P) field experiment into a chemical transport model, Sulfur Transport Eulerian Model, version 2K1 (STEM-2K1). Whether data assimilation would produce better analyzed fields is examined. It is found that assimilating ozone observations from one of two independent flights improves model prediction of the other flight ozone measurements, which are withheld as validation data. The adjusted initial fields after only assimilating the total reactive nitrogen (NO _y ) observations lead to better predictions of NO, NO₂, and PAN, based on their agreement with the withheld measurements. One experiment simultaneously assimilating the observations of O₃, NO, NO₂, HNO₃, PAN, and RNO₃ demonstrates that the model is able to match those measurements well by changing the initial fields. In addition, the model predictions of NO _y improve significantly after assimilating the aforementioned multiple observation species, which are independent of the withheld NO _y measurements. In the paper, we also show that the key species whose initial mixing ratios would significantly affect the agreement between model and measurements can be identified using adjoint sensitivity analysis. Such information can be used to reduce the number of control variables in the 4D-Var data assimilation. To speed up the optimization process in the 4D-Var, we enforce the concentration upper bounds through the limited memory–Broyden-Fletcher-Goldfarb-Shanno-B (L-BFGS-B) algorithm, and this proves to be effective.