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The conventional sensor calibration aims at mathematically relating the steady-state sensor responses to the target analyte concentrations to realize environment monitoring. However, commonly used chemical sensors usually require a relatively long time (say, minutes) to reach a steady state, and exhibit delayed responses when the analyte concentration changes quickly over time. To reduce the lag time and achieve real-time monitoring, this paper takes a statistical modeling approach. Based on the experimental data collected following the proposed design of experiments strategies, transfer function models are estimated to calibrate the dynamic behavior of a sensor. Such dynamic calibration models enable the use of transient sensor signals (as opposed to the steady-state responses) to track the rapid change in the target analyte concentration. In this paper, the dynamic modeling method is employed to calibrate a high-temperature electrochemical carbon monoxide (CO) sensor, and the empirical results have shown that the proposed method can reduce the time lag of a sensor by an order of magnitude (from minutes to seconds) compared with the steady-state calibration.