Indoor contaminants jeopardize people's health and may even cause serious consequences under extreme conditions. Therefore, the prompt and accurate identification of indoor airborne contaminant characteristics is significant for indoor health and safety. In this paper, we used an inverse Markov chain model, combined with the regularization proposed in our previous research, to identify periodic source strength under steady airflow in a multi-zone building. The impact of different measurement noise (0.05％, 0.1％, 0.2％) on the inverse results was investigated. The results showed that the greater the noise, the greater the oscillation of the inverse result. Furthermore, we also investigated the effect of adjusting the calculation time step (5 s, 10 s, 20 s, 30 s) and adding digital filters (Sliding window filter and Butterworth low pass filter) on the inverse source release rate. The results showed that properly increasing the calculation time step can reduce the impact of measurement noise. The root mean square error (RMSE) of the inverse source strength with 0.1％ noise decreased from 22.89 under a 5-s time step to 0.9793 under a 30-s time step. It was also found that adding digital filters could reduce the oscillation of the inverse source results, and the performance of the filters also depends on the calculation time steps.