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.

室内污染物会危害人们的健康，甚至可能在极端条件下造成严重后果。因此，迅速，准确地识别室内空气中的污染物特性对于室内健康和安全至关重要。在本文中，我们使用了逆马尔可夫链模型，结合我们先前研究中提出的正则化，来确定多区域建筑物在稳定气流下的周期性源强度。研究了不同测量噪声（0.05％，0.1％，0.2％）对反演结果的影响。结果表明，噪声越大，逆结果的振荡越大。此外，我们还研究了调整计算时间步长（5 s，10 s，20 s，30 s）并在反源释放速率上添加数字滤波器（滑动窗口滤波器和Butterworth低通滤波器）。结果表明，适当增加计算时间步长可以减少测量噪声的影响。具有0.1％噪声的反源强度的均方根误差（RMSE）从5s时间步长下的22.89降低到30s时间步长下的0.9793。还发现增加数字滤波器可以减少反源结果的振荡，并且滤波器的性能还取决于计算时间步长。30秒时间步长下的9793。还发现增加数字滤波器可以减少反源结果的振荡，并且滤波器的性能还取决于计算时间步长。30秒时间步长下的9793。还发现增加数字滤波器可以减少反源结果的振荡，并且滤波器的性能还取决于计算时间步长。