As indoor air pollution has emerged as a social issue since the COVID-19 pandemic, pollution management in large-scale facilities has been recognized as an important task. For this purpose, this study proposes real-time pollution level detection using sensors and efficient control path setting using Dijkstra algorithm as key technologies. In addition, by introducing outlier determination algorithm and priority algorithm, we propose ways to increase the reliability of the data and enable efficient control work. The outlier determination algorithm describes the process of identifying and processing outliers based on sensor data in an environmental monitoring system. It describes in detail the process of averaging the recent 10 sensor data, calculating the Z-score to detect outliers, and removing and replacing the data determined to be outliers. The priority algorithm describes the process of establishing an efficient control path in consideration of the pollution level of each region. It suggests how to select the most polluted areas first and use them as a starting point to set the control path. In addition, it introduces an iterative process of detecting and responding to the pollution level in real time, which allows the system to be continuously optimized and to respond to environmental pollution. Through this, it is expected to increase the reliability and efficiency of the environmental monitoring system through outlier judgment algorithms and priority algorithms, thereby quickly identifying and responding to pollution situations.