Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO Sensor and Automated Data Segmentation.

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO monitoring in classrooms has become commonplace. CO data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO mass balance equations.