Sensor-Based Ozone Monitoring and Forecasting in a Synchrotron Radiation Laboratory Using Autoregressive Integrated...

Ozone monitoring in laboratory environments is essential for ensuring personnel safety and maintaining stable experimental conditions, particularly in enclosed facilities where ozone may accumulate during high-energy radiation operations. This study investigates the short-term prediction of ozone concentration using data obtained from a sensor-based ozone monitoring system deployed at the National Synchrotron Radiation Research Center (NSRRC). Ozone concentration measurements were collected using a UV absorption-based ozone analyzer and analyzed as a time-series dataset under controlled experimental conditions. Three forecasting models-Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and linear regression-were evaluated for short-term ozone concentration prediction. Experimental results indicate that the ARIMA model provides superior predictive performance for the small-sample dataset used in this study. In the Right direction, ARIMA achieved R² values of 89.5%, 86.3%, and 81.1% at distances of 5 cm, 10 cm, and 15 cm, respectively, while also demonstrating stable performance in the Up direction. The results highlight the effectiveness of classical time-series models for sensor data analysis in environments with limited sensing data. The proposed framework demonstrates the potential of integrating sensing devices with predictive data analytics to support real-time environmental monitoring and safety management in laboratory facilities.