Predicting and Analyzing Indoor Air Quality in Inpatient Wards Using IoT-Based Long-Term Data and Machine Learning

Indoor air quality (IAQ) plays a crucial role in safeguarding the health of both patients and healthcare workers in hospital environments. Accurate IAQ analysis and prediction are vital for optimizing ventilation, filtration, and other control measures to maintain a safe indoor atmosphere. This study investigates IAQ in hospital spaces by utilizing long-term data from internet of things (IoT) sensors installed in general wards and negative pressure isolation wards. Given the significant influence of outdoor air, IAQ requires continuous monitoring across different seasons and extended periods. In this study, IAQ was measured over nearly a year, capturing seasonal variations and long-term trends. Clustering algorithms were applied to identify complex patterns and detect anomalies in key IAQ parameters, including temperature, CO₂ concentration, and particulate matter 2.5 μm (PM_2.5). These clustering results were then integrated into a long short-term memory (LSTM) model to enhance IAQ prediction for subsequent time steps. The findings indicate that incorporating clustering results as input variables substantially improves IAQ prediction accuracy. Notably, the root mean squared error for PM_2.5 prediction decreased from 8.51 to 3.99 when clustering results were included. This study underscores the potential of leveraging IoT sensors and machine learning techniques for real-time IAQ monitoring and forecasting in hospital settings. These insights can support the development of effective control strategies to maintain a healthy and comfortable indoor environment for both patients and healthcare workers.