Understanding indoor ozone behavior is essential for evaluating human health risks, as people spend 70% to 90% of their time indoors. Ozone, a key air pollutant formed by chemical reactions between nitrogen oxides and volatile organic compounds under sunlight, contributed to nearly 490,000 deaths worldwide in 2021 due to long-term exposure. Traditional exposure assessments relying on outdoor data fail to account for indoor factors like ventilation, indoor sources, and building materials that significantly affect actual ozone levels. Mechanistic models require detailed indoor parameters that are difficult to obtain at scale, while linear regression models struggle with nonlinear environmental relationships, creating an urgent need for accurate, scalable prediction methods.
Researchers from Fudan University and the Chinese Academy of Sciences have addressed this gap by developing a machine learning model capable of predicting hourly indoor ozone concentrations across 18 Chinese cities. The study, published in Eco-Environment & Health on July 9, 2025, used random forest algorithms trained on low-cost sensor measurements combined with meteorological and ventilation data. By comparing two models—one excluding and one including window-status information—the researchers demonstrated that incorporating ventilation behavior significantly improved prediction accuracy, representing a major advancement toward more realistic ozone exposure assessment.
The team collected over 8,200 hours of indoor ozone data using portable electrochemical sensors in 23 households. Predictor variables included outdoor ozone levels from high-resolution random-forest and MERRA-2 datasets, meteorological parameters such as temperature, humidity, wind, solar radiation, boundary-layer height, and surface pressure, and window-opening status recorded manually by volunteers. Including window behavior increased cross-validation R² from 0.80 to 0.83 and reduced RMSE from 7.89 to 7.21 ppb. The model accurately captured hourly ozone fluctuations and regional differences, performing better in southern than northern China and during cold rather than warm seasons. Predictor-importance analysis identified surface pressure, temperature, and ambient ozone as dominant factors, with ventilation emerging as a crucial behavioral determinant. Diurnal comparisons revealed indoor ozone concentrations were 40% lower than outdoor levels during the day, highlighting the buffering effect of indoor environments.
"Most exposure studies still rely on outdoor ozone data, but that's only half the story," said Prof. Xia Meng, senior author of the study. "Our findings show that ventilation behavior—something as simple as whether a window is open or closed—can change exposure dramatically. By integrating such behavioral data with meteorological information through machine learning, we can finally estimate indoor ozone more precisely at large scales. This will strengthen epidemiological studies and help guide public-health interventions in urban and residential settings." The research is detailed in the journal article available at https://doi.org/10.1016/j.eehl.2025.100170.
This research introduces a practical, low-cost strategy for predicting indoor ozone exposure in real time across large geographic areas. The model can be integrated into health-risk assessments, smart-home monitoring systems, and public-health surveillance platforms, enabling policymakers and scientists to better understand indoor-outdoor exposure differences. Future work could extend the framework to other pollutants such as fine particulate matter or nitrogen dioxide, incorporate smart sensors for automated window tracking, and expand monitoring to diverse climatic zones. Ultimately, this machine-learning approach bridges environmental modeling with daily life, promoting healthier indoor environments in rapidly urbanizing regions.
