Machine Learning Model Predicts Indoor Ozone Exposure with Unprecedented Accuracy

A new machine learning model developed by researchers from Fudan University and the Chinese Academy of Sciences can predict hourly indoor ozone concentrations with high accuracy, using easily accessible data including outdoor ozone levels, meteorological conditions, and window-opening behavior. The study, published in Eco-Environment & Health on July 9, 2025, addresses a significant limitation in current exposure assessment methods that rely primarily on outdoor measurements despite people spending 70% to 90% of their time indoors.

Ozone is a key air pollutant formed by chemical reactions between nitrogen oxides and volatile organic compounds under sunlight. In 2021, long-term ozone exposure contributed to nearly 490,000 deaths worldwide. Traditional exposure models have struggled to accurately assess indoor ozone levels because they either require detailed indoor parameters that are difficult to obtain at scale or use linear regression approaches that cannot capture complex environmental relationships. This has created an urgent need for accurate, scalable models that can predict indoor ozone exposure based on accessible data.

The research team collected over 8,200 hours of indoor ozone data using portable electrochemical sensors in 23 households across 18 Chinese cities. They trained random forest algorithms using predictor variables including outdoor ozone levels from high-resolution datasets, meteorological parameters such as temperature, humidity, wind, solar radiation, boundary-layer height, and surface pressure, and window-opening status recorded manually by volunteers. By comparing two models—one excluding and one including window-status information—the researchers demonstrated that incorporating ventilation behavior significantly improved prediction accuracy.

Including window behavior raised cross-validation R² from 0.80 to 0.83 and lowered RMSE from 7.89 to 7.21 parts per billion. The model accurately captured hourly ozone fluctuations and regional differences, performing better in southern than northern China and in cold rather than warm seasons. Predictor-importance analysis showed surface pressure, temperature, and ambient ozone as dominant factors, with ventilation emerging as a crucial behavioral determinant. Diurnal comparisons revealed that 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."

The 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. The full study is available at https://doi.org/10.1016/j.eehl.2025.100170. 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.