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Machine Learning Model Predicts Indoor Ozone Exposure Using Window Behavior

Breakthrough machine learning model predicts indoor ozone exposure using window behavior data from 18 Chinese cities. Study reveals ventilation dramatically affects air pollution risks indoors.

This research matters because it addresses a critical gap in air pollution science: while we know outdoor ozone causes nearly 490,000 deaths annually worldwide, people spend 70-90% of their time indoors where exposure levels differ significantly. Traditional models have struggled to account for behavioral factors like ventilation, leaving public health officials and researchers with incomplete exposure assessments. This machine learning approach bridges environmental modeling with daily human behavior, enabling more accurate epidemiological studies and targeted interventions. For urban residents, particularly in rapidly developing regions, this means better-informed building codes, ventilation guidelines, and personal protection recommendations. The model's scalability allows for widespread implementation in smart cities and health monitoring systems, potentially reducing ozone-related health risks for millions of people who previously had no way to measure their actual indoor exposure.