Many studies assessing urban air-pollution health impacts assign outdoor PM₂.₅ at a single residential address, ignoring children’s daily movements between home and school. This simplification may misclassify exposure and bias short-term risk estimates, potentially misguiding school-area prevention priorities. Using 2,543 laboratory-confirmed influenza cases among school-aged children in Guangzhou (2014–2019) and hourly monitoring–derived PM₂.₅ concentration fields, we tested whether mobility-aware exposure assignment changes short-term PM₂.₅–influenza risk estimates. We constructed a residence-based model (RBM) and a multi-context activity-weighted model (MCAWM) that reallocates hourly PM₂.₅ across home, school, and street-network commute corridors using stylized school-day schedules and open-source street networks. Although RBM and MCAWM lag-0 daily PM₂.₅ estimates were highly correlated (r = 0.93), 15.8% of child-days differed by ≥ 10 µg/m³. Per 10 µg/m³, cumulative relative risks were 1.04 (95% CI: 1.02–1.06) for RBM and 1.10 (1.06–1.14) for MCAWM, with improved fit and slightly more persistent lag effects under MCAWM. The concentration-dependent difference in cumulative relative risk between MCAWM and RBM increased above ~ 20 µg/m³, with a breakpoint near 45 µg/m³ indicating more rapid RBM underestimation at higher concentrations. Stratification by school environment revealed the largest mobility gains in high-density, low-greenspace schools. Scenario-based population attributable fractions indicated that, at 50 µg/m³, residence-only exposure would miss roughly 0.20 additional attributable cases per 1,000 children per season. A synthetic “ground-truth” experiment and sensitivity analyses suggest these gains reflect structural reductions in exposure misclassification rather than model tuning. Aligning hourly PM₂.₅ with children’s home–school–commute activity spaces indicates that residence-only assignment tends to attenuate the estimated PM₂.₅–influenza relationship and can understate preventable burden, supporting scalable targeting of school-area interventions such as school greening, near-school traffic management, and lower-exposure commuting corridors.
Jing et al. (Thu,) studied this question.