Background Introduced in Central London in 2019, the Ultra-Low Emission Zone (ULEZ) aims to improve air quality and population health. This study reports on the one-year findings of a natural experiment study of the ULEZ’s effect on children’s health and focuses on changes to children’s travel to school. We hypothesised that implementing the ULEZ in London would encourage children to switch to active travel to school.

Methods Children’s Health in London and Luton (CHILL) is a prospective parallel cohort study of 3414 multi-ethnic children aged 6–9 years attending 85 primary schools in Central London (intervention) and Luton (control with similar baseline air quality). Baseline and one-year follow-up data were collected from 3173 children who reported their mode of travel to school ‘today’ (day of annual health assessment) and ‘usually’. Active modes included walking, cycling, scootering, and taking public transport. Inactive modes included taking a car and taxi. Binomial logistic regressions were performed to analyse the impact of the ULEZ on the likelihood of switching from inactive to active travel modes, or vice-versa. Models were adjusted for by age, ethnicity, sex, parent’s employment and occupation, and baseline car ownership.

Results At baseline, 88% of children in the intervention group reported having used an active form of travel to school, compared to 59% in the control group. Results revealed that, relative to the control group, the intervention group was more likely to switch from inactive to active modes both ‘today’ (OR 2.59, 95%CI 1.65–4.06) and ‘usually’ (1.98, 1.07–3.68). Moreover, the intervention group was less likely to shift from active to inactive modes ‘today’ (0.28, 0.20–0.39) and ‘usually’ (0.22, 0.13–0.34) compared to the control group.

Conclusion Early results indicate that the ULEZ was associated with a shift in children’s travel to school towards more sustainable and active travel modes. Future analyses will include methods to more accurately define exposure groups and travel modes using GPS and accelerometer data.