Objective To investigate the relationships between average exposure concentrations of air pollutants (O₃-8 h, PM₁₀, and PM_2.5) and childhood allergic rhinitis in Baotou, and to provide a scientific basis for developing effective intervention measures and protecting children's health.

Methods From March to April 2024, a questionnaire survey was conducted among all eligible children aged 3-8 years attending schools within 2 km of eight environmental-monitoring stations in Baotou. The survey collected information on demographics, behaviors, genetic factors, environmental factors, and diagnosed rhinitis. After rigorous quality control, 5 775 valid questionnaires were included for analysis (3 035 boys and 2 740 girls). Daily air-pollution data from the eight monitoring stations between 2016 and 2023 were collected, and average exposure concentrations of O₃-8 h, PM₁₀, and PM_2.5 were calculated. Restricted cubic spline models and multivariable logistic regression were applied to examine the associations of air pollutant average exposure concentrations with the risk of childhood allergic rhinitis. The data were stratified based on confounding factors to assess whether the associations of O₃-8 h, PM₁₀, and PM_2.5 average exposure concentrations with childhood allergic rhinitis were consistent across subpopulations. The interaction term, average pollutant exposure concentration×subpopulation variable, was included to evaluate interactions between air pollutants and confounding factors.

Results O₃-8 h average exposure concentration showed a U-shaped relationship with childhood allergic rhinitis risk. Compared to the medium-exposure group, the low-and high-exposure groups showed 1.37 (95%CI: 1.21-1.56) times and 1.44 (95%CI: 1.23-1.69) times higher rhinitis risks. PM₁₀ and PM_2.5 average exposure concentrations showed J-shaped relationships with childhood allergic rhinitis risk. For PM₁₀, the medium-and high-exposure groups had rhinitis risks 1.87 (95%CI: 1.62-2.16) and 2.51 (95%CI: 2.17-2.91) times higher than the low-exposure group, respectively. For PM_2.5, the medium-and high-exposure groups showed rhinitis risks 1.26 (95%CI: 1.08-1.47) and 7.43 (95%CI: 6.26-8.82) times higher than the low-exposure group, respectively. In subgroup analyses stratified by sex, whether living with parents, and parental allergy history, the associations of O₃-8 h, PM₁₀, and PM_2.5 with childhood allergic rhinitis were consistent with the main analysis. There was a significant interaction between PM₁₀ average exposure concentration and the presence of environmental pollution within 100 m from the children's homes (P_interaction=0.023).

Conclusion The risk of allergic rhinitis in children in Baotou exhibited a U-shaped nonlinear association with O₃-8 h average exposure concentration and J-shaped nonlinear increases with PM₁₀ and PM_2.5 average exposure concentrations. The presence of environmental pollution within 100 m around children's homes may exacerbate the effect of PM₁₀ exposure on allergic rhinitis risk. These findings underscore the need for further reductions in pollutant levels and attention to microenvironmental pollution under current air quality conditions to alleviate the burden of allergic rhinitis in children.