Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China.

PM2.5is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the computed tomography scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5exposure and pulmonary nodules was limited. To examine potential associations of exposures to PM2.5and its major chemical constituents with the prevalence of pulmonary nodules. A total of 16 865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5and its components on the risk of pulmonary nodules, respectively. Each 1 mg m-3increase in PM2.5(OR 1.011 (95% CI: 1.007-1.014)) was positively associated with pulmonary nodules. Among five PM2.5components, in single-pollutant effect models, every 1μg m-3increase in organic matter (OM), black carbon (BC), and NO3-elevated the risk of pulmonary nodule prevalence by 1.040 (95% CI: 1.025-1.055), 1.314 (95% CI: 1.209-1.407) and 1.021 (95% CI: 1.007-1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5components was 1.076 (95% CI: 1.023-1.133) fold. Notably, NO3-BC and OM contributed higher risks of pulmonary nodules than other PM2.5components. And the NO3-particles were identified to have the highest contribution. The impacts of PM2.5components on pulmonary nodules were consistent across gender and age.These findings provide important evidence for the positive correlation between exposure to PM2.5and pulmonary nodules in China and identify that NO3-particles have the highest contribution to the risk.