Association between Combined Exposure to Underground Air Pollutants and Hypertension in Coal Miners: A Cross-Sectional Study

Background Coal miners are chronically exposed to underground air pollutants (e.g., coal dust, CO, NO₂), with a notably higher hypertension (HTN) incidence than the general population. Previous studies focused on single pollutants, yet the health effects of occupational multipollutant mixtures remain unclear. This study aimed to elucidate the association between combined exposure and HTN, identifying dominant contributors. Methods Data were collected from 3,432 coal miners employed by three large coal mining companies in Shaanxi Province, China. We assessed cumulative exposure to five air pollutants: coal dust, carbon monoxide (CO), carbon dioxide (CO₂), nitrogen monoxide (NO), and nitrogen dioxide (NO₂). Blood pressure measurements were taken, with HTN defined as a systolic blood pressure ≥ 140 mmHg and/or a diastolic blood pressure ≥ 90 mmHg. Four statistical methods were used to evaluate the associations between mixed exposure and HTN in coal miners: multivariate logistic regression, weighted quantile regression (WQS), quantile g-computation (comp), and Bayesian kernel machine regression (BKMR). Results Multivariate logistic regression analysis indicated that all five air pollutants were positively correlated with an increased risk of HTN after adjusting for covariates. Furthermore, all three mixed models—WQS (OR: 1.08; 95% CI: 1.04–1.11), comp (OR: 1.07; 95% CI: 1.05–1.08), and BKMR—consistently demonstrated a significant positive correlation between coexposure to mixtures of underground air pollutants and HTN. Within the mixture, CO (WQS: 0.611; comp: 0.435) and NO₂ (WQS: 0.236; comp: 0.209) emerged as the primary contributors. Conclusion Our study revealed a significant association between exposure to mixtures of underground air pollutants and an elevated risk of HTN, with CO and NO₂ being the primary contributors. The implementation of ventilation improvements, real-time monitoring, and prospective cohorts is critical for risk mitigation and mechanistic validation.