ArticleScientific reports2025
Assessing the impact of air pollution on lung function in South Korea using Bayesian kernel machine regression.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigates the relationship between air pollution and lung function in the South Korean adult population using Bayesian Kernel Machine Regression (BKMR). By integrating 2017 Korea National Health and Nutrition Examination Survey (KNHANES) data with air pollution data, the study examines the individual and joint effects of key air pollutants-[Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and CO-on lung function indicators, including COPD (binary) and [Formula: see text]FVC (continuous). The findings reveal that [Formula: see text] and [Formula: see text] have negative effects on lung function, both individually and interactively. As the concentrations of these pollutants increase, the probability of developing COPD and the decline in [Formula: see text]FVC become more pronounced. This study highlights the compounded risks posed by pollutant mixtures, providing critical insights for public health interventions and air quality policy improvements in South Korea. Future research directions include addressing time-lagged effects and regional variations to enhance the understanding of these relationships.
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