Abstract

Background
This study aimed to identify personal and environmental factors influencing malondialdehyde (MDA) levels, measured using thiobarbituric acid reactive substances (TBARS) assay, among residents of Ulsan, South Korea.
Methods
A total of 909 adult residents of Ulsan, recruited between 2022 and 2024, participated in a survey and urine analysis. The dependent variable was the natural log-transformed TBARS value (Ln_TBARS). The study examined 47 independent variables, including personal characteristics, socioeconomic factors, work-related exposures, residential area, and living environment. Residential areas were classified into three groups (A, B, and C) according to potential exposure to industrial hazardous air pollutants, determined jointly by proximity to large-scale industrial complexes and modelled concentrations of benzene, toluene, and xylene. Stepwise multiple regression, least absolute shrinkage and selection operator (LASSO) regression, and random forest (ntree = 500) were applied and compared to identify factors influencing oxidative stress levels among Ulsan residents.
Results
Residential area had the greatest impact on oxidative stress across all three models. Group C showed significantly lower Ln_TBARS levels than those in group A (stepwise: β = −0.395, p < 0.001; LASSO coefficient = −0.529; random forest: %IncMSE = 17.07%). Age and female sex were also consistently associated with oxidative stress across all three models (stepwise: β = 0.319, p < 0.001; LASSO coefficient = 0.014; random forest: %IncMSE = 12.67%, and stepwise: β = 0.152, p < 0.001; LASSO coefficient = 0.154; random forest: %IncMSE = 6.93%).
Conclusions
Residential area was the strongest predictor of oxidative stress across all three analytical models, with higher urinary MDA among residents of areas closer to industrial complexes; older age and female sex were also consistent predictors. These findings, robust across conventional statistical and machine-learning approaches, support systematic environmental health monitoring and targeted policy measures for residents near industrial zones.

• Keywords: Least-squares Analysis; Oxidative stress; Random Forest; Regression Analysis; Residential Characteristics