Backward modeling of urinary test reliability for assessing PAH health risks: An approximation solution for naphthalene
2021
Li, Zijian | Zhang, Xiaoyu | Fu, Yisha | Xu, Yupeng | Chen, Jinru | Lu, Shaoyou
Urine sample tests are one of the most common methods of estimating human exposure to polycyclic aromatic hydrocarbons (PAHs) and assessing population health risks. To evaluate the reliability of the urine test and the impact of other PAH elimination routes on the health risk estimated by this test, we proposed a backward modeling framework integrating other common elimination routes of PAH metabolites to calculate the overall intake rate of the parent PAH based on the levels of corresponding main metabolites in urine. Due to limited biotransformation data, we selected naphthalene as an example to evaluate model performance and collected urine samples from 234 random adults in Shenzhen. The overall intake rates of naphthalene were then simulated and compared to current literature data. The simulated intake rates of naphthalene ranged from 3.70 × 10⁻³ mg d⁻¹ to 1.95 mg d⁻¹ and followed a lognormal distribution with a median value of 6.51 × 10⁻² mg d⁻¹. The results indicated that, if naphthalene exposure occurred only via food for the population of Shenzhen, the literature data fell within the most frequent interval [3.70 × 10⁻³, 4.45 × 10⁻²] but were lower than the simulated median value. However, if other exposure routes were considered, the allocation factor-adjusted literature data were close to the simulated median values. In addition, under normal physiological conditions, the simulated results were more sensitive to 1-hydroxynaphthalene (1-OHN) and 2-hydroxynaphthalene (2-OHN) levels in urine than other biometric variables, which is due to the limited load of 1-OHN and 2-OHN in human elimination routes. Furthermore, the suggested safety levels of 1-OHN and 2-OHN in urine to protect 99% of the general population of Shenzhen were 6.40 × 10⁻⁶ and 3.75 × 10⁻⁵ mg L⁻¹, which could be used as regulatory indicators based on the high reliability of the model.
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