Simultaneous determination of multiple isomeric hydroxylated polycyclic aromatic hydrocarbons in urine by using ultra-high performance liquid chromatography tandem mass spectrometry

Urinary Metabolites of Polycyclic Aromatic Hydrocarbons of Rural Population in Northwestern China: Oxidative Stress and Health Risk Assessment

Polycyclic aromatic hydrocarbon (PAH) exposure is suspected to be linked to oxidative damage. Herein, ten PAH human exposure biomarkers [hydroxylated PAH metabolites (OH-PAHs)] and five oxidative stress biomarkers (OSBs) were detected in urine samples collected from participants living in a rural area (n = 181) in Northwestern China. The median molar concentration of ΣOH-PAHs in urine was 47.0 pmol mL-1. The 2-hydroxynaphthalene (2-OHNap; median: 2.21 ng mL-1) was the dominant OH-PAH. The risk assessment of PAH exposure found that hazard index (HI) values were <1, indicating that the PAH exposure of rural people in Jingyuan would not generate significant cumulative risks. Smokers (median: 0.033) obtained higher HI values than nonsmokers (median: 0.015, p < 0.01), suggesting that smokers face a higher health risk from PAH exposure than nonsmokers. Pearson correlation and multivariate linear regression analysis revealed that ΣOH-PAH concentrations were significant factors in increasing the oxidative damage to deoxyribonucleic acid (DNA) (8-hydroxy-2'-deoxyguanosine, 8-OHdG), ribonucleic acid (RNA) (8-oxo-7,8-dihydroguanine, 8-oxoGua), and protein (o, o'-dityrosine, diY) (p < 0.05). Among all PAH metabolites, only 1-hydroxypyrene (1-OHPyr) could positively affect the expression of all five OSBs (p < 0.05), suggesting that urinary 1-OHPyr might be a reliable biomarker for PAH exposure and a useful indicator for assessing the impacts of PAH exposure on oxidative stress. This study is focused on the relation between PAH exposure and oxidative damage and lays a foundation for the study of the health effect mechanism of PAHs.

Research progress in human biological monitoring of aromatic hydrocarbon with emphasis on the analytical technology of biomarkers

Aromatic hydrocarbons are unsaturated compounds containing carbon and hydrogen that form single aromatic ring, or double, triple, or multiple fused rings. This review focuses on the research progress of aromatic hydrocarbons represented by polycyclic aromatic hydrocarbons (including halogenated polycyclic aromatic hydrocarbons), benzene and its derivatives including toluene, ethylbenzene, xylenes (o-, m- and p-), styrene, nitrobenzene, and aniline. Due to the toxicity, widespread coexistence, and persistence of aromatic hydrocarbons in the environment, accurate assessment of exposure to aromatic hydrocarbons is essential to protect human health. The effects of aromatic hydrocarbons on human health are mainly derived from three aspects: different routes of exposure, the duration and relative toxicity of aromatic hydrocarbons, and the concentration of aromatic hydrocarbons which should be below the biological exposure limit. Therefore, this review discusses the primary exposure routes, toxic effects on humans, and key populations, in particular. This review briefly summarizes the different biomarker indicators of main aromatic hydrocarbons in urine, since most aromatic hydrocarbon metabolites are excreted via urine, which is more feasible, convenient, and non-invasive. In this review, the pretreatment and analytical techniques are compiled systematically for the qualitative and quantitative assessments of aromatic hydrocarbons metabolites such as gas chromatography and high-performance liquid chromatography with multiple detectors. This review aims to identify and monitor the co-exposure of aromatic hydrocarbons that provides a basis for the formulation of corresponding health risk control measures and guide the adjustment of the exposure dose of pollutants to the population.

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

Determination of Hydroxy Polycyclic Aromatic Hydrocarbons in Human Urine Using Automated Microextraction by Packed Sorbent and Gas Chromatography-Mass Spectrometry

A fast methodology for the determination of monohydroxy polycyclic aromatic hydrocarbons in human urine using a fully automated microextraction by packed sorbent coupled to a gas chromatograph-mass spectrometer is reported. Sample preparation requires simple hydrolysis, centrifugation, filtration, and dilution. The method does not require a derivatization step prior to analysis with gas chromatography and allows the measurement of up to three samples per hour after hydrolysis. Quantitation is carried out by a one-point standard addition allowing the determination of 6 analytes with good limits of detection (10.1-39.6 ng L^-1 in water and 0.5-19.4 µg L^-1 in urine), accuracy (88-110%) and precision (2.1-23.4% in water and 5.1-19.0% in urine) values. This method has been successfully applied to the analysis of six urine samples (three from smoker and three from non-smoker subjects), finding significant differences between both types of samples. Results were similar to those found in the literature for similar samples, which proves the applicability of the methodology.