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Abstract
Objective Our aim was to elucidate the polycyclic aromatic hydrocarbon (PAH) metabolites exposure levels of pregnant women in the underdeveloped region of Zunyi, southwest China.
Methods Sociodemographic information was collected via questionnaires, and urine samples were collected at the same time. A total of 3047 pregnant women participated in the study. Gas chromatography/mass spectrometry was used to detect the urine concentrations of 10 PAH metabolites. A generalised linear model (GLM) was used to identify predictive factors of PAH metabolites.
Results All PAH metabolites had a detection rate greater than 60% (67.21%–90.57%) except for 4-OH-PHE at 55.54%. The median concentrations were 0.02–0.11 µg/g Cre except for 1-OH-NAP, 2-OH-NAP, 2-OH-FLU and 9-OH-FLU (0.36–0.50 µg/g Cre). The cluster analysis identified the phenanthrene and fluorene metabolite clusters (containing no other metabolites), while naphthalene metabolites (1-OH-NAP, 2-OH-NAP) could not be clustered without other metabolites. GLM analysis identified that pregnant women with the following characteristics have high urinary concentration of PAH metabolites: overweight, in the last trimester of pregnancy, distance between their house and main traffic lines as <5 m, use fuel for cooking, passive smoking, renovated their residence for less than 3 years, middle family income and office workers.
Conclusion The results clarified pregnant women from the economically underdeveloped area could be the victims of PAHs. In addition, PAHs present a demographic and seasonal differential distribution, which will aid in the development of targeted interventions and reduce exposure to PAHs during pregnancy.
- polycyclic aromatic hydrocarbons
- pregnancy outcome
- air pollution
- epidemiology
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
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Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
Footnotes
Contributors YT and RZ: formal analysis, methodology, validation, data curation, writing-original draft. YL and SX: data curation, validation. XW and HZ: methodology, validation. SA, CH, WC, XL: investigation, conceptualisation. DF, JL, LW, YZha, HY and LZ: investigation, data curation. QL: project administration, investigation, validation, resources. XS and YZho: project administration, investigation, validation, resources, writing–review and editing, funding acquisition. XS is responsible for the overall content as the guarantor.
Funding This work was supported by the National Key R&D Program of China (2018YFC1004300, 2018YFC1004302) and Science & Technology Program of Guizhou Province, China (QKHHBZ (2020)3002).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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