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Prediction models and major controlling factors of antibiotics bioavailability in hyporheic zone

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Abstract

Recently, antibiotics have been frequently detected in the hyporheic zone (HZ) as a novel contaminant. Bioavailability assessment has gradually attracted more attention in order to provide a more realistic assessment of human health risks. In this study, two typical antibiotics, oxytetracycline (OTC) and sulfamethoxazole (SMZ), were used as target pollutants in the HZ of the Zaohe–Weihe River, and the polar organics integrated sampler was used to analyze the variation of antibiotics bioavailability. According to the characteristics of the HZ, the total concentration of pollutants, pH, and dissolved oxygen (DO) were selected as major predictive factors to analyze their correlation with the antibiotics bioavailability. Then the predictive antibiotic bioavailability models were constructed by stepwise multiple linear regression method. The results showed that there was a highly significant negative correlation between OTC bioavailability and DO (P < 0.001), while SMZ bioavailability showed a highly significant negative correlation with total concentration of pollutants (P < 0.001) and a significant negative correlation with DO (P < 0.01). The results of correlation analysis were further verified by Principal Component Analysis. Based on the experimental data, we constructed eight prediction models for the bioavailability of two antibiotics and verified them. The data points of the six prediction models were distributed in the 95% prediction band, indicating that the models were more reliable and accurate. The prediction models in this study provide reference for the accurate ecological risk assessment of the bioavailability of pollutants in the HZ, and also provide a new idea for predicting the bioavailability of pollutants in practical applications.

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Acknowledgements

The authors are also thankful to all experts involved for their valuable outputs and help. T.Z. contributed to formal analysis and writing—original draft. H.L. contributed to investigation, funding acquisition, and validation. M.Z. contributed to methodology and software. R.F. contributed to investigation. R.H. contributed to data curation. J.Z. contributed to writing—review & editing. Y.C. contributed to writing—review & editing. All authors reviewed the manuscript.

Funding

Henan Province Transportation Science and Technology Project (No. 2020G-2-10); the National Natural Science Foundation of China (No. 41977163); the key research and development program of Shaanxi (No. 2022ZDLSF06-06).

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Authors and Affiliations

  1. Henan College of Transportation, Zhengzhou, 450008, Henan, China

    Tao Zhu

  2. Henan Transport Investment Group Co., Ltd., Zhengzhou, China

    Hui Li

  3. Ocean University of China, Qingdao, 266100, Shandong, China

    Min Zhou

  4. Henan Provincial Department of Transport, Zhengzhou, 45000, Henan, China

    Min Zhou

  5. Key Laboratory of Subsurface Hydrology and Ecology in Arid Areas, Ministry of Education, Chang’an University, Xi’an, 710054, China

    Ruyi Feng, Ruixin Hu, Jianping Zhang & Yan Cheng

  6. School of Water and Environment, Chang’an University, Xi’an 710054, China

    Ruyi Feng, Ruixin Hu, Jianping Zhang & Yan Cheng

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  1. Tao Zhu
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Contributions

The authors are also thankful to all experts involved for their valuable outputs and help. T.Z.: Formal analysis, Writing-original draft. H.L.: Investigation, Funding acquisition, Validation. M.Z.: Methodology, Software. R.F.: Investigation. R.H.: Data curation. J.Z.: Writing-review & editing. Y.C.: Writing-review & editing. All authors reviewed the manuscript.

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Correspondence to Min Zhou.

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Zhu, T., Li, H., Zhou, M. et al. Prediction models and major controlling factors of antibiotics bioavailability in hyporheic zone. Environ Geochem Health 45, 5785–5797 (2023). https://doi.org/10.1007/s10653-023-01624-6

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