IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i4p3135-d1064693.html
   My bibliography  Save this article

Anaerobic Storage Completely Removes Suspected Fungal Pathogens but Increases Antibiotic Resistance Gene Levels in Swine Wastewater High in Sulfonamides

Author

Listed:
  • Xinyue Zhao

    (College of Life Sciences, Zhejiang University, Hangzhou 310058, China)

  • Mengjie Zhang

    (College of Life Sciences, Zhejiang University, Hangzhou 310058, China)

  • Zhilin Sun

    (College of Architecture Engineering, Zhejiang University, Hangzhou 310058, China)

  • Huabao Zheng

    (Zhejiang Province Key Laboratory of Soil Contamination Bioremediation, Zhejiang A&F University, Hangzhou 311300, China)

  • Qifa Zhou

    (College of Life Sciences, Zhejiang University, Hangzhou 310058, China)

Abstract

Wastewater storage before reuse is regulated in some countries. Investigations of pathogens and antibiotic resistance genes (ARGs) during wastewater storage are necessary for lowering the risks for wastewater reuse but are still mostly lacking. This study aimed to investigate pathogens, including harmful plant pathogens, and ARGs during 180 d of swine wastewater (SWW) storage in an anaerobic storage experiment. The contents of total organic carbon and total nitrogen in SWW were found to consistently decrease with the extension of storage time. Bacterial abundance and fungal abundance significantly decreased with storage time, which may be mainly attributed to nutrient loss during storage and the long period of exposure to a high level (4653.2 μg/L) of sulfonamides in the SWW, which have an inhibitory effect. It was found that suspected bacterial pathogens (e.g., Escherichia–Shigella spp., Vibrio spp., Arcobacter spp., Clostridium_sensu_stricto_1 spp., and Pseudomonas spp.) and sulfonamide-resistant genes Sul1 , Sul2 , Sul3 , and SulA tended to persist and even become enriched during SWW storage. Interestingly, some suspected plant fungal species (e.g., Fusarium spp., Ustilago spp. and Blumeria spp.) were detected in SWW. Fungi in the SWW, including threatening fungal pathogens, were completely removed after 60 d of anaerobic storage, indicating that storage could lower the risk of using SWW in crop production. The results clearly indicate that storage time is crucial for SWW properties, and long periods of anaerobic storage could lead to substantial nutrient loss and enrichment of bacterial pathogens and ARGs in SWW.

Suggested Citation

  • Xinyue Zhao & Mengjie Zhang & Zhilin Sun & Huabao Zheng & Qifa Zhou, 2023. "Anaerobic Storage Completely Removes Suspected Fungal Pathogens but Increases Antibiotic Resistance Gene Levels in Swine Wastewater High in Sulfonamides," IJERPH, MDPI, vol. 20(4), pages 1-11, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3135-:d:1064693
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/4/3135/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/4/3135/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feifan Shi & Xinyue Zhao & Qilu Cheng & Hui Lin & Huabao Zheng & Qifa Zhou, 2022. "High-Energy-Density Organic Amendments Enhance Soil Health," IJERPH, MDPI, vol. 19(19), pages 1-11, September.
    2. Anne Chao & John Bunge, 2002. "Estimating the Number of Species in a Stochastic Abundance Model," Biometrics, The International Biometric Society, vol. 58(3), pages 531-539, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:jss:jstsof:40:i09 is not listed on IDEAS
    2. Zhang, Hongmei & Ghosh, Kaushik & Ghosh, Pulak, 2012. "Sampling designs via a multivariate hypergeometric-Dirichlet process model for a multi-species assemblage with unknown heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2562-2573.
    3. Dankmar Böhning & Rattana Lerdsuwansri & Patarawan Sangnawakij, 2023. "Modeling COVID‐19 contact‐tracing using the ratio regression capture–recapture approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3818-3830, December.
    4. Balabdaoui, Fadoua & Kulagina, Yulia, 2020. "Completely monotone distributions: Mixing, approximation and estimation of number of species," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    5. Chun-Huo Chiu, 2023. "A Richness Estimator Based on Integrated Data," Mathematics, MDPI, vol. 11(17), pages 1-24, September.
    6. Maria De Angelis & Maria Piccolo & Lucia Vannini & Sonya Siragusa & Andrea De Giacomo & Diana Isabella Serrazzanetti & Fernanda Cristofori & Maria Elisabetta Guerzoni & Marco Gobbetti & Ruggiero Franc, 2013. "Fecal Microbiota and Metabolome of Children with Autism and Pervasive Developmental Disorder Not Otherwise Specified," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    7. David R Blair & Kanix Wang & Svetlozar Nestorov & James A Evans & Andrey Rzhetsky, 2014. "Quantifying the Impact and Extent of Undocumented Biomedical Synonymy," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-17, September.
    8. Dankmar Böhning & Herwig Friedl, 2021. "Population size estimation based upon zero-truncated, one-inflated and sparse count data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1197-1217, October.
    9. Maria De Angelis & Eustacchio Montemurno & Maria Piccolo & Lucia Vannini & Gabriella Lauriero & Valentina Maranzano & Giorgia Gozzi & Diana Serrazanetti & Giuseppe Dalfino & Marco Gobbetti & Loreto Ge, 2014. "Microbiota and Metabolome Associated with Immunoglobulin A Nephropathy (IgAN)," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-15, June.
    10. Chang Xuan Mao & Nan Yang & Jinhua Zhong, 2013. "On Population Size Estimators in the Poisson Mixture Model," Biometrics, The International Biometric Society, vol. 69(3), pages 758-765, September.
    11. Jérôme A. Dupuis & Michel Goulard, 2011. "Estimating Species Richness from Quadrat Sampling Data: A General Approach," Biometrics, The International Biometric Society, vol. 67(4), pages 1489-1497, December.
    12. Semyon K Kolmykov & Yury V Kondrakhin & Ivan S Yevshin & Ruslan N Sharipov & Anna S Ryabova & Fedor A Kolpakov, 2019. "Population size estimation for quality control of ChIP-Seq datasets," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-17, August.
    13. Dankmar Böhning & Ekkehart Dietz & Ronny Kuhnert & Dieter Schön, 2005. "Mixture models for capture-recapture count data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 29-43, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3135-:d:1064693. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.