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1H-NMR-based profiling of organic components in leachate from animal carcasses disposal site with time

Abstract

Leachate, generated by the decomposition of animal carcasses, presents many environmental, sanitary, and food safety hazards. However, research on the characteristics of leachate is lacking. In this study, we performed biochemical profiling of leachate from two animal species (pig and cattle) in two soil types (sandy loam and sandy soil) using 1H-NMR-based profiling, followed by multivariate data analysis. The leachate was collected from a well-controlled artificial burial site over a 31-week period. Principal components analysis (PCA) of the NMR data showed similar patterns between species and soil types. Organic components, including organic acids and phenols, predominated, and their levels increased with time. The methylamine level in leachate from pig carcasses 18 weeks following burial was significantly higher than that from cattle carcasses; leachate from cattle carcasses in sandy soil 1 week after burial contained unique components (specifically ethanol, formate, alanine, N-methylation, and taurine), in contrast with those from sandy loam soil. This study suggests that a NMR-based profiling approach is useful to characterize the organic components in leachate from animal carcasses over time.

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Acknowledgments

This study was supported by a grant from the Korea Basic Science Institute (C34705) and the National Research Foundation of Korea Grant funded by the Korean Government (MSIP) (2013, University-Institute cooperation program).

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Correspondence to Geum-Sook Hwang.

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Yong-Kook Kwon and Hyun-Whee Bae contributed equally to the work.

Responsible editor: Philippe Garrigues

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Kwon, YK., Bae, HW., Shin, S.K. et al. 1H-NMR-based profiling of organic components in leachate from animal carcasses disposal site with time. Environ Sci Pollut Res 21, 10453–10460 (2014). https://doi.org/10.1007/s11356-014-2992-7

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  • DOI: https://doi.org/10.1007/s11356-014-2992-7

Keywords

  • Leachate
  • Artificial pilot
  • Profiling
  • Organic components
  • H-NMR
  • Multivariate data analysis