A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites

Oh, Junseop; Kim, Ho-Rim; Yu, Soonyoung; Kim, Kyoung-Ho; Lee, Jehee; Park, Sunhwa; Kim, Hyun-Koo; Yun, Seong Taek

doi:10.1016/j.jhazmat.2023.131712

Cited by 4 publications

(1 citation statement)

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“…Previous studies have shown that such livestock mortality leachate contains high concentrations of inorganic and organic compounds (e.g., ammonium, alkalinity, chloride, sulfate, BOD, and COD) as a result of carcass decomposition 37 , 55 , 56 . The lower ORP in the leachate (median = -66 mv) compared to the surrounding groundwater (median = 115 mv) is due to anaerobic conditions prevailing in the burial pits 44 , 57 , 58 . The carcass leachate leakage from burial pits thus induces the subsequent increases of ionic concentrations in groundwater, exhibiting positive correlations with EC and TDS concentrations but negative correlations with ORP.…”

Section: Resultsmentioning

confidence: 97%

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

Oh,

Kim,

Kim

et al. 2024

Sci Rep

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This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

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