Identification of groundwater pollution from livestock farming using fluorescence spectroscopy coupled with multivariate statistical methods. (1st November 2021)

• Record Type: Journal Article
• Authors: Zhang, Yuanzheng; Liu, Yunde; Zhou, Aiguo; zhang, Li
• Is Part Of: Water research. Volume 206(2021)
• Publication Date: 2021-11-01
• Subjects: Groundwater pollution -- Dissolved organic matter -- EEM-PARAFAC -- Hierarchical cluster analysis -- Principal component analysis; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2021.117754
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Livestock farming altered the DOM content and optical properties in groundwater. A novel fluorescence method was established for identifying groundwater pollution. Tryptophan-like fluorescence would be a sensitive indicator of groundwater quality. Abstract: Extensive livestock farming has highly threatened groundwater quality, thereby necessitating a rapid and effective method to identify groundwater quality in such areas. Fluorescence spectroscopy has been recognized as an interpretable method for tracking anthropogenic influences on water quality, but its applicability in identifying the groundwater pollution from livestock farming remains unknown. In this study, the fluorescence characteristics of dissolved organic matter (DOM) in groundwater from a typical livestock farming area were investigated by using fluorescence excitation emission matrix (EEM)-parallel factor analysis (PARAFAC) coupled with multivariate statistical methods. The results showed that livestock farming significantly altered the content and composition of DOM in groundwater, and these effects were mainly observed in shallow groundwater in the study area. Hierarchical cluster analysis based on fluorescence parameters divided the groundwater samples into three clusters with significantly different pollution degrees: Cluster A, unpolluted; Cluster B, highly polluted; Cluster C, moderately polluted. In particular, the intensity of tryptophan-like fluorescence was high in the polluted groundwater but was almost undetectable in the unpolluted groundwater, suggesting that it is a potential indicator of groundwater quality. Principal component analysis based on the fluorescence parameters explained 91.5% of the variance with the first two principal components, and revealed that the degree of pollution dominated the fluorescence characteristics of groundwater in the study area. In addition, NO3 – was abundant in Clusters B and C, while it was low in Cluster A, validating the analysis results of fluorescence spectroscopy. These findings indicated that DOM fluorescence was sensitive to livestock farming pollution and could be applied to identify, monitor, and assess groundwater pollution from livestock farming. Graphical abstract: Image, graphical abstract