TY - JOUR
T1 - Model-based clustering of hydrochemical data to demarcate natural versus human impacts on bedrock groundwater quality in rural areas, South Korea
AU - Kim, Kyoung Ho
AU - Yun, Seong Taek
AU - Park, Seong Sook
AU - Joo, Yongsung
AU - Kim, Tae Seung
N1 - Publisher Copyright:
© 2014 Elsevier B.V.
PY - 2014/11/7
Y1 - 2014/11/7
N2 - Improved evaluation of anthropogenic contamination is required to sustainably manage groundwater resources. In this study, we investigated the hydrochemical measurements of 18 parameters from a total of 102 bedrock groundwater samples from two representative rural areas in South Korea. We used model-based clustering with a normal (Gaussian) mixture model to differentiate the contributions of natural versus anthropogenic processes to the observed groundwater quality. Water samples varied in hydrochemistry from a Ca-Na-HCO3 type to a Ca-HCO3-Cl type. The former type reflected derivation of major ions largely from water-rock interactions, while the latter type recorded varying degrees of anthropogenic contamination. Among the major dissolved ions, fluoride and nitrate were shown to be good indicators of the two types, respectively. The results of model-based clustering showed that the bivariate normal mixture model, which was based on the covariance of nitrate and fluoride, was more robust than multivariate analysis, and provided better discrimination between the anthropogenic and natural groundwater groups. Model-based clustering to measure the degree of cluster membership for each sample also showed a gradual change in groundwater chemistry due to mixing between the two water groups. This study provided an example of the successful application of model-based clustering to evaluate regional groundwater quality and demonstrated that better selection of the dimensional structure (i.e., selection of optimal variables and number of clusters) based on hydrochemistry was crucial in obtaining reasonable clustering results.
AB - Improved evaluation of anthropogenic contamination is required to sustainably manage groundwater resources. In this study, we investigated the hydrochemical measurements of 18 parameters from a total of 102 bedrock groundwater samples from two representative rural areas in South Korea. We used model-based clustering with a normal (Gaussian) mixture model to differentiate the contributions of natural versus anthropogenic processes to the observed groundwater quality. Water samples varied in hydrochemistry from a Ca-Na-HCO3 type to a Ca-HCO3-Cl type. The former type reflected derivation of major ions largely from water-rock interactions, while the latter type recorded varying degrees of anthropogenic contamination. Among the major dissolved ions, fluoride and nitrate were shown to be good indicators of the two types, respectively. The results of model-based clustering showed that the bivariate normal mixture model, which was based on the covariance of nitrate and fluoride, was more robust than multivariate analysis, and provided better discrimination between the anthropogenic and natural groundwater groups. Model-based clustering to measure the degree of cluster membership for each sample also showed a gradual change in groundwater chemistry due to mixing between the two water groups. This study provided an example of the successful application of model-based clustering to evaluate regional groundwater quality and demonstrated that better selection of the dimensional structure (i.e., selection of optimal variables and number of clusters) based on hydrochemistry was crucial in obtaining reasonable clustering results.
KW - Bedrock groundwater quality
KW - Hydrochemistry
KW - Model-based clustering
KW - Natural versus anthropogenic processes
KW - Normal (Gaussian) mixture model
UR - http://www.scopus.com/inward/record.url?scp=84906736623&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2014.07.055
DO - 10.1016/j.jhydrol.2014.07.055
M3 - Article
AN - SCOPUS:84906736623
SN - 0022-1694
VL - 519
SP - 626
EP - 636
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - PA
ER -