Combined use of multivariate statistical analysis and hydrochemical analysis for groundwater quality evolution: A case study in north chain plain

Abstract

Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis (MA) and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis (DA) method, which can afford 100% correct assignation according to the 3 different clusters (good water (GW), poor water (PW), and very poor water (VPW)) obtained from cluster analysis (CA). According to factor analysis (FA), 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.

Keywords

factor / groundwater quality / hydrochemical variable / industrial activity / multivariate statistical analysis

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