Using multivariate statistical techniques and geochemical modelling to identify factors controlling the evolution of groundwater chemistry in a typical transitional area between Taihang Mountains and North China Plain

Abstract

Identifying the key factors controlling groundwater chemical evolution in mountain-plain transitional areas is crucial for the security of groundwater resources in both headwater basins and downstream plains. In this study, multivariate statistical techniques and geochemical modelling were used to analyse the groundwater chemical data from a typical headwater basin of the North China Plain. Groundwater samples were divided into three groups, which evolved from Group A with low mineralized Ca-HCO 3 water, through Group B with moderate mineralized Ca-SO 4 -HCO 3 water, to Group C with highly saline Ca-SO 4 and Ca-Cl water. Water-rock interaction and nitrate contamination were mainly responsible for the variation in groundwater chemistry. Groundwater chemical compositions in Group A were mainly influenced by dissolution of carbonates and cation exchange, and suffered less nitrate contamination, closely relating to their locations in woodland and grassland with less pronounced human interference. Chemical evolution of groundwater in Groups B and C was gradually predominated by the dissolution of evaporites, reverse ion exchange, and anthropogenic factors. Additionally, the results of the inverse geochemical model showed that dedolomitization caused by gypsum dissolution, played a key role in the geochemical evolution from Group A to Group B. Heavy nitrate enrichment in most groundwater samples of Groups B and C was closely associated with the land-use patterns of farmland and residential areas. Apart from the high loads of chemical fertilizers in irrigation return flow as the main source for nitrate contamination, the stagnant zones, flood irrigation pattern, mine drainage, and groundwater-exploitation reduction program were also important contributors for such high mineralization and heavy NO 3 − contents in Group C. The important findings of this work not only provide the conceptual framework for the headwater basin but also have important implications for sustainable management of groundwater resources in other headwater basins of the North China Plain. K E Y W O R D S geochemical modelling, groundwater evolution, headwater basin, hydrochemistry, multivariate statistical analysis, North China Plain

Hydrogeochemical characterization of reservoir waters undergoing salinization processes in Northeastern Brazil

“…By calculating the correlations and ratios between the different ions, it is possible to obtain valuable information concerning the geochemical processes that gave rise to the chemical water characteristics. Reverse geochemical modeling simulates mass transfers caused by water-rock interaction processes and, thus, provides evidence for variations in the chemical composition of waters from two different samples (Xing et al, 2013;Liu et al, 2020a).…”

Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory

“…The ion sample data collected from inflows for each of the three sampling events were investigated by sampling event and as a pooled composite dataset using an agglomerative hierarchical clustering analysis (HCA) (Hastie et al, 2009). HCA creates clusters of samples based on the dissimilarity of the ion concentration magnitudes via Ward's linkage method that uses an analysis of variance to identify clusters and the Euclidian distance to measure the similarity between samples (Liu et al, 2020; Moya et al, 2015; Ward, 1963). By combining all data, it would be possible to determine if samples could be categorized consistently with local knowledge of the system and if composite HCA results similarly clustered samples into different source categories when compared to the results from individual events.…”

Hydrochemical Characteristics and Water Quality of Groundwater in the Thick Loess Deposits

“…6). The molar ratio of HCO3 -/Ca 2+ in groundwater is 2 or 4 for exclusive control of calcite or dolomite dissolution, respectively (Liu et al, 2020). In the flood season, a significant correlation existed between Ca 2+ and SO4 2-(r=0.5),…”

“…If Ca 2+ , Mg 2+ , SO4 2and HCO3are derived from the dissolution of calcite, dolomite and gypsum, the charge balance is expressed as (1/2 HCO3 -+SO4 2-]/(Ca 2+ +Mg 2+ ) = (Liu et al 2020). However, most samples (83.8% collected in the rainy season and 96.3% collected in the dry season) were located above the 1:1 equilibrium line (Fig.…”