Hydrogeochemistry of the formation waters in the San Francisco field, UMV basin, Colombia – A multivariate statistical approach

Abstract:In this study, the major chemical compositions of 159 shallow groundwater samples incorporated saturation index (SI) and Principal Component Analysis (PCA) were employed to evaluate the mainly geochemical processes that control the hydrogeochemical evolution of groundwater in a typical karst area, Bijie city, Guizhou Province. The groundwater samples in this study area were dominated of HCO 3 -Ca, HCO 3 -Ca-Mg, and HCO 3 -SO 4 -Ca types. The PCA suggested that four principal components could explain 88.85% of the total variance of 10 parameters, indicating that the hydrogeochemical evolution of groundwater was mainly controlled by the dissolution/precipitation of carbonates, gypsum, and halite minerals, cation exchange, and anthropogenic activities. To be specific, the enrichment of Ca 2+ , Mg 2+ , HCO 3 − and SO 4 2− in groundwater were primarily affected by the dissolution of dolomite and gypsum minerals, and the role of calcite dissolution was relatively weaker because most groundwater samples were saturated with respect to calcite. Besides, cation exchange was another factor that may affect the concentration of Ca 2+ and Mg 2+ in groundwater, and the concentration of SO 4 2− can also be influenced by coal mining activity. In addition, the concentrations of Na + and Cl − in groundwater were likely influenced by the dissolution of halite, cation exchange, and human activities.

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: Deuterium and Oxygen Isotopesmentioning

confidence: 99%

Hydrogeochemistry of Shallow Groundwater in a Karst Aquifer System of Bijie City, Guizhou Province

Jiang

Deng

et al. 2017

“…HCA is the most widely used cluster analysis technique in hydrogeology, and it can identify the initial relationship between any one sample and the entire dataset, typically visually illustrating the relationship with a dendrogram [33,34]. In this study, HCA was performed on standardized hydrochemistry datasets using Ward's linkage rule, which generates different groups based on an analysis of variance and then groups all nonresiduals into separate clusters, where each sample in a cluster is more similar to the other samples in the same cluster than to samples in other clusters.…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Quantification of Water Sources in a Coastal Gold Mine through an End-Member Mixing Analysis Combining Multivariate Statistical Methods

Liu

et al. 2020

Mixing calculations have been widely applied to identify sources of groundwater recharge, but these calculations have assumed that the concentrations of end-members are well known. However, the end-members of water remain unclear and are not easily available in practical applications. To better determine end-members and mixing ratios, an end-member mixing analysis combining multivariate statistical methods was used on a large, complex water chemistry dataset collected from the Shashandao gold mine in China. Multivariate statistical methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were applied to determine the specific end-members (these two methods verified each other). On the basis of the identified end-members, a maximum likelihood method was then used to estimate the mixing ratios of the water sources. The combined method proposed in this study can help to identify more accurate end-members and deal with uncertainty in end-member concentrations, and it can also adjust the concentrations until the optimal mixing ratios for the calculation are obtained. This method can be a powerful tool for groundwater management and in predicting water inrush in mining operations.

“…Analyses of water quality were conducted, including the parameters of pH, total alkalinity, total hardness, chloride, and sulfate. The water quality assessments by means of the methods of the Langelier Saturation Index (LSI), the Ryznar Stability Index (RSI), the Puckorius Scaling Index (PSI), and the Larson-Skold Corrosive Index (LSCI) were employed to estimate the tendency of scaling or corrosion [5][6][7][8][9][10][11][12][13][14]. The values of the LSI, the RSI, and the PSI are primarily used to estimate the scaling potential of calcium carbonate (CaCO 3 ) in water [14].…”

Section: Swhs Installed In the Three Remote-island Countiesmentioning

confidence: 99%

The Impact of Water Quality on the Use of Solar Water Heaters in Remote Islands of Taiwan

Fan

Chang

Chung

2016

Abstract:The use of solar water heaters (SWHs) in Taiwan's remote islands has been subjected to scaling and, in particular, corrosion problems due to sources of water. In this study, four different water quality indices including the Langelier Saturation Index (LSI), the Ryznar Stability Index (RSI), the Puckorius Scaling Index (PSI), and the Larson-Skold Corrosive Index (LSCI) were employed to assess the scaling and corrosion tendencies in SWHs caused by tap water and ground water in the Penghu, Kinmen, and Lienchiang counties, each of which is constituted of several remote islands. In general, the LSI, the RSI, and the PSI results show a slight scaling tendency in Penghu, but a corrosion tendency in Kinmen and Lienchiang. Nevertheless, all LSCI results show a serious steel corrosion tendency in these three counties. In addition, the chloride ion (Cl − ) concentrations are higher than 45 mg/L in either tap water (except for the Lieyu township in Kinmen) or ground water. This fact resulted in serious corrosion problems, as found in the currently installed SWHs, which were mainly made from 304 stainless steel. The metals with higher corrosion resistance to chloride ions are required in the manufacture of SWHs to be installed on these three remote-island counties.