Using multivariate statistical methods to assess the groundwater quality in an arsenic-contaminated area of Southwestern Taiwan

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

Comprehensive assessment of the impact of land use and hydrogeological properties on the groundwater quality in Taiwan using factor and cluster analyses

This study aims at making a comprehensive assessment of the impact of land use and the hydrogeological properties on groundwater quality. First, factor analysis (FA) is applied to reveal the main pollutant sources and hydrogeological processes controlling the groundwater quality. FA identifies the four most important factors. Factor 1 (seawater salinization) is characterized by a medium loading of land use type of aquaculture. It is recognized that the high scores for factor 1 in coastal areas are due to over-pumping from aquafarms. Focused land use management is required to prevent saline-water intrusion in coastal aquifers. Factor 3 (nitrate pollution) shows high correlations with the land use type of fruit farming and the gravel thickness in unsaturated layers. High scores for factor 3 are also found in the proximal area of the Chuoshui River Alluvial Fan and the northeastern mountain area in the Pingtung Plain. Fruit farmers should be educated to reduce the application of fertilizers and promote the organic fruit farming. The impacts of land use and the hydrogeological properties on both Factor 2 (arsenic enrichment) and Factor 4 (reductive dissolution of Fe²⁺ and Mn²⁺) are negligible. Second, cluster analysis (CA) is performed on computed scores of the four main factors to separates 123 monitoring wells into cluster 1 (low polluted zone), cluster 2 (nitrate polluted zone) and cluster 3 (hybrid polluted zone). The results obtained from CA provide practical applications such as reduce agrichemical use in the areas of cluster 2 and enforce intensive monitoring in the prioritizing areas of cluster 3. This study successively uses the FA and CA to extract the meaningful information present by geographical visualization of scores for 4 main factors and 3 distinct clusters zones. The results are essential for formulating sound groundwater resource and land use management policies to ensure groundwater sustainability.

Multivariate statistical and hydrochemical approaches for evaluation of groundwater quality in north Bahri city-Sudan

Groundwater has recently been considered one of the primary sources of water supply in Sudan. However, groundwater quality is continuously degraded due to overexploitation and long-term agricultural operations. The fossilized Cretaceous Nubian sandstone is the principal aquifer in the study area. This research aims to determine the major factors influencing groundwater quality and detect the suitability of groundwater for drinking and irrigation purposes by integrating hydrochemical and multivariate statistical methods. Hydrochemical plots such as Piper, Chadha, and Durov diagrams were applied to detect the groundwater facies and hydrochemical processes controlling the groundwater quality. They indicated Ca–Mg–HCO₃ water type as a dominant groundwater facies followed by Na–HCO₃ and Na–Cl types. Gibbs plots suggested that the dissolution of the minerals is the main factor influencing the water quality. The results of the Gibbs plot were further interpreted using saturation indices (SI). The SI values indicated that aragonite, calcite, and dolomite precipitated respectively in 58.33%, 75%, and 75% of groundwater samples. Multivariate statistical analyses, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were jointly employed to identify the structure of water quality data and deduce the main factors controlling groundwater quality. The statistical analysis revealed the effect of the physical and human-induced activities as the main factors influencing groundwater chemistry. These factors are rock-water interaction, agricultural practice, and organic contamination from septic tanks. Further, the suitability of groundwater for irrigation is determined using sodium adsorption ratio (SAR) and sodium percent (Na⁺%) indices. They carefully indicated that 75% of the groundwater samples in the study area are excellent for irrigation except for some sample location where the salinity hazard is stimulated by ion exchange. This integrated approach was effective in calibrating water quality assessment methodologies. The current research concluded that the implication of a groundwater quality monitoring scheme is crucial to ensure water supply sustainability in north Bahri city.

Difference in attenuation among Mn, As, and Fe in riverbed sediments

We report, for the first time, a detailed study at river water and hyporheic zone systems through collection and analyses of shallow sediments and selected source rocks, pore water, and river water from forty-two locations at the Chianan Plain (CP), SW Taiwan. The study was focused to understand the possible changes in the river water and sediment chemistry as a consequence of high arsenic (mean±SD=71.28±16.24μg/L, n=46) groundwater discharge to three major rivers in the plain. The study shows, except few locations, As concentration in river sediments corresponds to average As concentration in soil and upper crustal abundance and of source rock. Sequential extraction indicates that As is mostly bound to FeOOH. No enrichment of arsenic in river sediments or depletion of aqueous As and iron in pore water was observed down to the maximum sampling depth of 1.7m although manganese is enriched in sediments. Dissolved As concentrations in the river sediments are much lower compared to the hotspots in the CP aquifers. This suggests that no As attenuation processes are active or they cannot be detected in this zone. Mn precipitates at higher redox level compared to Fe and As and thus attenuates in the studied zone.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

Tropical soils with high aluminum concentrations cause oxidative stress in two tomato genotypes

Tropical and subtropical soils are usually acidic and have high concentrations of aluminum (Al). Aluminum toxicity in plants is caused by the high affinity of the Al cation for cell walls, membranes, and metabolites. In this study, the response of the antioxidant-enzymatic system to Al was examined in two tomato genotypes: Solanum lycopersicum var. esculentum (Calabash Rouge) and Solanum lycopersicum var. cerasiforme (CNPH 0082) grown in tropical soils with varying levels of Al. Plant growth; activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPOX), and glutathione reductase (GR) enzymes; stress-indicating compounds (malondialdehyde (MDA) and hydrogen peroxide); and morphology (root length and surface area) were analyzed. Increased levels of Al in soils were correlated with reduced shoot and root biomass and with reduced root length and surface area. Calabash Rouge exhibited low Al concentrations and increased growth in soils with the highest levels of Al. Plants grown in soils with high availability of Al exhibited higher levels of stress indicators (MDA and hydrogen peroxide) and higher enzyme activity (CAT, APX, GPOX, and GR). Calabash Rouge absorbed less Al from soils than CNPH 0082, which suggests that the genotype may possess mechanisms for Al tolerance.

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ 254 as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.

Identification of the hydrogeochemical processes in groundwater using classic integrated geochemical methods and geostatistical techniques, in Amol-Babol Plain, Iran

Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO₃, Ca-Na-HCO₃, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C₁, C₂, and C_3, have been classified using cluster analysis. The spatial distribution maps of Na⁺/Cl⁻, Mg²⁺/Ca²⁺, and Cl⁻/HCO₃⁻ ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area.

Characterization and assessment of potential environmental risk of tailings stored in seven impoundments in the Aries river basin, Western Romania

Background

The objective of this study was to examine the potential environmental risk of tailings resulted after precious and base metal ores processing, stored in seven impoundments located in the Aries river basin, Romania. The tailings were characterized by mineralogical and elemental composition, contamination indices, acid rock drainage generation potential and water leachability of hazardous/priority hazardous metals and ions. Multivariate statistical methods were used for data interpretation.

Results

Tailings were found to be highly contaminated with several hazardous/priority hazardous metals (As, Cu, Cd, Pb), and pose potential contamination risk for soil, sediments, surface and groundwater. Two out of the seven studied impoundments does not satisfy the criteria required for inert wastes, shows acid rock drainage potential and thus can contaminate the surface and groundwater. Three impoundments were found to be highly contaminated with As, Pb and Cd, two with As and other two with Cu. The tailings impoundments were grouped based on the enrichment factor, geoaccumulation index, contamination factor and contamination degree of 7 hazardous/priority hazardous metals (As, Cd, Cr, Cu, Ni, Pb, Zn) considered typical for the studied tailings. Principal component analysis showed that 47% of the elemental variability was attributable to alkaline silicate rocks, 31% to acidic S-containing minerals, 12% to carbonate minerals and 5% to biogenic elements. Leachability of metals and ions was ascribed in proportion of 61% to silicates, 11% to acidic minerals and 6% to the organic matter. A variability of 18% was attributed to leachability of biogenic elements (Na, K, Cl^-, NO₃^-) with no potential environmental risk. Pattern recognition by agglomerative hierarchical clustering emphasized the grouping of impoundments in agreement with their contamination degree and acid rock drainage generation potential.

Conclusions

Tailings stored in the studied impoundments were found to be contaminated with some hazardous/ priority hazardous metals, fluoride and sulphate and thus presents different contamination risk for the environment. A long term monitoring program of these tailings impoundments and the expansion of the ecologization measures in the area is required.