Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran

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.

Assessment of the water quality using multivariate statistics and the water quality index: a case study of the Yağlıdere Stream (Giresun) in the Eastern Black Sea region, Turkey

In this study, the water quality of the Yağlıdere Stream passing through Espiye (Giresun-NE Turkey) and Yağlıdere districts, where old and new mining activities are present, was evaluated, and characterized by using the water quality index (WQI), multivariate statistical, and GIS techniques. The downstream aquifer of the Yağlıdere Stream, which originates from Erimez Mountains and reaches the sea from the west of Espiye district, meets the domestic and drinking water needs of Yağlıdere and Espiye districts. In addition, activities such as energy production and fish farming are carried out along the stream. Therefore, it is of great importance to investigate the water quality of the stream. In order to evaluate the water quality, 50 water samples were taken from 10 sampling points (5 periods) along the flow direction of the stream. Parameters other than T, DO, Mg, F, NH3, CN, and HS show significant spatial variations indicating the influence of geogenic and anthropogenic activities. Some of the investigated parameters (T, DO, Ca2+, Mg2+, SO42-, NH3, CN, Fe, Se) exhibit important seasonal variations due to high seasonality in water temperature and water flow. Principal component analysis/factor analysis reveals that the parameters responsible for water quality changes in the Yağlıdere Stream are mainly related to the geogenic structure, mining wastes, agricultural activities, and domestic wastes. According to the WQI values, the water samples taken from the upstream and middle part of the stream are in the water quality between "Excellent" and "Good", on the other hand, in the downstream regions where anthropogenic and geogenic effects are dominant, the water samples are in the quality of "Poor water" and "Unsuitable for drinking". The most effective water quality parameters on WQI are Al, Fe, and Mn and the water quality in the basin is negatively affected by geogenic and anthropogenic effects.

Spatiotemporal evaluation and assessment of shallow groundwater quality for irrigation of a tropical coastal groundwater basin

The present study aims at analyzing groundwater quantity and quality simultaneously to identify its availability and suitability for irrigation. Various water quality indices were used to assess (i) origin of the groundwater sources (Gibbs diagram); (ii) salinity, alkalinity, and sodium hazard (sodium adsorption ratio, exchangeable sodium percentage, Kelly's ratio, US Salinity Laboratory diagram, Wilcox diagram); (iii) magnesium hazard (magnesium absorption ratio); (iv) carbonate and bicarbonate hazard (residual sodium carbonate); (v) hydro-chemical facie and evaluation (Piper diagram and Expanded Durov diagram); and (vi) statistical relationship among the variables, sample sites, and spatiotemporal grouping (principal component analysis and cluster analysis). The overall objective is to quantify the irrigation suitability of groundwater reserves. Gibb's diagram suggests that the groundwater quality is mainly controlled by rock-water interaction. Piper trilinear showed the presence of various types of hydro-chemical facies such as Ca-Mg-HCO3, mixed, and sodium bicarbonate. The expanded Durov diagram revealed the hydro-chemical evolution, grouping, and areal distribution of the groundwater samples. USSL diagram, Wilcox diagram, Kelly's ratio, magnesium hazard, and permeability index suggest that the groundwater quality is suitable for irrigation. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests confirmed the applicability of principal component analysis (PCA), which indicates that groundwater quality is controlled by rock-water interaction mainly. It also suggests that the groundwater has carbonate dissolution, which indicates the groundwater's hardness increased. Cluster analysis (CA) from the year 2000 to 2010 shows 4 to 8 groups present within the study area. Irrigation water quality suitability map and predicted groundwater potential zone map together act as a master tool for deciding tube well location, pumping schedule, and crop planning for the sustainability of the agriculture eco-system in the study area. The implementation of the aforementioned activities in the study area will further stop the advancement of the seawater intrusion front. The methodology shows the potential applicability for similar coastal groundwater basins worldwide with or without modification.

Accuracy assessment of inverse distance weighting interpolation of groundwater nitrate concentrations in Bavaria (Germany)

For the designation of nitrate vulnerable zones under the EU Nitrate Directive, some German federal states use inverse distance weighting (IDW) as interpolation method. Our study quantifies the accuracy of IDW with respect to the designation of areas with a groundwater nitrate concentration above the threshold of 50 mg NO₃/l using a dataset of 5790 groundwater monitoring sites in Bavaria. The results show that the absolute differences of nitrate concentrations between the monitoring sites are only weakly correlated within a range of no more than 0.4 km. The IDW cross-validated nitrate concentration of measurement sites shows a mean absolute error of 7.0 mg NO₃/l and the number of measurement sites above 50 mg NO₃/l is 44% too low by interpolation for all sites as a whole. The corresponding values for interpolation separately for the 18 hydrogeological regions in Bavaria are 7.1 mg NO₃/l and 38%. The sensitivity and the accuracy of nitrate concentration maps due to the variation of IDW parameters and the position of sampling points are analysed by Monte Carlo IDW interpolations using a Random Forest modelled map as reference spatial distribution. Compared to this reference map, the area with a concentration above 50 mg NO₃/l in groundwater is estimated by IDW to be 46% too low for the best IDW parametrization. Overall, IDW interpolation systematically underrates the occurrence of higher range nitrate concentrations. In view of these underestimations, IDW does not appear to be a suitable regionalization method for the designation of nitrate vulnerable zones, neither when applied for a federal state as a whole nor when interpolated separately for hydrogeological regions.

Control of organic contaminants in groundwater by passive sampling and multivariate statistical analysis

Organic contaminants in groundwater are among the most challenging chemical compound contaminants today, particularly when it comes to understanding their occurrence, origin, and relations in groundwater, as well as the transport processes, fate, and environmental impacts involved. This paper presents the use of active carbon fibre (AFC) passive sampling and multivariate statistical processing of the results to predict the possible occurrence of organic compounds (OCs) in groundwater and to determine the origin of various anthropogenic activity. This study aims to deepen our knowledge on the control of OCs in groundwater by introducing a multi-analytical and multi-elemental holistic approach, using the Dravsko polje aquifer, the largest intergranular aquifer in Slovenia, as an example. The occurrence of OCs in groundwater was determined by means of ACFs and compared against the characteristics of the recharge area and the type of compounds detected. We combined hierarchical cluster analysis (HCA) and principal component analysis (PCA) to identify the relationship between different OCs in groundwater. The relationships between their occurrence, environmental setting and type of compound were determined using multiple linear regression (MLR). From the total of 343 organic compounds detected using passive sampling, 47 were included in further statistical analysis. MLR shows that the environmental setting is one of the most important factors affecting the different types of pollutants in groundwater. MLR models were calculated for different sources of pollution (agricultural, urban, and industrial) based on the environmental setting, land use, agglomeration, infrastructure networks, and hydrogeological characteristics of the aquifer. By means of HCA and PCA, we identified the relationships between different OCs in groundwater. As expected, the strongest correlations were found between primary compounds and their degradation products (e.g. atrazine and desethylatrazine) and compounds of similar use (e.g. atrazine and propazine, also desethylatrazine and propazine, atrazine and simazine). Some of them were also found to have a similar molecular structure (e.g. palmitic and stearic acid, 5-methoxygramine and 5-methoxytryptamine). The use of the same substances in different environments (agricultural/urban) makes them markers of both (different) origins. Therefore, it is particularly important to determine the combination of markers of different origin using multivariate statistical methods, especially in the case of mixed land use. This study identifies the main factors influencing the distribution of groundwater OCs and thus contributes to a more comprehensive understanding of the vulnerability of shallow groundwater to surface-derived contamination in similar environments.

Assessment of hydro-geochemical properties of groundwater under the effect of desalination wastewater discharge in an arid area

Desalination to increase irrigation water supply for agricultural production is becoming important in water-scarce regions. While desalination has positive effects on the potential irrigation water quantity and quality, the technique may also be a considered potential source of groundwater pollution. The present study investigated the effects of desalination wastewater discharge on groundwater quality in an arid area in southern Iran for the 2012-2017 period. The chemical composition of the groundwater samples was evaluated considering pH, EC, Na⁺, Ca²⁺, Mg²⁺, SO₄²⁺, Cl^-, and HCO₃^-. The suitability of groundwater for drinking and irrigation purposes as well as spatial pattern of groundwater pollution was analyzed. The results showed that mean concentration of Na⁺, Ca²⁺, Mg²⁺, SO₄^2-, and Cl^- in all investigated wells increased from 148, 94, 46, 247, and 257 mg/L in 2012 to 282, 146, 71, 319, and 582 mg/L in 2017, respectively. Using Gibb's diagram, it was shown that the groundwater quality is slightly alkaline and primarily controlled by evaporation. Based on our findings, about 78% of the study aquifer displayed groundwater with good to excellent water quality that can be used for drinking and irrigation purposes. However, the eastern part of the aquifer was classified as unsuitable for use due to the disposal of desalination plant wastewater. The spatial distribution of WQI and other indices such as SAR, TDS, and TH showed that groundwater in the eastern part of the aquifer has deteriorated since the establishment of the desalination plants. To reverse this trend, it is important to implement regulations against wastewater discharge from desalination plants.

Modeling groundwater quality by using hybrid intelligent and geostatistical methods

Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin'anjiang River in Huangshan, China

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.