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Robust assessments of variations in freshwater availability are essential for current and future water resource management in the Pinios River Basin (PRB), which is one of the most productive basins of Greece in terms of agriculture. To support sustainable water resources management in the PRB, we set up and calibrated the mGROWA hydrological model at a high spatial (100 m) and temporal (daily) resolution for the period 1971–2000, with particular attention given to deriving crop-specific irrigation requirements. We developed and implemented a comprehensive methodological framework to overcome data scarcity constraints in the PRB, thus enabling the derivation of high-resolution spatially continuous estimates of many input variables required for the mGROWA model. We generated estimates of spatiotemporal variations in the water balance components actual evapotranspiration, irrigation requirements, total runoff, and groundwater recharge for the PRB. In addition, through the calculation of indices, such as the potential irrigation to groundwater recharge ratio (PIQR), we demonstrate a way to identify potential unsustainable water use in irrigated agriculture. The established mGROWA model can be used both as a hydrological reference model providing continuous decision support for water resources management, focusing on irrigation water use, and a basis for climate impact studies for the PRB.

Aquifer overexploitation in coastal aquifers has led to seawater intrusion that causes severe salinization effects on the groundwater system. The most widespread method for assessing groundwater vulnerability to seawater intrusion, the dominant cause of salinization in coastal aquifers, is the GALDIT method, with numerous applications globally. The present study proposes a modified version of the GALDIT method (GALDIT-Ι) to evaluate the vulnerability of salinization, including its potential additional sources. Both methods have been applied to Rhodope coastal aquifer, an intensively cultivated agricultural area subject to multiple salinization sources. The basic modifications of the proposed GALDIT-I method include different weighting factors and modification of classes for critical parameters, the use of a different indicator (TDS) for the estimation of the Impact factor and, overall, the address of the concept of groundwater salinization instead of seawater intrusion only. The differences in the results of the two methods were significant, as the modified version exhibited a more finite and realistic vulnerability capture, according to the area’s existing hydrogeological and hydrogeochemical knowledge. The original GALDIT method showed an area of nearly 80% as medium vulnerable with very limited spatial deviations. On the other hand, the proposed modified GALDIT method depicted high vulnerability hotspots away from the shoreline, indicating various salinity sources. The validation of the modified method showed that nearly 80% of the sampling points present very good to perfect match between the salinity assessment and the concentration of Cl−, indicating the successful validation of the method. Overall, the GALDIT-I method facilitated groundwater vulnerability assessment to salinization more accurately and exhibited a more discrete spatial assessment, thus, it could be regarded as a promising proactive tool for groundwater management and decision-making.

A novel index-based method (RIVA) for the assessment of intrinsic groundwater vulnerability is proposed, based on the successful concept of the European approach (Zwahlen 2003) and by incorporating additional elements that provide more realistic and representative results. Its concept includes four main factors: accounting for the recharge to the system (R), the infiltration conditions (I), the protection offered by the vadose zone (V), and the aquifer characteristics (A). Several sub-factors and parameters are involved in calculation of the final intrinsic vulnerability index. However, even though RIVA is a comprehensive method that produces reliable results, it is not data intensive, does not require advanced skills in data preparation and processing, and may safely be applied regardless of aquifer type, prevalent porosity, geometric and geo-tectonic setup, and site-specific conditions. Its development has incorporated careful consideration of all key existing groundwater vulnerability methods and their critical aspects (factors, parameters, rating, etc.). It has studied and endorsed their virtues while avoiding or modifying factors and approaches that are either difficult to quantify, ambiguous to assess, or non-uniformly applicable to every hydrogeological setup. RIVA has been successfully demonstrated in the intensively cultivated area of Kopaida plain, Central Greece, which is characterized by a complex and heterogeneous geological background. Its validation was performed by a joint compilation of ground-truth monitoring-based data, in-depth knowledge of the geological structure, hydrogeological setup, regional hydrodynamic evolution mechanisms, and also by the dominant driving pressures. Results of the performed validation clearly demonstrated the validity of the proposed methodology to capture the spatially distributed zones of different vulnerability classes accurately and reliably, as these are shaped by the considered factors. RIVA method proved that it may be safely considered to be a fair trade-off between succeeded accuracy, and data intensity and investment to reach highly accurate results. As such, it is envisaged to become an efficient method of performing reliable groundwater vulnerability assessments of complex environments when neither resources occur nor time to generate intensive data is available, and ultimately be valorized for further risk assessment and decision-making processes related to groundwater resource management.

Groundwater salinization occurs when high concentrations of water-soluble salts are present in groundwater systems and is regarded as one of the most worldwide, severe, and complex phenomena affecting coastal aquifers. Salinization might occur, for example, from: (i) from marine sources via seawater intrusion, seawater ingression, (ii) underground or terrestrial sources (e.g., natural soils and rocks through the dissolution of soluble minerals), and (iii) salt and saline fluids from anthropogenic activities.The analysis of salinization-related data by multivariate statistical methods is often undertaken in the context of efficient groundwater management. For example, clustering algorithms are used to delineate hydrogeohemically distinct water classes, whereas dimensionality-reduction algorithms are being used to decipher underlying natural and anthropogenic influences responsible for these distinct water classes. However, most of these algorithms do not explicitly account for spatial information and/or constraints, which can often have a significant impact on the classification of groundwater quality samples. In the context of the MEDSAL Project (www.medsal.net), such spatial effects are incorporated in the clustering procedure via the inclusion of pertinent hydrological attributes, namely, hydraulic head and conductivity data, along with pair-wise distances between sample locations.The application of the proposed spatially explicit clustering approach is illustrated using groundwater quality samples collected from the Rhodope coastal aquifer, located at north-eastern Greece. Sampling locations were grouped into four hydrogeochemically distinct water classes using k-means clustering with and without accounting explicitly for spatial information. Principal component analysis (PCA) was used to decipher underlying natural and anthropogenic influences responsible for these distinct water classes. The first four principal components (PCs) explained more than 83% of the total variance in water quality variables, from which the major component was found to be associated with salinization processes.

Groundwater salinization is a complex and dynamic process often related to multiple causes, such as seawater intrusion, soil salinization associated with water irrigation, and geogenic factors such as evaporate dissolution. Consequently, a reliable assessment of salinization risks depends heavily on the ability of statistical methods to accurately capture the spatial variability and interrelation among salinization indicators. Geostatistical methods are often used to identify and map salinization-affected regions, investigate how salinization indicators influence groundwater mechanisms, and eventually design optimal groundwater management policies.In the context of the MEDSAL Project (www.medsal.net), this study reviews the recent key applications of geostatistical methods to address problems relevant to groundwater salinization. The basic principles of geostatistics are briefly described, and several studies are discussed that employ geostatistical and multivariate tools for identifying salinization sources, clarifying the relationship among salinization indicators and groundwater processes, and facilitating uncertainty propagation in physically-based models of the groundwater systems affected by salinization.The literature review identifies most used methods and offers several recommendations in terms of future directions and challenges on the role of geostatistics for improved mapping of the spatial and/or spatiotemporal distribution of geochemical data related to salinization. These recommendations include the integration of geostatistics and machine learning methods for improved understanding and modeling of groundwater salinization processes, as well as the application of modern geostatistical simulation algorithms, accounting for diverse information sources, for exploring parameter uncertainty in spatially distributed hydrogeochemical models.

Machine learning (ML) models that are robust, efficient and exhibiting sound generalization capabilities rely on the assumption that they are trained with data that are independent and identically distributed (i.i.d). Violating this assumption may result in overfitting these highly flexible methods to the training data and underestimating spatial prediction errors. Making models appear more reliable than they are, could lead in a bias assessment of the model’s capability to generalize the learned relationship to independent data and consequently models with overall poor prediction accuracy.Spatial data are special kind of data that the i.i.d. does not hold most of the times due to their spatial autocorrelation. Cross-validation is a very common resampling method both for the tuning of ML models and for the assessment of their predictive capabilities. Studies have shown that using random cross-validation methods with spatial data could produce overoptimistic results due to the violation of the i.i.d assumption. In order to mitigate this problem, spatial cross-validation is proposed alternatively that splits the data into spatially disjoint subsets, which are subsequently used for cross-validation.In the context of the MEDSAL Project (www.medsal.net), multiple data of different covariates were collected in order to study groundwater salinization. Machine learning was applied to predict salinity concentration based on these data. In the current presentation some of the results of the ML analysis are shown along with the effect of the spatial autocorrelation in the ML models' prediction capabilities. This was implemented by comparing the prediction results of the ML models created with random cross-validation versus spatial cross-validation resampling methods. Possible spatial autocorrelation, along with time series autocorrelation, in water data are important issues that data analysts should study and address especially when pairing with ML analysis and modeling.

Groundwater resources are inevitably considered as the primary source of high-quality water for the Mediterranean region. In critical cases, groundwater is essential to complement inadequate, uncertain, expensive, or even lacking surface-water sources. Especially in coastal areas of the Mediterranean, which show increasing development and population growth, karst aquifers represent vital freshwater sources. Karst aquifers are rather complex water systems, and therefore, though to manage, predict, and protect. Groundwater modeling has proved to be a very effective tool for groundwater management. Physically-based modeling is usually applicable to porous aquifers; numerical modeling application to karst aquifers is very challenging because of their complexity, which combines discontinuity, conduit, and porous medium domains. The need to forecast groundwater quantity and quality in karst aquifer systems is high, with groundwater salinity being very critical. Artificial Intelligence (AI) algorithms have been proved to be an effective alternative in simulating groundwater quality and quantity variables. This study aims to develop and test the performance of 6 AI algorithms to forecast groundwater electrical conductivity (EC) in the highly complex, coastal karst aquifer system of Salento (Puglia, Southern Italy). The AI algorithms applied were: 1) Multilayer Perceptron (MLP), 2) Long short-term memory (LSTM), 3) Bidirectional LSTM (BiLSTM), 4) Convolutional Neural Network (CNN), 5) Recurrent Neural Networks (RNN), and 6) Support Vector Machine (SVR). Except for SVR, which is considered a machine learning (ML) algorithm, all the other approaches are deep learning (DL) neural network architectures. Models’ development was based on 3-year groundwater EC daily data from 7 sensors. Other variables used for EC modeling were groundwater level and temperature, precipitation, and air temperature. The above variables were combined in 11 input variable experiments. In addition, various realizations of training times windows were developed under five scenarios. The total number of trained EC models was 2184. The results show AI models can efficiently provide a 30-day groundwater EC forecast for a wide range of EC values varying from slightly saline (0.7-2 mS/cm) to very saline (25-45 mS/cm). BiLSTM proved to be the most effective algorithm, while the least but still effective algorithm was SVR, thus showing the superior performance of DL algorithms compared to legacy ML approaches. Experimental results showed that increasing the number of input variables did not improve the performance of models. In contrast, including 2-time windows for training (one short-term and one long-term) increased it.

The coastal aquifer of the Rhodope region (NE Greece) is a complex groundwater system impacted by various processes that increase groundwater salinization (seawater intrusion, trapped saline lenses, geothermal fluid impact, irrigation return). In the context of the MEDSAL Project (www.medsal.net), a thorough study of its hydrogeochemical characteristics was performed to assess the spatiotemporal variations of groundwater salinization and identify the dynamics of the phenomenon.To this aim, we used a combination of tools, including multivariate statistics analysis (MVSA) and hydrogeochemical modelling, to decipher the mechanism(s) of groundwater salinization and their evolution in time and space.Results from Hierarchical Cluster Analysis (HCA) classified water samples into four (4) diverse groups and seven (7) subgroups that denote different hydrogeochemical and salinization phases. The different processes that control hydrogeochemistry were further assessed using R-mode factor analysis. The outcomes outlined three (3) factors that supplemented the HCA. The dominant factor is related to the cascading processes of salinization, and the secondary factors are related to anthropogenic contamination (N surplus due to agricultural activities) and the impact from the substrate (water-rock interaction).Hydrogeochemical modelling further supported assessments and provided an overview of the spatiotemporal variability of factors and processes affecting groundwater chemistry. A set of saturation indices of key minerals related to the dominant processes identified by the MVSA were calculated and interpolated to capture the spatiotemporal dynamics. Results facilitated the development of a more representative conceptual model about salinization and the key hydrogeochemical processes affecting water quality in the area.

In this study, lead removal from aqueous solutions using biochar derived from olive mill solid and liquid wastes has been investigated by applying batch experiments and geochemical modelling. The batch adsorption experiments included the assessment of several key parameters such as the contact time (kinetic), initial concentration (isotherm), pH, adsorbent dose, and the presence of competitive cations, whilst the geochemical modelling focused on the involved adsorption mechanisms using the PHREEQC code. The kinetic studies showed that lead adsorption is a relatively fast process, where intraparticle diffusion is the rate-limiting step. Biochar dose, solution pH and the presence of competitive ions significantly affected the Pb adsorption effectiveness by the biochar. Especially the higher Pb removal percentages were observed in mono-elemental solutions with high biochar dose at mildly acidic solution pH values. The maximum Pb adsorption capacity of biochar was estimated as 40.8 mg g

Assessing the hydrogeochemical evolution of groundwater is a challenging task, which is further exacerbated when considering the multiple geogenic and anthropogenic impacts that affect its quality and the hydraulic interactions between different aquifer bodies. This study combined hydrogeochemical modelling and data analysis to assess this complex hydrogeological regime. Before modelling, the groundwater samples were clustered using a multivariate statistical method (hierarchical cluster analysis (HCA)). Then, the Geochemist Workbench (GWB) software was applied to model the hydrogeochemical groundwater evolution, including the dominant ion exchange process, and to explain the changes in groundwater chemistry towards its flow. The input data consisted of five key parameters from seventy-seven sampling points collected in two periods (accounting for the start and the end of the irrigation period). A data analytical approach based on the optimal mixing ratios between the interacting groundwater systems and recharge inputs was also performed as part of the methodological approach. It revealed a progressively temporal-dependent behaviour of the aquifer system during the irrigation period, resulting in seasonal changes in the hydrodynamic conditions and depletion of the upper aquifer layers. Specifically, the aquifer system was confirmed to undergo cation exchange as the dominant geochemical process that increases calcium concentrations. The complex hydrogeological regime was further evaluated by assessing the mixing ratios of the different aquifer layers. Hence, the aquifer system (bulk samples) was mixed with the irrigation water by 71% and 97% and with the lateral recharge by 76% and 29% for the beginning and at the end of the irrigation period, respectively. Overall, the joint assessments were confirmed by the hydrogeochemical status of the end-members and the modelling approach and explained the sequential changes in groundwater chemistry due to the dominant ion-exchange process and the mixing of different water bodies. The proposed methodological approach proved that it could be used as an exploratory and preliminary method for capturing the temporal dynamics in complex groundwater systems and supporting groundwater resource management.

A combined hydrogeochemical and hydrodynamic characterization for the assessment of key aspects related to groundwater resources management was performed in a highly productive agricultural basin of the Thessaly region in central Greece. A complementary suite of tools and methods—including graphical processing, hydrogeochemical modeling, multivariate statistics and environmental isotopes—have been applied to a comprehensive dataset of physicochemical analyses and water level measurements. Results revealed that the initial hydrogeochemistry of groundwater was progressively impacted by secondary phenomena (e.g., ion exchange and redox reactions) which were clearly delineated into distinct zones according to data processing. The progressive evolution of groundwater was further verified by the variation of the saturation indices of critical minerals. In addition, the combined use of water level measurements delineated the major pathways of groundwater flow. Interestingly, the additional joint assessment of environmental isotopes revealed a new pathway from E–NE (which had never before been validated), thus highlighting the importance of the joint tools/methods application in complex scientific tasks. The application of multivariate statistics identified the dominant processes that control hydrogeochemistry and fit well with identified hydrodynamic mechanisms. These included (as dominant factor) the salinization impact due to the combined use of irrigation water return and evaporitic mineral leaching, as well as the impact of the geogenic calcareous substrate (mainly karstic calcareous formations and dolostones). Secondary factors, acting as processes (e.g., redox and ion exchange), were identified and found to be in line with initial assessment, thus validating the overall characterization. Finally, the outcomes may prove to be valuable in the progression toward sustainable groundwater resources management. The results have provided spatial and temporal information for significant parameters, sources, and processes—which, as a methodological approach, could be adopted in similar cases of other catchments.

Olive mill wastes in the Mediterranean continue to be a management challenge due to the large volumes produced. Their recycling in agricultural systems may be improved, with multiple benefits for the environment and soil protection, while promoting the circular economy. Information on amounts and physicochemical properties of olive mill wastes will help the development of sustainable strategies to convert them into fertilizers or soil amendments in the Mediterranean Basin. To this end, we obtained up-to-date data on olive production and generation of wastes in Spain, Tunisia, and Greece and characterized representative samples in the laboratory in terms of their agronomic properties. We then made calculations of the total quantities of nutrients and matter which may be recycled in agriculture. More particularly, annual mineral ash contained in the waste streams of the three countries amounts to 137 thousand tons per year. Solid waste properties were more homogenous than wastewater properties, indicating that local management and production conditions in three-phase mills is more diverse, so standardization of conversion technologies is likely to be easier for two-phase mills.

A numerical analysis of the groundwater flow and contaminant transport equations, based on the variable density flow approach, is used for the construction of non-dimensional diagrams to predict seawater intrusion to confined coastal aquifers. The classical Henry’s seawater intrusion problem is analysed by using a finite element model. The model’s equations are written in non-dimensional form and the numerical solutions depend solely on three non-dimensional parameters:α=q΄/Κ0, β=(bΚ0)/(nDm), α΄=bS0/n (eq. 1 a,b,c)where q’ is the freshwater recharge rate (m/d), K0 the freshwater hydraulic conductivity (m/d), b the aquifer thickness, n the porosity (-), Dm the molecular diffusion coefficient (m2/d) and S0 the freshwater specific storage (1/m). Please note that hydraulic conductivity appears in two of the non-dimensional parameters, α and β.The non-dimensional formulation has led to the construction of non-dimensional diagrams of salt distribution for a homogeneous and isotropic confined aquifer with horizontal base and constant thickness that is uniformly recharged with freshwater. These diagrams illustrate the influence of the key hydrological and hydraulic parameters, and furthermore, can be used to predict the evolution of seawater intrusion in real case studies.The numerical simulations were carried out up to the equilibrium state for different values of the non-dimensional parameters of equation 1. By decreasing the value of parameter α=q΄/Κ0, seawater intrusion is advancing inland and the width of dispersion zone is increasing. By increasing the parameter β=(bΚ0)/(nDm), the seawater-freshwater transition zone is narrowing and shifted to the seaside at the upper part of the aquifer, while the intrusion of saltwater is advancing inland at the lower part of the aquifer. The distribution of the salts in the aquifer was found essentially identical for different values of the parameter α΄=bS0/n; hence this parameter exhibits very low sensitivity, which makes it of low importance, especially for real case studies.Overall, the non-dimensional diagrams – constructed by following the variable density flow approach and under specific assumptions – can be used for a quick and direct prediction of seawater intrusion in real aquifers. These diagrams would be useful for an initial prediction at the case studies of the PRIMA MEDSAL project (www.medsal.net), namely the coastal aquifers in Rhodope (Greece), Samos island (Greece), Bouficha (Tunisia), Bouteldja (Algeria), Tarsus (Turkey) and under specific assumptions to the karstic aquifer in Salento (Italy).

Groundwater resources constitute nearly one-third of the globe’s freshwater resources [...]

A novel index-based method (RIVA) for assessing intrinsic groundwater vulnerability was applied to Tirnavos basin (central Greece) to assess the susceptibility to surface-released contamination. Data from field surveys, previous studies, and literature were used to calculate the factors that compile the RIVA method. The aggregated results delineated the spatial distribution of groundwater vulnerability from very low to very high. The modelled results were successfully validated with ground-truth values of nitrates obtained from 43 boreholes. Overall, the modelled and the monitored values match more than 80%, indicating the successful application of the RIVA method. Few deviations were observed in areas dominantly affected by lateral crossflows and contamination from adjacent areas. RIVA proved an efficient method in terms of accuracy, data intensity, and investment to reach highly accurate results. Overall, RIVA proved to be a robust tool for reliable groundwater vulnerability assessments and could be further exploited for risk assessment and decision-making processes in the context of groundwater resource management.

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model.

The aims of this study are to investigate the potential origin of selected heavy metal(loid)s in the Shabestar plain, NW Iran, by means of multivariate statistical techniques (cluster analysis and factor analysis), as well as to determine the dominant factors that affect groundwater quality and to assess the health risk induced by metal(loid)s using the hazard quotients (HQ). Totally, 29 groundwater samples were collected from wells in August 2016, and the values of 23 parameters, namely pH, electrical conductivity, concentration of major elements (Ca2+, Mg2+, Na+, K+, HCO3−, SO42−, Cl−), minor elements (NO3−, F−, B, and Br−) and heavy metal(loid)s (Fe, Al, Cr, Mn, As, Zn, Pb, Cu, and Ni) were measured. The results indicate that some samples were found with As, Pb, and Zn concentrations exceeding WHO standards for drinking water. Results of correlation coefficients between the measured variables reflect the occurrence of weathering and dissolution of rocks, especially silicates and evaporites, with ion exchange and geochemical characteristics similar to the release of some heavy metal(loid)s. According to hierarchical cluster analysis, samples of cluster 1 are affected by alkalinity and accompanied by elements compatible with alkaline ambience (CO32− and Ni). Samples of subcluster 2-1 demonstrate the effect of salinity, attributed to evaporates, irrigation return flow, and influx of Urmia Lake’s brine, while, samples of sub-cluster 2-2 are influenced by agricultural activities. Factor analysis results illustrate the effects of five factors on the quality of groundwater. The factor analysis accounts for the 71.9% of total variance of groundwater quality for geogenic impacts, while 10% of the groundwater quality variance is controlled by agricultural activities which produce excessive amounts of NO3− along with Zn which is contained in fertilizers and agrochemicals. The results of the human health risk assessment show that As is the most dominant metalloid in inducing maximum noncarcinogenic risk among all the heavy metal(loid)s. Based on HI, 45 and 14% of the samples for children and adults, respectively, are found to be in high risk category.

Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R2), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models.

Combined assessments from different methodologies, including hydrogeochemical analysis, multivariate statistics and stable isotopes, were used in order to characterize the groundwater resources of a heterogeneous aquifer system in central Greece and to evaluate the overall environmental regime. Results outlined the driving factors that chiefly control groundwater chemistry and delineated the major pathways of groundwater flow. Following the results of the combined assessments, hydrogeochemistry is influenced both by geogenic and anthropogenic factors including the geological substrate, intense agricultural activities and ongoing geochemical processes which impact the concentrations of redox sensitive agents like NO3, Fe, Mn and SO4. Stable isotope evaluations supplemented the above assessments by providing critical information for the hydrodynamics of the heterogeneous aquifer system. Evaporation is the main factor influencing the isotopic composition of water resources, in addition to the slow percolation rates of the thick unsaturated zone. Comparisons between δ 18Ο and δD values for surface and groundwater samples revealed an interaction among water systems through the developed karstic network and/or the riverbeds of higher permeabilities. Eventually, the integrated conceptual approach of diverse methodologies was applied successfully for the identification of hydrogeological and hydrogeochemical assessments in the case of Kopaida basin; evaluations were cross-confirmed and supplemented when needed, hence providing essential information for strategic planning and water resources management.

Olive mill wastes continue to be a management challenge due to the large volumes produced, particularly due to their toxicity and impacts on the environment. Thermal conversion through pyrolysis or hydrothermal carbonization techniques can detoxify wastes while conserving nutrient contents. In this work, we produced up-to-date data on olive mill waste flows in Spain, Tunisia, and Greece and characterized representative samples in the laboratory. Assays of thermal conversion of olive mill wastewaters and solid wastes were also performed to understand biochar yields and final properties, and the total quantities of nutrients contained were estimated. Of particular note were the quantities of potassium in Tunisian wastewaters, representing 0.6% of the total mass and an annual flow of approximately 5000 t, and in the Spanish solid wastes, an average of 1.7% of the total mass is potassium, representing an annual flow of approximately 23,000 t. Concerning phosphorus, Spanish solid wastes had the highest contents (0.1%), double that of other countries&rsquo, wastes. Annually, olive mill wastes from the three countries are estimated to contain approximately 35 &times, 10^3 tons of potassium and 2.6 &times, 10^3 tons of phosphorus. With this resource converted to biochar, each year more than 700 km2 of soils could be enriched in 0.2% carbon with biochar at an application rate of 7 t ha&minus, 1.

MEDSAL is a research project (www.medsal.net) focusing on groundwater salinization in the Mediterranean area, funded by the PRIMA Program (Partnership for Research and Innovation in the Mediterranean Area), and running for 36 months starting from September 2019. MEDSAL constitutes a joint Euro-Mediterranean cooperation network of organizations from Mediterranean countries and associated states of the EU contributing national funds. The partnership involves eight academic partners from seven countries (plus an external collaborator – private firm), covering a wide range of academic experts in various scientific fields (e.g. hydrogeology, hydrogeochemistry, environmental isotopes, modeling, hydro-informatics, geostatistics, machine learning).MEDSAL aims at developing innovative methods to identify various sources and processes of salinization and at providing an integrated set of modeling tools that capture the dynamics and risks of salinization. Thereby, it aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation. MEDSAL encompasses six (6) test sites located in five (5) countries: Rhodope, Greece, (ii) Samos Island, Greece, (iii) Salento, Italy, (iv) Tarsus, Turkey, (v) Boufichia, Tunisia, and (vi) Bouteldja, Algeria.MEDSAL’s principal objectives are the following: a) Deliver new tools for the identification of complex salinization sources and processes, b) Exploit the potential of Artificial intelligence and Deep Learning methods to improve detection of patterns in multi-dimensional hydrogeochemical and isotope data, c) Elaborate tailor-made risk assessment and development of management plans by coupling salinization forecasts with climate change impacts and future scenarios, and d) Develop a public domain web-GIS Observatory for monitoring, alerting, decision support and management of coastal groundwater reserves around the Mediterranean.MEDSAL is expected to have a significant impact on water resources availability and quality by improving the identification and development of adequate strategies and measures for the protection and management of salinization in coastal aquifers. In this context, MEDSAL will provide innovative classification and detection methods of groundwater salinization types for Mediterranean coasts, also in complex karstic and data-scarce environments. These outcomes will be reached by better integration of hydrogeochemical and environmental isotope data with physical-based groundwater flow and transport models and advanced geostatistics. Artificial intelligence and deep learning methods will be also used to improve the detection of patterns in multi-dimensional hydrogeochemical and isotope data.

This research determined potential sources of certain trace elements in the surface waters of the Tabriz area in northwestern Iran. In total, 19 samples from surface waters in the Tabriz area, including the Aji-Chay, Ansa-Rood, Basmenj-Chay, Gomanab-Chay, Mehran-Rood and Sinikh-Chay Rivers, and their branches, were collected in December 2016. Samples were measured/analyzed with respect to electrical conductivity (EC), pH, major (Ca2+, Mg2+, Na+, K+, HCO3−, SO42−, Cl−) and minor (NO3−, F−, SiO2) elements, and certain trace elements (Fe, Mn, Al, Zn, Cd, Pb, Cr, Al and As). The order of abundance for trace elements was Mn > Fe > Al > Zn > Cr > As > Cd > Pb. Cluster analysis divided the samples into two clusters, and the accuracy of the clustering was determined to be 100% by discriminant analysis. The discriminant analysis introduced Cd and Cr as the best parameters to predict sample grouping. Values of EC and concentrations of the major ions and trace elements such as Cd, Pb, Cr and Al, were greater in cluster 2 than in cluster 1. The high EC and its dependent anions (e.g. SO42− and Cl−) in cluster 2 can be considered as one of the driving factors for their release into surface water by forming SO42− and Cl− complexes with metals. The concentrations of SiO2 and As were greater in cluster 1 than in cluster 2 due to the impact of the volcanic formations of Sahand Mountain. Factor analysis identified four factors, which cumulatively explained 80.1% of the variance of the hydrochemistry of the surface water, and which were related to both natural and anthropogenic processes.

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December&ndash, February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.

In recent years, water management in Iran has mostly focused on monitoring, understanding and mitigating issues of water quality. To understand the processes that can affect water quality, this study carried out a geochemical study on cold (5 well water samples and 6 cold springs) and thermal (9 hot springs) waters in the Sarein-Nir area in Iran. A total of 20 water samples were collected in July 2016, and physicochemical variables such as pH, electrical conductivity (EC) and major, minor and trace element concentrations were determined. Using multivariate statistics, the main geochemical processes and possible origin of selected trace elements were investigated. The maturity and mixing processes of the waters were investigated using stable isotope (δ18O and δD) and geothermometry techniques. It was found that water-rock interaction was the predominant process, and that denitrification and ion exchange processes also took place in the groundwater system of the area. Thermal waters had an elevated EC, whereas cold waters showed elevated pH and NO3 values, indicating the impact of anthropogenic activities on water quality. Alkalinity and temperature were recognized as the most important variables that control the release of trace elements into the groundwater. Most thermal water samples were immature and belonged to peripheral water types, indicating that these samples came in part from deep circulation. Geothermometry of the thermal waters showed that the springs of the Sarein region had high temperatures compared to the springs of the Nir region. Considering that the Sarein spring waters had lower deuterium ratios than spring waters in the Nir region, it was concluded that the recharge elevation of the Sarein springs was higher than that of the Nir springs.

Groundwater quality evaluation for irrigation and drinking purposes is of utmost importance in semi-arid and arid regions of the globe and is a great tool for sustainable development. This study focuses on Dalgan basin in southeast Iran with the objectives of monitoring and assessment of physicochemical parameters (pH, EC, TDS, and T), cations (K+, Na+, Ca2+, and Mg2+), major anions (Cl−, HCO3−, and SO42−), and calculation of groundwater quality indexes (WQI, and EWQI) for irrigation and drinking purposes. In this research, we also prioritize the wells based on the groundwater quality index using the analytic hierarchy process and data envelopment analysis (AHP- DEA) model. Use of multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicated that geogenic (i.e. interaction of water-rock, climatic conditions, high evaporation) and anthropogenic (i.e. agricultural fertilizer) sources are responsible for variation in physicochemical parameters in groundwater of the study area. The results of the study showed that the simultaneous use of these methods and indicators is very effective and suitable in determining groundwater quality and prioritizing wells of any region.

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012–May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.

The aim of the present study is to assess the future spatial and temporal distribution of precipitation and temperature, and relate the corresponding change to water resources’ quantitative status in Pinios River Basin (PRB), Thessaly, Greece. For this purpose, data from four Regional Climate Models (RCMs) for the periods 2021–2100 driven by several General Circulation Models (GCMs) were collected and bias-correction was performed based on linear scaling method. The bias-correction was made based on monthly precipitation and temperature data collected for the period 1981–2000 from 57 meteorological stations in total. The results indicate a general trend according to which precipitation is decreasing whilst temperature is increasing to an extent that varies depending on each particular RCM–GCM output. On the average, annual precipitation change for the period 2021–2100 was about − 80 mm, ranging between − 149 and + 35 mm, while the corresponding change for temperature was 2.81 °C, ranging between 1.48 and 3.72 °C. The investigation of potential impacts to the water resources demonstrates that water availability is expected to be significantly decreased in the already water-stressed PRB. The water stresses identified are related to the potential decreasing trend in groundwater recharge and the increasing trend in irrigation demand, which constitutes the major water consumer in PRB.

Major ion and selected trace element compositions were analyzed in 102 groundwater samples from central and northern areas of Euboea Island, Greece, where both serpentinite dissolution and anthropogenic activities (agricultural) are in action. By integrating hydrogeochemical and two multivariate statistical methods, hierarchical cluster analysis (HCA) and principal component analysis (PCA), the chemistry of these groundwaters was assessed with the aim to understand the evolution of groundwater and assign the major processes that exert control on its composition. HCA classified the groundwater samples into three chemically distinct groups (C1–C3) according to their dominant chemical composition. These three clusters were further categorized by their electrical conductivity values: C1 (median EC: 480 μS/cm), C2 (median EC: 608 μS/cm), C3 (median EC: 1020 μS/cm). PCA was performed to identify the underlying natural and anthropogenic processes affecting the chemistry of these groundwaters. The PCA results can be represented by two principal factors: (1) salinization by seawater intrusion and NO3− contamination; and (2) enrichment of groundwater with Mg2 +, HCO3− and Cr following groundwater–serpentinite interaction. A third trivial component is associated with more local effects of the geological substrate. The three components of the PCA account for 70% of the total variance in the data. Dissolved Cr concentrations (up to 71 μg/L) that rise environmental concerns are more pronounced in the groundwaters from the alluvial coastal area of Politika (C3 water samples). Results of this study demonstrate that appropriate measures should be taken to protect the vital groundwater resources in the alluvial coastal area including the regulation of the amount of chemical fertilizers applied to agricultural soils and the monitoring of groundwater pumping rates.

Surface and groundwater samples were collected from three key olive grove regions in Greece in order to assess their overall quality and outline major hydrogeochemical characteristics. The three study areas were selected for their significance to the national olive production as well as for their diverse physiographic characteristics and imposed cultivation practices. Results suggest that quality status in general is acceptable with few exceptions; however, issues related with salinization were identified which in turn could potentially lead to environmental degradation. Hydrogeochemical characteristics are affected by geogenic (natural) factors and anthropogenic influences to some extent. Results were confirmed by PoS index which classified samples according to their quality status. Overall, the controlling factors appear to be the geological setup, the hydrogeological regime, as well as the irrational cultivation practices and groundwater overexploitation. Environmental sustainability in the thr...

Twenty-seven (27) tap water samples were collected during October 2012 from the supply network of Thermi Municipality (central Macedonia, northern Greece) in order to assess their hydrogeochemical signatures and the overall quality status according to the European legislation and international standards. Samples were analysed for a total of 25 environmentally significant parameters including physicochemical properties (pH, EC, colour, turbidity and hardness), major and minor ions (Ca2+, Mg2+, , , , Cl−, , F−, and CN−) and trace elements (B, Sb, As, Cd, Cr, Cu, Pb, Hg, Ni, Se, and Mn). The vast majority of the parameters appeared in values below the maximum admissible concentration for potable waters. Individual elevated concentrations of B and may be attributed to natural (geogenic) factors related with local lithology and anthropogenic influences possibly deriving from agricultural practices (excessive use of N-fertilizers). The assessments of the analytical results were validated with the use of...

The present study aims to evaluate the possible source of major and some minor elements and heavy metals in the groundwater of Qareh-Ziaeddin plain, NW Iran with respect to chemical elements, saturation index, and multivariate statistics including correlation coefficient, cluster analysis, and factor analysis. Groundwater samples were collected in Jun 2016 and measured with respect to EC, pH, major and some minor elements and heavy metals including Fe, Mn, Zn, Cr, Pb, Cd, Al, and As. Among all the measured parameters, some of the samples exceed the World Health Organization (WHO) guideline value for EC, Na, Mg, HCO3, SO4, Cl, NO3, F, As, Zn, and Pb. The results of correlation analysis show that weathering and dissolution of minerals especially evaporites and silicates, water-rock interaction, and cation exchange are dominant occurred processes in the groundwater of the study area. Also, denitrification process is occurred in the groundwater system. Cluster analysis categorizes the samples into three distinct groups which are different based on their EC and dependent variables, e.g., Na, Ca, Cl, SO4 and pH, Pb, Cd, and As. It can be found that volcanic, evaporite, and clay formations have the least impact on the chemistry of the cluster 1 samples while clay and evaporite formations have the highest impact on the cluster 3 and also calcareous formations on cluster 2. Factor analysis shows that five factors, with total variance of 83%, are effective in the release of heavy metals and groundwater chemistry which are mostly geogenic.

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO3, Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na+, K+, Ca2+, Mg2+, SO42-, and Cl-), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO3-, SiO2, and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca2+, Mg2+, Na+ and K+, HCO3 −, SO4 2−, Cl−, F− and NO3 −) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO3 and mixed Ca–Na–HCO3. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.

In order to assess the potential impacts of climate change in the hydrologic regime of River Pinios Basin, an area-differentiated model for total run-off (Qt) estimation based on the GROWA model was applied with bias-corrected precipitation and temperature data from four regional climate models (RCMs) for the projected periods 2020–2050 (period A) and 2050–2080 (period B). Bias correction was performed using the linear scaling approach. As a reference basis, monthly precipitation data from 57 meteorological stations and average temperature data from 17 stations were analyzed for the period 1980–2000. Relative assessments were achieved by comparing reference to projected periods values for Qt, after incorporating bias-corrected projected climate data from the four RCMs driven by several general circulation models (GCMs) as input data to the hydrological model. Results showed that all RCM–GCM combinations lead to a considerable decrease in total run-off with variable rates between the examined proje...

Summary Groundwater quality assessment and evaluation is of paramount importance in strategic planning and management at river basin scale or even larger. Depending on the available infrastructure data upon which such assessments are carried out, significant variations in terms of measured parameters and time span covered occur frequently and pose objective difficulties to environmental assessments. Still, there is a need for evaluation across such basins at regional, national or even continental scales under a common reference base. Existing methods so far focus on the comparative evaluation of a single parameter or a common set of parameters that needs to be available throughout all examined basins. Moreover, existing approaches and practices are assessing groundwater in comparison to the quality standards set for a specific use despite the fact that often these resources are covering a multitude of functions. This paper presents an index that attempts to perform a comparative assessment of groundwater quality across basins controlled by the same or different factors, subject to the same or different pressures and characterized by different availability of water quality measurements spread over the same or different time periods. It serves as an easy to implement and unbiased approach to identify water quality controlling factors. The proposed method offers on the spot assessment of groundwater quality characteristics visualized in a way that is easily conceived and comprehended.

The Micro Prespa basin is a trilateral catchment area of significant importance with a unique ecosystem closely related to the homonymous lake. In this frame, a fully operational monitoring project was carried out including continuous real-time measurements in Micro Prespa Lake with the use of a multi-sensor probe, as well as periodical sampling and analyses of all available water systems for an extended set of 85 parameters. Four main interacting water systems were identified, including alluvial and karstic aquifers, Micro Prespa Lake and adjacent drainage network. The results outlined that general environmental conditions are satisfying in respect to the relative legislation and the hydrogeochemical signatures. However, trends of environmental pressures were ascertained as a result of natural (geogenic) factors, embracing seasonal peaks for Ni, Pb, and NH4 mainly in groundwater systems. Based on chlorophyll a records, Micro Prespa is classified as oligotrophic to slightly mesotrophic, subjected to seasonal variations. Heavy metal concentrations are low, except Ni which appears to have elevated values during the dry hydrological period. Finally, the hydrogeochemistry of drainage network is primarily influenced by surface runoff of the surrounding mountainous areas, hence elevated phosphorus values of the Aghios Germanos stream are possibly linked with the leaching of the granitic formations on the east.

Aji-Chay River is one of the most important surface reservoirs of northwest of Iran, because it passes through Tabriz city and discharges to Urmia Lake, one of the largest permanent salty lakes in the world. The main objectives of the present study are to evaluate its overall water quality and to explore its hydrogeochemical characteristics, including the potential contamination from heavy metals and metalloids such as Co, Pb, Zn, Cd, Cu, Cr, Al and As. For this purpose, 12 water samples were collected from the main river body and its tributaries within Tabriz plain. The Piper diagram classified water samples mainly into Na–Cl and secondary into Ca–HCO3 and mixed Ca–Mg–Cl types, denoting a profound salinization effect. The cross-plots showed that natural geochemical processes including dissolution of minerals (e.g., carbonates, evaporites and silicates), as well as ion exchange, are the predominant factors that contribute to fluvial hydrogeochemistry, while anthropogenic activities (industrial and agricultural) impose supplementary effects. Cluster analysis classified samples into two distinct clusters; samples of cluster B appear to have elevated electrical conductivity (EC) values and trace metals concentrations such as Co, Pb and Cd, while SiO2 and Zn are low in comparison with the samples of the cluster A. The main processes controlling Aji-Chay River hydrogeochemistry and water quality were identified to be salinization and rock weathering. Both are related with geogenic sources which enrich river system with elevated values of Na+, Cl−, Ca2+, Mg2+, K+, SO4 2− and EC as a direct effect of evaporites leaching and elevated values of Pb and Cd as an impact from the weathering process of volcanic formations. According to the US salinity diagram, all of the water samples are unsuitable for irrigation as having moderate to bad quality.

Στην παρούσα εργασία χρησιμοποιήθηκε ένας πολύ υψηλής ποιότητας ζεολιθικός τόφφος τύπου-HEU (88% κ.β. κλινοπτιλόλιθος-ευλανδίτης) ως πρόσθετο υλικό σε φυσικά εδάφη, με στόχο την ελάττωση της συγκέντρωσης των παραγόμενων εκπλυμάτων νιτρικών ιόντων, υπό συνήθεις συνθήκες άρδευσης. Ο ζεολιθικός τόφφος αναμίχθηκε με το έδαφος σε τρεις αναλογίες (0.2, 0.4 και 0.6%) οι οποίες αντιστοιχούν σε εφαρμογή 500, 1000 και 1500 kg ανά στρέμμα. Στο δείγμα αναφοράς (μάρτυρας – απουσία ζεολιθικού τόφφου) και στα τρία μίγματα εδάφους-ζεολιθικού τόφφου πραγματοποιήθηκε πείραμα διάρκειας 10 εβδομάδων, στο οποίο, διοχετεύτηκαν σταθερές δόσεις αρδευτικού νερού εμπλουτισμένου με νιτρικά ιόντα. Τα αναλυτικά αποτελέσματα των εκπλυμάτων έδειξαν ότι η προσθήκη ζεολιθικού τόφφου κατά 0,2% δεν ήταν αποτελεσματική και το μίγμα συμπεριφέρθηκε ανάλογα του δείγματος 2146 αναφοράς. Αντιθέτως, η προσθήκη ζεολιθικού τόφφου σε περιεκτικότητες 0,4% και 0,6% έδωσε σημαντικά αποτελέσματα, καθώς η συγκέντρωση νιτρικών ιόντωνμειώθηκε κατά 81% και 86% αντίστοιχα, σε σχέση με το έκπλυμα του μάρτυρα. Συνεπώς, οι επιπτώσεις από την εφαρμογή πολύ υψηλής ποιότητας ζεολιθικού τόφφου στα αγροτικά εδάφη, μπορεί να έχει σημαντικά θετικές επιπτώσεις στην περιβαλλοντική προστασία και στην αειφόρο γεωργία, γεγονός που είναι συνδεδεμένο άμεσα με τους στόχους της νέας κοινής αγροτική πολιτικής της Ε.Ε και την σχετική νομοθεσία, A HEU-type zeolitic tuff of very-high quality (88 wt.% clinoptilolite-heulandite) has been used as an additive to natural soils, aiming to reduce the produced nitrate load in leachates, following a common irrigation scheme. Zeolitic tuff has been added and mixed with agricultural soil in three different proportions (0.2, 0.4 and 0.6%) corresponding to an application of 500, 1000 and 1500 kg per acre. The control soil (without zeolitic tuff) and the three mixtures were exposed in a ten weeks experiment, in which, specific doses of irrigation water enriched in nitrates were added. Results of leachates analyses revealed that the addition of 0.2% zeolitic tuff is not effective and the mixture of soil-zeolitic tuff appears to have similar behaviour with the untreated (reference) soil sample. On the contrary, zeolitic tuff additions by 0.4% and 0.6% showed remarkable results and reduced the nitrate load of leachates by 81 and 86%, respectively. Hence, the impacts from the application of very-high quality HEUtype zeolitic tuff in agricultural soils could be rather positive towards environmental protection and rational farming, an in line with the goals and objectives of the new common agricultural policy imposed by European Union

The present study investigates the hydrogeochemical regime of a complex aquifer system in a highly cultivated area of Thessaly, central Greece. To do so, totally forty (40) groundwater samples were collected for three aquifer units with diverse geological and hydrogeological attributes and analyzed for 77 parameters. Data processing was accomplished with the joint use of classic hydrogeochemical techniques including major ion molar ratios and graphical interpretation, as well as multivariate statistical methods including R-mode factor (FA) and hierarchical cluster analysis (HCA). Results showed that major ion hydrogeochemistry is characterized by the prevalence of calcium (median = 81 mg/L) and bicarbonates (median = 308 mg/L) in the following descending order of concentrations for cations Ca2+>Mg2+>Na+>K+ and anions HCO3 −>NO3 −>SO4 2−>Cl−, respectively. Nitrate values are elevated (median = 23 mg/L), especially in the porous quaternary aquifer, indicating the ongoing agricultural impact from the excessive use of nitrogen fertilizers and manure. The results of multivariate statistics highlighted four factors that chiefly control 81.4 % of overall hydrogeochemistry, related with both geogenic and anthropogenic impacts. The geogenic impact is mainly attributed to the geological substrate and secondarily to the ongoing geochemical (redox) conditions which in turn enrich or deplete groundwater solution with different ions; anthropogenic impact is mainly related with the extensive agricultural practices which favor nitrate enrichment and salinization due to irrigation water return flow.

Edipsos area, situated in northern Euboea, has been well known since ancient times for the existence of thermal springs. In order to assess the hydrogeochemical conditions, thermal and cold water samples were collected and analyzed by ICP method for major and trace elements. The results revealed the direct impact of seawater, a process which is strongly related to the major tectonic structures of the area. Seawater impact was confirmed by the Cl/Br and Na/Cl ionic ratios, as well as from statistical processing and graphical interpretation of the analytical results, which classified the sampled waters into three groups (two for cold waters and one for the thermal ones). Trace element ranges for thermal waters are: As (44–84 ppb), Pb (23–154 ppb), Ag (1–2 ppb), Mn (31–680 ppb), Cu (61–97 ppb), Cs (66–244 ppb), Se (0–76 ppb), Li (732–3269 ppb), Fe (0–1126 ppb), Sr (14000–34100 ppb), B (4300–9600 ppb). Compared with the chemical composition of other thermal springs from the Hellenic Volcanic Arc, Edipsos thermal waters are enriched in Ca2+, Na+, Cl−, SO4 2−, Li, B and K+, reflecting the influence from seawater. Cold waters are free of heavy metals compared with other natural waters and are characterized by good quality based on the major element chemistry. Finally, several geothermometers were applied in order to assess the reservoir temperatures, but none of them appear to be applicable, mainly due to the impact of seawater on the initial hydrogeochemistry of the geothermal fluids.

Kopaida plain is a cultivated region of Eastern Greece, with specific characteristic related with the paleogeographic evolution and the changes in land use. The present article examines the contamination that derives from nitrates, in terms of contaminant levels, definition of sources and spatial distribution of contaminant plume. For this purpose, 50 water samples were collected from the karstic aquifer and analyzed for 15 parameters including major ions, trace elements, physicochemical parameters, and stable isotopes. The assessment of the above parameter values along with the notes derived by the statistical process revealed the existence of nitrate contamination which has been spatial defined with the aid of spatial interpolation techniques. The correlation of NO3− concentrations with the stable isotope values, defined the infiltration conditions and showed contaminant transport. Nitrate values revealed the potential environmental threat for local people, as 10% of the samples exceeded the parametric value of 50 ppm and 54% of them are above 25 ppm, indicating no optimal quality conditions. The origin of nitrate contamination seems to derive exclusively from the application of N-fertilizers, since the rest of potential sources were not verified by analytical data and field works.

The area of Agia (Thessaly, Central Greece) is a highly cultivated region, whose hydrogeochemical status is influenced both by natural and manmade factors. Chemical analyses of 40 groundwater samples from the examined area, revealed that geology has a significant impact to aquifers chemistry, mainly expressed through the presence of carbonate rocks and evaporitic minerals of the Neogene formations of the area., as well as the ultrabasic rocks of the substrate. The manmade influence is assessed through the elevated values of nitrates (values up to 168,7 mg/L). Furthermore, the analytical results of the groundwaters revealed that NO3-, Fe and Se, in several samples, exceeded the EC and SMCL (USEPA) limits. More specifically, the 20% of NO3- samples exceeded the EC limits as well as the 2,5% of Se in samples. The 2,5% of Fe in samples exceeded both the EC and SMCL (USEPA) limits.