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Unlabelled Image • Mechanisms of groundwater salinization are studied at the coastal aquifer scale. • Withdrawals in greenhouses areas near the coast led to groundwater salinization. • BEX and SAR temporal-spatial trends discriminate natural vs. anthropic salinization. [ABSTRACT FROM AUTHOR]

Invasive alien species represent one of the main environmental threats to native biodiversity and can also strongly alter the biogeochemical cycles within an ecosystem. This study aims to define the distribution of the invasive alien species Cotula coronopifolia L. within the protected wetland "Variconi" (Campania region, southern Italy) and evaluate the potential role of water geochemical features as interpretation tools for pattern distribution. The presence of C. coronopifolia was assessed in the field, and a distribution map was drawn; concomitantly thirty-nine water samples were collected from groundwater and surface water bodies for chemical analyses. The results showed that C. coronopifolia preferentially colonized the sector of the wetland characterized by high halinity, while it is totally absent in retrodunal and sandy coastal area with very high halinity. The cartography presented can be used as a tool to help target future management interventions. Through our multidisciplinary approach, new evidence has been provided on the ecology of this invasive alien plant that occupies several wetlands worldwide. The replicability of this method may be useful to assess the level of invasion of an alien species but also to predict its evolution as a function of environmental parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Time series regression analysis and SWAT model identify pollution sources. • Leaching susceptibility maps for sediment, P and N were created for the basin. • Agricultural N and treated wastewater N loads were low while sewage was 84 ± 5 % An accurate evaluation of river water quality could be challenging due to the complex hydrological and anthropogenic processes which affect its nature. Reliable water quality data are mandatory to identify long-term trends and regional variability at the watershed scale. In this study, a combined approach using time series, regression, and multivariate statistical analysis along with SWAT modelling was applied to identify the relevant hydrogeochemical processes and the nutrients sources within the Aspio watershed (Ancona, Italy). The analysis detected different processes: i) the geogenic origin of Cl- and SO 4 2-, ii) the heavy metals (Cu and Ni) and hydrocarbons pollution due to runoff from urban and industrial areas, and iii) the agricultural contribution of pesticides, nitrogen, and phosphorous. A SWAT model was implemented to quantify the nutrients load in the Aspio river. A calibration for streamflow, river sediment yield, and for nutrients load was obtained considering agricultural, urban, and wastewater treatment plant contributions. Agriculture and treated wastewater contributed to the overall nitrogen load only for 4% and 12% respectively, while the majority was due to leakage from urban sewage (84%). A scenario with only fertilizers' load (excluding other sources) highlighted that nitrogen and phosphorous export from agricultural lands did not significatively impact the Aspio river. The spatial representation of runoff susceptibility also showed how the highest susceptibility for nitrogen and phosphorous loads is due to areas located close to urban settlements. [ABSTRACT FROM AUTHOR]

The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQI IRRI and GQI POT , while the distance from the coastline and the rivers, are the most important for GQI SWI. The spatial modelling of GQI IRRI and GQI POT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQI IRRI , while the SVM model has the highest prediction for the GQI POT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQI IRRI and GQI POT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the "very high and high quality" classes while the southern portion shows "very low and low quality" classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices. [Display omitted] • Groundwater suitability for human utilization has been assessed in the Campania region (South Italy) using a combined approach. • Three machine learning methods (RF, BRT, and SVM) have been tested to identify the areas related to potable and irrigation indicators. • The results show as almost 80% of resources are suitable for human utilization while the remaining 20% could benefit a preliminary treatment. [ABSTRACT FROM AUTHOR]

Abstract The aim of the present study was to quantify nitrate (NO 3 −) removal via denitrification in an intensively cropped lowland of the Po River delta (Northern Italy). These agricultural lands are characterized by fine textured soils, generally poor of labile organic matter and amended mainly with synthetic fertilizers. Laboratory core incubations in water saturation conditions were performed on two contrasting soil types distinguished by different soil textures (silty-loam and silty-clay) either amended with NO 3 − alone or a combination of NO 3 − and acetate. Denitrification was evaluated by concomitant measurements of NO 3 − consumption and N 2 production via N 2 :Ar analyses by Membrane Inlet Mass Spectrometry (MIMS). The water-logged soils showed higher capacity to reduce NO 3 − to N 2 when supplied with acetate as an organic substrate, while, without acetate amendment, NO 3 − removal was limited due to lack of labile organic substrates. Transient nitrite (NO 2 −) accumulation was documented in acetate amended mesocosms, due to concurrent presence of elevated pH values and use of highly oxidized substrates (like acetate). This study suggests that agricultural practices aimed to increase the availability of labile organic matter, such as acetate, are beneficial in buffering reactive N excess in soils and to reduce NO 3 − leaching towards groundwater and surface-water. Highlights • Quantify NO 3 − removal via denitrification in intensively cropped lowland soils • Laboratory core incubations in water saturation conditions on two soil types • The soils showed higher capacity to reduce NO 3 − to N 2 when supplied with acetate. • Without acetate NO 3 − removal was limited by the lack of labile organic substrates. • The addition of labile substrates is beneficial to buffer reactive N excess in soils. [ABSTRACT FROM AUTHOR]

Abstract. The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley–Taylor (P-T) and Hargreaves–Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman–Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (Rs) estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950–2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ETo and Rs) giving more weight to the monthly coefficients of the months with higher ETo values (or Rs values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ETo and Rs fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ETo estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ETo values by 40 and 25 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ETo values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P-T and H-S evapotranspiration methods for both reference crops and a global map for the revised annual coefficient of the H-S radiation formula and (c) global maps that indicate the optimum locations for using the standard P-T and H-S methods and their possible annual errors based on reference values. The database can support estimations of ETo and solar radiation for locations where climatic data are limited and it can support studies which require such estimations on larger scales (e.g. country, continent, world). [ABSTRACT FROM AUTHOR]

The objective of the study is to provide high resolution global grids of revised annual coefficients for the Priestley-Taylor (P-T) and Hargreaves-Samani (H-S) evapotranspiration methods after calibration based on ASCE-standardized Penman-Monteith method (ASCE method includes two reference crops: short clipped grass and tall alfalfa). The analysis also includes the derivation of global grids of revised annual coefficients for solar radiation Rs estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950-2000. The method for deriving annual coefficients of P-T and H-S methods was based on partial weighted averages (p.w.a.) of their mean monthly values, which eliminate the effect of monthly coefficients that occur during periods where ETo and Rs fall below a specific threshold. Five resolution global grids (30 arc-sec, 2.5, 5, 10 arc-min and 0.5 deg) of annual coefficients for each method were developed. The new coefficients were validated based on data from 140 stations located at various climatic zones of USA and Australia with expanded observations up to 2016. Nine statistical criteria including Taylor diagrams were used in the validation procedure. The validation procedure for ETo estimations of short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed in comparison to the typical methods reducing the ETo RMSE of estimated values by 39 % and 36 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 30% in comparison to the typical H-S radiation formula (the given results are based on the finer resolution grid). All the statistical criteria indicated better performance of the revised coefficients of all resolutions versus the typical coefficients used in the original methods. Finally, a raster database was built consisting of: a) global maps of revised annual coefficients for the ETo methods of P-T and H-S for both reference crops and the Rs H-S formula, b) global maps which indicate the optimum locations for using the original P-T and H-S methods and their expected error based on reference values. The provision of the database aims to improve ETo and Rs estimations which are used in hydrologic/climatic applications when climatic data are limited. [ABSTRACT FROM AUTHOR]

The use of dispersants in particle size analysis is a common practice, but this could cause bias on the gravitational separation of the different particle fractions in natural soil. The study highlights the results obtained in gravitational separation of silty and clayey fractions by using hydrogen peroxide (H2O2) and sodium hexametaphosphate ((NaPO3)6) in different combinations. The efficiency of the different treatments was verified by comparison against the results obtained on the same sediments without any treatment. The separation method is based on Stokes law to calculate the settling time of particles in deionized water under controlled temperature. This method was applied to three different agricultural soils of the Po River Plain. The sample treated with H2O2 and (NaPO3)6 at 4% showed the best results in terms of particle size degree of purity, ranging from 95% to 97% for clay and from 91% to 95% for silt. The degree of purity indicates the percentage of sediment with the particles of the provided grain sizes. [ABSTRACT FROM AUTHOR]

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO 3 −, F−, Cl−, SO 4 2−, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results. Unlabelled Image • Novel Gaussian Process (GP) algorithm was used for NO 3 − and Sr2+ concentration prediction. • GP algorithm had a higher performance followed by RF, M5P and RT. • Different input variables had a different impact on the result. • HCO 3 − and Mg2+ have a significant effect on the prediction of NO 3 − and Sr2+. • Both variables with high and low correlation coefficient should be used as an input. [ABSTRACT FROM AUTHOR]