Abstract: Permeable reactive barriers (PRBs) are used for groundwater remediation at contaminated sites worldwide. This technology has been efficient at appropriate sites for treating organic and inorganic contaminants using zero-valent iron (ZVI) as a reductant and as a reactive material. Continued development of the technology over the years suggests that a robust understanding of PRB performance and the mechanisms involved is still lacking. Conflicting information in the scientific literature downplays the critical role of ZVI corrosion in the remediation of various organic and inorganic pollutants. Additionally, there is a lack of information on how different mechanisms act in tandem to affect ZVI-groundwater systems through time. In this review paper, we describe the underlying mechanisms of PRB performance and remove isolated misconceptions. We discuss the primary mechanisms of ZVI transformation and aging in PRBs and the role of iron corrosion products. We review numerous sites to reinforce our understanding of the interactions between groundwater contaminants and ZVI and the authigenic minerals that form within PRBs. Our findings show that ZVI corrosion products and mineral precipitates play critical roles in the long-term performance of PRBs by influencing the reactivity of ZVI. Pore occlusion by mineral precipitates occurs at the influent side of PRBs and is enhanced by dissolved oxygen and groundwater rich in dissolved solids and high alkalinity, which negatively impacts hydraulic conductivity, allowing contaminants to potentially bypass the treatment zone. Further development of site characterization tools and models is needed to support effective PRB designs for groundwater remediation.

Abstract: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.

Abstract: Pyrite is common in roll-front type uranium deposit in Chu-sarysu basin, Kazakhstan. Combined in-situ microstructural, isotopic and chemical analysis of pyrite indicates variation in precipitation conditions and in fluid composition. Broad-scale δ34S heterogeneity indicates a complex multi-facet evolution. First generation authigenic framboïdal aggregates are biogenic as demonstrated by the lowest δ34S values of -48‰ to -28‰. The latest generation pyrites are probably hydrothermal with greater δ34S variation (-30‰ to +12‰). This hydrothermal pyrite commonly displays variable enrichment of several trace elements especially As, Co and Ni. Strong variation in δ34S values and variable trace element enrichment is interpreted in terms of continuous variations in fluid composition.

Abstract: Qanat is a type of drain that extract water from aquifers by gravity. Significant amount of fresh water used in Middle East and other parts of the world are supplied by qanats. Despite their importance, discharge variation of these type of wells received almost no attention. The aim of this research is to obtain a Laplace domain solution of discharge variation of a qanat installed in an anisotropic unconfined aquifer subjected to arbitrary areal recharge and nearby pumping well(s) discharge. A new semi-analytical solution of drawdown is obtained first to implement the effects of arbitrary areal recharge and nearby pumping well(s) using the principle of superposition. Then, the discharge variation solution of the qanat is obtained from the drawdown solution. To establish a constant-head boundary condition at the qanat periphery, the qanat is discretized into several segments. The results of this study are presented in dimensionless discharge-dimensionless time curves. The effects of hydraulic as well as geometric parameters on the discharge variation of the qanat due to arbitrary areal recharge, falling of water table from its initial position and discharge of nearby wells are explored. We also investigate the influences of distance and screen depth and location of the nearby well on the discharge variation of the qanat. The results of this study can be utilized for multiple purposes: 1) to predict discharge of qanat in response to rainfall and nearby pumping well(s); 2) to estimate the aquifer parameters using hydrograph of the qanat; 3) to determine optimal location and pumping pattern of the nearby wells to minimize their influences on the discharge of the qanat; 4) to calculate water budget of aquifers drained by a qanat. The equation presented in this work can also be used to estimate discharge of a horizontal drain installed in cropland subjected to arbitrary irrigation pattern.

Abstract: Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.