Passive in situ remediation of metal-polluted water with caustic magnesia: evidence from column experiments

Passive remediation consists of a permeable system that enables the water to pass through while retaining metals by means of biogeochemical reactions. Conventional passive treatments are based on calcite dissolution. This increases the pH to values between 6 and 7, which are insufficiently high to precipitate divalent metals. Alternative treatments are based on sulfate reduction with organic matter in order to precipitate metal sulfides. However, redox reactions are usually too slow to treat large groundwater flows as currently found in gravel aquifers (>50 m/a). Caustic magnesia obtained from calcination of magnesium carbonate was tested as an alternative material to devising passive remediation systems. Caustic magnesia reacts with water to form magnesium hydroxide, which dissolves, increasing the pH to values higher than 8.5. Then zinc and lead are mainly precipitated as hydroxides, copper is precipitated as hydroxysulfate, and manganese(II) is oxidized and precipitated as manganese(III) oxides. Thus, metal concentrations as high as 75 mg/L in the inflowing water are depleted to values below 0.04 mg/L. Magnesia dissolution is sufficiently fast to treat flows as high as 100 m/a. The new precipitates may lead to a permeability drop in the porous treating system. Mixtures of caustic magnesia and an inert material such as silica sand (approximately 50% of each) have been shown to be as reactive as pure magnesia and permeable for a longer time (more than 10 months and 1000 pore vol).     

A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology

Forecasting systems for foreseeing water levels and flow rates have become necessary to mitigate climate change negative impacts. Most of these systems are based on powerful tools such as Artificial Intelligence (AI) methods. This paper presents a comprehensive review of AI methods for high-flow extremes prediction. The review starts with an overview of the state-of-the-art AI techniques and examples of their application, followed by a SWOT analysis to benchmark their predictive capability based on set of criteria. Finally, the most suitable AI methods for short-term and/or long-term prediction, based on a rigorous suitability assessment are proposed. As a result, Fourteen AI methods have been identified. Their evaluation revealed that the methods that averagely behave the best for achieving high-flow extremes prediction are ANNs, SVMs, wavelets and Bayesian methods, at all-time scales. The latter, as stochastic methods, have the privilege by their cheap computation cost, their reliability and ability to handle hydrological uncertainty, and their capacity to perform causal relationships between features. This study also urges researchers to further explore the predictive potential of decision trees, ensembles, CNNs, MARS, GP and agent-based methods for high-flow extremes.

     

The Social Sustainable Aquifer Yield: An Indicator for the Analysis and Assessment of the Integrated Aquifers Management

Integrated Aquifers Management (IAM) demands innovative tools and methods that are able to consider as much perspectives as possible. This research is aimed to design, apply and provide an indicator named Social Sustainable Aquifer Yield (SSAY), expressed in units of time that includes pure hydrological variables as well as social ones. The indicator is defined as the relation between the Residence Time, which is the relation between aquifer Storage (S) and Recharge (R) (S/R), and the relation between the aquifer Pumping (P) and the new variable named Aquifer Social Yield (ASY). The whole indicator is defined by this formula: (S/R)/(P/ASY). The assessment of the residence time is essential in aquifers with at least one of the following features: i) high hydraulic diffusivity, and ii) small volume of reserves. Finally, the variable ASY is defined as the average perception from the stakeholders about the maximum acceptable aquifer exploitation. This indicator has been successfully applied in the aquifers located in southern Jaen province (South Spain) belonging to the Water System SE4-Jaén Water Supply. The results probe the high utility of the indicator, especially in regards to the public participation processes.     

Particle In-Flight and Coating Properties of Fe-Based Feedstock Materials Sprayed with Modern Thermal Spray Systems

New developments in the field of thermal spraying systems (increased particle velocities, enhanced process stability) are leading to improved coatings. Innovations in the field of feedstock materials are supporting this trend. The combination of both has led to a renaissance of Fe-based feedstocks. Using modern APS or HVOF systems, it is now possible to compete with classical materials for wear and corrosion applications like Ni-basis or metal-matrix composites. This study intends to give an analysis of the in-flight particle and spray jet properties achievable with two different modern thermal spraying systems using Fe-based powders. The velocity fields are measured with the Laser Doppler Anemometry. Resulting coatings are analyzed and a correlation with the particle in-flight properties is given. The experiments are accompanied by computational fluid dynamics simulations of spray jet and particle velocities, leading to a comprehensive analysis of the achievable particle properties with state-of-the-art HVOF and APS systems.