Abstract: Laboratory experiments and point monitoring of reservoir sediments have proven that stable sulfate reduction (SSR) can lower the concentrations of toxic metals and sulfate in acidic groundwater for a long time. Here, we hypothesize that SSR occurred during in situ leaching after uranium mining, which can impact the fate of acid groundwater in an entire region. To test this, we applied a sulfur isotope fractionation method to analyze the mechanism for natural attenuation of contaminated groundwater produced by acid in situ leaching of uranium (Xinjiang, China). The results showed that δ34S increased over time after the cessation of uranium mining, and natural attenuation caused considerable, area-scale immobilization of sulfur corresponding to retention levels of 5.3%–48.3% while simultaneously decreasing the concentration of uranium. Isotopic evidence for SSR in the area, together with evidence for changes of pollutant concentrations, suggest that area-scale SSR is most likely also important at other acid mining sites for uranium, where retention of acid groundwater may be strengthened through natural attenuation. To recapitulate, the sulfur isotope fractionation method constitutes a relatively accurate tool for quantification of spatiotemporal trends for groundwater during migration and transformation resulting from acid in situ leaching of uranium in northern China.

Abstract: Cities have experienced rapid urbanization-induced harsh climatic events, especially flooding, inevitably resulting in negative and irreversible consequences for urban resilience and endangering residents’ lives. Numerous studies have analyzed the effects of anthropogenic practices (land use changes and urbanization) on flood forecasting. However, non-structural mitigation’s effectiveness, like Nature-Based Solutions (NBS), has yet to receive adequate attention, particularly in the Middle East and North Africa (MENA) region, which have become increasingly significant and indispensable for operationalizing cities efficiently. Therefore, our study investigated the predictive influence of incorporating one of the most common NBS strategies called low-impact development tools (LID) (such as rain gardens, bio-retention cells, green roofs, infiltration trenches, permeable pavement, and vegetative swale) during the urban planning of Alexandria, Egypt, which experiences the harshest rainfall annually and includes various urban patterns. City characteristics-dependent 14 LID scenarios were simulated with recurrence intervals ranging from 2 to 100 years using the LID Treatment Train Tool (LID TTT), depending on calibrated data from 2015 to 2020, by the Nash-Sutcliffe efficiency index and deterministic coefficient, and root-mean-square error with values of 0.97, 0.91, and 0.31, respectively. Our findings confirmed the significant effectiveness of combined LID tools on total flood runoff volume reduction by 73.7%, revealing that different urban patterns can be used in flood-prone cities, provided LID tools are considered in city planning besides grey infrastructure to achieve optimal mitigation. These results, which combined multiple disciplines and were not explicitly mentioned in similar studies in developing countries, may assist municipalities’ policymakers in planning flood-resistant, sustainable cities.

Abstract: Engineering heritage refers to a broad variety of items of social, economic, aesthetic or historic relevance, including roads, dams, buildings and supply networks. Due to their utilitarian nature, their heritage value is often overlooked. This occurs even with those infrastructures that have played an essential role in underpinning the daily existence of entire civilizations. Underground water-supply networks provide an excellent example. Although there are exceptions, water networks tend to be functional in design, rather than monumental. Moreover, they present intricate linear layouts that often span several kilometres. This means they are costly to maintain once their operational life is over, and that they are prone to abandonment and destruction. Devising a priori protection strategies is important to preserve these valuable cultural assets. The following pages present a method to map linear structures based on archive records and sparse material remains. The method is illustrated through its application to the Amaniel qanat, a water-supply gallery built in Madrid, Spain, in the early 17th Century. An appraisal of the known remains was carried out first, leading to an inventory of galleries, shafts, shaft caps and deposits. This was followed by a thorough survey of over one thousand handwritten manuscripts, including physical descriptions of the aqueduct, budget accounts or water metering campaigns, among other documents. Known remains and written evidence were matched against original and auxiliary maps to reconstruct the itinerary of the aqueduct. This led to the identification of sectors where it is still possible to find remains in good condition. Thus, a priori mapping is advocated a valuable technique to locate and preserve these remains, as well as to devise non-invasive surveys and establish heritage protection zones.

Abstract: Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques.

Abstract: Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words – it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost.