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A coupled methodology of nitrogen isotopes, hydrogeochemical characterization, multivariate statistical analysis, and SIAR Bayesian modeling has been employed to identify the sources of NO3− and N transformation processes in three alluvial aquifers (Schinos, Thiva, and Central Evia) located in central Greece where geogenic Cr(VI) co-occurs with agricultural activity and rural development. Hexavalent chromium concentrations exceed 50 μg/L in many sampling stations of the studied groundwater bodies, while nitrate contamination is evident in all three study areas with concentrations well over 50 mg/L. The mean δ15N-NO3– and δ18Ο-NO3− values are 6.67 ± 1.77‰ and 2.68 ± 1.77‰ in C. Evia, 8.72 ± 4.74‰ and 3.96 ± 4.57‰ in Schinos and 4.44 ± 1.71‰ and 2.91 ± 1.02‰ in Thiva, respectively. Domestic sewage and N-bearing fertilizers are contributing in various degrees to the observed nitrification which is the dominant transformation process of N in the studied aquifers. Multivariate statistics indicated that the main processes identified in the study areas are salinization, silicate dissolution, and groundwater contamination due to fertilizer use. It is suggested that ultramafic rock–related alluvial aquifers must be closely monitored in terms of nutrient inputs as an effective measure for controlling Cr(VI) release in groundwater. [ABSTRACT FROM AUTHOR]

Μining waste, processing by-products and mine water discharges pose a serious threat to the environment as in many cases they contain high concentrations of toxic substances. However, they may also be valuable resources. The main target of the current review is the comparative study of the occurrence of rare earth elements (REE) in mining waste and mine water discharges produced from the exploitation of coal, bauxite, phosphate rock and other ore deposits. Coal combustion ashes, bauxite residue and phosphogypsum present high percentages of critical REEs (up to 41% of the total REE content) with ΣREY content ranging from 77 to 1957.7 ppm. The total REE concentrations in mine discharges from different coal and ore mining areas around the globe are also characterised by a high range of concentrations from 0.25 to 9.8 ppm and from 1.6 to 24.8 ppm, respectively. Acid mine discharges and their associated natural and treatment precipitates seem to be also promising sources of REE if their extraction is coupled with the simultaneous removal of toxic pollutants. [ABSTRACT FROM AUTHOR]

A wide range of dyes are being disposed in water bodies from several industrial runoff and the quantity is rapidly increasing over the years. From an environmental safety point of view, it is urgent to improve the removal process of dyes. It is important to understand the stochastic and highly redundant behavior of the process of dye removal (D R) in wastewater treatment. This leads to better utilization of Machine Learning (ML) models for both optimization as well as prediction of the D R process efficiency. In this review, 200 papers (Years, 2006–2021) have been systematically reviewed from the Web of Science and Scopus-indexed journals, covering a total of 84 journals. All applied ML models have been thoroughly studied in the review and analyzed in terms of their architecture setup, hyper-parameters selection, performance, advantages, and disadvantages. A wide range of optimization methods for hyper-parameters tuning were analyzed and discussed scientifically. Explicit information about the data sizes, splitting structure for training-validation-testing along with input and output selection approaches have been logically reviewed and discussed. Data availability, transparency, and reusability have been reported adequately. Various software for data-driven modeling have been discussed with their pros and cons. Trends in statistical evaluators (among about 60 types) have been discussed with their pros and cons including their sensitivity with the data fluctuations. Moreover, the most popular performance metrics have reported. In addition, the D R mechanism has reviewed and discussed inclusively. Extensive media used for the decolorization were discussed thoroughly, including their physical and chemical characteristics, along with feasibility and equilibrium data based on Langmuir model. The cost of the applied media in the decolorization process reported adequately. Finally, the research gap and future road map of the next 5 years, which bridge the gap of the domain are scientifically drafted along with the limitations. This critical review not only provides the appraisal of growth of D R research integrated with ML in the last couple of decades but also scouts the potential studies where all experimental, chemical and modeling processes should be taken under consideration. [Display omitted] • An extensive review on dye removal modeling established over 2 decades. • All machine learning predictive models and optimizers are exhibited and assessed. • Data availability, application of hyper-parameter optimization are analyzed. • Removal media, dye color, and economical global assessment are appraised. • 5 years of future prospective road map are suggested to be explored. [ABSTRACT FROM AUTHOR]