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Selenium (Se) is an essential micro-nutrient for living organisms, but elevated concentrations in water can adversely affect health. In this research, we investigate the removal of selenium oxyanions (selenate and selenite) in aqueous systems by integration of adsorption on modified zeolites and microbial reduction. Dynamic sorption-reduction experiments were conducted using two sets of zeolite columns for the removal of selenite and selenate oxyanions, respectively. In each case, one column was fully packed with natural, unmodified zeolites, while the other column was composed of 80% natural and 20% iron-coated zeolites, by mass. The initial selenium concentration, selenite (SeIV) or selenate (SeVI), was 790 μg/L, the pH was 7.5, and the flow rate was 3 mL/min. Initially, as expected, the higher selenate removal (34%) was observed with coated zeolite, twice as high compared to the results with unmodified zeolite. Maximum selenite removal was 89% in the column with modified zeolite. Within approximately 14 days, as the biofilm developed inside the columns, selenium reduction in all four columns reached approximately 99%. Biofilm microbial community composition, assessed by 16S rRNA sequencing, is consistent with the presence of mainly selenium-reducing bacteria (Veillonella, Bacteroides, and Escherichia). Selenium oxyanions were reduced to elemental selenium, visible within the bioreactors as red-color aggregates. [ABSTRACT FROM AUTHOR]

Selenium is an essential micro-nutrient for living organisms, but elevated concentrations of it in water can adversely affect health. Nitrate is often found in selenium-contaminated water and negatively correlates with selenium removal. In this study, we investigate the effect of nitrate co-existence on selenium bioremediation in chemically modified zeolite columns. Dynamic sorption-reduction experiments were conducted using natural and iron-coated zeolite columns to remove selenite and selenate oxyanions separately, with and without nitrate anions. Anaerobic sludge was included as microbial inoculum, while lactate was the sole electron donor. The initial selenium concentration (SeIV or SeVI) was 790 µg/L, the nitrate concentration was 620 mg/L, the pH was 7.5, and the flow rate was 3 mL/min. Before introducing nitrate ions, selenium reduction in all four columns reached approximately 99%. However, after introducing nitrate ions, selenate and selenite reduction efficiencies were reduced to approximately 93% and 60%, respectively. Biofilm microbial community composition, assessed by 16S rRNA sequencing, was distinct between the communities with and without nitrate anions. Specifically, in the absence of nitrate, biofilm communities are mainly composed of selenium-reducing bacteria (Veillonella, Bacteroides and Escherichia). In contrast, the presence of nitrate led to mostly denitrifying bacteria (Anaeromusa-Anaeroarcus, Lentimicrobium, Azospirillum and Endomicrobium). Further, comparison of diversity indices (Shannon index, Faith PD and Pielou's) shows alteration in all indices in the presence of nitrate. [ABSTRACT FROM AUTHOR]

This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite [ Se (IV) ] removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite's ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of Se   (IV) , and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, X G B o o s t , L i g h t G B M , and C a t B o o s t models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error ( M A E ), Root Mean Square Error ( R M S E ), Mean Absolute Percentage Error ( M A P E ), and coefficient of determination ( R 2 ). The C a t B o o s t model provided the best fit for a breakthrough prediction with a coefficient of determination R 2 equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model's accurateness. A feature importance assessment indicated that Se   (IV) initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed Se   (IV) sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation. [ABSTRACT FROM AUTHOR]

Selective removal of arsenic from aqueous solutions with high salinity is required for safe disposal of the concentrate and protection of the environment. The use of drinking water treatment solids (DWTS) to remove arsenic from reverse osmosis (RO) concentrate was studied by batch sorption experiments. The impacts of solution chemistry, contact time, sorbent dosage, and arsenic concentration on sorption were investigated, and arsenic sorption kinetics and isotherms were modeled. The results indicated that DWTS were effective in removing arsenic from RO concentrate. The arsenic sorption process followed a pseudo-second-order kinetic model. Multilayer adsorption was simulated by Freundlich equation. The maximum sorption capacities were calculated to be 170 mg arsenic per gram of DWTS. Arsenic sorption was enhanced by surface precipitation onto the DWTS due to the high amount of calcium in the RO concentrate and the formation of ternary complexes between arsenic and natural organic matter (NOM) bound by the polyvalent cations in DWTS. The interactions between arsenic and NOMin the solid phase and aqueous phase exhibited two-sided effects on arsenic sorption onto DWTS. NOMin aqueous solution hindered the arsenic sorption onto DWTS, while the high organic matter content in solid DWTS phase enhanced arsenic sorption. [ABSTRACT FROM AUTHOR]

Radioactive strontium-90 (90Sr2+) is a fission byproduct of uranium and plutonium production, and therefore understanding its environmental fate is of particular importance for predicting the evolution of long-term risk from historical releases. The nonradioactive strontium cation, Sr2+, is a chemical analog for 90Sr2+ that is often used in studies designed to understand the environmental behaviors of 90Sr2+. The focus of this work was on understanding the dynamics of remobilization of strontium following evaporation to dryness in porous media. Evaporation is ubiquitous in the unsaturated zone, and has the potential to significantly impact the dynamics of transport by driving adsorption or precipitation on solid surfaces. For this work, a series of transport experiments were conducted examining the behavior of strontium over a range of pH values, ionic strengths, and concentrations. Saturated transport experiments were conducted, followed by experiments designed to examine the release and transport following evaporation to dryness. Results show increasing saturated retardation with increasing pH, decreasing ionic strength, and decreasing concentration, with the concentration exhibiting the strongest effect. Breakthrough curves at low concentrations were also found to be consistent with significant rate-limited desorption. Remobilization elution curves measured following evaporation to dryness exhibited the high initial effluent concentrations, exceeding the influent strontium concentration, most likely caused by the initial dissolution and accumulation of strontium by the advancing solution. Concentrations at later times were found to be largely consistent with the dynamics of saturated transport for the systems studied. [ABSTRACT FROM AUTHOR]