Melamine-based hyper-cross-linked resin for efficient removal of total petroleum hydrocarbons (C10-C32) from aqueous solutions.

Petroleum hydrocarbons (PHCs) are produced from industrial discharges, storage leakages, accidental spills, and operational failures. The hazardous nature of PHCs causes serious health risks and threatens the entire aquatic habitat. In this research work, the investigation of the removal of total petroleum hydrocarbons (TPHs) from the contaminated water is carried out utilizing a novel hypercross-linked resin, MAICY, which is generated by condensation of commercially available precursors. The chemical structures of MAICY have been examined extensively by FESEM, FT-IR, solid (CP-MAS) 13C-NMR, and TGA. A comprehensive analysis for adsorption parameters of TPHs has been performed, and different models such as Langmuir and Freundlich isotherms have been employed where the Freundlich isotherm was found to be the best fit for removal of THPs (R2= 0.9991). The results revealed that the performance of MAICY for the adsorption of TPHs from contaminated water gives a maximum adsorption capacity (qe) of 146 mg.g-1. The results of various parameters hinted that the contact time (0.25-4 h), the dosage of adsorbent (0.17 g/L), pH (7), and concentration of TPHs (26.5 mg/L) have controlled the overall adsorptive performance. Moreover, the kinetic data of qe(expt.) and qe(calc.) for adsorption of TPHs disclosed the regression values (R2) for pseudo-first order (R2= 0.9921) and pseudo-second order (R2= 0.9891). Additionally, based on CHI factor (X2) error estimations, the data was shown to be more consistent with pseudo-first-order kinetics. Moreover, MAICY demonstrated excellent reusability and recycling properties for up to four consecutive adsorption-desorption cycles.

Removal of N-Nitrosodiphenylamine from contaminated water: A novel modeling framework using metaheuristic-based ensemble models.

Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA). Initially, models were trained by using OVAT data, with their hyperparameters subsequently fine-tuned through Bayesian optimization. In the second phase, the particle swarm optimization (PSO) technique was adopted to identify optimal parameters, specifically time, concentration, temperature, pH, and dose, to ensure the highest removal. The adopted analytical method enhances both prediction accuracy and removal efficiency. Utilizing OVAT data for NDPhA removal, the XGBoost regressor significantly outperformed other models. With a correlation coefficient of 0.9667 in the testing dataset, the XGBoost model exhibited its accuracy, emphasized by its low mean squared errors of 28.45 and mean absolute errors of 0.0982. Feature importance analysis consistently identified time and concentration as the most critical factors across all models.

Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability.

Water scarcity threatens agriculture and food security in arid regions like Saudi Arabia. The nation produces significant quantities of municipal wastewater, which, with adequate treatment, could serve as an alternative water source for irrigation, thereby reducing reliance on fossil and non-renewable groundwater. This study assessed the appropriateness of using treated wastewater (TWW) for irrigation in a dry coastal agricultural region in Eastern Saudi Arabia and its impact on groundwater resources. Field investigations were conducted in Qatif to collect water samples and field measurements. A multi-criteria approach was applied to evaluate the TWW's suitability for irrigation, including complying with Saudi Standards, the Irrigation Water Quality Index (IWQI), the National Sanitation Foundation water quality index (NSFWQI), and the individual irrigation indices. In addition, the impact of TWW on groundwater was assessed through hydrogeological and isotope approaches. The results indicate that the use of TWW in the study area complied with the Saudi reuse guidelines except for nitrate, aluminum, and molybdenum. However, irrigation water quality indices classify TWW as having limitations that necessitate the use for salt-tolerant crops on permeable and well-drained soils. Stable isotopic analysis (δ2H, δ18O) revealed that long-term irrigation with TWW affected the shallow aquifer, while deep aquifers were minimally impacted due to the presence of aquitard layer. The application of TWW irrigation has successfully maintained groundwater sustainability in the study area, as evidenced by increased groundwater levels up to 2.3 m. Although TWW contributes to crop productivity, long term agricultural sustainability could be enhanced by improving effluent quality, regulating irrigation practices, implementing buffer zones, and monitoring shallow groundwater. An integrated approach that combines advanced wastewater treatment methods, community involvement, regulatory oversight, and targeted monitoring is recommended to be implemented.

Emerging iron-based mesoporous materials for adsorptive removal of pollutants: Mechanism, optimization, challenges, and future perspective.

Iron-based materials (IBMs) have shown promise as adsorbents due to their unique physicochemical properties. This review provides an overview of the different types of IBMs, their synthesis methods, and their properties. Results found in the adsorption of emerging contaminants to a wide range of IBMs are discussed. The IBMs used were evaluated in terms of their maximum uptake capacity, with special consideration given to environmental conditions such as contact time, solution pH, initial pollutant concentration, etc. The adsorption mechanisms of pollutants are discussed taking into account the results of kinetic, isotherm, thermodynamic studies, surface complexation modelling (SCM), and available spectroscopic data. A current overview of molecular modeling and simulation studies related to density functional theory (DFT), surface response methodology (RSM), and artificial neural network (ANN) is presented. In addition, the reusability and suitability of IBMs in real wastewater treatment is shown. The review concludes with the strengths and weaknesses of current research and suggests ideas for future research that will improve our ability to remove contaminants from real wastewater streams.

Desalination and environment: A critical analysis of impacts, mitigation strategies, and greener desalination technologies.

The desalination of seawater is perceived as one of the most viable processes to fulfill the mounting demand for freshwater. Despite enormous economic, social, and health benefits offered by desalination, there are several concerns regarding its prospective environmental impacts (EIs). The objective of this work is to critically evaluate the potential EIs of seawater desalination, and assess the prospects of greener desalination. The EIs of desalination on marine environment, land, groundwater, and air quality was systematically reviewed. An attempt has been made to analyze the actuality of these so-called impacts with reference to evidence from real desalination plants. The mitigative measures to counterbalance these unfavorable impacts are critically appraised. Furthermore, the brine management technologies for the disposal of reject stream, the recovery of precious materials and water, and the production of useful chemicals are also reviewed. Current challenges to minimize the adverse impacts of desalination and prospects of sustainable greener desalination to overwhelm global water scarcities are also discussed. The current desalination approaches have moderate and minor negative EIs. However, with proper mitigation and utilization of modern technologies, these impacts can be lessened. Furthermore, by employing various modern techniques, reject brine can be utilized for several useful applications while reducing its adverse impacts simultaneously. Recent advancements in desalination technologies have also offered many alternative approaches that provide a roadmap towards greener desalination. This review article will be beneficial for all the stakeholders in the desalination industry.

Characterisation and microbial community analysis of lipid utilising microorganisms for biogas formation.

In the anaerobic process, fat-oil-grease (FOG) is hydrolysed to long-chain fatty acids (LCFAs) and glycerol (GLYC), which are then used as substrates to produce biogas. The increase in FOG and LCFAs inhibits methanogenesis, and so far, most work investigating this inhibition has been carried out when FOG or LCFAs were used as co-substrates. In the current work, the inhibition of methanogenesis by FOG, LCFAs and GLYC was investigated when used as sole substrates. To gain more insight on the dynamics of this process, the change of microbial community was analysed using 16S rRNA gene amplicon sequencing. The results indicate that, as the concentrations of cooking olive oil (CO, which represents FOG) and LCFAs increase, methanogenesis is inhibited. For instance, at 0.01 g. L-1 of FOG, the rate of biogas formation was around 8 ml.L-1.day-1, and this decreased to <4 ml.L-1.day-1 at 40 g.L-1. Similar results were observed with the use of LCFAs. However, GLYC concentrations up to 100g.L-1 did not affect the rate of biogas formation. Acidic pH, temperature > = 45°C and NaCl > 3% led to a significant decrease in the rate of biogas formation. Microbial community analyses were carried out from samples from 3 different bioreactors (CO, OLEI and GLYC), on day 1, 5 and 15. In each bioreactor, microbial communities were dominated by Proteobacteria, Firmicutes and Bacteroidetes phyla. The most important families were Enterobacteriaceae, Pseudomonadaceae and Shewanellaceae (Proteobacteria phylum), Clostridiacea and Ruminococcaceae (Firmicutes) and Porphyromonadaceae and Bacteroidaceae (Bacteroidetes). In CO bioreactor, Proteobacteria bacteria decreased over time, while those of OLEI and GLYC bioreactors increased. A more pronounced increase in Bacteroidetes and Firmicutes were observed in CO bioreactor. The methanogenic archaea Methanobacteriaceae and Methanocorpusculaceae were identified. This analysis has shown that a set of microbial population is selected as a function of the substrate.

Synthesis of silver sulfide modified carbon materials for adsorptive removal of dibenzothiophene in n-hexane.

Carbon nanotube (CNT) and graphene oxide (GO) as common nanostructures were modified with silver sulfide (Ag2S) using chemical vapor deposition. The raw and modified materials were tested for the removal of Dibenzothiophene (DBT) from a model fuel in batch mode adsorption experiments. The maximum adsorption capacities of DBT were 52.18 and 49.65 mg g-1, using CNT-Ag2S and GO-Ag2S, respectively. The adsorption isotherm was modeled using Freundlich, Langmuir and Temkin models using linear and non-linear regression. The squared correlation coefficient (R2) and HYBRID error function were used to determine the best adsorption model. IR spectroscopy was used to study the DBT adsorption mechanism, and it was found that the DBT molecules lie flat on the surface of the developed adsorbents. Significant improvement was achieved in the adsorption of DBT using CNT-Ag2S and GO-Ag2S, where the maximum adsorption capacity increased by 127% and 117% respectively, which indicates a stronger interaction between DBT and the modified adsorbents.