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Acid mine drainage is the reaction of surface water with sub-surface water located on sulfur bearing rocks, resulting in sulfuric acid. These highly acidic conditions result in leaching of non-biodegradeable heavy metals from rock which then accumulate in flora, posing a significant environmental hazard. Hence, reliable, cost effective remediation techniques are continuously sought after by researchers. A range of materials were examined as adsorbents in the extraction of heavy metal ions from acid mine drainage (AMD). However, these materials generally have moderate to poor adsorption capacities. To address this problem, researchers have recently turned to nano-sized materials to enhance the surface area of the adsorbent when in contact with the heavy metal solution. Lately, there have been developments in studying the surface chemistry of nano-engineered materials during adsorption, which involved alterations in the physical and chemical make-up of nanomaterials. The resultant surface engineered nanomaterials have been proven to show rapid adsorption rates and remarkable adsorption capacities for removal of a wide range of heavy metal contaminants in AMD compared to the unmodified nanomaterials. A brief overview of zeolites as adsorbents and the developent of nanosorbents to modernly applied magnetic sorbents and ion imprinted polymers will be discussed. This work provides researchers with thorough insight into the adsorption mechanism and performance of nanosorbents, and finds common ground between the past, present and future of these versatile materials.

Groundwater is the most important natural resource for drinking water to many people around the world, especially in rural areas where the supply of treated water is not available. Drinking water resources cannot be optimally used and sustained unless the quality of water is properly assessed. To this end, an attempt has been made to develop a suitable methodology for the assessment of drinking water quality on the basis of 11 physico-chemical parameters. The present study aims to select the fuzzy aggregation approach for estimation of the water quality index of a sample to check the suitability for drinking purposes. Based on expert’s opinion and author’s judgement, 11 water quality (pollutant) variables (Alkalinity, Dissolved Solids (DS), Hardness, pH, Ca, Mg, Fe, Fluoride, As, Sulphate, Nitrates) are selected for the quality assessment. The output results of proposed methodology are compared with the output obtained from widely used deterministic method (weighted arithmetic mean aggregation) for the suitability of the developed methodology.

Coagulation is an essential and easy process to treat water and wastewater and also to adopt for point of use solutions. Coagulants have played a significant role in providing safe and potable water. Nevertheless, the ill effects of chemical coagulants, such as health effects and substantial sludge quantities, cannot be ignored. Under given conditions, the search for alternative coagulants has been the need of the hour, and researchers have presented those natural coagulants are promising alternatives. The exploration and evaluation of plant-based coagulants have shown that these are fit to substitute chemical coagulants sustainably. Previous studies have presented the efficacy of various coagulants but could not fill in the gap existing in terms of a cumulative database of natural coagulants. In these lines, the focus of the current review is to present the history of natural coagulants, the science involved and studies carried out to evaluate them at different levels. Furthermore, a cumulative database of 57 natural coagulants with their efficacy in removing impurities from raw water is presented.

The present investigation has dealt with the biosorption of copper and zinc ions on the surface of egg-shell particles in the liquid phase. Various rate models were evaluated to elucidate the kinetics of copper and zinc biosorptions, and the results indicated that the pseudo-second-order model was more appropriate than the pseudo-first-order model. The curve of the initial sorption rate versus the initial concentration of copper and zinc ions also complemented the results of the pseudo-second-order model. Models used for the mechanistic modeling were the intra-particle model of pore diffusion and Bangham’s model of film diffusion. The results of the mechanistic modeling together with the values of pore and film diffusivities indicated that the preferential mode of the biosorption of copper and zinc ions on the surface of egg-shell particles in the liquid phase was film diffusion. The results of the intra-particle model showed that the biosorption of the copper and zinc ions was not dominated by the pore diffusion, which was due to macro-pores with open-void spaces present on the surface of egg-shell particles. The thermodynamic modeling reproduced the fact that the sorption of copper and zinc was spontaneous, exothermic with the increased order of the randomness at the solid–liquid interface.

In this study, unmodified biosorbent was obtained from Arachis hypogea husk and applied to remove hexavalent chromium [Cr(VI)] from aqueous media through batch technique. The independent variables (contact time, pH of the solution and initial Cr(VI) concentration) influencing the adsorption process were optimized by central composite design (CCD) found in response surface methodology of the Design-Expert software 12.0.0 at a fixed temperature of 30 ± 0.5 °C. Furthermore, equilibrium sorption isotherms and kinetics studies were also investigated. The ANOVA component of the CCD indicated that all the process independent variables investigated had significant impacts on the sorption capacity of Cr(VI) by Arachis hypogea husk. The obtained experimental data showed that at the optimized 120 min contact time, 8.0 pH of the aqueous solution and 50 mg/L initial Cr(VI) concentration resulted in an optimum adsorption capacity of 2.355 mg/g. Equilibrium sorption isotherm and kinetic studies showed that Redlich–Peterson adsorption isotherm and pseudo-second-order kinetic models fitted well to the equilibrium data. The unmodified adsorbent from Arachis hypogea husk was found to be efficient for Cr(VI) decontamination from the aqueous media.

Karoon River is the most important river in Iran that supplies the drinking and industrial water to many cities and villages in Khuzestan. As a consequence of hospital and municipal wastewater discharge into this river, antibiotic resistance will be increased in microbial inhabitants of this ecosystem. Furthermore, microbial load is also undergone continual variation. The aim of this study was to evaluate seasonal microbial quality and antibiotic resistance in bacterial isolates. For this purpose, five stations were selected and samples were harvested during four seasons. Bacterial count was performed through culturing on Mueller–Hinton agar, and bacterial isolates were identified. Antibiotic resistance profiles of isolates as well as resistance to Hg were investigated. As a result, it was found that the least quality was in summer season while the best quality was in winter that is related to the reduction of water volume and recreational activities in summer and increasing rain and dilution of contaminants in winter season. All isolates were sensitive to Hg while antibiotic resistance and multidrug resistance were found in bacterial isolates. Based on the obtained results, it can be concluded that the microbial quality of Karoon River has variations depending on the season and it is necessary that be monitored in order to control and prevent epidemic bacterial infections and antibiotic resistance distribution.

In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.