Applied Water Science最新文献

Antibiotics are among the most widely used pharmaceutically active compounds. Possessing the capability to adversely impact the ecological system, existence of antibiotics in the environment is an escalating concern. With the purpose of removing two widely used antibiotics efficiently from aqueous solutions, the competency of two biochar (BC)-based sorbents derived from spent coffee (SC) grounds was investigated. Both pristine (SCBC) and nickel (II) oxide-impregnated (Ni-SCBC) biochars were utilized as sustainable and cost-effective sorbents to remove daunorubicin (DAYN) and tigecycline (TIGY) from single synthetic aqueous solutions and binary combinations. Batch adsorption experiments were controlled implementing Box–Behnken design. The removal efficiency of Ni-SCBC was superior compared to SCBC (TIGY: 67.06%, DAYN: 94.30%). Results of characterizations showed that impregnation with NiO changed the degree of crystallization with a remarkable increase in the surface area from 49.23 m²/g in SCBC to 86.06 m²/g in Ni-SCBC. Adsorption of DAYN and TIGY (single solutions) conformed well to Freundlich, and Langmuir isotherms, respectively. A maximum adsorption capacity (q_max) of 136.62 mg/g (DAYN) and 73.15 mg/g (TIGY) was reported in single solutions, compared to 23.50 mg/g (DAYN) and 58.42 mg/g (TIGY) in binary mixture. Adsorption kinetics onto Ni-SCBC fitted well with the pseudo-second-order (PSO) and Elovich models. Acquired results demonstrated that SCBC and Ni-SCBC are promising adsorbents for remedying antibiotics.

Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

A novel photocatalytic reactor was developed to degrade methylene blue in water using titanium dioxide (TiO₂) as a catalyst and fluorinated ethylene propylene (FEP) tubing as a transmitter for ultraviolet (UV) radiation. The reactor was operated by continuously flowing the solution through narrow tubes that were exposed to UV radiation. The efficiency of the reactor was evaluated by comparing it to a previous study that used quartz glass tubing. The study also investigated the effects of flow rate, initial concentration, pH, TiO₂ dose and UV radiation without a catalyst on the degradation of methylene blue. The results showed that the FEP tubing was a more efficient UV transmitter than quartz glass. The efficiency of the reactor was also affected by the flow rate and pH of the solution. The highest degradation efficiency was achieved at a flow rate of 10 mL/min and a pH of 7.0. The use of TiO₂ as a catalyst also significantly improved the degradation efficiency, with an almost doubling of the degradation rate when compared to the case without a catalyst. This study demonstrates the potential of the FEP tubing-based photocatalytic reactor for the degradation of methylene blue in water. The reactor is easy to operate and can be scaled up for industrial applications.

In an attempt to create wastewater treatment “green” techniques that are both economically feasible and sustainable without using any dangerous chemicals, barley grain (Hordeum vulgare L.) water extract was used to phyto-synthesize silver nanoparticles (Ag°). Barley grains served as a natural reductant and stabilizer at the same time. The role of different synthesis conditions and their effect on the efficiency of the green synthesis process were studied and confirmed with characterization using several techniques (UV–vis, SEM, EDX, sizing distribution, and FTIR). The Ag°9 formula catalytic reduction was inspected against p-nitrophenol (PNP) and methylene blue (MB) as a model of nitroaromatic components and dyes, respectively. The removal studies were conducted using the target pollutants in a single or mixed liquid state. Remarkably, the Ag°9 particle size was around 20 nm, and its final concentration in the current formula was 2.2 × 10⁻⁷ mol L⁻¹. The adsorption mechanism of the PNP and MB was pseudo-second order. The good fit with the pseudo-second-order kinetic model suggests that chemisorption occurs in the sorption process. The formula catalytic activity to remove PNP and MB was 99 and 66% at levels 60 and 500 µL from the Ag°9 formula, respectively, within less than 5 min.

This study aimed to forecast dam inflows and subsequently predict its capability in producing HEPP using machine learning and evolutionary optimization techniques. Mahabad Dam, located in the northwest of Iran and recognized as one of the nation’s key dams, served as a case study. First, artificial neural networks (ANN) and support vector regression (SVR) were employed to predict dam inflows, with optimization of parameters achieved through Harris hawks optimization (HHO), a robust optimization technique. The data of temperature, precipitation, and dam inflow over a 24-year period on a monthly basis, incorporating various lag times, were used to train these machines. Then, HEPP from the dam was predicted using temperature, precipitation, dam inflow, and dam evaporation as input variables. The models were applied to data covering the years 2000 to 2020. The results of the first part indicated both hybrid models (HHO-ANFIS and HHO-SVR) improved the prediction performance compared to the single models. Based on the results of Taylor’s diagram and the error evaluation criteria, the HHO-ANFIS hybrid model (RMSE, MAE, and NSE of 3.90, 2.41, and 0.86, respectively) exerted better performance than HHO-SVR (RMSE, MAE, and NSE of 4.39, 2.70, and 0.86, respectively). The results of the second part showed that using the HHO algorithm to optimize single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively) predicted HEPP better than single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively). The results of Taylor’s diagram also showed that the HHO-ANFIS model exerted better performance. The findings of this study indicated the promising performance of machine learning models optimized by metaheuristic algorithms in the simultaneous prediction of dam inflows and HEPP in multi-purpose dams for better management and allocation of surface water resources.

This study examined the treatment of surface water using a mixed natural (linseed) and chemical (alum)-based coagulant in terms of color, turbidity, and chemical oxygen demand (COD) (%) removal in a laboratory jar test. Experimental results showed that using a combined coagulant has shown higher removal of color (99.72%) and turbidity (97.76%) at pH values of 3.5 using a 1.5 g/L dosage and a stirring time of 38.58 min. Similarly, at the same pH value and 2.5 g/L dosage, the COD removal was 96%. To determine the optimum value with the highest percent removal efficiency of the coagulation–flocculation process, several experimental parameters including blended dosage, pH, COD concentration, and initial turbidity have been studied in terms of the percent of color, chemical oxygen demand, and turbidity removal. The optimum value was found for the highest removal of color-97.75%, turbidity-96.86%, and COD-90.33% with the pH values of 7.0, at a dosage of 2.5 g/L and a stirring time of 40 min, respectively. Statistical techniques of response surface methodology were used in experimental design and optimization, in order to calculate the confidence intervals to assess population parameter precision. An ANOVA-95% confidence interval ensures that the high reliability optimizes the result. The findings proved the excellent adsorption potential and high performance of the blended coagulant in the removal of contaminants from surface water.

Acid mine drainage (AMD) is a mining-associated environmental problem that mainly pollutes water resources worldwide, making it imperative to find sustainable remediation solutions. To find effective treatment solutions for AMD, it will be beneficial to understand how this area of research has evolved over the years. Thus, this work provides a bibliometric analysis and narrative review of previous research articles that have focused on AMD treatment and management over the past 47 years and highlights the associated challenges and how to overcome them. Research articles addressing the treatment and management of AMD were retrieved from the Scopus database, using specific search criteria. The Scopus Analyze Tool and VOSviewer were used to analyze the publications to provide information on the publication distribution, countries of publication, authorship, keywords, field of study, and author affiliations, while the narrative review provides an overview of how AMD treatment technologies have evolved over these years. The top ten most published countries are developed countries except for South Africa (ranking number 4). This review revealed that several approaches have been developed for AMD treatment and management. It was observed that AMD treatment methods have not drastically changed over the years. Instead, earlier treatment techniques are being improved to develop new and more effective ones. The most recent remediation approach involves the valorization of AMD for the recovery of new materials in economically viable amounts. This is a sustainable approach to AMD treatment; however, it comes with challenges that can be overcome through more research in this area.

Contamination of water resources by heavy metals causes health problems for humans. This study attempts to investigate the heavy metal contamination levels, health risks and sources of appraisal of groundwater and surface water in the mountain-bounded catchment and low-grade basement rock-dominated area of ​​the Irob, Tigray. Eighteen grab water samples (13 borehole water, 2 spring water and 3 surface water) were collected and analyzed for pH, electrical conductivity, total dissolved solids and heavy metals using standard procedures. The findings were contrasted with those of the standards set by the World Health Organization and the United States Environmental Protection Agency. Integrated techniques, including indexed and statistical methods, were used to determine the contamination levels of metals, risks to human health and sources. The result shows that the pH value, electrical conductivity and total dissolved solids fluctuated between 7.4 and 7.9, 516 and 2410 µs/cm and 396.7 mg/l and 1719 mg/l, respectively. The findings indicate that 94.4% of the water samples had levels of contamination above the critical limit for all three indices: the heavy metal evaluation index (HEI), the degree of contamination (cd) and the heavy metal pollution index. The hazard index of metals for adults and children was greater than 1. 88.9% of the water samples showed a cancer risk value above the recommended value (CR > 1 × 10^–4) for Cd and Cr for both adults and children. Multivariate statistical analyses indicate that weathering of bedrocks and partly anthropogenic influences are responsible for the metal contamination. The study concludes that some water samples sources are unfit for human consumption that can pose health risks over time. Therefore, it is recommended to treat contaminated water sources to protect and sustain public health.

The contamination of natural mineral bottled drinking water with trace, heavy metals and rare-earth elements is a growing concern globally, especially in regions with limited access to safe drinking water sources. In South Africa, the safety of bottled water remains a critical issue due to potential health risks associated with heavy metal exposure. This study aimed to evaluate physicochemical parameters alongside to assess the health risks due to heavy metals in natural mineral bottled drinking water available in South Africa, using inductively coupled plasma mass spectrometry analysis. A total of 21 bottled water samples from different brands were collected and analysed for heavy metal concentrations including Pb, Cd, As, Cr, Ni, Ba and Hg, as well as physicochemical parameters such as pH, conductivity, dissolved oxygen and total dissolved solids among others. The results revealed physicochemical parameters were within acceptable ranges, indicating overall water quality. Varying levels of heavy metals across different brands observed with concentrations of Al, Fe, Mg, Ca, K and Mn in some samples exceeding permissible limits set by WHO and SAWQG for drinking water quality. The mean concentration of the heavy metals is in the decreasing toxicity order of Sr > Al > Ba > Mn > Cu > Cr > Zn > Fe > As > Co > U > Ni > Cs > Pb > Cd > Hg. The mean values of the chronic daily intake (CDI) for the concentration of heavy metals for adults are in the order of Cr > Fe > Sr > Ba > Mn > Cu > Zn > Li > V > As > Ni > Be > Pb > Hg > Cd and were below acceptable limits. The estimated values of both HQ and HI (with mean of 2.07E−03 and range of 0.00 to 1.76E−02) for the heavy metals are all found to be less than 1. The total mean value of ILCR is 4.67E−06, and range of 0.00 to 2.76E−05, which is insignificant and within the permissible level of the cancer risk guidance limit of 1.00E−06 to 1.00E−04. The results show that consumption of natural mineral bottled drinking water of South Africa is safe and may not cause any significant health risk to the populace. However, the long-time potential effects due to the few exceeded metals levels needed to be considered. Our findings contribute to the ongoing discourse on water quality assurance, offering insights into the overall integrity of the natural mineral bottled water supply chain in South Africa. This research not only serves as a foundation for regulatory measures but also underscores the significance of maintaining high-quality standards in the bottled water industry for public health and environmental sustainability.

Emerging contaminants such as ceftriaxone are a significant issue in the environment. They have led to a series of ecological, environmental, and health issues, and it is urgent to find a green and secure method to remove antibiotics from water effectively. In this research, the CuCoFe₂O₄@Gum Arabic (GA)/Activated Carbon (AC) as an innovative bio-based matrix magnetic nanocatalyst was synthesized for the efficient degradation of ceftriaxone from aqueous media. The structure of CuCoFe₂O₄@GA/AC was characterized via FESEM, EDS, Mapping, XRD, FTIR, VSM, and DRS analyses. The structural analysis of the catalyst revealed its synthesis at the nanometer scale (40–50 nm), exhibiting high magnetic strength (Ms: 5.38 emu/g) and favorable optical properties with a bandgap of 3.6 eV. Under optimized conditions, including a pH of 5, 60 min of irradiation time, 0.24 g/L photocatalyst dose, and ceftriaxone concentration of 5 mg/L, the removal efficiency from synthetic and real samples was 94.43% and 62.5%, respectively. The photocatalytic degradation process of ceftriaxone followed pseudo-first-order and Langmuir–Hinshelwood kinetic models. Furthermore, analysis of the process mechanism indicated a prominent role of the superoxide radical. The catalyst had a high recovery capability and chemical stability. The photocatalytic degradation of ceftriaxone by CuCoFe₂O₄@GA/AC showcased remarkable efficiency, indicating its potential utility in the treatment of wastewater contaminated with antibiotics.