 Subjects -> WATER RESOURCES (Total: 160 journals)
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- Synthesis of a nanocomposite with holocellulose extracted from barley
straw and montmorillonite, and optimization of the removal of methylene
blue dye using the synthesized adsorbent
Abstract: Abstract In this study, holocellulose was extracted from milled barley straw with different mesh sizes using Laccase enzyme. After extraction, a dual composite was made using montmorillonite clay to remove methylene blue dye from synthetic effluent. Results of different analysis methods including scanning electron microscope, Fourier transform infrared spectroscopy, and BET revealed that prepared nanocomposite presented desired specifications, and for smaller mesh sizes, derived holocellulose had higher quality due to high specific surface area. Response surface methodology was employed to reduce the number of experiments for methylene blue adsorption experiments and to achieve an empirical model for prediction of adsorption efficiency at different operating conditions. The effect of different factors including solution temperature, pH, initial dye concentration, and mesh size of milled barley straw on dye adsorption performance by prepared composite was examined. Maximum removal efficiency was obtained about 95% at temperature of 32 °C, pH of 8, initial dye concentration of 4 mg L−1, and mesh size of 70. Also, isotherm studies were performed on experimental data using Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherm models and results revealed that the adsorption process follows Langmuir model with maximum predicted adsorption capacity of 159 mg g−1, which implies monolayer adsorption. Moreover, thermodynamic study revealed that adsorption of methylene blue is endothermic and spontaneous while enthalpy and Gibbs free energy of adsorption are positive and negative, respectively. Finally, adsorption kinetic study determined that the pseudo-second-order kinetics model with correlation coefficient of about 1 best fitted the experimental results which is the characteristic of chemisorption process.
PubDate: 2023-11-24
- Decontamination of levofloxacin from water using a novel chitosan–walnut
shells composite: linear, nonlinear, and optimization modeling
Abstract: Abstract Chitosan–walnut shells (Ch–W) composite was tested for the removal of levofloxacin from water. Various experimental factors were examined at different contact time intervals. The prepared composite exhibited maximum uptake capacity of 7.43 mg g−1 for levofloxacin with 0.5 g L−1 Ch–W dose at 45 min and pH = 7. Linear and nonlinear isotherm/kinetic models have been investigated, and the pertinency of the models was confirmed by correlation coefficients (R2) and error functions. Consequently, the adsorption of levofloxacin could be more accurately described by the nonlinear pseudo-second-order and Langmuir as well as Temkin models (R2 ˃ 0.98). Optimization modeling of levofloxacin was performed using a central composite design. The independent parameters; initial concentration, pH, and Ch–W dose, were selected while levofloxacin removal was the response. The maximum levofloxacin removal was 75.7% and 94.2% at concentrations of 4 mg L−1 and 2 mg L−1, respectively. Furthermore, 3D surface plots with the interaction effects of the investigated factors are presented. The developed model was reliable for further study and prediction owing to the closeness between the experimental and predicted values. The individual and interacting factors were found to be significant except for “concentration x dose” based on the ANOVA. The models confirmed the experimental results with R2, R2adjusted, and R2predicted values ˃ 0.939. The continuity adsorption cycles were tested for reuse and revealed that the removal of levofloxacin was decreased ~ 23% after six cycles. Hence, the prepared composite has the potential to remove antibiotics from water.
PubDate: 2023-11-24
- A machine learning-based approach to predict groundwater nitrate
susceptibility using field measurements and hydrogeological variables in
the Nonsan Stream Watershed, South Korea
Abstract: Abstract Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control and management in rural mixed-land-use areas. Several groundwater nitrate prediction models have been developed; in particular, a nitrate concentration model that uses dissolved ions in groundwater as an input variable can produce accurate results. However, obtaining sufficient chemical data from a target area remains challenging. We tested whether machine learning models can effectively determine nitrate contamination using field-measured data (pH, electrical conductivity, water temperature, dissolved oxygen, and redox potential) and existing geographic information system (GIS) data (lithology, land cover, and hydrogeological properties) from the Nonsan Stream Watershed in South Korea, an area where nitrate contamination occurs owing to intensive agricultural activities. In total, 183 groundwater samples from different wells, mixed municipal sites, and agricultural activities were used. The results indicated that among the four machine learning models (artificial neural network (ANN), classification and regression tree (CART), random forest (RF), and support vector machine (SVM)), the RF (R2: 0.74; RMSE: 3.5) and SVM (R2: 0.80; RMSE: 2.8) achieved the highest prediction accuracy and smallest error in all groundwater parameter estimates. Land cover, aquifer type, and soil drainage were the primary RF and SVM model input variables, representing agricultural activity-related and hydrogeological infiltration effects. Our research found that in rural areas with limited hydro-chemical data, RF and SVM models could be used to identify areas at high risk of nitrate contamination using spatial variability, GIS-aided visualization, and easily accessible field-measured groundwater quality data.
PubDate: 2023-11-24
- Parallelization of AMALGAM algorithm for a multi-objective optimization of
a hydrological model
Abstract: Abstract A calibration procedure is essential step to achieve a realistic model simulation particularly in hydrological model which simulates water cycle in the basin. This process is always faced with challenges due to selection of objective function and highly time-consuming. This study aimed to take advantage of parallel processing to accelerate the computations involved with simulation process of hydrologic model linked with the multi-objective optimization algorithm of AMALGAM for multi-site calibration of SWAT hydrologic model parameters. In order to illustrate how meaningful SWAT model calibration trade-off between the four objective functions involved in AMALGAM optimization program, the Pareto solution sets were provided. Furthermore, it is implemented a group of model runs with a number of cores involved (from one to eight) to demonstrate and evaluate the running of parallelized AMALGAM with taking advantages of “spmd” method to decrease the running time of the SWAT model. The results revealed the robustness of the method in reducing computational time of the parameter calibration significantly. This strategy with 4-objective functions focuses on high streamflow (Nash–Sutcliffe coefficient), low streamflow (Box–Cox transformed root–mean–square error), water balance (runoff coefficient error), and flashiness (slope of the flow duration curve error) provided an efficient tool to decide about the best simulation based on the investigated objective functions. This study also provides a strong basis for multi-objective optimization of hydrological and water quality models and its general analytical framework could be applied to other parts of the world.
PubDate: 2023-11-22
- Assessment of water productivity improvement strategies using system
dynamics approach
Abstract: Abstract This study utilizes a system dynamics approach (SD) to assess the effects of water productivity improvement strategies on the Qazvin plain, Iran, and the uncertainty of the individual and interactive effects. The key indicators included in the important strategies are cropping pattern scenarios (CPS), deficit irrigation (DI), and modern irrigation systems development (MISD). Plain-scale results show that CPS 7, CPS 4, and CPS 8 had the highest physical water productivity (WPp) at 2.11, 1.99, and 1.95 kg/m3, respectively, representing a 21, 14, and 12 percent increase over CPS 1. Compared with CPS 1, CPS 4, CPS 6, and CPS 8 showed the highest values of WPe (5678, 5568, and 5503 Rials/m3, respectively). At the field scale, under DI, WPp increased for all crops (except corn, which was the most sensitive), but WPe is only increased for tomato, canola, pea, and barley and reduced for corn, potato, beans, lentils, and sugar beet. The WPe was affected by the DI, the irrigation system type, and the CPS. CPS 7 and CPS 6 had the highest and lowest water requirements, respectively, with 11,699 and 8207 m3/ha. Volume decline in aquifers is significantly affected by both CPS and DI. The CPS6, CPS8, and CPS2 were better than other scenarios. By modifying the cropping pattern, it is possible to prevent aquifer decline, thus improving the aquifer status (CPS5). MISD increased both field and plain WPp for all crops. The MISD improved groundwater resources and reduced demand by increasing efficiency to improve aquifer condition.
PubDate: 2023-11-20
- Sedimentary abundance and major determinants of river microplastic
contamination in the central arid part of Iran
Abstract: Abstract The proliferation of anthropogenic activities around the Central Iranian Rivers shows a warning alarm of river microplastic (MP) pollution. In the Zayandeh-rood River, the mean abundance of sedimentary MPs trapped at the mouth of 21 modified sub-catchments was 588 items/kg d.w and followed the order: downstream (1701 items/kg d.w) > midstream (269.2 items/kg d.w) > upstream (57.2 items/kg d.w). The widespread distribution of fiber and fragment forms across all stations and the high MP abundance near the discharge of the largest wastewater treatment plant indicate their origin from both point and non-point sources. Using the linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN), we assessed the contribution of three types of variables including the sediment physio-chemical properties, river geometry and land-use characteristics. According to both modeling results, the mean annual number of local people and tourist visitors (0.35 million people) are the most important determinants of river MP pollution whose contribution dominates through the use of plastic products and their direct and indirect release into the environment. The ANN model (R2 = 0.99) outperformed the MLR model (R2 = 0.80) and showed the importance of total organic carbon (TOC)-rich regions as MP hotspots. To alleviate the river MP pollution, suggested measures involve altering plastic usage and disposal practices among visitors and reducing the TOC content in the industrial/municipal wastewater entering the river.
PubDate: 2023-11-20
- Seasonal variations of potentially toxic elements (PTEs) in drinking water
and health risk assessment via Monte Carlo simulation and Sobol
sensitivity analysis in southern Iran's largest city
Abstract: Abstract The escalating concern over the presence and health implications of potentially toxic elements (PTEs) in drinking water has underscored the need for rigorous risk assessments. Our study aimed to quantify both the non-carcinogenic and carcinogenic health risks associated with exposure to selected PTEs—namely arsenic (As), chromium (Cr), and cadmium (Cd). Also, we evaluated ingestion and skin contact exposures to risks during summer and winter using metrics such as the hazard quotient (HQ), hazard index (HI), and cancer risk (CR) for children, adult males, and adult females. For all demographic groups and exposure pathways, the HQ values remain below the established safety threshold (HQ < 1). Notably, As consistently had the highest average HI value across children, male adults, and female adults. Seasonal variations were statistically significant (p < 0.05) for As and Cr, but not Cd. During the summer, the average total carcinogenic risks (TCR) from drinking water exposure were 7.61 × 10–6, 8.94 × 10–6, and 1.12 × 10–5 for children, male adults, and female adults, respectively. In the winter, these values were 1.18 × 10–5, 1.40 × 10–5, and 1.75 × 10–5, respectively. The fuzzy C-means clustering analysis provided insights into our dataset's Cr, Cd, and As distribution patterns. Results indicate that As, Cr, and Cd mean concentrations were below the World Health Organization health-based guidelines. The CR values for children and adults from drinking water exposure were slightly above or below the US Environmental Protection Agency’s standards. These findings can inform research and policy-making regarding the risk of PTEs in drinking water and highlight the need to monitor Shiraz water regularly.
PubDate: 2023-11-18
- Utilizing of aquifer hydraulic parameters to assess the groundwater
sustainability in the new reclamation area of Moghra Oasis: Western
Desert—Egypt
Abstract: Abstract The sustainability of groundwater aquifers requires evaluating several parameters, the most important of which are hydraulic parameters. Therefore, the essential aim of this research is to develop a management plan for the Moghra aquifer in order to prevent the expanding of water level decline and degradation of groundwater quality due to overexploitation and scarcity of recharge. To achieve this goal, all aquifer hydraulic parameters such as transmissivity, hydraulic conductivity, radius of interference, specific capacity, resulted drawdown, well loss, formation loss, well efficiency, and optimum safe yield had been measured for 40 groundwater wells drilled in the new reclaimed areas of Moghra Oasis. Based on geographic information system (GIS), hydrogeological cross sections and thematic maps for all parameters were created such as aquifer thickness, water table, groundwater flow direction, drawdown and groundwater salinity maps. The results revealed that clay and shale beds separated the three water-bearing formations of the Moghra aquifer. The aquifer-saturated thickness ranged from 30 and 102 m, and the groundwater level was below the mean sea level for all wells (ranges from − 72 to − 26.6 m). The calculated hydraulic parameters based on the analysis of long-duration pumping tests indicated that the studied aquifer has a wide variety of transmissivity (T) between 631 and 3768 m2/day, hydraulic conductivity (K) between 13.4 and 104.6 m/day, radius of influence from 126.3 to 581.3 m and specific capacity between 377.14 and 883.72 m2/day. On the other hand, the evaluation of existing drilled wells performance based on the results of step tests showed that well loss coefficient ranges between 0.0004749 and 0.0676 (h2/m5), formation loss coefficient varies from 3.34 × 10–8 to 4.80 × 10–6 (h/m2), well efficiency (γ) ranged from 50.53 to 98.08%, and optimum safe yield ranged from 40 to 98 (m3/h). Results of aquifer mapping and pumping tests can be more important for solving water scarcity issues, non-polluting water issues, health issues, and source of fresh water on the surface of the earth. The characterization of aquifer parameters in the study area, however, should be a significant component in the scientific planning and sustainability of groundwater.
PubDate: 2023-11-18
- The discharge coefficient of SMBF flumes under free and submerged
conditions
Abstract: Abstract In this study, the discharge coefficients (Cd) of SMBF flumes under free and submerged flow conditions were analytically investigated. The dimensionless parameters involved in the discharge coefficient, derived from the dimensional analysis, are the contraction ratio [rcrn = ratio of flume width (w) to channel width (B)], relative head (hw: the ratio of the upstream head (h) to the \(w\) ) and, in the case of submerged flow, also the submergence ratio [ \(S_{{\text{r}}} = h_{{\text{t}}} /h_{{\text{u}}}\) : downstream flow depth ( \(h_{{\text{t}}}\) ) to upstream flow depth (hu)]. Cd decreases logarithmically from 1.2 to 0.75 in the range of \(h_{w}\) between 0.4 and 1.8. The submerged condition does not reduce the \(C_{{\text{d}}}\) , but it reduces the discharge capacity (up to 50%), so that in some cases, to pass a given flow discharge, \(h_{{\text{u}}}\) should increase by about 100% compared to the free condition.
PubDate: 2023-11-18
- Impact of climate change-induced warming on groundwater temperatures and
quality
Abstract: Abstract The impacts of climate change-induced warming on our ecosystems can no longer be neglected, but our understanding of consequences for groundwater ecosystems in general and groundwater quality in particular is alarmingly incomplete. In this review, we therefore provide an overview of the current state of knowledge related to the impact of global warming on our precious groundwater resources. Groundwater warming in shallow aquifers is closely associated with increasing average land surface temperatures and has already reached + 1 K compared to pe-industrial times. Until the end of the twenty-first century, temperature increases in local groundwater of up to + 10 K are possible. Monitoring data, laboratory and field experiments all provide evidence that such temperature increases are sufficient to substantially modify groundwater quality through numerous and interlinked biogeochemical processes, which we have summarized in a conceptual overview. Warming impacts on groundwater are highly site-specific and spatially heterogeneous, which complicates their assessment and prediction. Locally, shallow unconfined and nutrient-rich floodplain aquifers are most susceptible to warming-induced changes. Importantly, processes affecting water quality are not only modified by a long-term rise in groundwater temperatures, but also in the short-term during weather extremes, which is of great relevance for riverbank filtration. At the regional scale, aquifers in cold regions impacted by permafrost thawing are especially vulnerable to warming. As the majority of temperature-sensitive processes affecting groundwater quality are not or only very slowly reversable, we pressingly require comprehensive mechanistic understanding before it is too late to develop suitable countermeasures and management strategies.
PubDate: 2023-11-14
- Geophysical assessment of seawater intrusion: the Volturno Coastal Plain
case study
Abstract: Abstract In coastal alluvial plains, the variability of sedimentary inputs, tectonic and eustatism causes a complex subsurface geology which influences the position of fresh/saltwater interface. Furthermore, in these areas densely populated, the over-pumping of freshwater, coupled with the climate change events, promotes the landward migration of freshwater/saltwater boundary. This research illustrates the ability of geophysical tools to recognize the presence of salt/brackish water at Volturno Coastal Plain, Southern Italy. This area is characterized by a peculiar geological setting, due to the proximity at Somma–Vesuvio and Campi Flegrei volcanic areas, which profoundly influences the circulation of groundwater. The subsurface is mainly characterized by: (i) two denser layers located at − 10 m and − 20 m depth which in part prevents the vertical migration of groundwater, (ii) facies heteropy that facilitates the hydraulic connection between the different geological bodies, (iii) a discontinuous Campanian Ignimbrite deposits which favor the hydraulic connection between deeper and shallower aquifers. In this geological framework, 2D-ERT and 3D-ERT integrated with Downhole, Multichannel Analysis of Surface Waves and boreholes made possible to recognize the presence of two main zones with salt and brackish waters, respectively. The first zone, characterized by very low resistivity (≤ 1 Ωm) typical of salt water, stretches 1.5 km inland from the coast. The second zone, with a resistivity between 2 and 5 Ωm typical of brackish water, continues for other 3 km inland. This knowledge is useful for the engagement of all stakeholders (farmers, ranchers and policy makers) in the sustainable use of fresh water and for making water management plan operational tools.
PubDate: 2023-11-14
- Pilot plant evaluation of membrane distillation for desalination of
high-salinity brines
Abstract: Abstract Membrane distillation (MD) is a hybrid thermal-membrane desalination process that can use either low-grade waste heat and/or solar energy with hydrophobic membranes to desalinate high-salinity brines and produce high quality distillate. A research consortium was launched to investigate the application of the MD process, at lab and pilot scale, for desalination of concentrated brines. Bench scale results showed the presence of antiscalants in the concentrated brines minimized the scale precipitation potential and offered stable membrane permeability performance. Various MD technologies were screened, and two suitable technologies were selected for field-testing. Pilot unit A was based on multi-effect vacuum showed a stable flux of 6.2 LMH with excellent salt rejection (> 99.9%) from the concentrated brine discharged from thermal desalination plant in Qatar. That pilot unit was also field tested on hypersaline groundwater in Texas (USA) to generate fresh water for reservoir fracking in unconventional oil production operations. The MD unit was coupled with humidification/dehumidification (HDH) unit to achieve zero liquid discharge (ZLD) for inland applications. The MD unit was operated at 40% recovery producing distillate of < 20 mg/L total dissolved solids (TDS) and observed a stable flux of 5 LMH. Key challenges that are critical for large-scale deployment of MD technology were identified at the end of the field-testing program. Finally, a review of active MD technologies was conducted to highlight recent promising developments for full-scale applications.
PubDate: 2023-11-10
- Enhancing daily rainfall prediction in urban areas: a comparative study of
hybrid artificial intelligence models with optimization algorithms
Abstract: Abstract Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.
PubDate: 2023-11-09
- Linear and nonlinear regression analysis of phenol and P-nitrophenol
adsorption on a hybrid nanocarbon of ACTF: kinetics, isotherm, and
thermodynamic modeling
Abstract: Abstract This study aimed to create activated carbon thin film (ACTF) as a hybrid nanocarbon via a simple and efficient method through a single-step mixing process using thermal functionalization techniques. TEM, BET, BJH, FTIR, XRD, and TGA analyses were used to investigate the prepared ACTF. The results exhibited that ACTF has a porous structure with a high surface area of 318 m2/g and important functional groups, which are considered significant adsorption sites. The adsorption performance of ACTF for phenol and p-nitrophenol (PNP) removal from aqueous solutions using batch adsorption mode was studied. Evaluations were conducted on experimental factors influencing the adsorption process, such as pH, initial phenol and PNP concentrations, adsorbent dose, contact time, and temperature. Under the optimized conditions, the phenol and PNP were removed with a maximum efficiency of 89.98% and 92.5%, respectively. The results of linear and nonlinear isotherms and kinetic models of phenol and PNP showed that both pollutants were well fitted with the Freundlich model (R2 = 0.99, χ2 = 0.13, RMSE = 1.6), (R2 = 0.99, χ2 = 0.42, RMSE = 2.8), and the pseudo-second-order model (R2 = 0.999, χ2 = 0.03, RMSE = 0.31), (R2 = 0.99, χ2 = 0.01, RMSE = 0.24), for phenol and PNP, respectively. According to the calculated thermodynamic results, the adsorption of phenol and p-nitrophenol onto the ACTF surface was a spontaneous and exothermic reaction. The regeneration experiments showed that the spent ACTF could be reused up to the fifth cycle while maintaining noteworthy removal efficiency.
PubDate: 2023-11-07
- Evaluation of the leaching of microplastics from discarded medical masks
in aquatic environments: a case study of Mashhad city
Abstract: Abstract The COVID-19 pandemic has led to a significant increase in the global use of face masks, with reports indicating that approximately 129 billion people worldwide use them every month. Many masks contain MPs, which can pose environmental and health risks. The aim of this study is to assess the properties of MPs that are released from ten different mask brands. The masks that were selected were weighed, immersed in deionized distilled water, stirred, and MPs that were released into the water were collected using a cellulose ester membrane. The collected MPs were then analyzed using an optical microscope to observe their shape and color. The results showed that the rates of MPs released from N95 masks, surgical masks, and 3D masks were 54, 23, and 23%, respectively. The N95 mask had the highest percentage of MPs due to its heavy weight. The observed shapes of MPs, in terms of abundance percentage, were filamentous > spherical > irregular > fragmented. Furthermore, the majority of MPs were found to be transparent or black in color. This study offers valuable insights into the mechanisms behind the release of MPs from disposable face masks, shedding light on the critical issue of microplastic pollution resulting from mask waste.
PubDate: 2023-11-07
- Synthesis and characterization of nanocomposites containing Buchholzia
coriacea pod decorated with multi-walled carbon nanotubes for remediation
of rhodamine B dye from aqueous solution
Abstract: Abstract A novel nanocomposite (WKM) was synthesized by crosslinking Buchholzia coriacea pod (WK) and f-MWCNTs for the efficient adsorption of rhodamine B (RhB) from wastewater. The uptake of RhB onto WK and WKM was influenced by initial pH, adsorbent dose, the temperature of the adsorptive system, contact time and initial RhB concentration. Optimum experimental conditions of 100 min agitation time, 0.05 g dosage, solution pH 3.0 and 100 mg dm−3 initial concentration were obtained. The uptake of RhB onto WK and WKM was well expressed by Freundlich isotherm and a monolayer maximum uptake capacity of 67.82 mg g−1 and 87.58 mg g−1 was determined for WK and WKM, respectively. The adsorption of RhB onto WKM and WK followed the Elovich and pseudo-first-order kinetic models, respectively. Thermodynamic parameters such as ∆G°, ∆H° and ∆S° reveal that the uptake of RhB onto WK and WKM was spontaneous and endothermic. Meanwhile, WKM demonstrated excellent reusability. Hence, this study presents a nanocomposite (WKM) with robust potential for RhB removal.
PubDate: 2023-11-07
- An in-depth comparative analysis of data-driven and classic regression
models for scour depth prediction around cylindrical bridge piers
Abstract: Abstract The study focuses on the critical concern of designing secure and resilient bridge piers, especially regarding scour phenomena. Traditional equations for estimating scour depth are limited, often leading to inaccuracies. To address these shortcomings, modern data-driven models (DDMs) have emerged. This research conducts a comprehensive comparison involving DDMs, including support vector machine (SVM), gene expression programming (GEP), multilayer perceptron (MLP), gradient boosting trees (GBT) and multivariate adaptive regression spline (MARS) models, against two regression equations for predicting scour depth around cylindrical bridge piers. Evaluation employs statistical indices, such as root-mean-square error (RMSE), coefficient of determination (R2), mean average error (MAE) and normalized discrepancy ratio (S(DDRmax)), to assess their predictive performance. A total of 455 datasets from previous research papers are employed for assessment. Dimensionless parameters Froude number \(\left( {Fr = \frac{U}{{\sqrt {gy} }}} \right)\) , Pier Froude number \(Fr_{P} = \frac{U}{{\sqrt {g^{\prime } D} }}\) , and the ratio of scour depth to pier diameter \((\frac{\text{y}}{{\text{D}}})\) are carefully selected as influential model inputs through dimensional analysis and the gamma test. The results highlight the superior performance of the SVM model. In the training phase, it exhibits an RMSE of 0.1009, MAE of 0.0726, R2 of 0.9401, and SDDR of 2.9237. During testing, the SVM model shows an RMSE of 0.023, MAE of 0.017, R2 of 0.984, and SDDR of 5.301. Additionally, it has an average error of − 0.065 and a total error of − 20.642 in the training set and an average error of − 0.005 and a total error of − 0.707 in the testing set. Conversely, the M5 model exhibits the lowest accuracy. The statistical metrics unequivocally establish the SVM model as significantly outperforming the experimental models, placing it in a higher echelon of predictive accuracy.
PubDate: 2023-11-07
- TiO2-functionalized biochar from pistachio nutshells: adsorptive removal
and photocatalytic decolorization of methyl orange
Abstract: Abstract Pistachio nutshells-derived biochar (PNS-BC) was utilized as a cost-effective adsorbent for competently removing a model dye, methyl orange (MO) from wastewater. Three concentrations of TiO2; 1%, 2%, and 3% were used to decorate the biochar. Analysis of morphology, stability, and structure of the three adsorbents (PNS, PNS-BC, and the TiO2 functionalized biochar; TiO2@PNS-BC) was extensively explored using various characterization techniques. The synergistic photocatalytic-adsorptive efficiency of the three adsorbents was compared. In this regard, a Box-Behnken (BB) design-based multivariate scheme was inaugurated with the target of maximizing MO removal (%R) while using the minimum possible of chemicals and resources. The impact of five variables; %TiO2, dose of TiO2-PNS, reaction time, dye concentration, and pH on the magnitude of %R was investigated. Results show that 97.69% removal of MO could be recognized over 120 min using adsorption compared to 99.47% removal over 30 min using 3% TiO2@PNS-BC as a photocatalyst. A 3% TiO2@PNS-BC was the best catalyst (compared to 1% and 2%) with a decolorization rate constant of 0.12741 min−1, ~ 1.5 × faster compared to the decolorization of MO using adsorption alone. Adsorption of MO conformed well to Langmuir isotherm. A maximum adsorption capacity (qmax) of 142.38 mg/g was achieved. Adsorption kinetics fitted well with the pseudo-second order (PSO) model. Results obtained indicated that biochar of PNS is a promising, cost-effective, and economical adsorbent.
PubDate: 2023-11-06
- Zinc chloride activated carbon derived from date pits for efficient
biosorption of brilliant green: adsorption characteristics and mechanism
study
Abstract: Abstract It is critical to remove dyes from wastewater as they cause harm to human and aquatic life due to their carcinogenic, toxic, and mutagenic effects. Here, low-cost activated carbons (CPs) were produced from the date (Phoenix dactylifera L.) pits. The prepared CPs were chemically activated utilizing zinc chloride to obtain activated carbons from date pits (ZCPs). The physicochemical properties, chemical composition, and morphology of ZCPs material and the active surface functional groups involved in adsorption were identified using N2 adsorption–desorption isotherm, scanning electron microscopy, point of zero charges (pHPZC), and Fourier transforms spectroscopy. The ZCPs biocomposite was applied for the Brilliant green (BG) removal from aqueous solutions, where the efficiency was assessed as functions of pH value, foreign ions, the initial dye concentration, dose of adsorbent, adsorption time, and temperature. The outcomes showed that the prepared ZCPs biocomposite exhibited high uptake of BG with a qe of 247.752 mg/g. The isotherm and kinetic studies show that the adsorption process of BG dye onto ZCPs biocomposite followed Langmuir, and pseudo-second-order models, respectively. From the estimated thermodynamic functions, it was found that the nature of the BG dye adsorption process onto the prepared ZCPs adsorbent was endothermic and spontaneous. With a relative standard deviation of less than 3%, the prepared ZCPs were successfully applied for the removal of BG from real water samples with a recovery of more than 90%. The plausible mechanism of BG adsorption onto the prepared ZCPs can be assigned to various interactions, such as pore–filling, electrostatic attraction, H-bonding, and π–π stacking.
PubDate: 2023-11-04
- Feasibility analysis of synthesized polyaniline nanocomposites loaded by
Co-doped ZnO nanorods for aqueous pollutants oxidation
Abstract: Abstract Polyaniline (PANI) nanocomposites (NCs) based on cobalt (Co)-doped ZnO nanorods were fabricated (PANI-NCs) using chemical oxidation polymerization technique. Co-doped ZnO nanorods were synthesized using hydrothermal route. Microstructure characterization and UV–Visible absorption measurement confirm the formation of wurtzite ZnO nanostructured crystals. Introducing effect of Co-doped ZnO nanorods into PANI matrix on microstructural, optical, surface morphology and electrical properties of the investigated NCs were studied. The characterization of the fabricated NCs was examined using X-ray diffraction (XRD), Fourier transform infrared (FTIR), high-resolution transmission electron microscopy (HR-TEM) and four-probe DC electrical conductivity. Also, the photocatalytic activity of the fabricated NCs was examined using UV irradiation for Procion Blue dye wastewater oxidation. The photocatalytic experimental parameters were studied and the results revealed high photocatalytic activity reached to complete dye removal within 60 min of irradiation time (at pH 7.0 and room temperature). Finally, the data fitted with first-order kinetic model.
PubDate: 2023-11-04
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