Safety Of Water Resources Research Articles

Mixtures of toxic organic micropollutants compromise the safety of water resources in urban agglomerations in low- and medium-income countries: The example of Lahore, Pakistan

Contamination of water resources with mixtures of organic micropollutants (OMP) including pesticides, pharmaceuticals, and industrial chemicals is a serious threat to aquatic organisms and human health. Long-term exposure to such pollutants may cause detrimental effects even at very low concentrations. Water resources in urban agglomerations in low- and medium-income countries may be under particular pressure due to high population densities, significant industrial activities, and limited water treatment and management resources. In these areas, many inhabitants directly rely on healthy urban water resources. Using the agglomeration of Lahore, Pakistan, as a case, we studied the occurrence, spatial distribution, and toxic risks of OMP mixtures in different urban water resources using target screening with liquid chromatography high-resolution mass spectrometry. In total, 266 of 576 target analytes were detected in at least one of the 200 samples taken from groundwater, canals, River Ravi and drains within the urban agglomeration. Notably, very high concentrations ranging from 10 to over 100 μg L−1 were found for highly toxic pesticides including several fungicides such as picoxystrobin, the transformation product phthalamic acid, and imazalil, the insecticide etofenprox but also industrial chemicals stemming for example from traffic such as 2-naphthalene sulfonic acid. Our study revealed high toxic risks particularly for invertebrates, fish and algae, with etofenprox as a dominant risk driver. This compound is extensively used in Lahore to control insect vectors of malaria and dengue fever in the urban agglomeration. Mixture risks were assessed using a toxic unit (TU) approach based on organism group specific effect concentrations, complemented with a risk quotient (RQ) approach using the lowest predicted no effect concentrations (PNECs). Acute and chronic risk thresholds were frequently exceeded, often by many orders of magnitude. These very high mixture risks strongly exceed those with previous studies in Europe, Africa and South America.

Does Pb2+ Form Holodirected or Hemidirected Solvation Geometries in Water?

The hydration properties of the Pb2+ ion in aqueous solution have been investigated by using a synergic approach based on Classical and Car-Parrinello molecular dynamics (CPMD) simulations and extended X-ray absorption fine structure (EXAFS) spectroscopy. A definite answer has been given to the main question on the Pb2+ hydration structure, which concerns the formation of either holodirected or hemidirected solvation geometries, such terms referring to the arrangements of the ligands that can be directed either throughout the surface of an encompassing globe around Pb2+ or throughout only part of the globe, respectively. Our CPMD results show that the Pb2+ ion in water forms a hemidirected 4-fold cluster with a well-defined distorted pyramidal geometry. This cluster is directed throughout one side of the first shell globe, while the other side contains either two or three very mobile water molecules that do not form a well-defined geometry around the Pb2+ ion. The Pb2+ first shell structural arrangement determined from the CPMD simulation was confirmed by the EXAFS experimental results. A homodirected Pb2+ first shell complex like the 8-fold SAP structure obtained from the classical MD simulations cannot be reconciled with the EXAFS experimental data. These findings represent a significant step forward in the understanding of the solvation chemistry of the Pb2+ ion, which is fundamental to improving the efficiency of lead removal procedures that are crucial to the safety of water resources.

Inorganic–organic hybrid nonwoven fabrics with colorimetric detection capability for borate ions in aqueous solutions

Abstract A nonwoven fabric sheet with an immobilized colorimetric reagent was prepared to detect borate anions in aqueous media. The fabric comprised immobilized chromotropate and 1-butanesulfonate anions and chromotropate, as the colorimetric reagent, per anion exchange capacity of the layered double hydroxide. The resulting sheet quantitatively detected borate anions through photoluminescence measurements. Thus, it can be used for onsite quantitative analysis of dissolved boron in water, with promising potential for water quality monitoring and, consequently, sustainable and safe water resources globally.

Assessment on the Effect of Rapid Urbanization on Groundwater Quality of Mirpur Area, Dhaka City, Bangladesh

Dhaka’s population is increasing day by day as more people are migrating towards Dhaka city for improving their level of living and for availing the cities amenities. Hence, this is creating pressure on the overall water quality of Dhaka city. The work intended to assess the groundwater standard in Mirpur area of Dhaka City, Bangladesh by analyzing eight (08) different physicochemical water quality parameters. In December 2023, six (06) separate water samples were taken throughout the dry season from six (06) sites across the area. The results showed that all sites had pH levels between 7.18 to 7.50, which is within acceptable ranges for drinking water quality and indicates neutral to slightly alkaline conditions. Total dissolved solids (TDS) continued to be far below safety limits (210–250 ppm), indicating that the water's mineral content was quite low. Electrical conductivity (EC) measurements fell within acceptable ranges (290-350 μS/cm), indicating suitable groundwater quality. Dissolved oxygen (DO) levels varied (3.7-9.3 mg/L), with some sites exhibiting satisfactory oxygenation while others raised concerns about potential water quality issues. Total hardness (TH) levels (330-900 mg/L) exceeded recommended limits (200-500 mg/L) at all sites, suggesting potential issues with water quality. However, chemical oxygen demand (COD) levels were minimal or zero (0-3 mg/L), indicating minimal organic pollution. Iron concentrations were generally low (0-0.05 mg/L). Total chlorine (TC) concentrations varied (0- 0.03 mg/L) across sites. In summary, the study emphasizes how crucial it is to continuously monitor and regulate the quality of groundwater in order to guarantee the sustainability and safety of water resources in the Mirpur region.

Design and realization of a water quality monitoring system based on the Internet of Things

ABSTRACT Water quality monitoring is a key means of ensuring the safety of water resources, protecting the ecological environment and maintaining public health. This paper describes a water quality monitoring system based on the Internet of Things (IoT), which supports real-time monitoring, remote control and intelligent early warnings. It is concluded that the system can effectively collect, process and display data on water quality. To further improve the level of intelligence of the water quality monitoring system, the ability of machine learning algorithm to analyze and predict the water quality data has also been separately tested. In this paper, the hardware design, software design, test results and algorithm evaluation of the system are described. The actual performance and reliability of the system in practical application under various field conditions have not yet been verified. This new system is compared with other water quality monitoring systems.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 mL of deionized water to prepare a single salt solution (0.02 %–1.00 %) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86 % and 99.66 %, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Assessment of Theoretical and Test Performance Considerations of Concentrated Solar Water Purification System “Parabosol” in Underserved Regions

Water is a fundamental human right and a prerequisite for sustainable development because it is an essential element of existence. Notwithstanding, a huge part of the world’s population continues to face challenges in accessing clean and safe drinking water. This situation is particularly pronounced in arid and underdeveloped regions where there is a global water crisis that is a huge threat to human health, economic development, and environmental stability. Designed with solar energy, the award-winning “Parabosol” enhances water evaporation and purifies it simultaneously. Parabosol does not require any expensive machines or complicated infrastructural frameworks, making it both cost-effective and efficient for such vulnerable communities. Transporting it easily allows for quick deployment in remote areas during emergencies, ensuring a clean, dependable water supply for basic household use. This innovative measure, which reduces the risk of waterborne diseases and increases access to safe water resources within communities, could greatly contribute to public health promotion efforts. It is intended for daily performance that corresponds to the minimum needs of one family unit (no less than 35 L per person). The processing capacity of each station varies between 120 and 180 L of water per day (depending on geographical and environmental conditions), depending on geographical and meteorological (solar radiation values) factors. However, experimental values are around 250 L. Parabosol illustrates a novel model with its distinctive design and functionality, highlighting the critical role of clean energy in the development of a more sustainable and resilient future. Additionally, unlike macrosystems that require a substantial initial investment and ongoing operating costs, Parabosol is a portable solution that has the potential to address the issue of clean water scarcity in the future.

A selective and sensitive mercury sensor for drinking water based on fluorescence quenching of pure rhodamine B

The escalating threat of industrial pollutants, particularly heavy metals, in water sources poses a significant risk to global populations. Among these heavy metals, mercury stands out as a severe contaminant with detrimental health implications. This paper introduces a novel and efficient method for the selective detection of mercury ions in drinking water, employing laser-induced fluorescence with pure rhodamine B as the sensing probe. The method achieves a low detection limit of 7 ppb, closely approaching the World Health Organization’s maximum permissible limit. The simplicity of the procedure, coupled with the use of pure rhodamine B, distinguishes this approach from others relying on complex chemical procedures and derivatives of rhodamine B. The sensing mechanism involves the fluorescence quenching of rhodamine B due to complex formation with tetraiodomercurate. Noteworthy is the method’s selectivity, demonstrated by its resistance to interference from common ions present in water (e.g. Magnesium, calcium, sodium, and potassium), ensuring accurate detection of mercury ions. Extensive testing with tap water samples, considering potential interference, validates the robustness of the sensor, with recovery percentages of 99.25% and 109.2%. In summary, this study contributes a practical solution to the critical challenge of mercury detection in drinking water, addressing issues of sensitivity, selectivity, and on-site applicability. The proposed method holds promise for widespread implementation, enhancing efforts to safeguard public health and ensure the safety of water resources.

Groundwater quality parameters prediction based on data-driven models

Groundwater quality assessment is essential for achieving safe and sustainable water resources, specifically in regions that rely mainly on groundwater. This study focuses on evaluating groundwater quality metrics in the Alnekheeb basin located in Iraq to obtain a more suitable and sustainable water source, which plays a pivotal role in policy development and strategies for more efficient utilization of groundwater. In this regard, three groundwater water quality metrics presented in hardness, sodium absorption ratio (SAR), and salinity are purportedly predicted using two AI-driven models, namely the Radial Basis Neural Network (RBF-NN) and the Probabilistic Neural Network (PNN). Furthermore, this study investigates the influence of input parameters on the performance of the proposed models. Several water quality parameters, including SO4, Cl, NO3, Ca, Mg, Na, HCO3, and CO3, are used for the development modelling. The effectiveness of the proposed models is assessed using various statistical indicators and graphical presentations. According to the evaluation results, adding more input variables can sometimes increase the efficacy of the proposed models with regard to prediction accuracy. Moreover, the findings show that the PNN model provides a promising performance in predicting the groundwater’s water quality (WQ) matrices, showing superior performance compared to the RBFNN model.

Evaluating Groundwater Safety: Heavy Metal Contamination of Selected Boreholes across Uyo Metropolis, Akwa Ibom State, Nigeria

Study’s Novelty/Excerpt This study offers a comprehensive analysis of heavy metal concentrations in borehole water within Uyo Metropolis, Akwa Ibom State, highlighting significant public health risks associated with these contaminants. Utilizing Atomic Absorption Spectrophotometry (AAS), the research uniquely identifies iron and nickel concentrations that exceed World Health Organization (WHO) and Nigerian Standard for Drinking Water Quality (NSDWQ) guidelines, with iron levels reaching 0.551 mg/L and nickel levels at 0.298 mg/L. These findings emphasize the urgent need for policy interventions and infrastructure investments to mitigate the pervasive risk to consumers and ensure the safety of water resources. Full Abstract In light of growing concerns about water quality and its effects on public Health, this study offers an in-depth analysis of heavy metal concentrations in selected boreholes water within Uyo Metropolis in Akwa Ibom State. The research also emphasizes the potential health risks associated with these heavy metals, particularly as some have been found to exceed the acceptable drinking water limits set by the World Health Organization (WHO). Borehole water samples from ten strategically selected locations were collected and analyzed for heavy metals using Atomic Absorption Spectrophotometry (AAS) to quantify the concentrations of copper, zinc, iron, chromium, and nickel. The results revealed that all water samples contained iron concentration (0.551 mg/L), surpassing the guidelines set by the World Health Organization (WHO) and Nigerian Standard for Drinking Water Quality (NSDWQ), indicating a pervasive risk to consumers. Additionally, elevated levels of nickel (0.298 mg/L) were detected in several samples, further exacerbating the public health implications. The findings underscore the critical need for policy intervention and infrastructure investment to ensure the safety and sustainability of safe water resources for human use.

Fungal removal of cyanotoxins in constructed wetlands: The forgotten degraders

Harmful cyanobacterial blooms have increased globally, releasing hazardous cyanotoxins that threaten the safety of water resources. Constructed wetlands (CWs) are a nature-based and low-cost solution to purify and remove cyanotoxins from water. However, bio-mechanistic understanding of the biotransformation processes expected to drive cyanotoxin removal in such systems is poor, and primarily focused on bacteria. Thus, the present study aimed at exploring the fungal contribution to microcystin-LR and cylindrospermopsin biodegradation in CWs. Based on CW mesocosms, two experimental approaches were taken: a) amplicon sequencing studies were conducted to investigate the involvement of the fungal community; and b) CW fungal isolates were tested for their microcystin-LR and cylindrospermopsin degradation capabilities. The data uncovered effects of seasonality (spring or summer), cyanotoxin exposure, vegetation (unplanted, Juncus effusus or Phragmites australis) and substratum (sand or gravel) on the fungal community structure. Additionally, the arbuscular mycorrhizal fungus Rhizophagus and the endophyte Myrmecridium showed positive correlations with cyanotoxin removal. Fungal isolates revealed microcystin-LR-removal potentials of approximately 25 % in in vitro biodegradation experiments, while the extracellular chemical fingerprint of the cultures suggested a potential intracellular metabolization. The results from this study may help us understand the fungal contribution to cyanotoxin removal, as well as their ecology in CWs.

Nitrogen and sulfur-doped biochar supported magnetic CuZnFe2O4 as a sustainable adsorbent for efficient reactive black dye 5 removal from industrial wastewater

AbstractThe shortage of clean and safe water resources, due to the growing pollution and the high cost of water treatment techniques, has become a real threat. Herein, CuZnFe2O4@N,S-doped biochar (CZF@N,S-BC), a novel magnetic, cleaner, and completely green-based composite, was fabricated using the aqueous extract of Beta vulgaris (sugar beet) leaves for the efficient removal of reactive black dye 5 (RB5) from industrial wastewater discharge. With the aid of numerous techniques, including Fourier-Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), Scanning Electron Spectroscopy (SEM), Vibrating Sample Magnetometer (VSM), and zeta potential analyses, CZF@N,S-BC was well-characterized. The results revealed the successful fabrication of CZF@N,S-BC with good magnetic saturation of 12 emu/g and a highly positively charged surface of 32 mV at pH 2. The removal efficiency of RB5 was reached 96.5% at equilibrium time 60 min, and adsorbent dose of 80 mg. The equilibrium data fitted well with the Freundlich isotherm model, while the adsorption kinetics followed a pseudo-second-order model (PSO), with a maximum adsorption capacity of 276.57 mg/g. The thermodynamics results confirmed the physical interaction between the composite and RB5. Additionally, the composite also demonstrated exceptional reusability, maintaining a removal efficiency of 57.27% even after six consecutive cycles. To evaluate the performance of CZF@N,S-BC composite in a real water matrix, the composite was subjected to remove RB5 from a real wastewater sample obtained from an industrial discharge of a textile dyeing industry. Also, a plausible mechanism of RB5 removal by the composite was intensively discussed using XPS before and after adsorption.

Eu2CuO4 nanostructures: A simple co-precipitation pathway, characterization, and promising photocatalytic application for dye removal under visible light

Contamination of water has become an international concern, and finding efficient and sustainable methods to eliminate harmful pollutants is crucial for ensuring that clean and safe water resources are available. A promising approach in this regard is photocatalysis, which uses light energy to initiate chemical reactions that break down organic contaminants into harmless byproducts. This study presents a nanophotocatalyst based on europium copper oxide (Eu2CuO4) for the elimination of hazardous pollutants. The fabrication of Eu2CuO4 nanostructures was achieved through a simple and rapid co-precipitation process. According to diffuse reflectance spectroscopy (DRS), Eu2CuO4 has an optical bandgap of 1.56 eV, making it suitable for use in visible light. A variety of techniques were used to characterize Eu2CuO4 nanostructures, including XRD, FESEM, FTIR, VSM, HRTEM, BET, EDS, and LC-MS. In order to maximize effectiveness, several variables were considered, such as the amount of Eu2CuO4 used, the concentration of toxic pollutants, and the pH of the solution. It was found that Eu2CuO4 was quite effective in degrading various organic pollutants, particularly methyl orange (MO) of water. Using 50 mg of Eu2CuO4 in the presence of 20 ppm MO and exposing it to visible radiation for 100 min resulted in 94.9 % of MO being destroyed. A further analysis revealed that hydroxyl radicals (•OH) and holes (h+) played a dominant role in pollutant photodegradation. In light of this finding, Eu2CuO4 may serve as a valuable candidate for developing new materials that are capable of successfully removing contaminants from water.

Forecasting the environmental safety of water resources using neural networks

In this study, a neural network model has been developed to analyze the suitability of water based on its chemical and physical characteristics. The ecological significance of the task is due to the need for effective monitoring of the quality of water resources, which are an essential element of ecosystems and directly affect human health and the environment. The model has demonstrated a high accuracy of 87%, which confirms its effectiveness for automated analysis of water quality. The results of the study indicate the importance of parameters such as pH and concentration of pollutants for determining the suitability of water. The developed model can be implemented into environmental monitoring systems, providing more rapid and accurate detection of pollution and improving decision-making processes in water resources management.

Green synthesis of Ag-ZnFe2O4@graphene nanocomposite for photocatalytic and electrochemical applications

In light of environmental, health, and research considerations, both the photocatalytic degradation of methylene blue and the electrochemical sensing of dopamine hold practical significance for mitigating environmental pollution, ensuring the safety of water resources, and addressing neurological issues. In this study, we successfully synthesized the Ag-ZnFe2O4@graphene nanocomposite using Artocarpus heterophyllus leaf extract as a sustainable and eco-friendly fuel for combustion, followed by the hydrothermal method. This method yielded 20–30 nm sized ZnFe2O4 with well-distributed Ag dots on the surface, accompanied by graphene sheets. This composition enhances the stability and surface area of the catalyst for photocatalytic and electrochemical sensing applications. The complete degradation of the carcinogenic methylene blue (MB) was achieved within a 180-min timeframe. The heightened photocatalytic performance can be attributed to improved charge carrier separation, and reduction in the electron-hole (e−/h+) recombination. Additionally, the composite materials exhibited remarkable electrochemical sensing capabilities for dopamine detection, achieving a low limit of detection (LOD) of 1.6 μM. This study underscores the feasibility of utilizing bio-waste, such as Artocarpus heterophyllus leaf extract, for catalyst synthesis and highlights the versatile potential of this catalyst in multifunctional applications. Our work not only advances the field of green nanomaterial synthesis but also paves the way for sustainable and cost-effective catalyst production with diverse functionalities.

Heavy Metal Remediation from Water/Wastewater Using Bioadsorbents - A Review

This paper aims to emphasize heavy metals’ impact on water and its removal mechanism with a focus on adsorption. Furthermore, factors affecting bio adsorption, such as temperature, pH, RPM, and initial heavy metal concentration, have been studied for different heavy metals and bioadsorbents. A comparison of their adsorption capacities and efficiencies has been made. This review reviewed different bioadsorbents for their suitability in removing cadmium, lead, and copper ions from water and wastewater, typically by using adsorption as a methodology. A suitable summary compares various heavy metal removal techniques and their advantages and limitations. For adsorption, the characteristics of bioadsorbents and their activation steps have been consolidated. Furthermore, the effects of operational parameters and adsorption mechanisms have been discussed in the review. Apart from assessing the suitability of bioadsorbent, a novel bioadsorbent has been suggested for copper ions removal. The findings shall be significantly useful in applying bioadsorbent in water/wastewater treatment fields to reduce heavy metal pollution. Thorough and well-planned research in this field can facilitate the creation of sustainable and durable technology for wastewater treatment, addressing the increasing demand for safe and dependable water resources, focusing on making it cost-effective and recyclable.

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling.

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

Technological suitability and improvement for shaping environmental performance: A life cycle perspective on Fenton-based wastewater treatment processes

Water resource management and wastewater treatment are designed to ensure a safe and clean water resource in the pursuit of sustainable development goals. Chemical-based wastewater treatment processes typically involve significant consumption of resources, by means of chemical or material uses, which potentially lead to significant environmental impacts. This study intends to gain a comprehensive understanding of the environmental impacts associated with two Fenton-based wastewater treatment processes, namely, fluidized-bed reactor (FBR-Fenton) and conventional Fenton (C-Fenton) methods, for four industrial wastewater tests (organic wastewater from pulp mill, pharmaceutical, oil-containing, and polyethylene terephthalate (PET) rinsing), from a technical suitability perspective. The present study adopts ISO 14040 guidelines for life cycle assessment (LCA) and uses the IPCC and ReCiPe methods for environmental impact assessment. The results show that the FBR process has relatively lower environmental impacts than the C-Fenton process according to the IPCC and ReCiPe impact results, partly due to differences in chemical usage and electricity consumption. The results obtained from the IPCC method show that the C-Fenton process had a relatively higher impact on global warming potential than the FBR process, with impacts in the range of 20.5–141.9 kg CO2eq/1 kg COD and 12.0–41.9 kg CO2eq/1 kg COD, respectively, mainly due to variations in the amount of sodium hydroxide or sulfuric acid required to achieve the desired degree of water quality from different raw industrial wastewater sources. Furthermore, the reduction in the use of high-impact chemicals contributes to remarkable environmental benefits for the processes, while process adjustment may not guarantee environmental benefits. This result implies that the environmental performance of Fenton-based processes significantly varies depending on the treatment of raw wastewater characteristics; that is, the environmental advantage of a wastewater treatment technique strongly depends on the determination of a suitable process. This information can be used to support the determination and advancement of future wastewater treatment technologies aimed at improving environmental outcomes.

Sodium alginate hydrogel-encapsulated trans-anethole based polymer: Synthesis and applications as an eradicator of metals and dyes from wastewater

Clean and safe water resources are essential for environmental safety and human health. Hydrogels and biomass polymers have attracted considerable attention in recent years, considering their nontoxicity, controllable performance, and high adsorption capacity. The interpenetrating network described here is a combination of a biomass polymer and a hydrogel adsorbent was established, the biomass polymer microspheres were first prepared with the combination of biomass monomer trans-anethole and maleic anhydride copolymer. A simple, environmentally friendly, and facile method of incorporating biomass polymer into sodium alginate biopolymer was developed by introducing the cross-linking agents calcium chloride and glutaraldehyde into the biomass polymer. Furthermore, the biomass polymer sodium alginate hydrogel (BP@SA/H) was characterized by FTIR, XPS, SEM, and XRD. In order to test materials' performance, the removal of pollutants and the adsorption study were also investigated after and before adsorption toward metals and dyes in water. We examined the factors influencing the materials, adsorption capability, such as initial concentration, time, absorbent amount, and pH. Moreover, the maximum adsorption values for Pb+2 and Cd+2 were 734.9 and 722 mg/g. While the adsorption toward RhB dye are 745 mg/g. In addition, the adsorption results were investigated using kinetic and isothermal models, demonstrating that biomass polymer hydrogel adsorption is chemisorption. Therefore, the as-developed biomass polymer sodium alginate hydrogel (BP@SA/H) is an exceptional multifunctional material that can be used to remove hazardous pollutants from wastewater.

Investigating the Impact of pH Levels on Water Quality: An Experimental Approach

Abstract: Water quality is a critical aspect of environmental health and human well-being. pH, as a fundamental parameter, plays a significant role in determining the chemical characteristics of water. This experimental study aims to investigate the impact of pH levels on water quality. The research involved collecting water samples from various sources and subjecting them to controlled pH variations in a laboratory setting. Multiple parameters, including dissolved oxygen, nutrient availability, microbial activity, heavy metal contamination, algal blooms, turbidity, and ecological health, were assessed to evaluate the effects of pH on water quality. The results revealed that pH fluctuations had a substantial influence on these parameters, indicating a direct relationship between pH levels and water quality. The study findings contribute to the understanding of the complex interactions between pH and water quality, providing valuable insights for water resource management and environmental conservation efforts. Further research is warranted to explore additional factors that may influence the relationship between pH and water quality and to develop sustainable strategies for maintaining optimal pH levels to ensure safe and healthy water resources. I have mainly focus in this research paper the effect of pH and TDS in deferent temperature of deferent water sample.