Water, Air, & Soil Pollution最新文献

Lignin is an amorphous natural polymer with multifunctional phenolic network accounting for approximately 15–30% of lignocellulosic biomass. Different functional groups like carboxyl, hydroxyl, phenolic and methoxy in lignin molecule may act as suitable interaction sites for the removal of pollutants. Lignin, as an abundant and renewable bioresource, can be used to create nanofibrous membranes via electrospinning technique. Such nanofibrous membranes with elevated specific surface area, uniform fiber diameter, and high porosity may serve as highly efficient adsorbents for the entrapment of detrimental pollutants like dyes, heavy metals, particulates, etc. Hence, numerous research has been carried out to develop lignin based electrospun membranes in recent years. For example, a low-cost electrospun nano-fibrous membrane (ENM) comprising of alkali lignin/polyvinyl alcohol (PVA) was developed for effective removal of Safranine T dye. Similarly, palm fronds and banana bunch waste biomass derived lignin was successfully employed as an electrospun material for the removal of methylene blue dye. An alkali lignin/PVA ENM was developed for the adsorption of fluoxetine contaminant from its solution. This review paper provides the up-to-date information on the development and usage of eco-friendly, sustainable and cost-effective electrospun lignin nano-adsorbents for the removal of various environmental contaminants.

Rivers play pivotal role in transporting plastic litter into the ocean. The present study aimed to estimate microplastics (MPs) in Palar River (PR), that receives waste inputs of agricultural, industries, fishing, and domestic origin. Water and sediment samples were collected during low tide, at seven locations from inner check dam to mouth region. Highest concentration of MPs in water are observed at PR-6 (28.01 × 10⁴ particles.km⁻²) and the lowest was recorded at PR-4 (2.6 × 10⁴ particles.km⁻²). In sediment, highest count was detected at PR-1 (8.8 × 10² ± 226 particles.kg⁻¹ d.w) and the lowest was recorded in PR-6 (2.5 × 10² ± 14 particles.kg⁻¹d.w). MPs distribution in water is more in the outer mouth (PR-6) in comparison to inner region (PR-4). Fibres were dominating morphotype of MPs, comprised of 79.4% in water and 89.8% in sediment samples. According to size classification, the majority of particles (41.02% in water and 56.1% in sediment) were in the size range of 1 μm to 1000 μm. Characterization of MPs through Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy revealed that High Density Polyethylene (HDPE) is the dominant polymer indicating the use of plastic fishing nets and ropes along with the outfall from nearby industries may be the main contributor to the microplastic pollution in the river.

Magnetic solid-phase extraction (MSPE) is a highly effective method for separating metal ions from aquatic solutions, making it a popular choice for metal ion separation. This study investigated the behavior of Fe₃O₄@SiO₂-SH core–shell composites for the adsorption and recovery of silver ions. The quality of the synthesized adsorbent was verified using XRD, FT-IR, EDS, FE-SEM, and EDAX-map analyses. Magnetic properties and surface charge changes were assessed using VSM and Zeta potential analyses. By implementing the classic OFAT method, the study evaluated the effect of various parameters on silver adsorption, such as pH, composite dosage, initial Ag⁺ concentration, temperature, and secondary metal ions. Results showed that the proposed composite had a high maximum adsorption capacity (112 mg/g) at pH = 6, ambient temperature, 0.1 g/L adsorbent dosage, and silver concentrations greater than 25 mg/L. The composite demonstrated good selectivity in Ag⁺ separation from a bimetallic solution containing silver and mercury ions. The results also showed that 1M HNO₃ and 1M HCl solutions released over 78% and 70% of absorbed ions from the composite surface, respectively. However, the HCl solution enabled the precipitation of almost all desorbed Ag⁺, providing a selective recovery of silver ions.

Mangrove soil is crucial for maintaining the health and nutrient balance of coastal ecosystems. It often serves as the final sink for commercial, aquaculture, and agricultural contaminants. This necessitates developing and understanding precise insights into mangrove health and sustainability through tailor-made evaluation indices. Currently, there are few studies regarding the assessment of mangrove ecosystem sustainability. The Thai Binh Mangrove is the third-largest province in terms of mangrove coverage in northern Vietnam. This mangrove has not undergone a direct assessment of soil quality using ground-level data. Therefore, this study focuses on evaluating the current state of the Thai Binh Mangrove in Vietnam, identifying key factors contributing to its degradation. Soil samples were collected from 38 stations across the mangrove and analyzed for pH, heavy metals, and nutrients over two years, from 2022 to 2023. The mangrove soil quality is assessed using Principal Component Analysis (PCA) and Cluster Analysis (CA). The Pollution Load Index (PLI) is used to characterize soil pollution, and it ranges from 0.29 to 1.09. Additionally, the Ecological Risk Index (ERI) is used to assess the ecological impact of soil pollution, revealing that the area falls within the low-risk category. The study also proposes measures like aquaculture, ecotourism, and increased awareness and education regarding the sustainability of mangroves and soil quality. The study underscores the significance of international cooperation, treaties, and legislative reform in assisting decision-makers with sustainable mangrove management.

In this investigation, the purifying power of a biodegradable coagulant extracted from banana peel waste was studied and compared with that of a synthesized coagulant, hydroxyapatite. The biocoagulant of banana peel powder was prepared and characterized using different techniques, such as Fourier-transform infrared spectroscopy, scanning electron microscopy coupled with energy-dispersive X-ray microanalysis, X-ray diffraction, and X-ray fluorescence spectrometry. The coagulation-flocculation process was adopted to treat water loaded with copper, zinc, and suspended matter. The experimental design methodology was employed for the optimization of the experimental conditions. The Box Behnken design with three factors (dose, granulation, and initial pH) defined in 15 experiments was chosen. Within the pH range of 6 to 8, there was a noticeable increase in the rate of elimination, and this noteworthy trend was confirmed by the observable changes in the point of zero charge. The biocoagulant shows a very good coagulation capacity comparable to that of hydroxyapatite in Zn removal. The obtained values for the removal of Zn, Cu, and turbidity using banana peel powder and hydroxyapatite were 86 %, 96 %, and 97 %, and 68 %, 99 %, and 97 %, respectively. Banana peel powder has the potential to serve as a cost-effective and eco-friendly option for wastewater treatment in the context of waste valorization and environmental protection.

Rainfall prediction, based on meteorological data and models, forecasts the possible rainfall conditions for a period in the future. It is one of the important issues in meteorology and hydrology, and holds significant scientific and social value for enhancing human society's adaptive capacity, reducing the risk of natural disasters, promoting sustainable development, and protecting the environment. This study proposes a rainfall prediction model based on CEEMDAN-VMD-BiLSTM, which couples CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), and BiLSTM (Bidirectional Long Short-Term Memory). The model first employs CEEMDAN and VMD, two decomposition algorithms, for a secondary decomposition of the original data, followed by prediction using the BiLSTM network. The study integrates the characteristics of CEEMDAN, which include adaptability, completeness, denoising capability, and high precision, the characteristic of VMD in extracting trend information, and the ability of the BiLSTM model to better capture contextual information in sequence data and solve long-term dependency issues, thereby increasing the accuracy of rainfall prediction. The research selected Zhongwei City in the Ningxia Hui Autonomous Region as the study object and used 20 years of monthly rainfall data from 2001 to 2020 as the research data. The model was compared with standalone BiLSTM models, CEEMDAN-BiLSTM coupled models, and VMD-BiLSTM coupled models. The model was validated using four indicators: RMSE, MARE, MAE, and NSE. The results showed that the maximum relative error of the CEEMDAN-VMD-BiLSTM neural network rainfall prediction coupled model was 7.22%, and the minimum relative error was -7.03%. The prediction qualification rate was 100%. The overall NSE value of the model ranged from 0.63 to 0.97, with most values between 0.86 and 0.97. The excellent rate was about 84.6%, and the good and above rate was 92.3%. In the rainfall prediction for Zhongwei City, the prediction accuracy of this coupled model was better than the other three models. In summary, the CEEMDAN-VMD-BiLSTM rainfall prediction model proposed in this paper combines the advantages of various methods and has shown good predictive effects in experiments, providing an effective prediction method for rainfall.

This study investigated the radioactivity of groundwater and bottom silt from wells in southern Sinai, Egypt. Eight well sites were chosen (Abu Redis, Abu Zenima, and Al-Tor) and composite samples of water and silt were created from each. Southern Sinai well water (Egypt) was safe for drinking based on tested elements (²²⁶Ra < 300 Bq/L, ²³²Th < 100 Bq/L). However, some bottom silt samples showed elevated ²²⁶Ra, ²³²Th, and ²²²Rn-, potentially posing health risks through inhalation or ingestion. Further investigation is needed on these specific silt samples due to potential internal and external radiation exposure.

The quality of water is significantly impacted by the presence of Cr⁶⁺ and Ni²⁺ ions. This study investigates the effectiveness of a flow-by porous graphite electrode cell in removing these contaminants from simulated industrial wastewater. We explore the impact of various factors on the removal process, demonstrating the method's potential for efficient removal. The initial concentration of nickel and chromium ions (20 to 80 mg/l and 20 to 100 mg/l, respectively), the feed flow rate (0.28 to 1.11 ml/s), current density (0.2 to 2.25 mA/cm²) and pH all influence the removal rate and efficiency. A higher feed flow rate negatively affects the removal efficiency of both Ni²⁺ and Cr⁶⁺ ions. Nickel removal efficiency decreased by 34.9% at 20 ppm and 26% at 80 ppm, representing the highest and lowest reductions in efficiency, respectively. Chromium removal efficiency decreased by 19% at 100 ppm and 6.5% at 50 ppm, indicating the highest and lowest reductions in efficiency, respectively, under the same flow rate change. Under optimal conditions, the removal efficiency for Ni²⁺ was 99.47% after 15 min of operation at a current density of 1.96 mA/cm², a flow rate of 0.28 ml/s, and a pH of 8 and the removal efficiency for Cr⁶⁺ was 99.97% after 10 min of operation at a current density of 2.25 mA/cm², a flow rate of 0.28 ml/s, and a pH of 2. The flow-through porous electrode system achieves efficient heavy metal removal with operating costs of 0.24 USD/m³ for nickel and 0.38 USD/m³ for chromium at optimal conditions.

Manganese oxide (MnO_x) on the surface of the filter material can be used to effectively remove ammonium (NH₄⁺) and manganese ions (Mn²⁺) from water, but overgrow oxide film gradually shortens backwashing interval after several years of long-term filtration system operation. Different influent pollutant loading result in different durations for chemical peeling film. A growth kinetics model for MnO_x was established by adjusting the different initial concentrations of Mn²⁺ in the influent, which provided a theoretical basis for determining a specific time point for film peeling and recovered the shortened backwashing intervals in the filter columns. The variation in film thickness demonstrated a linear dependence on time, confirming the high accuracy of the kinetics model for film growth. The pseudo-first-order kinetic model better fits among adsorption and oxidation kinetic models of Mn²⁺. Hydrogen peroxide (H₂O₂) was identified as an effective agent in the chemical peeling film process. Hydroxyl radicals, generated by H₂O₂, destroy coordination bonds, producing extremely low solubility (≡MnO₂), which was then removed during the backwashing process.

Graphical Abstract

Antibiotics are intensively used in various sectors, such as human and animal husbandry, and in the current scenario, they are considered global contaminants in different environmental compartments. Constant antibiotic exposure leads to antimicrobial resistance in environmental microorganisms. However, the correlation between antibiotic exposure and resistance is still indistinct and unproven in environmental microbiota. The release of antibiotic-resistant bacteria from municipal wastewater into rivers poses a public health concern. This study aimed to explore the antibiotic resistance profiling of bacterial pathogens isolated from wastewater, sludge, water, and sediment samples of the Ganges River of three North Indian cities (Kanpur, Prayagraj and Varanasi) for summer and winter seasons. Antibiotic resistance profiles were done for 12 antibiotics through the disc diffusion method. PCR analysis of genomic and plasmid DNA, the prevalence of six Antibiotic Resistance Genes was evaluated in 103 bacterial isolates isolated from wastewater, sludge, river water and sediment samples of North Indian cities. This study gives insight into the role of wastewater treatment plants in the abundance and spread of antibiotic-resistant bacteria and genes through effluent in the receiving river environment. The consumption of antibiotics is very high in India, and Antibiotic Resistance Genes are mostly abundant in wastewater treatment plants and river water and sediment, which indicates that the river has the potential and probable reservoir of Antibiotic Resistance Genes.