Environmental Science and Pollution Research最新文献

Mercury is a global pollutant that poses significant risks to human health and the environment. Natural sources of mercury include volcanic eruptions, while anthropogenic sources include industrial processes, artisanal and small-scale gold mining, and fossil fuel combustion. Contamination can arise through various pathways, such as atmospheric deposition, water and soil contamination, bioaccumulation, and biomagnification in food chains. Various remediation strategies, including phytoremediation, bioremediation, chemical oxidation/reduction, and adsorption, have been developed to address mercury pollution, including physical, chemical, and biological approaches. The effectiveness of remediation techniques depends on the nature and extent of contamination and site-specific conditions. This review discusses the challenges associated with mercury pollution and remediation, including the need for effective monitoring and management strategies. Overall, this review offers a comprehensive understanding of mercury contamination and the range of remediation techniques available to mitigate its adverse impacts.

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method.

Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R²) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO₃, Cl, Mg, and SO₄ were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.

Enhanced phosphorus management, geared towards sustainability, is imperative due to its indispensability for all life forms and its close association with water bodies' eutrophication, primarily stemming from anthropogenic activities. In response to this concern, innovative technologies rooted in the circular economy are emerging, to remove and recover this vital nutrient to global food production. This research undertakes an evaluation of the dead-end filtration performance of a mixed matrix membrane composed of modified bentonite (MB) and polyvinylidene fluoride (PVDF) for efficient phosphorus removal from water media. The MB:PVDF membrane exhibited higher permeability and surface roughness compared to the pristine membrane, showcasing an adsorption capacity (Q) of 23.2 mgP·m^-2. Increasing the adsorbent concentration resulted in a higher removal capacity (from 16.9 to 23.2 mgP·m^-2) and increased solution flux (from 0.5 to 16.5 L·m^-2·h^-1) through the membrane. The initial phosphorus concentration demonstrates a positive correlation with the adsorption capacity of the material, while the system pressure positively influences the observed flux. Conversely, the presence of humic acid exerts an adverse impact on both factors. Additionally, the primary mechanism involved in the adsorption process is identified as the formation of inner-sphere complexes.

The present study focuses on the elaboration of magnetic nanocomposites by the in situ incorporation of magnetite (Fe₃O₄) nanoparticles (NPs) with spherical and nanoflower-like morphologies in graphitic carbon nitride (g-C₃N₄) sheets using two different synthetic routes. Nanomaterials are characterized by TEM, SEM, XRD, FTIR, BET, zetametry, vibrating sample magnetometry, and UV-vis absorption spectroscopy. The decoration of the carbon nitride matrix with the magnetic NPs enhanced optical and textural properties. The influence of the morphology of the magnetic NPs on the adsorptive and photocatalytic properties of the nanocomposites under different pH conditions (4.5, 6.9, and 10.6) was assessed from batch tests to remove methylene blue (MB) from aqueous solutions. In extreme pH conditions, the nanocomposites exhibited lower or equivalent MB removal capacity compared to the pure g-C₃N₄. However, at neutral medium, the nanocomposite with incorporated Fe₃O₄ nanoflowers showed a significantly higher removal efficiency (80.7%) due to the combination of a high adsorption capacity and a good photocatalytic activity in this pH region. The proposed nanocomposite is a promising alternative to remove cationic dyes from water by magnetic assistance, since no pH adjustment of the polluted effluent is required, reducing costs and environmental impact in the dyeing industry.

This research is to develop dictated metrics using a multi-proxy approach such as spatial-temporal analysis, statistical evaluation, and hydrogeochemical analysis. We have collected 45 groundwater samples located in the Tamirabarani river basin. To evaluate the aptness of developed metrics for agriculture and domestic needs and eleven years dataset has been analyzed and compared with national and international standards BIS, ICMAR, and WHO Monitoring and all the analysis results revealed that the concentration of calcium (Ca-1679 to 4937 mg/L; and Cl ions 236 to 1126 mg/L) and chloride ions was on the higher side in locations. These higher values may be attributed to the regional point sources as untreated water disposal and off-peak sources as agriculture practices. According to the results of the principal component analysis, the post-monsoon season accounted for an 84.2% variance. The major analyzed cations and anions have been observed in the following order: Na⁺  > Ca²⁺  > Mg²⁺  > K⁺ and Cl^-  > HCO₃^-  > SO₄^2-  > NO₃^- respectively. Ca-Mg-HCO₃, Mg-Ca-Cl, Na-C1, and infused waters have been discovered in the basin region, indicating that anion and cation dominance is not prevalent. This specifies that groundwater quality in this region is significantly degraded and suffers from extensive salinity due to the urban pollutants mixed with unprotected river sites.

In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, downy mildew, anthracnose, and black rot. However, the existing manual detection methods are time-consuming and require more efforts. In this paper, an effectual grape leaf disease finding and percentage of pesticide detection approach is devised usingan optimized deep learning scheme. Here, the input image is pre-processed and then, black spot segmentation is done using proposed Taylor Remora Optimization Procedure (TROA). The TROA is the combination of Taylor concept and Remora Optimization Algorithm (ROA). After that, the multi-classification of grape leaf disease is performed to classify the disease as Black rot, Black measles, Isariopsis leaf spot and healthy. Accordingly, the training process of the Deep Neuro-Fuzzy Optimizer (DNFN) is done using Sine Cosine Butterfly Optimization (SCBO). Then, pesticide classification is done using Deep Maxout Network (DMN) and the training of the DMN is done using the Monarch Anti Corona Optimization (MACO) algorithm. Finally, the pesticide percentage level detection is performed using Deep Belief Network (DBN), which is trained by the TROA. The devised scheme obtained highest accuracy of 0.9327, sensitivity of 0.9383, and 0.9429. Thus, this method can assist as an effectual decision provision system for assisting the farmers to find the percentage of pesticide affected in grape leaf diseases.

The global outbreak of the COVID-19 pandemic has given rise to a significant health emergency to adverse impact on environment, and human society. The COVID-19 post-pandemic not only affects human beings but also creates pollution crisis in environment. The post-pandemic situation has shown a drastic change in nature due to biomedical waste load and other components. The inadequate segregation of untreated healthcare wastes, chemical disinfectants, and single-use plastics leads to contamination of the water, air, and agricultural fields. These materials allow the growth of disease-causing agents and transmission. Particularly, the COVID-19 outbreak has posed a severe environmental and health concern in many developing countries for infectious waste. In 2030, plastic enhances a transboundary menace to natural ecological communities and public health. This review provides a complete overview of the COVID-19 pandemic on environmental pollution and its anthropogenic impacts to public health and natural ecosystem considering short- and long-term scenarios. The review thoroughly assesses the impacts on ecosystem in the terrestrial, marine, and atmospheric realms. The information from this evaluation can be utilized to assess the short-term and long-term solutions for minimizing any unfavorable effects. Especially, this topic focuses on the excessive use of plastics and their products, subsequently with the involvement of the scientific community, and policymakers will develop the proper management plan for the upcoming generation. This article also provides crucial research gap knowledge to boost national disaster preparedness in future perspectives.