Clean-soil Air Water最新文献

The microbial community on plastic surfaces, known as the plastisphere, plays a vital role in the bioremediation of complex environmental pollutants at urban waste disposal sites. Since non-culturable strains that survive on polymer substrates are very common, only a small group of microbes has been identified to date. This study examined the predictive functional dynamics of beneficial microbial communities associated with the plastisphere at four plastic-rich urban disposal sites near Kolkata, West Bengal, India. Collection of disposed polyethylene carry bags were followed by biofilm extraction, gDNA isolation, library preparation, and sequencing. Results showed that Betaproteobacteria (0.35%–37.02%), Gammaproteobacteria (3.10%–22.38%), Dehalococcoides sp. (0.05%–10.15%), Flavobacterium sp. (0.02%–8.31%), and Spirulina sp. (0.06%–1.21%) were the most prevalent genera with strong bioremediation potential. Functional profiling identified essential metabolic pathways, such as general function prediction (10.37%–20.45%), followed by amino acid transport and metabolism (13.64%–15.85%), cell wall/membrane/envelope biogenesis (5.49%–18.18%), and coenzyme transport and metabolism (2.27%–14.29%). Key enzymes like Lipase (0.04%–66.67%), Cytochrome p450 (1.90%–36.63%), OpdA (0.03%–33.33%), and Laccase Dehalogenase (2.46%–15.03%), that aid in environmental pollutant remediation were also predicted. The present study provides a comprehensive metagenomic profiling to identify key microbial taxa and their functional genes involved in pollutant degradation in the said locations. This work offers insights for developing a predictive framework for targeted bioremediation strategies in pollution mitigation.

This study investigates groundwater quality in the Northern Ranebennur taluk of Haveri district, Karnataka, focusing on the geological, hydrological, and anthropogenic factors affecting water suitability for agriculture. A total of 150 samples from 50 villages were analyzed for parameters such as pH, electrical conductivity (EC), total hardness (TH), and sodium content. Alongside hydrochemical assessment, a bibliometric analysis was conducted to map global and regional research trends on groundwater quality and management, and this was further combined with machine learning approaches to provide both a literature-driven perspective and a predictive framework for irrigation water suitability. Results showed a pH range of 6.6–8.2 and average EC values of 3.30 dS/m, indicating varied salinity. Sodium absorption ratio (SAR) ranged from 4.74 to 24.30, highlighting issues with soil permeability. While TH was classified as soft (6.71–62.00 mg/L), samples varied from fresh to brackish water (TDS: 435.20–3628.80 mg/L). Despite favorable conditions indicated by magnesium absorption ratio (MAR) and Kelley's Index, 91.33% of samples were only moderately suitable for irrigation, with many classified as “doubtful to unsuitable” based on the Wilcox and US Salinity Hazard diagrams. While predictive models, such as principal component regression (PCR), LASSO, Ridge Regression, and support vector machine regression (SVMR), performed well for most water quality indicators, including IWQ and TH, they still struggle to accurately predict Kelley's Index due to the complex interactions between ions. The study emphasizes the role of geochemical processes in groundwater quality and highlights the need for improved predictive modeling and groundwater management strategies to mitigate salinity and sodicity risks.

Arsenic (As) dynamics in intensively irrigated agroecosystems are intricately linked to Fe/Mn (oxyhydr)oxide transformations and organic soils. This study investigates depth-resolved As accumulation and methodological comparability in fluvo–aquic soils, a critical food production region. Combining x-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP–MS), we analyzed pronounced surface As enrichment (0–100 cm) with concentrations reaching 95 mg kg⁻¹. Strong correlations (Adj. R² = 0.828) between XRF and ICP–MS validated their complementary utility for As determination in fluvo–aquic soils. As distribution correlated positively with Fe/Mn oxides (p < 0.01) and clay content, highlighting redox-driven adsorption mechanisms. Subsurface depletion patterns underscored legacy effects of long-term groundwater irrigation. Methodological trade-offs emphasized XRF field adaptability versus ICP–MS accuracy in capturing vertical geochemical gradients. Spatial heterogeneity analyses identified Fe/Mn cycling and soil texture as primary controls on As retention, providing critical insights for predictive models of contaminant mobility. These findings establish a framework for optimizing monitoring protocols and mitigating As exposure risks in global agricultural systems, balancing analytical precision with practical field deployment.

As global concerns over climate change and carbon emissions rise, understanding the dynamics of carbon reduction becomes crucial. Technological advancements, energy intensity, economic growth, and trade openness have significantly reshaped modern carbon emission systems. This study aims to investigate the impact of trade openness, energy intensity, technological innovation, and economic growth on carbon emissions in 40 Belt and Road Initiative (BRI) countries from 2010 to 2022. Using method of moments quantile regression (MMQR) and Dumitrescu–Hurlin panel causality tests, the findings reveal a nonlinear relationship between GDP and carbon emissions, supporting the Environmental Kuznets Curve (EKC) hypothesis, where economic growth initially increases emissions but later leads to reductions as economies develop. Trade openness is found to significantly increase emissions, particularly at higher quantiles, highlighting the environmental challenges posed by deeper international trade openness. Although technological innovation tends to increase emissions at early stages of economic development, its impact weakens as economies mature, suggesting that innovation alone is not sufficient unless focused on green technologies. High-energy intensity, mainly driven by fossil fuel dependency, is a persistent driver of emissions, with the effect intensifying at higher levels of economic development. These findings emphasize the importance of policies that foster sustainable growth, energy intensity reduction, and green technological innovation. BRI countries should prioritize renewable energy adoption, international climate cooperation, and innovation in low-carbon technologies to achieve their environmental and developmental objectives.

The high demand for polyurethane (PU) material has led to the generation of waste PU, prompting the need for effective recycling methods. This study developed a waste recycling method that utilizes thermoplastic polyurethane (TPU) and PU as a substrate. Thermo-compression was utilized to generate TPU/PU blend material. Various PU additions were set as ratio wt% of a sample piece, and the optimal PU additions were determined after a series of thermal and mechanical tests. Within the range of 20% PU addition, the density of TPU/PU blend material remained relatively stable, with no significant change observed. The density consistently hovered around 1.185 kg/m³. The optimal elasticity coefficient was identified at 12.5% PU addition, reaching a value of 3.365 MPa. This enhancement in the coefficient of elasticity increased the rigidity of TPU/PU blend material. Meanwhile, the maximum shore hardness was recorded at 10% PU addition, reaching a value of 44.33 A. Scanning electron microscope and Fourier-transform infrared spectroscopy analyses revealed an improved degree of phase mixture within the TPU/PU blend material, particularly notable within 20% PU addition. Thermal conductivity reached its maximum value of 0.318 W/mK with a 15% PU addition. This indicates that adding PU contributed to improved thermal conductivity, which can be crucial in certain applications. This study demonstrates a sustainable mechanical recycling approach to produce TPU/PU blend material with enhanced thermal and mechanical performance, directly contributing to UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production) through green material development and circular economy practices.

Groundwater (GW) contamination and its monitoring are continuously posing a significant challenge in cities like Delhi and its surroundings territories. The study analyzed seven hydro-chemical parameters such as calcium, chloride, magnesium, pH, potassium, total hardness (TH), and electrical conductivity (EC) for 488 GW samples to calculate Weighted Arithmetic Water Quality Index (WA-WQI), and further prediction of water quality was performed using the machine learning (ML) approaches. This study employed six ML models, artificial neural network (ANN), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), extreme gradient boosting (XGBoost), and support vector machine (SVM) to predict WA-WQI. The prediction performance was assessed using R², root mean square error (RMSE), mean absolute error (MAE), centered RMSE (CRMSE), and mean absolute percentage error (MAPE). The obtained results revealed that ANN demonstrated the highest accuracy with lowest errors in MAE of 4.518, MAPE of 3.276, RMSE of 8.294, and CRMSE of 8.236. The ANN model exhibited the highest R² value of 0.99 among all examined models. The performance evaluation order found was ANN > SVM > XGBoost > RF > KNN > DT, and Taylor's diagram was employed to evaluate the efficacy of each model. The validation results show that ANN models with specific samples yielded promising results with R² value of 0.99, MAE of 3.746, MAPE of 2.550, RMSE of 5.520, and CRMSE of 5.373. The outcomes of the study indicate that ANN is the best performing model, providing a cost-efficient alternative for predicting GW quality, significantly reducing extensive laboratory testing, offering practical support for real-time monitoring, and helping policy makers to take decisions for mitigation and improving water quality.

Rapid growth in plastic waste and construction–demolition debris had strained disposal systems while the clay-brick industry remained energy intensive, creating demand for masonry units that consumed wastes without kiln firing. Fly ash and sugarcane bagasse ash had provided reactive or microfiller functions, and a polyolefin-rich plastic stream had supplied a thermoplastic binder, motivating a composite route. The objective was to engineer and optimize a plastic–mineral brick using plastic waste, fly ash, construction–demolition fines, and a fixed 5% bagasse ash, ensuring that the compressive strength and water absorption met code-relevant thresholds. A special-cubic response surface mixture design was implemented; all wastes were characterized, melt-mixed at 110°C–120°C, molded to standard dimensions, and tested for compressive strength and 24-h cold-water absorption per IS 3495, while a composite desirability function enabled simultaneous optimization. The best formulation—23.64% plastic waste, 35% fly ash, and 41.36% CDW with 5% SBA—achieved 14.06 N/mm² compressive strength and 2.9% absorption, and strong interaction effects, particularly between plastic and CDW, were identified; the optimized solution reached a desirability of 1.000. These properties placed the units within the IS 1077 Class-15 strength band with absorption far below the specification limit, indicating suitability for partitions, infill masonry, façade panels, and pedestrian-grade pavers. The findings signaled a practical pathway to divert heterogeneous wastes into durable building products through low-temperature processing and statistically guided proportioning. Future work was proposed to extend field-scale durability monitoring and to integrate cradle-to-gate environmental accounting with pilot manufacturing.

This study investigated the use of fog-spraying deposition of wood cellulose nanofiber (WCNFs) suspensions on tissue paper (TP) to create air filter media. High-purity WCNFs, confirmed using ATR-FTIR spectroscopy, were found to be composed of cellulose I_β structures via x-ray diffraction with a crystallinity of 67%. The measured zeta potential of the aqueous suspension of WCNFs was −13 ± 1 mV, indicating the absence of acid hydration during their isolation. This study used nine grammage levels of sprayed WCNFs (ranging from 0.3 to 10 g/m²) and investigated the influence of tert-butanol (TB) on the performance of the medium. After coating, the specimens were freeze-dried and imaged using FE-SEM to confirm the proper distribution of WCNFs on the TP substrate. The key findings revealed that increasing the grammage level from 0.3 to 2 g/m² led to increased particulate matter (PM) adsorption and a significant pressure drop. However, increasing the grammage level from 2 to 10 g/m² decreased the adsorption efficiency, particularly for PM size of 0.3 µm (PM_0.3). The study concluded that the specimens prepared with a deposition of 2 g/m² grammage level of WCNFs containing TB were the optimal treatment, demonstrating an adsorption efficiency of 94.1% for PM_0.3 and a pressure drop of 123 Pa, compared to the corresponding values for bare TP, which were only 9.1% and 18 Pa, respectively.

The growing environmental impact of tire waste necessitates innovative recycling methods. With global tire production expected to reach 2.67 billion units by 2027, repurposing tire-derived materials in concrete offers a sustainable solution. This study investigates the effects of steel fibers from waste tires on concrete incorporating coconut shells (CSs), palm kernel shells (PKSs), and recycled aggregates. The research examines how different fiber percentages influence fresh properties and strength. A concrete mix ratio of 1:2.19:2.46 (Cement: Fine Aggregate: Coarse Aggregate) was used, with an admixture at 1% by cement weight. Natural aggregates were replaced with recycled concrete aggregates (RCA), CSs, and PKS at 25%, 50%, 75%, and 100%, while steel fibers were added at 0.25%, 0.5%, 0.75%, and 1% by cement weight. Slump, split tensile, and compressive strength tests were conducted. Results showed that steel fibers enhanced cohesion but reduced slump due to increased stiffness. After 28 days of curing, the optimal mix contained 1% steel fibers and 25% aggregate replacements, achieving a balance between workability and strength. In addition, a life cycle and economic assessment indicated that these sustainable mixes can lower CO₂ emissions by 15%–25% and reduce costs by 2%–4%, confirming their potential for affordable, low-carbon construction in Kwara state, Nigeria.