Environmental Science and Pollution Research最新文献

Enhancement of dynamic characteristics of sand through bio-cementation is one of the prospective ground improvement techniques for sustainable development considering seismic loading scenarios. Microbially induced calcite precipitation (MICP) has already been established as an efficient and low-cost and sustainable bio-cementation technique. In the present study, engineering characteristics of poorly graded standard Ennore sand of India have been improved through the bio-cementation effects of Sporosarcina pasteurii bacteria using the MICP technique. Microstructure images obtained through scanning electron microscope and crystalline form of minerals present obtained through X-ray diffraction analyses are presented for the bio-cemented sand. Finally, dynamic characteristics of the bio-cemented sand are investigated through strain-controlled unconsolidated undrained cyclic triaxial testing varying the shear strain range from 0.3 to 1.5% with 1.0-Hz frequency. Variations of shear modulus and damping ratio of bio-cemented sand with shear strain are reported considering treatment durations of 7 and 14 days with treatment intervals of 12 and 24 h, respectively. It is observed that the improvement in shear modulus of bio-cemented sand for lesser strain rate approximately ranges from 37% to more than 80% compared to untreated sand. Furthermore, for strain higher than 1.0%, the margin of improvement varies from 50 to 70%. Moreover, the damping of bio-cemented sand was found to be lesser than untreated sand, and the variation is more significant for the higher number of loading cycles. This study is helpful in the assessment of dynamic characteristics of bio-cemented sand specifically applicable to seismic design.

The swift industrial expansion has posed significant environmental challenges, particularly in the context of water pollution. Industrial effluents consist of substantial amounts of harmful pollutants that enter the main rivers via various tapped and untapped drains/local water streams, causing alterations in their physical and chemical properties. This study investigated 153 grossly polluting industries (GPIs) that were identified to release their effluents into the main rivers through different drains within multiple sectors in the industrial zone of four northern states of India in 2023. Physicochemical analysis, multivariate statistical analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and spatial analysis were conducted to evaluate the impact of these discharges. The results show significant variations in mean concentrations, such as pH (6.55-8.42), biochemical oxygen demand (6-707.83 mg/l), chemical oxygen demand (20-1504.25 mg/l), total suspended solids (5-417 mg/l), total dissolved solids (560-9908 mg/l), and chloride (101-4360.7 mg/l) across all the sectors. PCA results indicated that two principal loadings significantly influence the wastewater chemistry. PC1 accounts for 49.85% of the variance, reflecting organic and nutrient pollution, while PC2 contributes 19.128% of the total variance, reflecting the dominance of chloride, dissolved solids, and chemical oxygen demand. HCA classified the GPIs into six clusters for their substantial roles in releasing highly polluted (C3, C4, C5, and C6), moderately polluted (C2), and less polluted (C1) wastewater. Overall findings reveal the alarming magnitude of industrial wastewater discharge into the rivers, emphasizing the urgent need for improved regulatory frameworks, stricter enforcement of environmental laws, and greater corporate responsibility.

Since its discovery, carbon quantum dots (CDs) have been widely applied in cell imaging, drug delivery, biosensing, and photocatalysis due to their excellent water solubility, chemical stability, fluorescence stability biocompatibility, low toxicity, and preparation cost. However, the low fluorescence yield and poor surface structure limit the application of CDs. Heteroatom doping is considered an ideal method to improve CDs' optical and electrical properties. From this perspective, eco-friendly biomass and its derivatives are perfect carbon precursors for CDs because they contain the heteroatoms needed to modify CDs, and their complex chemical composition gives CDs a wide variety of surface functional groups. Besides, converting biomass waste into high-value-added CDs is also an innovation in biomass waste treatment. Therefore, this paper focuses on the carbon precursors of biomass CDs. At the same time, food packaging occupies an essential position in the industry, and fluorescent CDs with good fluorescence properties, high chemical stability, and good photobleaching properties have great application potential in packaging innovation techniques that have emerged in recent years, but relevant reports are scarce and scattered. Considering that the surface morphology, chemical structure, and optical and electrical properties of biomass CDs are primarily affected by the carbon precursors' chemical structure and preparation method, this paper also focuses on the synthesis method of biomass CDs and its application in anti-counterfeiting packaging, intelligent packaging, antioxidant packaging, and antibacterial packaging.

The present review provides the first analysis and synthesis of the available scientific information on the effects of anthropogenic contaminants on cephalopod embryos, paralarvae, and juveniles. We evaluated 46 articles published between 1970 and 2023 that focused on trace elements (69%), pharmaceutical compounds (11%), persistent organic compounds (11%), and plastics (9%). To date, the greatest scientific effort has originated from Europe and Asia (France [57%], China [9%], Italy [7%], and Spain [4%]), with few reports available from the rest of the world. Most studies focused on species of economic importance (cuttlefish [69%], octopuses [18%], and squid [13%]), with few reports on species of low commercial value or that reside in remote habitats such as nautiluses. Although 28 contaminants have been evaluated, cadmium, copper, zinc, fluoxetine (FLX), polycyclic aromatic hydrocarbons (PAHs), organophosphorus compounds, and tributyltin (TBT) were the only contaminants associated with adverse effects on various biological, physiological, and ethological processes during early life phases. Despite these advances, the present review demonstrates the crucial need for ecotoxicology studies that focus on (i) embryotoxicology and the interactions among toxic agents during the early stages of cephalopod development, (ii) survival and recruitment, and (iii) species that inhabit coastal and oceanic environments that have not yet been the focus of previous studies, especially those in countries with few published records. With this information, critical areas can be identified, marine biodiversity monitoring programs can be developed, and effective conservation strategies can be created that include measures to mitigate marine pollution.

Due to the industry's rapid growth, the presence of organic pollutants, especially antibiotics, in water and wastewater resources is the main concern for wildlife and human health. Therefore, these days, a significant challenge is developing an efficient, sustainable, and eco-friendly photocatalyst. Natural biological models have numerous advantages compared to artificial model materials. Biological models with unique multi-level structures and morphology can be used to create porous bio-templates to produce hierarchical materials. So, in this work, for the first time, this was achieved by using sorghum grain seeds as a bio-template (natural waste material) and urea as a precursor, through a simple and environmentally friendly method. We believed that natural waste materials with high carbon atom content could be used as both a carbon doping agent and a bio-template, thus improving the physical and optical properties of the resulting materials. In comparison to previous studies on the synthesis of C-doped g-C₃N₄, our work offers a greener and more cost-effective approach to synthesis, while also reducing waste material. We succeeded in the photo-degradation of a series of organic pollutants such as phenol, bisphenol A (BPA), and amoxicillin (AMX) in an aqueous solution under solar light illumination.

Granite sludge dust (GSD), a significant byproduct of granite processing globally, poses severe environmental and public health challenges, with India alone generating 200 million tons annually. The conventional use of GSD in soil stabilization and construction materials is limited to 20-30%, underscoring the urgent need for sustainable repurposing solutions within the circular economy catering to broader bulk utilization. Unlike traditional techniques, repurposing granite dust using microbially induced calcite precipitation (MICP) offers a sustainable low-impact and eco-friendly ground improvement solution. It also reduces waste and associated environmental pollution. MICP leverages bacterial enzymes to catalyze urea hydrolysis, leading to calcite (CaCO₃) precipitation stabilizing the solids matrix. This study evaluates the efficacy of MICP in strength enhancement of GSD enabling its repurposing in low-volume roads. To assess this, unconfined compressive strength (UCS), wetting and drying (WD) durability, and X-ray diffraction (XRD) tests were conducted. Additionally, to assess the efficacy of MICP in mitigation of both wind and rainfall-induced erosion of GSD from waste containments, percentage weight loss in wind tunnel tests along with air quality parameters PM_2.5, PM₁₀, and drip erosion tests were conducted respectively. MICP treatment with Bacillus megaterium resulted in significant strength gain of up to 1355 kPa UCS, suitable for low-volume pavement subbases, enhanced durability up to two wetting and drying cycles, substantial reductions in PM_2.5 and PM₁₀ levels due to wind erosion, and improved resistance to rainfall-induced erosion sustaining the 10-min test. This low-carbon-intensive technique endorses circular economy goals by transforming GSD into a sustainable construction material addressing waste management, infrastructure resilience, and environmental sustainability. Further, the surficial application of MICP contributes to eco-friendly infrastructure and pollution control of GSD storage facilities.

Conventional power generation methods have led to adverse environmental impacts. Thus, the need for a strategic transition to alternative energy sources arises. This study presents a comprehensive approach to sustainable solar energy deployment using multi-criteria decision-making (MCDM) techniques. The research aims to identify suitable sites for utility-scale solar photovoltaic (PV) installations, estimate potential energy output, and assess solar PV deployment's environmental and economic impacts. The methodology integrates analytic hierarchy process (AHP), fuzzy logic, and geographic information systems (GIS) to evaluate land suitability across four scenarios. The analysis considers technical, economic, environmental, and social factors, including solar radiation, proximity to infrastructure, land use, topography, and stakeholder opinions. Results reveal that Scenario 1 analytic hierarchy process multi-criteria-decision-making (AHP-MCDM) identified 2824.1 km² of suitable land, while the combined approach in Scenario 3 yielded 666.9 km². The study estimates potential annual energy generation ranging from 19.69 to 109.15 GWh/km²/year, depending on the scenario and solar PV technology used. Environmental impact assessments indicate potential annual CO₂ emission reductions of up to 51,365.84 tons, with associated cost savings of US $3.28 million. The research provides valuable insights for policymakers and investors, highlighting 16 optimal sites for utility-scale solar farm development across Jamaica, with the most promising in the Westmoreland, Manchester, and St. Mary parishes. These findings contribute to Jamaica's renewable energy goals and offer a replicable, sustainable solar energy planning framework in similar geographical contexts.

This research presents a straightforward and economically efficient design for a microbial fuel cell (MFC) that can be conveniently integrated into a borehole to monitor natural attenuation in groundwater. The design employs conventional, transparent, and reusable PVC bailers with graphite tape and granular activated carbon to create high surface area electrodes. These electrodes are connected across redox environments in nested boreholes through a wire and variable resistor setup. The amended electrodes were installed in pre-existing boreholes surrounding a groundwater plume near a former gasworks facility. Among all the MFC locations tested, the MFC at the plume fringe exhibited the highest electrical response and displayed significant variations in the differential abundance of key bacterial and archaeal taxa between the anode and cathode electrodes. The other MFC configurations in the plume center and uncontaminated groundwater showed little to no electrical response, suggesting minimal microbial activity. This straightforward approach enables informed decision-making regarding effectively monitoring, enhancing, or designing degradation strategies for groundwater plumes. It offers a valuable tool for understanding and managing contaminant degradation in such environments.

Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicting two water quality parameters including total dissolved solids (TDS) and pH, in an area with semi-arid climate conditions. The study area under consideration is an aquifer located in the Sirjan plain, Kerman, Iran. The developed models include standard multilayer perceptron neural network (MLPNN), classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and their ensemble versions in bagging (BG) and boosting (BT) ensemble structures. The analysis revealed that standard MLs yield comparable results in predicting TDS. The MLPNN, exhibiting a standard root mean square error (SRMSE) of 0.085, demonstrated superior accuracy in predicting TDS when contrasted with CART and CHAID models. Predicting pH poses a greater challenge for the models. Ensemble techniques significantly enhanced the accuracy of regular models. On average, the bagging and boosting techniques resulted in a 22.68% improvement in the accuracy of regular models, which represents a statistically significant enhancement. The boosting method, with an average SRMSE of 0.0602, is more accurate than bagging. Based on the results, the CHAID-BT with SRMSE of 0.0790 and CHAID-BG with SRMSE of 0.0330 are ranked the most accurate models for predicting TDS and pH, respectively. The performance of ensemble techniques in predicting TDS is more remarkable. In practical implementation, ensemble techniques can be considered an alternative method with high accuracy for sustainable water resources management in semi-arid regions, helping to address water shortages, climate change, water pollution, etc.

The content of 39 metals and metalloids (MMs) in submicron road dust (PM₁ fraction) was studied in the traffic zone, residential courtyards with parking lots, and on pedestrian roads in parks in Moscow. The geochemical profiles of PM₁ vary slightly between different types of roads and courtyards but differ significantly from those in parks. In Moscow, compared to other cities worldwide, submicron road dust contains less As, Sb, Mo, Cr, Cd, Sn, Tl, Ca, Rb, La, Y, U, but more Cu, Zn, Co, Fe, Mn, Ti, Zr, Al, V. Relative to the upper continental crust, PM₁ is highly enriched in Sb, Zn, Cd, Cu, W, Sn, Bi, Mo, Pb. In the courtyards, where contact between pollutants and the population is most frequent and occurs over an extended period, the level of PM₁ pollution with MMs (from strong to extreme) is comparable to that on large roads. Source identification was conducted using correlations, elemental ratios, and absolute principal component analysis with multiple linear regression (APCA-MLR). In the traffic zone, non-exhaust and exhaust vehicle emissions contribute significantly to the MM concentrations in PM₁ (especially for Bi, Sb, Sn, V, Fe, Cu, W, Mo); soil particles, abrasion of steel surfaces, industrial emissions, tire and road wear with carbonate dust resuspension contribute less. In the courtyards, the contribution of the road wear with carbonate dust resuspension and soil particles increases by up to 16% due to the poor condition of the road surface, frequent construction works, and large contact areas of roads with soils. In parks, the contribution of anthropogenic sources sharply decreases by 20-48% due to the increased soil resuspension rate. The spatial distribution pattern of MMs in submicron road dust should aid in the development of more effective road surface washing strategies, ultimately minimizing the risk to public health.