Search Results (39)

Reducing emissions is increasingly critical for mitigating the environmental impact of the iron and steel industry. Achieving this transition requires an accurate evaluation of carbon emission intensity for steel production, which relies on an in-depth analysis of carbon metabolism mechanisms across the entire steel production chain. Existing approaches predominantly focus on carbon tracing within material flows, which cannot deeply integrate carbon migration pathways with energy flows and thus fail to reveal the actual sources and transmission mechanisms of carbon emissions. To address this gap, this study develops a carbon metabolism simulation model of the steel manufacturing process that considers the coupling of material production with the energy network. The differentiated carbon metabolism patterns are characterized in terms of carbon fixation, migration, and dissipation to support more accurate carbon emission accounting and enable the formulation of targeted decarbonization strategies. The results show that the coking process fixes 72.51% of its carbon input. The sintering and pelletizing process shows typical carbon dissipation characteristics, with nearly 100% of input carbon discharged. Carbon emissions from steelmaking and the rolling process are mainly induced by indirect energy consumption. The total carbon dissipation of integrated steel production system is 440.62 kg-C/t-CS, with the ironmaking process contributing the largest share of 33.92%. Full article

Accurately locating and reliably diagnosing insulation defects in oil-immersed transformers remains challenging. To overcome this, a micro-robot is employed to autonomously identify partial discharge (PD)-induced carbon traces on the insulation surface of the core components. Accurately capturing the multi-scale complex features of surface-discharge carbon traces under low-illumination conditions is critical for effective defect detection. Therefore, to address the obscurity of carbon trace features caused by insufficient illumination inside oil-immersed transformers, a Retinex-based image enhancement algorithm with superimposed illumination estimation is proposed. By transforming the original image into the HSI color space and integrating negative-image illumination fusion, this algorithm decouples brightness from chromaticity and preserves dark-region details, thereby reducing color distortion and enhancing carbon trace features. Furthermore, to handle the significant scale variations in carbon traces, a C2f module integrated with spatial and channel synergistic attention (SCSA) is designed. This module employs multi-scale depthwise separable convolutions and wide-channel self-attention to enhance cross-scale feature representation and reduce redundancy. Moreover, to address the feature resolution degradation in the fast spatial pyramid pooling module, which hinders the accurate perception of tiny carbon traces, a poly kernel inception atrous spatial pyramid pooling module (PKI-ASPP) is adopted. This preserves precise morphological details and minimizes the missed and false detection rates for tiny carbon traces. Finally, to tackle the difficulties in fusing complex morphological features, a deformable large kernel attention (DLKA) module is introduced into the neck network. This adapts to irregular carbon trace shapes, significantly improving the localization and learning of complex morphologies. Experiments on a transformer PD carbon trace dataset demonstrate that the proposed model significantly improves perceptual capabilities for carbon traces with massive scale variation. The improved model outperforms the baseline across all evaluation metrics, with mAP50 improved by 2.7% and mAP50-95 improved by 7.9%. These results indicate that the proposed method is highly reliable, providing solid technical support for internal surface discharge intensity detection and insulation condition assessment in oil-immersed transformer maintenance. Full article

This study provides a comprehensive characterization of carbon dust adhesion on the anode surface induced by the anode effect (AE) in the Hall–Héroult process. The primary objective was to verify the hypothesis of electrophoretic carbon particle transport and its subsequent stabilization on the electrode substrate. Unlike previous studies conducted in horizontal configurations where gravitational sedimentation could interfere with observations, this research employs a unique vertical electrode setup to provide direct physical evidence of purely electrophoretic transport. Authentic industrial carbon dust was used as a tracer material, its presence on the high-purity graphite surface being definitively confirmed through the detection of trace markers (Mg, Ca) via SEM-EDS. The multiscale structural analysis revealed that spike initiation occurs through a dynamic arc-induced nucleation mechanism. Morphological observations suggest that micro-arc discharges during the AE provide the extreme localized energy for direct carbon-to-carbon “welding,” creating a conductive, porous scaffold on the vertical anode wall. XRD analysis identified crystalline cryolite (Na₃AlF₆) and chiolite (Na₅Al₃F₁₄) within this structure. It was demonstrated that these fluoride phases represent the solidified product of molten, acidic electrolyte infiltration into the carbonaceous matrix via capillary action, rather than acting as binders that crystallize during the process. Raman spectroscopy confirmed the disordered, amorphous nature of the captured dust (high D-band intensity), distinguishing it from the highly ordered graphite substrate. Confocal microscopy visualized the topographical evolution from isolated clusters to interconnected three-dimensional “islands” as a function of AE duration. The results demonstrate that the anode effect serves as a critical flashpoint where synergistic electrophoretic forces and localized thermal anomalies initiate the growth of stable, conductive carbon–matrix composite spikes, providing new insights for mitigating current efficiency losses in industrial smelters. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

Investigating the cycling characteristics of dissolved inorganic carbon (DIC) in karst groundwater within arid and semi-arid regions is crucial for understanding its role in the global carbon cycle and its contribution to atmospheric carbon sinks. This study is centered on the Liulin Spring area of North China, based on sampling data from April 2019. We employed hydrogeochemical analysis and environmental isotopic tracing methods to (1) characterize the spatial distribution of DIC along the groundwater flow path; (2) elucidate the sources of HCO₃⁻; (3) calibrate groundwater ¹⁴C ages. Results indicate that the HCO₃⁻ concentration initially increases and then decreases along the flow path, peaking in the spring discharge zone. Conversely, δ¹³C values initially decrease and then increase, reaching a minimum in the discharge zone, exhibiting a negative correlation with the HCO₃⁻ concentration. The contribution of soil/biogenic CO₂ dissolution to HCO₃⁻ ranges from 26% to 62%, with the highest values (56–62%) observed in recharge, runoff, and discharge zones and lower values (26–49%) observed in stagnant zones; this contribution generally decreases towards the western boundary. Calibrated ¹⁴C ages are significantly reduced and align better with expected groundwater dynamics. Full article

This study characterizes the hydrochemistry and geochemical signature of the Upper Cretaceous geothermal aquifer in the Zharkunak zone (Eastern Ili Depression, SE Kazakhstan) using certified analytical datasets from five deep wells (5539, 1-RT, 3-T, 1-TP, and 2-TP). The waters are hyperthermal (89–103 °C), alkaline (pH 8.1–9.0), and weakly mineralized (TDS 0.3–1.0 g/L), with sodium-dominated facies ranging from Na–HCO₃–SO₄ to Na–SO₄–Cl. Hydrochemical analysis indicates that water–rock interaction and cation exchange are the primary controls on fluid evolution, with limited influence from evaporation or external salinity sources. Elevated fluoride (up to ~10 mg/L) and dissolved silica (H₂SiO₃, often >50 mg/L) reflect prolonged high-temperature interaction with silicate-rich lithologies under low Ca²⁺ conditions. Trace elements and radon activity (up to 0.32 nCi/L) further support deep, fault-controlled circulation pathways. PHREEQC modeling indicates near-equilibrium to slight supersaturation with respect to silica phases, suggesting a potential risk of silica scaling during cooling, while carbonate scaling remains limited. Although the dataset is based on discharge conditions from a limited number of wells, the results demonstrate that the Zharkunak system has strong geothermal utilization potential, with management considerations related to fluoride, radon, and silica scaling. Future work should focus on integrating isotopic analyses and reactive transport modeling to better constrain subsurface processes and long-term system behavior. Full article

The Northwestern Black Sea shelf, strongly influenced by Danube discharge and coastal activities, provides an effective setting for separating lithogenic controls from localized anthropogenic inputs. We applied a multi-proxy geochemical–radiometric approach to Romanian shelf waters and surface sediments. A CTD–Rosette was used to quantify nutrients, chlorophyll-a, TOC, and TN. Dissolved metals and PAHs were measured in seawater, while surface sediments were analyzed for CaCO₃, TOC, trace metals, and γ-emitting radionuclides. Multivariate statistics (PCA/FA) were used to resolve the dominant environmental controls. Summer stratification was characterized by the bottom-layer maxima of PO₄³⁻, SiO₄⁴⁻, and NH₄⁺ and a pronounced subsurface chlorophyll-a maximum at 12–16 m. Surface-water Σ16PAH ranged from 134 to 347 ng L⁻¹ and was dominated by low-molecular-weight compounds, with episodic nearshore enrichment in high-molecular-weight species. In sediments, CaCO₃ ranged from 7.6 to 29.9% and TOC from 0.11 to 0.96%. Trace metals were generally low. Pb and Hg peaked at nearshore station S23, whereas mean Ni (38.88 ppm) slightly exceeded the 35 ppm guideline, consistent with natural Fe/Mn-oxide association. PCA/FA identified a terrigenous axis (Fe-Al-Ti-V-Ni-Cr), a carbonate axis (CaCO₃; Sr where available), and an anthropogenic factor (Pb, Hg, HMW-PAHs). γ-spectrometry provided a compatible radiometric baseline that supports the multi-proxy interpretation. Full article

Mixing zones in carbonate coastal aquifers are dynamic interfaces where freshwater and seawater converge, triggering complex biogeochemical processes. This study investigates diagenetic barite (BaSO₄) precipitation within such a mixing zone in the dolomitic aquifer of the Sierra de Gádor (SE Spain). Three sectors were analyzed: two active mixing zones—one associated with submarine discharge and the other affected by marine intrusion—and an uplifted, fossilized Pleistocene mixing zone. Mineralogical, petrographic, and geochemical analyses reveal extensive dissolution of the dolomitic bedrock, forming polygonal voids and fracture-controlled porosity, frequently covered by Fe and Mn oxides. Barite crystals were identified exclusively in the Fe oxide precipitates at depths where 80% of seawater is reached. The saturation index for barite in groundwater suggests near-equilibrium conditions across the fresh–brackish–saline transition; however, barite precipitation is localized where Fe oxides act as a geochemical barrier, concentrating Ba and enabling nucleation. SEM imaging shows well-formed euhedral barite crystals up to 100 µm in size. This form of crystallization would be similar to the marine diagenetic barite formation models involving organic matter degradation and Ba remobilization, translated to a coastal aquifer setting in this study. Trace metal analyses show significant enrichment of Pb (up to 20 wt%) and other elements (Zn, Ni, and Co), suggesting potential for ore-forming processes if redox conditions shift. This work proposes a conceptual model for diagenetic barite formation in coastal aquifers, emphasizing the role of Fe and Mn oxides as reactive substrates in metal cycling at the land–sea interface. Full article

The results from previous environmental studies on the physicochemical properties of bottom sediments from the Odra River estuary (SW Baltic Sea) and their contamination by pharmaceutically active compounds (PhACs) were compiled and analyzed by the use of various statistical methods (Principal Component Analysis, ANOVA/Kruskal–Wallis, Spearman correlation analysis, Partial Least Squares Discriminant Analysis, and Cluster Analysis). These studies included data on 130 PhACs determined in sediment samples collected from 70 sites across the Odra River estuary as well as the site distance to wastewater treatment plant discharge, PhACs’ physicochemical properties (Kd, Kow, pKa, solubility, metabolism), and sales data. Additionally, total organic carbon, total nitrogen, total phosphorus, acid volatile sulfides, clay mineral content, and trace elements such as As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sn, and Zn were analyzed. Clay mineral content and TP were identified as the key physicochemical factors influencing the spatial distribution of PhACs in bottom sediments, exerting a greater impact than the distance of sampling sites from WWTP discharge points. The distribution of PhACs in the estuary was also influenced by the Kd and solubility of the compounds. More soluble pharmaceuticals with low adsorption affinity to sediments were detected more frequently and transported to distant locations, whereas less soluble compounds with high adsorption affinity settled down in bottom sediments near contamination sources. Neither the proportion of a drug excreted unchanged, nor its prescription frequency and sales volume, influenced the spatial distribution of PhACs. In general, Kd may be a useful parameter in the planning of environmental monitoring and tracing migration of PhACs in aquatic environments. Full article

The information on the seasonal variability of the chemical composition of the Arctic rivers is necessary for the proper assessment of the status of river runoff and the influence of anthropogenic and natural factors. Spring freshet is an especially important period for the Arctic rivers with a sharp maximum of water discharge. The Kolyma River is the least studied large river with a basin located solely in the permafrost zone. The change in the concentration of dissolved organic carbon (DOC), major, trace, and rare earth (RE) elements was studied at the peak and waning of the spring freshet of 2024 in the lower reaches of the Kolyma River. The concentration of elements was determined in filtrates <0.45 μm and in suspended solids > 0.45 μm. The content of coarse colloids (0.05–0.45 μm) was estimated by the intensity of dynamic light scattering (DLS). It was shown that the freshet peak is characterized by a minimal specific conductivity, concentration of major cations, and chemical elements migrating mainly in solution (Li, Sr, and Ba). During the freshet decline, the concentration of these elements increases with dynamics depending on the water exchange. The waters from the Kolyma River main stream have a maximal content of coarse colloids and concentration of <0.45 μm forms of hydrolysates (Al, Ti, Fe, Mn, REEs, Zr, Y, Sc, and Th), DOC, P, and heavy metals (Cu, Ni, Cd, and Co) at the freshet peak. A decrease of 8–10 times for hydrolysates and coarse colloids (0.05–0.45 μm) and of 3–6 times for heavy metals was observed at the freshet waning during the first half of June. This indicates a large-scale accumulation of easy soluble forms of hydrolysates, DOC, and heavy metals in the seasonal thawing topsoil layer on the catchment upstream in the previous summer, with a flush out of these elements at the freshet peak of the current year. In the large floodplain watercourse Panteleikha River, the change in concentration of major cations and REEs, Zr, Y, Sc, and Th at the freshet is less accented compared with the Kolyma River main stream due to a slower water exchange. Yet, <0.45 μm forms of Fe, Mn, Co, As, V, and P show an increase of 4–6 times in the Panteleikha River in the second half of June compared with the freshet peak, which indicates an additional input of these elements from the thawing floodplain landscapes and bottom sediments of floodplain watercourses. The concentration of the majority of chemical elements in suspended matter (>0.45 μm) of the Kolyma River is rather stable during the high-water period. The relative stability in the chemical composition of the suspended solids means that the content of the suspension and not its composition is the key to the share of dissolved and suspended forms of chemical elements in the Kolyma River runoff. Full article

For large oil-immersed transformers, their metal-enclosed structure poses significant challenges for direct visual inspection of internal defects. To ensure the effective detection of internal insulation defects, this study employs a self-developed micro-robot for internal visual inspection. Given the substantial morphological and dimensional variations of target defects (e.g., carbon traces produced by surface discharge inside the transformer), the intelligent and efficient extraction of carbon trace features from complex backgrounds becomes critical for robotic inspection. To address these challenges, we propose the DCMC-UNet, a semantic segmentation model for carbon traces containing adaptive illumination enhancement and dynamic feature fusion. For blurred carbon trace images caused by unstable light reflection and illumination in transformer oil, an improved CLAHE algorithm is developed, incorporating learnable parameters to balance luminance and contrast while enhancing edge features of carbon traces. To handle the morphological diversity and edge complexity of carbon traces, a dynamic deformable encoder (DDE) was integrated into the encoder, leveraging deformable convolutional kernels to improve carbon trace feature extraction. An edge-aware decoder (EAD) was integrated into the decoder, which extracts edge details from predicted segmentation maps and fuses them with encoded features to enrich edge features. To mitigate the semantic gap between the encoder and the decoder, we replace the standard skip connection with a cross-level attention connection fusion layer (CLFC), enhancing the multi-scale fusion of morphological and edge features. Furthermore, a multi-scale atrous feature aggregation module (MAFA) is designed in the neck to enhance the integration of deep semantic and shallow visual features, improving multi-dimensional feature fusion. Experimental results demonstrate that DCMC-UNet outperforms U-Net, U-Net++, and other benchmarks in carbon trace segmentation. For the transformer carbon trace dataset, it achieves better segmentation than the baseline U-Net, with an improved mIoU of 14.04%, Dice of 10.87%, pixel accuracy (P) of 10.97%, and overall accuracy (Acc) of 5.77%. The proposed model provides reliable technical support for surface discharge intensity assessment and insulation condition evaluation in oil-immersed transformers. Full article

Carbonate geothermal reservoirs, characterized by widespread distribution, a high discharge capacity, and favorable reinjection conditions, have become a key target for geothermal resource development. However, the karst geothermal reservoir system in the Juancheng geothermal field exhibits significant heterogeneity, leading to substantial disparities in productivity among multiple geothermal wells and severely restricting efficient regional exploitation. This study systematically investigates the hydraulic characteristics and development potential of the karst geothermal reservoir in the Juancheng geothermal field using sodium fluorescein tracing experiment technology. The results reveal that the reservoir system contains multiple flow channels with distinct permeability differences. The dominant flow pathways, controlled by fault structures, exhibit an apparent velocity of up to 10.98 m/h, significantly higher than other regions in the study area. In contrast, low-permeability zones, influenced by the burial depth of the Ordovician strata, show poor connectivity due to limited karst development, with the lowest apparent velocity of only 1.03 m/h. By integrating pumping test data and tracer response characteristics, the dominant flow direction (northeast) demonstrates a stronger recharge capacity and water abundance, offering a higher development value. Conversely, the southeast low-permeability zone has weaker water production and constrained recharge conditions, resulting in a relatively limited development potential. Additionally, it is recommended that the direction of future geothermal well placement in the Juancheng geothermal field should avoid being parallel to the fault strike to prolong the thermal breakthrough arrival time. In regions with deeper Ordovician strata burial, denser well network deployment is suggested to enhance the reservoir utilization efficiency. Full article

To enhance the efficiency and accuracy of detecting insulation faults such as discharge carbon traces in large oil-immersed transformers, this study employs an inspection micro-robot to replace manual inspection for image acquisition and fault identification. While the micro-robot exhibits compactness and agility, its limited battery capacity necessitates the critical optimization of its 3D inspection path within the transformer. To address this challenge, we propose a hybrid algorithmic framework. First, the task of visiting inspection points is formulated as a Constrained Traveling Salesman Problem (CTSP) and solved using the Ant Colony Optimization (ACO) algorithm to generate an initial sequence of inspection nodes. Once the optimal node sequence is determined, detailed path planning between adjacent points is executed through a synergistic combination of the A algorithm*, Rapidly exploring Random Tree (RRT), and Particle Swarm Optimization (PSO). This integrated strategy ensures robust circumvention of complex 3D obstacles while maintaining path efficiency. Simulation results demonstrate that the hybrid algorithm achieves a 52.6% reduction in path length compared to the unoptimized A* algorithm, with the A*-ACO combination exhibiting exceptional stability. Additionally, post-processing via B-spline interpolation yields smooth trajectories, limiting path curvature and torsion to <0.033 and <0.026, respectively. These advancements not only enhance planning efficiency but also provide substantial practical value and robust theoretical support for advancing key technologies in micro-robot inspection systems for oil-immersed transformer maintenance. Full article

Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects. The characteristics of carbon traces, such as multiple sizes, diverse morphologies, and irregular edges, pose severe challenges for segmentation accuracy and inference speed. In this paper, a feasible real-time network (deformable-spatial-Canny segmentation network, DSC-SeNet) was designed for carbon trace segmentation. To improve inference speed, a lightweight unilateral feature extraction framework is constructed based on a shallow feature sharing mechanism, which is designed to provide feature input for both semantic path and spatial path. Meanwhile, the segmentation model is improved in two aspects for better segmentation accuracy. For one aspect, to better perceive diverse morphology and edge features of carbon trace, three measures, including deformable convolution (DFC), Canny edge operator, and spatial feature refinement module (SFRM), were adopted for feature perception, enhancement, and aggregation, respectively. For the other aspect, to improve the fusion of semantic features and spatial features, coordinate attention feature aggregation (CAFA) is designed to reduce feature aggregation loss. Experimental results showed that the proposed DSC-SeNet outperformed state-of-the-art models with a good balance between segmentation accuracy and inference speed. For a 512 × 512 input, it achieved 84.7% mIoU, which is 6.4 percentage points higher than that of the baseline short-term dense convolution network (STDC), with a speed of 94.3 FPS on an NVIDIA GTX 2050Ti. This study provides technical support for real-time segmentation of carbon traces and transformer insulation assessment. Full article

Endocrine-disrupting compounds (EDCs) are emerging pollutants that can potentially accumulate in aquatic ecosystems at significant levels, with the potential to impact the health of both animals and humans. Many scientists have correlated human exposure to high concentrations of EDCs with critical physiological impacts, including infertility, thyroid imbalance, early sexual development, endometriosis, diabetes, and obesity. Several substances, such as heavy metals, belong to this family, ranging from natural to synthetic compounds, including pesticides, pharmaceuticals, and plastic-derived compounds. Domestic sewage represents a significant source of EDCs in the surrounding aquatic ecosystems. To this day, most rural and urban domestic wastewater in the municipality of Maricá is directly discharged into local aquatic environments without any treatment. The present study aimed to assess the potential contamination of the riverine and lagoonal environment in the municipality of Maricá. Water and sediment samples were collected seasonally at 18 sites along the Maricá watershed and the main lagoon, into which most of the watershed’s contributors flow. Water physico-chemical parameters (pH, reduction–oxidation potential—Eh, dissolved oxygen levels, salinity, turbidity, temperature, and fecal coliforms) were analyzed to characterize the urban influence on the aquatic environment. Sediment samples were also analyzed for grain size, total organic carbon percentage, potential bioavailable fraction of trace metals (Cd, Pb, Cu, Cr, Hg, Ni, Zn), and metalloid As. Finally, the sediment toxicity was assessed using yeast estrogen screen (YES) assays. The results obtained already demonstrate the presence of estrogenic effects and raise concerns about water quality. The current study indicates that, despite the absence of agricultural and industrial activities in the city of Maricá, EDCs are already present and have the potential to impact the local ecosystem, posing potential risks to human health. Full article