Search Results (35)

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

Due to their antibacterial and antifungal properties, parabens are commonly used as biocides and preservatives in food, cosmetics, and pharmaceuticals. Parabens have been reported to exist in various water matrices at low concentrations, which renders the need for sample preparation before their quantification using analytical techniques. Thus, sample preparation methods such as solid-phase extraction (SPE), rotating-disk sorptive extraction (RDSE), and vortex-assisted dispersive liquid–liquid extraction (VA-DLLE) that are commonly used for parabens extraction and preconcentration have been discussed. As a result of sample preparation methods, analytical techniques now detect parabens at trace levels ranging from µg/L to ng/L. These compounds have been detected in water, air, soil, and human tissues. While the full impact of parabens on human health and ecosystems is still being debated in the scientific community, it is widely recognized that parabens can act as endocrine disruptors. Furthermore, some studies have suggested that parabens may have carcinogenic effects. The presence of parabens in the environment is primarily due to wastewater discharges, which result in widespread contamination and their concentrations increased during the COVID-19 pandemic waves. Neglecting the presence of parabens in water exposes humans to these compounds through contaminated food and drinking water. Although there are reviews that focus on the occurrence, fate, and behavior of parabens in the environment, they frequently overlook critical aspects such as removal methods, policy development, and regulatory frameworks. Addressing this gap, the effective treatment of parabens in water relies on combined approaches that address both cost and operational challenges. Membrane filtration methods, such as nanofiltration (NF) and reverse osmosis (RO), demonstrate high efficacy but are hindered by maintenance and energy costs due to extensive fouling. Innovations in anti-fouling and energy efficiency, coupled with pre-treatment methods like adsorption, help mitigate these costs and enhance scalability. Furthermore, combining adsorption with advanced oxidation processes (AOPs) or biological treatments significantly improves economic and energy efficiency. Integrating systems like O₃/UV with activated carbon, along with byproduct recovery strategies, further advances circular economy goals by minimizing waste and resource use. This review provides a thorough overview of paraben monitoring in wastewater, current treatment techniques, and the regulatory policies that govern their presence. Furthermore, it provides perspectives that are critical for future scientific investigations and shaping policies aimed at mitigating the risks of parabens in drinking water. Full article

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer. Full article

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot. Full article

The quality of eggshells is critical to the egg production industry. The addition of trace elements has been shown to be involved in eggshell formation. Organic trace elements have been found to have higher biological availability than inorganic trace elements. However, the effects of organic trace elements additive doses on eggshell quality during the laying period of commercial laying hens required further investigation. This experiment aims to explore the potential mechanisms of different doses of organic trace elements replacing inorganic elements to remodel the eggshell quality of egg-laying hens during the laying period. A total of 360 healthy hens (Lohmann Pink, 45-week-old) were randomly divided into four treatments, with six replications per treatment and 15 birds per replication. The dietary treatments included a basal diet supplemented with inorganic iron, copper, zinc and manganese at commercial levels (CON), a basal diet supplemented with organic iron, copper, zinc and manganese at 20% commercial levels (LOT), a basal diet supplemented with organic iron, copper, zinc and manganese at 30% commercial levels (MOT), and a basal diet supplemented with organic iron, copper, zinc and manganese at 40% commercial levels (HOT). The trial lasted for 8 weeks. The results of the experiment showed that the replacement of organic trace elements did not significantly affect the production performance of laying hens (p > 0.05). Compared with inorganic trace elements, the MOT and HOT groups improved the structure of the eggshells, enhanced the hardness and thickness of the eggshells, increased the Haugh unit of the eggs, reduced the proportion of the mammillary layer in the eggshell, and increased the proportion of the palisade layer (p < 0.05). In addition, the MOT and HOT groups also increased the enzyme activity related to carbonate transport in the blood, the expression of uterine shell gland-related genes (CA2, OC116, and OCX32), and the calcium and phosphorus content in the eggshells (p < 0.05). We also found that the MOT group effectively reduced element discharge in the feces and enhanced the transportation of iron (p < 0.05). In conclusion, dietary supplementation with 30–40% organic micronutrients were able to improve eggshell quality in aged laying hens by modulating the activity of serum carbonate transport-related enzymes and the expression of eggshell deposition-related genes. Full article