Search Results (107)

With intensifying watershed pollution pressures and growing ecological vulnerability, scientifically revealing and enhancing the water environmental carrying capacity is crucial for ensuring the long-term health of the basin and the sustainable socioeconomic development of the region. However, the dynamic regulatory mechanisms linking narrow-sense and broad-sense water environmental carrying capacity remain poorly understood, limiting the development of integrated management strategies. This study systematically investigated the changing trends of both the narrow-sense and broad-sense water environmental carrying capacity in the Harbin section of the Songhua River basin through model calculations, along with the regulatory mechanisms of its key influence indicators. The results of the study on the carrying capacity of the water environment in the narrow sense show that permanganate, total phosphorus, and ammonia nitrogen exhibited partial carrying capacity across water periods, while dissolved oxygen decreased during flat and dry periods, with only limited capacity remaining at the Ash River estuary and in the Hulan River. The biochemical oxygen demand in the Ash River was consistently overloaded, and total nitrogen showed insufficient capacity except during the abundant water period. Broad-sense analysis indicated that improving urbanization quality, water supply infrastructure, and drinking water safety could effectively reduce future overload risks, with projections suggesting a transition from critical to loadable levels by 2030, though latent threats persist. Correlation analysis between narrow- and broad-sense indicators informed targeted control strategies, including stricter regulation of nitrogen- and phosphorus-rich industrial discharges, restoration of aquatic vegetation, and periodic dredging of riverbed sediments. This work is the first to dynamically integrate pollutant and socio-economic indicators through a hybrid modelling framework, providing a scientific basis and actionable strategies for improving water quality and achieving sustainable management in the Songhua River Basin. Full article

We present a highly sensitive and selective gas sensor based on an advanced silicon-on-insulator tunnel field-effect transistor (SOI-TFET) architecture, enhanced through the integration of customized conducting polymers. In this design, traditional metal gates are replaced with distinct functional polymers—PPP-TOS/AcCN, PP-TOS/AcCN, PP-FE(CN)₆³⁻/H₂O, PPP-TCNQ-TOS/AcCN, and PPP-ClO₄/AcCN—which enable precise molecular recognition and discrimination of various target gases. To further enhance sensitivity, the device employs an oppositely doped source region, significantly improving gate control and promoting stronger band-to-band tunneling. This structural modification amplifies sensing signals and improves noise immunity, allowing reliable detection at trace concentrations. Additionally, optimization of the subthreshold swing contributes to faster switching and response times. Thermal stability is addressed by embedding a P-type buffer layer within the buried oxide, which increases thermal conductivity and reduces lattice temperature, further stabilizing device performance. Experimental results demonstrate that the proposed sensor outperforms conventional SOI-TFET designs, exhibiting superior sensitivity and selectivity toward analytes such as methanol, chloroform, isopropanol, and hexane. Beyond gas sensing, the unique polymer-functionalized gate design enables integration of microbial biosensing capabilities, making the platform highly versatile for biochemical detection. This work offers a promising pathway toward ultra-sensitive, low-power sensing technologies for environmental monitoring, industrial safety, and medical diagnostics. Full article

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

The pursuit of cleaner environments and healthier ecosystems has driven the development of innovative strategies for detecting and mitigating toxic pollutants. Among emerging nanomaterials, carbon dots (CDs) have gained prominence due to their low toxicity, excellent biocompatibility, high fluorescence efficiency, and environmental sustainability. This review critically analyzes the transformative role of CDs in environmental sensing and remediation. Highlighting their versatile applications, including bioimaging, photocatalysis, and sensitive biochemical sensing, we examine how CDs support the next generation of pollutant detection and degradation technologies, such as contaminant adsorption, membrane filtration, and photocatalytic breakdown. Furthermore, we discuss advances in sensor architectures integrating CDs and outline pathways for their expanded use in environmental monitoring. By mapping the intersection of nanotechnology, environmental science, and sensor innovation, this review anticipates future developments that could redefine pollution control through the strategic deployment of carbon dots. Full article

In the past decade, nanozymes have been attracting increasing interest in academia due to their stable performance, low cost, and easy modification. With the catalytic signal amplification feature, nanozymes not only find wide use in traditional “lock-and-key” single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. In particular, the rise of machine learning in recent years has greatly advanced the design, construction, signal processing, and utilization of sensor arrays. The constructive collaboration of nanozymes, sensor arrays, and machine learning is accelerating the development of biochemical sensors. To highlight the emerging field, in this minireview, we created a concise summary of machine-learning-assisted nanozyme-based sensor arrays. First, the construction of nanozyme-involved sensor arrays is introduced from several aspects, including nanozyme materials and activities, sensing variables, and signal outputs. Then, the roles of machine learning in signal treatment, information extraction, and outcome feedback are emphasized. Afterwards, typical applications of machine-learning-assisted nanozyme-involved sensor arrays in environmental detection, food analysis, and biomedical sensing are discussed. Finally, the promise of machine-learning-assisted nanozyme-based sensor arrays in biochemical sensing is highlighted, and some future trends are also pointed out to attract more interest and effort to promote the emerging field for better practical use. Full article

The refractive index is an important optical property of materials which can be used to understand the composition of materials. Therefore, refractive index sensing plays a vital role in biological diagnosis and therapy, material analysis, (bio)chemical sensing, and environmental monitoring. Conventional optical refractive index sensors based on optical fibers and ridge waveguides have relatively large sizes of a few millimeters, making them unsuitable for on-chip integration. Photonic crystals (PCs) have been used to significantly improve the compactness of refractive index sensors for on-chip integration. However, PC structures suffer from defect-introduced strong scattering, resulting in low transmittance, particularly at sharp bends. Valley photonic crystals (VPCs) can realize defect-immune unidirectional transmission of topological edge states, effectively reducing the scattering loss and increasing the transmittance. However, optical refractive index sensors based on VPC structures have not been demonstrated. This paper proposes a refractive index sensor based on a VPC Mach–Zehnder interferometer (MZI) structure with a high forward transmittance of 0.91 and a sensitivity of 1534%/RIU at the sensing wavelength of λ = 1533.97 nm within the index range from 1.0 to 2.0, which is higher than most demonstrated optical refractive index sensors in the field. The sensor has an ultracompact footprint of 9.26 μm × 7.99 μm. The design can be fabricated by complementary metal–oxide semiconductor (CMOS) fabrication technologies. Therefore, it will find broad applications in biology, material science, and medical science. Full article

Amino acids (AAs) and tea polyphenols (TPs) are essential quality indicators in tea, impacting sensory attributes and economic value. Hyperspectral technology enables efficient, real-time detection of these compounds on field-grown tea leaves. “The original spectra were preprocessed using fractional-order derivatives (0.1–1.0 orders) to enhance subtle spectral features. Compared to fixed integer-order derivatives (e.g., first or second order), fractional-order derivatives allow continuous tuning between 0 and 1, thereby amplifying minor absorption peaks while effectively suppressing noise amplification”. The Competitive Adaptive Reweighted Sampling (CARS) method selects optimal spectral bands, and Partial Least Squares Regression (PLSR) models were built with raw spectral reflectance as independent variables and AA and TP content as dependent variables. Results show that FOD had better prediction accuracy compared to classical integer-order derivatives, e.g., the optimal FOD order of 0.7 for AA prediction increased the R² from 0.73 to 0.80 and reduced the RMSE from 0.30% to 0.25%, while for TP prediction, a FOD order of 0.1 raised the R² from 0.40 to 0.42 and lowered the RMSE from 4.03% to 3.96%. In addition, CARS shows a better performance over the correlation coefficient (CC) method in model accuracy, contributing to more accurate selection of sensitive bands for the content prediction of tea ingredients. Our FOD–CARS–PLSR models achieved an R² of 0.80 and RMSE of 0.25% for AAs, and an R² of 0.42 and RMSE of 3.96% for TPs in fresh tea leaves. Beyond tea quality monitoring, this flexible preprocessing and modeling framework can be readily adapted to estimate biochemical or biophysical properties in other crops, soils, or vegetated ecosystems, offering a generalizable tool for precision agriculture and environmental sensing. Full article

Addressing hazards from dangerous pollutants requires specialized techniques and risk-control strategies, including detection, neutralization and disposal of contaminants. Smart polymers, designed for specific contaminants, provide powerful solutions for hazardous compound challenges. Their remarkable performance capabilities and potential applications present exciting opportunities for further exploration and development in this field. This editorial aims to provide a comprehensive overview of smart materials with unique features and emerging polymeric technologies that are being developed for isolation, screening, removal, and decontamination of hazardous compounds (e.g., heavy metals, pharmaceutically active contaminants, hormones, endocrine-disrupting chemicals, pathogens, and energetic materials). It highlights recent advancements in synthesis methods, characterization, and the applications of molecularly imprinted polymers (MIPs), along with alternative smart polymeric platforms including hydrogels, ion-imprinted composites, screen-printed electrodes, nanoparticles, and nanofibers. MIPs offer highly selective recognition properties, reusability, long-term stability, and low production costs. Various MIP types, including particles and films, are used in applications like sensing/diagnostic devices for hazardous chemicals, biochemicals, pharmaceuticals, and environmental safety. Full article

Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R²) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R² = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities. Full article

Fiber Bragg grating (FBG) sensors have emerged as advanced tools for monitoring a wide range of physical parameters in various fields, including structural health, aerospace, biochemical, and environmental applications. This review provides a comprehensive overview of FBG sensor technology, focusing on their operating principles, key advantages such as high sensitivity and immunity to electromagnetic interference, and common challenges like temperature-strain cross-sensitivity and the high cost of interrogation systems. Additionally, this review compares FBG sensors with other sensing technologies and highlights recent innovations in design, packaging, and implementation techniques. Finally, future research directions are discussed to enhance the performance, scalability, and long-term reliability of FBG-based sensing systems. Full article

Borophene, as a novel two-dimensional (2D) material, has garnered significant interest due to its exceptional optoelectronic properties, including anisotropic plasmonic response high carrier mobility, etc. In this work, we theoretically propose a borophene-based anisotropic metamaterial perfect absorber using the finite-difference time-domain (FDTD) method. The research results show that the proposed metamaterial exhibits triple-band perfect electromagnetic absorption characteristics when the polarization direction of electromagnetic wave is along the zigzag direction of borophene, and the resonant absorption wavelengths can be adjusted by varying the carrier mobility of borophene. Furthermore, as an application of the proposed perfect absorber, we investigate the refractive sensing properties of the borophene-based metamaterial. When the carrier density of borophene is 4.0 × 10¹⁹ m⁻², the maximum refractive index sensitivity of the designed absorber is up to 867 nm/RIU, with a figure of merit of 11.71 RIU⁻¹, which has promising applications in the field of biochemical sensing and special environmental detection. Full article

This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow. Full article

With the rapid advancement of unmanned aerial vehicles (UAVs) in recent years, UAV-based remote sensing has emerged as a highly efficient and practical tool for environmental monitoring. In vegetation remote sensing, UAVs equipped with hyperspectral sensors can capture detailed spectral information, enabling precise monitoring of plant health and the retrieval of physiological and biochemical parameters. A critical aspect of UAV-based vegetation remote sensing is the accurate acquisition of canopy reflectance. However, due to the mobility of UAVs and the variation in flight altitude, the data are susceptible to scale effects, where changes in spatial resolution can significantly impact the canopy reflectance. This study investigates the spatial scale issue of UAV hyperspectral imaging, focusing on how varying flight altitudes influence atmospheric correction, vegetation viewer geometry, and canopy heterogeneity. Using hyperspectral images captured at different flight altitudes at a Chinese fir forest stand, we propose two atmospheric correction methods: one based on a uniform grey reference panel at the same altitude and another based on altitude-specific grey reference panels. The reflectance spectra and vegetation indices, including NDVI, EVI, PRI, and CIRE, were computed and analyzed across different altitudes. The results show significant variations in vegetation indices at lower altitudes, with NDVI and CIRE demonstrating the largest changes between 50 m and 100 m, due to the heterogeneous forest canopy structure and near-infrared scattering. For instance, NDVI increased by 18% from 50 m to 75 m and stabilized after 100 m, while the standard deviation decreased by 32% from 50 m to 250 m, indicating reduced heterogeneity effects. Similarly, PRI exhibited notable increases at lower altitudes, attributed to changes in viewer geometry, canopy shadowing and soil background proportions, stabilizing above 100 m. Above 100 m, the impact of canopy heterogeneity diminished, and variations in vegetation indices became minimal (<3%), although viewer geometry effects persisted. These findings emphasize that conducting UAV hyperspectral observations at altitudes above at least 100 m minimizes scale effects, ensuring more consistent and reliable data for vegetation monitoring. The study highlights the importance of standardized atmospheric correction protocols and optimal altitude selection to improve the accuracy and comparability of UAV-based hyperspectral data, contributing to advancements in vegetation remote sensing and carbon estimation. Full article

Soda lakes are extreme saline–alkaline environments that harbor metabolically versatile microbial communities with significant biotechnological potential. This study employed shotgun metagenomics (NovaSeq PE150) to investigate the functional diversity and metabolic potential of microbial communities in Ethiopia’s Chitu and Shala Lakes. An analysis of gene content revealed 554,609 and 525,097 unique genes in Chitu and Shala, respectively, in addition to a substantial fraction (1,253,334 genes) shared between the two, underscoring significant functional overlap. Taxonomic analysis revealed a diverse phylogenetic composition, with bacteria (89% in Chitu Lake, 92% in Shala Lake) and archaea (4% in Chitu Lake, 0.8% in Shala Lake) as the dominant domains, alongside eukaryotes and viruses. Predominant bacterial phyla included Pseudomonadota, Actinomycetota, and Gemmatimonadota, while Euryarchaeota and Nitrososphaerota were prominent among archaea. Key genera identified in both lakes were Nitriliruptor, Halomonas, Wenzhouxiangella, Thioalkalivibrio, Aliidiomarina, Aquisalimonas, and Alkalicoccus. Functional annotation using the KEGG, eggNOG, and CAZy databases revealed that the identified unigenes were associated with various functions. Notably, genes related to amino acid, carbohydrate, and energy metabolism (KEGG levels 1–2) were predominant, indicating that conserved core metabolic functions are essential for microbial survival in extreme conditions. Higher-level pathways included quorum sensing, two-component signal transduction, and ABC transporters (KEGG level 3), facilitating environmental adaptation, stress response, and nutrient acquisition. The eggNOG annotation revealed that 13% of identified genes remain uncharacterized, representing a vast untapped reservoir of novel enzymes and biochemical pathways with potential applications in biofuels, bioremediation, and synthetic biology. This study identified 375 unique metabolic pathways, including those involved in pyruvate metabolism, xenobiotic degradation, lipid metabolism, and oxidative stress resistance, underscoring the microbial communities’ ability to thrive under fluctuating salinity and alkalinity. The presence of carbohydrate-active enzymes (CAZymes), such as glycoside hydrolases, polysaccharide lyases, and oxidoreductases, highlights their role in biomass degradation and carbon cycling. Enzymes such as alkaline proteases (Apr), lipases (Lip), and cellulases further support the lakes’ potential as sources of extremophilic biocatalysts. These findings position soda lakes as reservoirs of microbial innovation for extremophile biotechnology. Future research on unannotated genes and enzyme optimization promises sustainable solutions in bioenergy, agriculture, and environmental management. Full article

Brassinosteroids, as unique plant steroid hormones that bear structural similarity to animal steroids, play a crucial role in modulating plant growth and development. These hormones have a positive impact on plant resistance and, under stressful conditions, stimulate photosynthesis and antioxidative systems (enzymatic and non-enzymatic), leading to a reduced impact of environmental cues on plant metabolism and growth. Although these plant hormones have been studied for several decades, most studies analyze the primary site of action of the brassinosteroid phytohormone, with a special emphasis on the activation of various genes (mainly nuclear) through different signaling processes that influence plant metabolism, growth, and development. This review explores another issue, the secondary influence (the so-called mode of action) of brassinosteroids on changes in growth, development, and chemical composition, as well as thermodynamic and energetic changes, mainly during the early growth of corn seedlings. The interactions of brassinosteroids with other phytohormones and physiologically active substances and the influence of these interactions on the mode of action of brassinosteroid phytohormones were also discussed. Seen from a cybernetic point of view, the approach can be labeled as “black box” or “gray box”. “Black box” and “gray box” are terms for cybernetic systems, for which we know the inputs and outputs (in an energetic, biochemical, kinetic, informational, or some other sense), but whose internal structure and/or organization are completely or partially unknown to us. The findings of many researchers have indicated an important role of reactive species, such as oxygen and nitrogen reactive species, in these processes. This ultimately results in the redistribution of matter and energy from source organs to sink organs, with a decrease in Gibbs free energy from the source to sink organs. This quantitative evidence speaks of the exothermic nature and spontaneity of early (corn) seedling development and growth under the influence of 24-epibrassinolide. Based on these findings and a review of the literature on the mode of action of brassinosteroids, a hypothesis was put forward about the secondary effects of BRs on germination and the early growth of plant seedlings. Full article