Search Results (2,109)

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

Pollution of rivers and large water bodies, including reservoirs, by wastewater from various sources is one of the most critical issues in the Donetsk region, requiring continuous monitoring and assessment of surface water quality. The research aims to assess the state of the Kalmius River under anthropogenic pressure, as well as to find correlations between the species composition, photosynthetic activity of phytoplankton, and the degree of water pollution. This study presents the results of biomonitoring of the Kalmius River and its tributaries within Donetsk City, which are under intense anthropogenic pressure. Pollution of the river channel by phenol, anionic surfactants, Ferrum ions, chlorides, and sulfates was identified. Based on the combinatorial pollution index, the water in the Kalmius River and its tributaries can be classified as polluted. The pigment composition of water samples was analyzed, and the species composition of river phytoplankton was determined. Dominant species include Chlorella vulgaris Beij., Dictyosphaerium pulchellum H.C.Wood, Scenedesmus quadricauda Brébisson, and Oscillatoria agardhii M.A.Gomont. Photosynthetic activity of the river’s algal flora was assessed based on chlorophyll fluorescence induction curves of natural phytoplankton. A correlation was established between surface water pollution levels and changes in the photosynthetic apparatus of microalgae cells. A strong negative correlation was found between the content of nitrate nitrogen in the aquatic environment and the photosynthetic activity, pigment composition, and abundance of the main dominant forms of phytoplankton, particularly the microalgae of the genus Cyclotella. The data obtained shows that the Kalmius River’s pollution has a significant impact on phytoplankton biodiversity, leading to the growth of cyanobacteria species. Full article

Recent advances in satellite missions, particularly the Landsat, Sentinel, and Gaofen series, have led to the rapid accumulation of high-quality remote sensing data with frequent revisits. As these data have become more widely available, Satellite Image Time Series (SITS) have become an important tool for monitoring Earth surface dynamics. SITS now supports a wide range of applications, including precision agriculture, Land Use/Cover Change (LULCC) monitoring, environmental management, and disaster response. This growth has also promoted the development of advanced SITS classification datasets. However, existing reviews have mainly focused on SITS classification algorithms or specific applications, while systematic comparisons of public SITS benchmark datasets remain limited. This lack of synthesis makes it difficult for researchers to navigate fragmented resources and select datasets that match specific scientific or operational tasks. To address this gap, this paper provides a comprehensive review and analysis of 29 publicly available medium-to-high-resolution SITS classification benchmark datasets released between 2017 and 2025. These datasets are intended for training, testing, and validating land-cover classification algorithms, rather than for direct use as operational map products. We conduct a detailed statistical and comparative analysis of these datasets, focusing on their key characteristics across spectral, temporal, and spatial dimensions, as well as their labeling systems. In addition, this review summarizes the SITS classification algorithms that have been developed and benchmarked using these datasets. Finally, we identify the main challenges in constructing and applying SITS classification datasets and discuss future research directions, particularly in data reconstruction, multimodal fusion, change analysis, and advanced model architectures. This survey provides the research community with a systematic overview of SITS classification benchmark datasets and aims to support continued progress in this rapidly developing field. Full article

Wire laser cladding (WLC) of bronze on stainless steel offers a promising approach for combining the structural strength of steel with the superior tribological and corrosion properties of copper alloys. In this study, the influence of key process parameters, including wire preheating current, deposition speed, laser power, and wire feed speed on melt pool temperature and clad geometry was investigated using response surface methodology (RSM). Experiments were performed using a robot-assisted coaxial wire feeding laser cladding system, and real-time thermal monitoring was conducted using an infrared camera. The results showed that defect-free bronze clads with good metallurgical bonding and limited dilution were achieved across the investigated parameter range. Statistical analysis revealed that melt pool temperature is primarily governed by laser power and deposition speed, with a significant interaction between these parameters. Clad height was mainly influenced by wire feed speed and deposition speed, whereas clad width was controlled by laser power and deposition speed. The side angle was affected by deposition speed, laser power, and wire feed speed, reflecting the balance between vertical buildup and lateral spreading. Overall, the study demonstrates that stable and high-quality clads can be achieved by properly balancing energy input and material supply. The developed models provide valuable insight for optimizing process parameters in wire laser cladding of bronze on stainless steel. Full article

Open environmental monitoring datasets are increasingly used in water-pollution research because they provide broad spatial and temporal coverage and support reproducible large-scale analyses. However, their interpretation may depend strongly on preprocessing decisions, particularly when many observations are reported below the limit of quantification (LOQ). This study evaluated the sensitivity of inferred heavy-metal pollution patterns to preprocessing choices in open European surface-water monitoring data. Publicly available Waterbase records for cadmium, lead, and nickel were restricted to rivers and lakes. After removing missing values and a subset of implausible extreme observations above 1000 µg/L, the main analytical dataset contained 1,475,409 observations. Below-LOQ records accounted for 66.6% of cadmium, 57.3% of lead, and 36.1% of nickel observations. A separate censoring-analysis dataset (1,259,636 observations) was used to compare three scenarios: removal of below-LOQ observations, substitution with half the LOQ, and substitution with the full LOQ. Censoring treatment substantially affected concentration summaries, with the strongest sensitivity observed for cadmium, followed by lead, whereas nickel was comparatively more stable. The effect persisted after station-year aggregation and also altered hotspot identification. These findings show that although open monitoring data are valuable for pollution research, robust interpretation requires explicit and transparent reporting of preprocessing decisions. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

Dam constructions alter the river flow, leading to a cascade of physical, chemical, and biological changes in the ecosystem’s structure and function. This study presents a systematic framework for assessing the impact of these built structures on adjacent surface water bodies. The approach integrates mandatory long-term monitoring data with a multivariate statistical approach (Partial Least Squares Discriminant Analysis, PLS-DA) to provide a robust assessment of fourteen of Bulgaria’s major and significant reservoirs’ influence on nearby rivers and streams. Datasets for studied reservoirs include basic physicochemical parameters, and for 8 out of 14 dams—potentially toxic elements (PTEs). To assess the influence of each reservoir on the river, two sampling locations were selected per dam: upstream (U) and downstream (D). Results for the water quality parameters, identified as significant discriminators in each PLS-DA model, are presented. A clear upstream dominance was observed for Pchelina, Saedinenie, and Ticha, a strong downstream pattern was observed for Dospat and Yovkovtsi, and a mixed spatial pattern for the remaining dams. The hierarchical clustering revealed three groups of parameters studied. The first cluster (EC, NO₂⁻, NO₃⁻, TN) likely reflects diffuse inputs. The second cluster (TP, PO₄³⁻) describes the relationship between total and dissolved phosphorus fractions. The third cluster (pH, NH₄⁺, DO, BOD) highlights organic matter decomposition and oxygen dynamics. The results highlight that reservoir impacts are governed by the interplay of hydrological conditions, catchment characteristics, and in-reservoir biogeochemical processes, leading to distinct functional behaviours such as retention, transformation, or release of substances. Full article

We examined the effect of the coronavirus disease 2019 (COVID-19) pandemic on air quality in the Kanto region of Japan using multiple satellites and ground-based observations. The vertical column density (VCD) of nitrogen dioxide (NO₂) derived from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) showed decreases of 38% and 27%, on average, respectively, in March of 2020, compared with the same month in 2015–2019, for OMI and in 2019 for TROPOMI. Surface NO₂ concentrations measured by the Atmospheric Environmental Regional Observation System (AEROS) also declined by up to 22% relative to the 2015–2019 mean, which is consistent with previously reported reductions. To investigate interactions between ozone (O₃) and NO_x, we calculated the ratio of non-methane hydrocarbon (NMHC) and NO_x and potential ozone (PO) surface concentrations from the AEROS data. The results indicated that the ozone formation regime in the Kanto region remained within the NMHC-limited domain during the COVID-19 period and was unchanged from the previous five years. Nevertheless, the baseline O₃ concentration decreased by 2.5–8.5 ppbv, depending on site (urban vs. suburban) and year (2020 vs. 2021). Diurnal variations in PO concentrations (defined as O₃ + NO₂-0.1NO_x), which is the net O₃ concentration produced by photochemical reactions and/or transport excluding the NO titration effect, showed significant reductions of 6.3 ppbv in 2020 and 3.2 ppbv in 2021, suggesting that lower PO levels were mainly attributed to the reductions in baseline O₃ concentrations in 2020. These findings highlight how pandemic-related emission reductions affected chemical processes and dynamics related to both NO_x and O₃ in a major Japanese metropolitan region. Full article

We present the development of an infrared sensor based on a meta surface utilizing Dolmen plasmonic nanostructures. This meta surface is engineered to enhance the absorption of infrared light at a specific wavelength. The sensor is optimized for high sensitivity and selectivity in the infrared spectrum. This straightforward meta surface sensor shows promise for various applications, including gas sensing, biosensing, and security. The design is compact and easy to fabricate with studied fabrication tolerance ensuring reliable performance. The sensor was tested for water-based sensing applications, and we tested its performance by using different materials such as ZrN, TiN, Cr, and Au on silicon dioxide. In a separate configuration, a gold nanostructure was fabricated on a silicon layer over a silicon dioxide base to examine the resulting plasmonic response. The results surpass those of other water quality sensors, underscoring the potential of this design for high-performance sensing. The sensor’s high sensitivity and low fabrication costs make it a promising technology for future sensing and monitoring applications. Full article

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s⁻¹ wind speed, and 32.36 mm precipitation—yielding average PM_2.5, PM₁₀, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m⁻³, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m⁻³ for PM_2.5, PM₁₀, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production. Full article

Field measurements of ship motions at berth are often sparse, heterogeneous, and collected across multiple vessels and locations, limiting the applicability of conventional response-modelling approaches. This study presents a statistical framework for analysing sea-state-conditioned motion responses using long-term monitoring data with incomplete overlap between degrees of freedom (DoF). Each DoF is analysed independently and conditioned on significant wave height ($H_s$) and peak wave period ($T_p$), with directional values retained across the full angular range (0–360°) and examined separately. A two-stage quality-control procedure combining plausibility checks and robust regression removes inconsistent response–sea-state pairs while preserving dominant behaviour. Motion response envelopes are derived by binning observations in sea-state space and computing median and upper-percentile statistics. To quantify sampling uncertainty, bootstrap resampling provides 95% confidence intervals for envelopes and derived metrics, ensuring robust comparative conclusions. Results show systematic growth in motion variability with increasing $H_s$, with surge exhibiting the strongest translational sensitivity and roll the largest amplification. Synthetic sea surfaces generated using a spectral random-phase approach reproduce prescribed sea-state characteristics, supporting physical interpretation. The study contributes a data-driven framework for heterogeneous berth datasets, robust quality control, uncertainty-aware response envelopes, and statistically consistent synthetic seas, aligning field-based monitoring with practical port operability assessment. Full article

Ammonia (NH₃) from pig houses contributes to air-quality degradation and odor, yet farm-level emissions are highly sensitive to housing design, slurry chemistry and management. This study developed and validated a minute-resolution dynamic model for indoor NH₃ concentration and house-level emission in a mechanically ventilated finishing pig house. Volatilization from the slurry surface was computed from total ammonia nitrogen (TAN), pH and temperature using established mass-transfer formulations, and coupled between two zones (pit headspace and room airspace) via advection and diffusion across the slatted-floor open area. Over one production cycle, key drivers and indoor NH₃ were monitored; discrete TAN observations were upsampled to minute resolution by linear interpolation. Model coefficients were optimized by a genetic algorithm with chronological 70/30 splits for calibration and validation in the grower and finisher phases, respectively. The calibrated model reproduced minute-scale dynamics (validation RMSE 1.53–1.76 ppm, R² 0.87–0.88; MAPE 9.95–10.87%). Sobol’s global sensitivity analysis identified ventilation rate as the dominant driver of indoor concentration, and TAN and slurry pH as the principal drivers of emissions. The model provides decision support for minute-scale monitoring and management, and can be integrated with factor-control methods and ICT-based supervisory systems. Full article

Blasting-induced ground vibrations, typically quantified by peak particle velocity (PPV), pose one of the most critical environmental challenges in surface mining and can damage nearby structures and disrupt surrounding ecosystems. Consequently, the development of reliable and accurate predictive models is essential for designing safe, environmentally responsible, and sustainable blasting operations. This study develops a robust predictive framework using a harmonized database of 506 blasting events, from which 386 high-quality records were retained after preprocessing to model PPV as a function of charge per delay (Q), monitoring distance (R), and rock mass rating (RMR). Several machine learning (ML) algorithms, including artificial neural networks trained using the Levenberg–Marquardt algorithm (ANN-LM), adaptive neuro-fuzzy inference systems (ANFIS), Gaussian process regression (GPR), and decision trees (DT), were evaluated alongside conventional empirical models such as the USBM, Ambraseys–Hendron, Langefors–Kihlstrom, and BIS. To further enhance predictive capability, two optimization strategies, Bayesian optimization (BO) and differential evolution (DE), were applied to the GPR model, producing optimized BO-GPR and DE-GPR variants. Model performance was assessed using the correlation coefficient (r), variance accounted for (VAF), mean absolute error (MAE), and relative root mean square error (RRMSE). Results indicate that the BO-GPR model achieved the best predictive performance during testing for both the two-input (Q, R) and three-input (Q, R, RMR) configurations, with r values of 0.97426 and 0.98381, respectively, and VAF values exceeding 94%. SHAP analysis revealed monitoring distance as the dominant attenuating factor controlling PPV. The optimized framework provides an accurate, interpretable tool for vibration prediction and precision blast design, supporting environmentally responsible, sustainable mining operations. Full article

Surimi manufacturing involves complex, multi-step operations in which small changes in raw material condition, formulation, and heating history can markedly alter texture, water retention, and visual quality. This review critically examines peer-reviewed studies that apply artificial intelligence to surimi and surimi-based products, focusing on work validated directly in surimi systems. Current evidence mainly supports non-destructive quality evaluation and integrity screening using imaging and vibrational spectroscopy. These applications include deep learning for classifying gel surface images, as well as chemometric and machine learning analysis of infrared, near-infrared, and hyperspectral data for quality prediction and adulteration detection. Process-linked monitoring during thermal treatment is also beginning to emerge, with one time-resolved hyperspectral imaging study demonstrating quality tracking during heating. Major barriers to industrial adoption include limited and narrowly sampled datasets, batch effects and validation designs that may overestimate predictive performance, and practical deployment challenges such as stable sensing in wet environments, instrument drift, and calibration transfer across devices and sites. The review also outlines forward-looking directions, including digital twins, adaptive control strategies, and automation, and identifies data standardization, external validation, and maintenance strategies as priorities for translating laboratory demonstrations into reliable industrial applications. Full article

Volatile Organic Compounds (VOCs) are important indicators of environmental pollution and metabolic activity, making their sensitive and selective detection highly relevant for applications in health monitoring and air quality assessment. Graphene, owing to its exceptional charge transport properties, large surface area, and tunable surface chemistry, is a promising candidate for advanced gas and VOCs sensing. Here we report chemoresistive sensors based on pristine graphene and graphene decorated with platinum (Pt), palladium (Pd), and gold (Au) nanoparticles toward both aromatic (benzene, toluene, and xylene) and non-aromatic (ethanol, methanol, and acetone) vapor compound detection. The detection is achieved at room temperature, and the results demonstrate that graphene functionalized with noble metal nanoparticles shows significant enhancements in sensitivity compared to pristine graphene, mainly against ethanol, toluene and xylene vapors for the Au–graphene sensors. A comparative study with Multi-Walled Carbon Nanotube (MWCNT) sensors decorated with the same type of nanoparticles revealed clear advantages of graphene, attributed to the microstructure and porous structure of graphene powders, which facilitate efficient charge transfer upon vapor adsorption. Full article