Introduction Grassland degradation in karst regions is typically characterized by increased bedrock exposure, vegetation fragmentation, and soil structural instability. However, the mechanisms by which these changes affect hillslope erosion resistance remain poorly quantified. Methods In this study, natural grassland plots under four levels of degradation, defined by bedrock exposure rates (RER) of 0%, 20%, 40%, and 55%, were selected in representative limestone and dolomite areas in Guizhou Province, China. Undisturbed soil samples from the 0∼20 cm layer were collected for aggregate stability tests and undisturbed soil scouring experiment. Using 9 diagnostic indicators of soil structural function, the effects of degradation on soil retention capacity were quantitatively assessed. Results The results showed that with increasing RER, the proportion of macroaggregates (>2 mm) decreased by 31.6%, and mean weight diameter (MWD) declined by 58.3%. Relative dispersion index (RSI) and relative mechanical Breakdown index (RMI) increased to 1.96 and 2.21, respectively, with the most severe structural breakdown occurring under fast wetting conditions. In the scouring experiments, sediment concentration peaked at 1.8 g/min within the first minute in the 55% RER plots, significantly higher than in the 0% RER plots. Meanwhile, the soil resistance coefficient declined by more than 50%. Composite functional evaluation revealed that MWD, RSI, Anti-scourability coefficient (AS), and root surface area were the most sensitive indicators across degradation levels. Limestone grassland (LG) demonstrated stronger performance in maintaining structural integrity and erosion resistance compared to Dolomite grassland (DG). Discussion These findings provide a scientific basis for identifying early warning signs of erosion resistance loss and offer theoretical support for ecological restoration and degradation threshold identification in karst grassland ecosystems.

Against the backdrop of environmental crises, countries worldwide are striving to curb ecosystem degradation. China faces unbalanced ecological development, hindering its overall green transition. Based on balanced panel data of 30 Chinese provinces from 2010 to 2020, this study employed a comprehensive methodological framework integrating the entropy weight method, dynamic qualitative comparative analysis, one-way analysis of variance, and Kruskal-Wallis test to explore the interaction mechanisms of diverse policy instruments and the differentiated pathways of environmental governance. Results indicate that leaders’ awareness of technological innovation constitutes a necessary condition for high environmental performance. Political, administrative, and legal factors can be combined into seven synergistic policy instrument pathways enhancing environmental performance, which exhibit strong explanatory power across periods, confirming their effectiveness and stability. Moreover, distinct regions exhibit clear preferences for specific environmental regulation pathways. This study deepens the understanding of how government regulation shapes environmental performance from the perspective of configurational theory, elucidates the interaction mechanisms among environmental policy instruments, and provides insights for transnational and cross-regional entities to implement differentiated environmental governance and advance holistic green development.

Methane (CH4) emission quantification in oilfield environments remains challenging due to spatially dispersed sources and complex near-surface atmospheric conditions, creating uncertainties in conventional monitoring approaches and limiting emission mitigation efforts. To address this issue, a UAV–AirCore system was deployed over the Shengli Oilfield in Dongying, China, to obtain high-resolution three-dimensional CH4 concentration observations using a two-stage flight strategy that combined horizontal plume screening with vertical spiral profiling to constrain plume geometry. Based on these observations, an integrated Emission-Partition inversion framework was developed by coupling a multi-source Gaussian dispersion model with a hybrid Particle Swarm Optimization–Interior Point Penalty Function (PSO–IPPF) algorithm, enabling the joint retrieval of emission rates and effective release heights. The proposed framework successfully reconstructed observed concentration fields with a coefficient of determination (R 2 ) of approximately 0.85. For two investigated areas, retrieved emission intensities were 3.22 ± 0.12 g/s and 3.68 ± 0.04 g/s, corresponding to relative uncertainties of 3.7% and 1.1%, respectively, while effective emission heights exhibited uncertainties of approximately 9%. Comparisons with mass balance, nonlinear least-squares fitting (NLSF), OTM 33A, and inventory-based estimates demonstrated consistent emission magnitudes, with the proposed method showing lower dispersion and improved robustness under complex atmospheric conditions. These results indicate that integrating three-dimensional UAV observations with hybrid inversion optimization enhances parameter identifiability and provides a stable and scalable solution for refined methane emission monitoring in industrial regions.

Introduction Effective detection of environmental pollution relies on reliable methods for determining pollutant concentrations, with cellular damage reflecting pollutant toxicity as a vital detection tool. This study presents a novel quantitative method for predicting environmental pollutant concentrations using cell images and a hybrid model. Methods The approach processes conventional optical microscope images by extracting grayscale statistical features and constructing a hybrid predictive framework that integrates stepwise regression for feature selection and multilayer perceptron for nonlinear modeling, enabling accurate mapping from image-based damage features to pollutant concentrations. Results Experiments show that the model performs consistently well across five cell types: HeLa, A549, HUVEC, PC12, and HaCaT. For example, it achieves an R 2 of 0.9911 on the HeLa test set, demonstrating strong generalization ability and robustness. Discussion The method does not require expensive equipment or complex sample preparation, offering an innovative, rapid, and low‐cost solution for monitoring environmental pollutant concentrations.

Introduction Karst regions in Southwest China are ecologically fragile and face complex governance challenges during rapid socio-economic transformation. Clarifying the evolutionary characteristics and governance constraints of ecological civilization construction in these regions is critical for promoting sustainable development and precision governance. Methods This study developed a multidimensional evaluation framework comprising 29 indicators to assess ecological civilization construction in typical karst regions. Using Guizhou Province as a case study, construction levels were systematically measured at the provincial, municipal, and county scales for three key temporal nodes (2012, 2018, and 2023). Spatial autocorrelation models were applied to reveal spatio-temporal differentiation patterns and spatial dependence characteristics. Results The results show that from 2012 to 2023, the comprehensive ecological civilization index in Guizhou Province increased significantly, with an average annual growth rate of 10.02%, indicating the effectiveness of the green transition during the modernization process. However, pronounced spatial non-equilibrium persists, forming a county-level pattern characterized by “high-value clustering in the southeast and low-value clustering in the west,” accompanied by a continuously strengthening positive spatial autocorrelation. Discussion Further analysis reveals that the observed spatial disparities are driven by deep-seated mechanisms, including governance cost differentials imposed by karst habitat constraints, path dependency of resource-based industries, and cultural disconnection during rapid urbanization. These findings provide a theoretical basis for differentiated and precision-oriented governance strategies in ecologically fragile regions and offer a valuable “China Solution” for achieving global sustainable development goals in similar karst environments.

The integration of trend-based Environmental Stress Severity (ESS) and Artificial Neural Network (ANN) modeling provides a robust framework for quantifying natural environment dynamics and forecasting stress in arid ecosystems. This study focuses on the Makkah Watersheds, Saudi Arabia. ESS was derived from Kendall’s τ and significance thresholds applied to long-term Landsat indices (NDVI, NDWI, LST; 1991–2023) and hydroclimatic drivers from TerraClimate (temperature, precipitation, AET, CWD). Trend analysis revealed significant declines in NDVI (20.48 km 2 ) and NDWI (99.57 km 2 ) and an increase in LST (156.5 km 2 ) at p < 0.05. Hydroclimatic pressures intensified as CWD expanded (1,936 km 2 ) while AET (176 km 2 ) and precipitation (128 km 2 ) contracted. The optimized ANN models demonstrated strong predictive performance for Environmental Stress Severity, with the baseline ANN achieving R 2 = 0.881 (MAE = 0.200), and further improvements obtained using metaheuristic optimization (ANN-PSO: R 2 = 0.890, MAE = 0.186; ANN-GWO: R 2 = 0.883, MAE = 0.196). Zonal and landcover diagnostics showed the highest stress in sparsely vegetated zones (e.g., WS5: 3.49 ± 0.67, WS4: 3.31 ± 0.71), whereas built-up areas exhibited lower means (e.g., WS1: 2.88 ± 0.72). Right-skewed ESS distributions in natural covers highlight asymmetric dryland vulnerability, characterized by a larger tail of highly stressed pixels. The workflow is scalable, temporally sensitive, and directly actionable for restoration planning, land-use regulation, and climate-risk adaptation in drylands.

Introduction Monitoring and real-time detection of soil heavy metal pollution are crucial to ensuring the safety of agricultural food products. Conventional methods for determining heavy metal concentrations require substantial time and costs. This study investigates intelligent modeling approaches for online detection of heavy metal contamination in soils. Based on Internet of Things communication, large‐scale near-infrared (NIR) spectral data were collected from distributed sensors for federated analysis. Methods In chemometric studies, the improved binary firefly algorithm (IBFA) is proposed for evolutionary variable selection, and the modified method of maximum information coefficient (MMIC) is designed to estimate the nonlinear correlation of unevenly distributed samples. Experimental soil data are collected from the Karst geology on the north side of Guangxi ZAR, China. The NIR calibration model is established by fusion of the IBFA and the MMIC methods (denoted as IBFA + MMIC). The fusion model is applied for quantitative prediction targeting of four heavy metals in the Karst soil samples. Results The IBFA + MMIC model can observe correlation coefficients higher than 0.9 and the lowest prediction errors during model training. It is tested with the correlations very close to 0.9, while the testing errors are acceptably low. These results outperform the counterpart models established by other cross combinations of firefly algorithm (FA)/IBFA and maximum information coefficient (MIC)/MMIC. Discussion The proposed modeling methodology is effectively validated for quantitative NIR analysis of different heavy metals in Karst soil data. Meanwhile, it provides critical technical support for the federated analytical performance of distributed sensing data, thereby facilitating precision soil management practices.

Under the global imperative for energy conservation and emission reduction, a critical yet underexplored question is how environmental regulation (ER) specifically affects carbon emission efficiency (CEE). In this paper, the Super-Slacks-Based Measure (SBM) model with undesirable outputs is used to measure the CEE of 30 Chinese provinces from 2010 to 2022. Based on the results, the Tobit model is used to study the impact of ER on CEE empirically. The results show that (1) China’s CEE is low, and the eastern region has the highest CEE, followed by the central and northeastern regions, and the lowest is the western region. (2) There is a U-shaped curve relationship between ER and CEE, namely, the impacts of ER on CEE are first inhibitory and then promotive; Currently, ER is having a negative effect on CEE, and the inflection point has yet to emerge. (3) Opening up level and population density have positive influences on CEE, while industrial structure exerts a negative impact. This research provides a reference for improving the CEEs of China and other developing countries. Policy implications include the need to strengthen and optimise the regulations to promote the high-quality development of the Chinese economy. The results offer a reference for policymakers.

Rural sustainability is key to maintaining prosperity in developed rural areas, yet many existing evaluation methods focus on material resources while overlooking villagers’ perceptions. This study introduces an ecological–livelihood–perception ( ELP ) model that extends the sustainable livelihoods framework (SLF) by refining its natural capital dimension to explicitly incorporate ecological quality and villagers’ environmental perceptions, alongside a psychological perception derived from a self-fulfilling prophecy theory. We conducted a structured survey of 100 households in Guoliang village. Then, 21 standardized indicators spanning the natural, financial, physical, social, human, and ecological dimensions were introduced to evaluate the survey data. After reliability screening, principal component analysis (PCA) was applied to construct a single composite score for each capital, capturing the dominant variance in the data without multicollinearity. PCA combined with entropy-based weighting was applied to reduce collinearity and derive indicator weights. Descriptive results show that normalized indicator means range from 0.15 to 0.86, and indicator weights are relatively balanced, ranging from 0.044 to 0.051, with the highest-weight indicator reaching 0.051. The sustainable individual perception ( SIP ) index was calculated from qualitative items and was linked to the six capital scores through an ordinary least squares regression. The results reveal a threshold pattern: once a basic level of natural capital is met, it leads to further gains, especially in social and ecological assets, which are strongly associated with higher psychological optimism. Households exhibiting high SIP consistently possess balanced and multidimensional capital portfolios, whereas those with low SIP show deficits across most dimensions. The ELP model, therefore, provides a more comprehensive tool for rural sustainability evaluation by integrating objective livelihood assets with subjective perceptions, offering practical insights for targeted policy interventions and development strategies.

Introduction The importance of silicon for soil functioning has only recently received the attention it deserves in soil science (and ecology). To identify the actual effects of Si on ecosystem functioning, a precise knowledge about different Si soil fractions (plant-available, amorphous Si, and Si bound to organic matter, short-range ordered aluminosilicates (SROAS), or iron oxides/hydroxides) is required. Sequential extraction techniques are a common tool to quantify different soil fractions in soils of humid-temperate climates, whereas their reliability in other climatic zones or contrasting soils remains unclear. This study tested to what extend sequential extraction is applicable on soils from the High Andes and Amazon Basin, two contrasting regions with distinct soil formation processes. Methods To ensure method validity, we analyzed Si, iron (Fe), and aluminum (Al) in extracts and conducted X-ray diffraction (XRD) to detect mineral changes pre- and postextraction. Amorphous Si was more abundant in the High Andes soils, yet plantavailable Si was unexpectedly lower compared to Amazon Basin soils. Results Silicon occluded in Fe oxides/hydroxides or SROAS showed no differences between the two contrasting soils. XRD results revealed the following limitations: dithionite extraction did not completely remove Fe from hematite in Andean soils, and powder XRD lacked precision for amorphous Si quantification. Among the tested methods, CaCl2 extraction (for plant-available Si) and a two-step protocol—density separation for biogenic Si (bASi), followed by Tiron extraction for total amorphous Si (tASi)—were the most effective for these soils. Discussion These findings, with different methods not being able to characterize the soil used, underscore the need to refine Si extraction techniques for environments. Expanding methods originally developed in temperate soils is critical to understanding Si’s role in biogeochemical cycles and its broader significance for different ecosystem performance.