Search Results (219)

Metal anodes promise improvements in energy density and cost; however, their performance is determined within the first several nanometers at the interface. This review reports on how polymer-based artificial solid electrolyte interphases (SEIs) are engineered to stabilize Li and aqueous-Zn anodes, and how these designs are now evaluated against operando readouts rather than post-mortem snapshots. We group the related molecular strategies into three classes: (i) side-chain/ionomer chemistry (salt-philic, fluorinated, zwitterionic) to increase cation selectivity and manage local solvation; (ii) dynamic or covalently cross-linked networks to absorb microcracks and maintain coverage during plating/stripping; and (iii) polymer–ceramic hybrids that balance modulus, wetting, and ionic transport characteristics. We then benchmark these choices against metal-specific constraints—high reductive potential and inactive Li accumulation for Li, and pH, water activity, corrosion, and hydrogen evolution reaction (HER) for Zn—showing why a universal preparation method is unlikely. A central element is a system of design parameters and operando metrics that links material parameters to readouts collected under bias, including the nucleation overpotential (η_nuc), interfacial impedance (charge transfer resistance (R_ct)/SEI resistance (R_SEI)), morphology/roughness statistics from liquid-cell or cryogenic electron microscopy (Cryo-EM), stack swelling, and (for Li) inactive-Li inventory. By contrast, planar plating/stripping and HER suppression are primary success metrics for Zn. Finally, we outline parameters affecting these systems, including the use of lean electrolytes, the N/P ratio, high areal capacity/current density, and pouch-cell pressure uniformity, and discuss closed-loop workflows that couple molecular design with multimodal operando diagnostics. In this view, polymer artificial SEIs evolve from curated “recipes” into predictive, transferable interfaces, paving a path from coin-cell to prototype-level Li- and Zn-metal batteries. Full article

National parks face ecological threats from climate change and human activities. Sanjiangyuan National Park (SNP), a major ecological area in China, lacks a systematic evaluation of its ecological environmental quality changes and their driving factors. This study explores these dynamics to provide a scientific basis for regional ecological management. By constructing the remote sensing ecological index (RSEI) and using the optimal multivariate-stratification geographical detector (OMGD) model, we assessed ecological changes from 2014 to 2024. The results showed the RSEI remained stable at approximately 0.66, peaking at 0.732 in 2022, indicating a general improvement in ecological quality. The vegetation coverage rate (NDVI) increased from 0.591 to 0.680. Driving factor analysis revealed considerable regional variation, with temperature and human activities as the primary drivers. Higher RSEI values were associated with conditions where precipitation was moderate (~100 mm), evapotranspiration levels were high (>50 mm), temperatures were above average (>4 °C), and nighttime light indices were low (<0.6). These findings suggest that specific combinations of these factor thresholds may enhance ecological quality, informing protection strategies for SNP and providing a reference for similar plateau ecosystems. Full article

Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the Composite Remote Sensing Ecological Index (CoRSEI), which integrates both desert and non-desert systems. These indices are compared with the traditional Remote Sensing Ecological Index (RSEI) in the Tarim River Basin from 2000 to 2023. Principal component analysis (PCA) revealed that RSEI maintained the highest structural compactness (average PCA1 = 87.49%). In contrast, ARSEI (average PCA1 = 78.62%) enhanced sensitivity to albedo and vegetation (NDVI) in arid environments. Spearman correlation analysis further demonstrated that ARSEI was more strongly correlated with NDVI (ρ = 0.49) and precipitation (ρ = 0.62) than RSEI, confirming its improved responsiveness under water-limited conditions. CoRSEI exhibited higher internal consistency and spatial adaptability (mean values ranging from 0.45 to 0.56), with slight ecological improvements observed between 2000 and 2023. Ecological drivers varied across habitat types. In desert areas, evapotranspiration, precipitation, and soil moisture were the main determinants of ecological status, showing high coupling and synchrony. In non-desert regions, soil moisture and precipitation remained dominant, but vegetation indices and disturbance factors (e.g., fire density) exerted stronger long-term influences. Partial dependence analyses further confirmed nonlinear, region-specific responses, such as the threshold effects of precipitation on vegetation growth. Overall, our findings highlight the importance of differentiated ecological modeling. ARSEI enhances sensitivity in desert ecosystems, whereas CoRSEI captures landscape-scale variability across desert and non-desert regions. Both indices contribute to more accurate long-term ecological assessments in hyper-arid environments. Full article

Human harvesting exerts significant pressure on intertidal ecosystems, yet its impact on community structure remains insufficiently understood. To assess these effects, we investigated macrobenthic communities on Lvhua Island and adjacent islets by integrating ecological surveys, questionnaire data, and Remote Sensing Ecological Indices (RSEI). We analyzed species composition, biomass, density, and diversity indices across seven sampling sites. Results showed distinct spatial variation: the eastern Lvhua Island exhibited higher biomass and density than the west, with the remote Manduishan islet highest and the South of West Lvhua near the pier the lowest. Harvesting hotspots were dominated by Chlorostoma rusticum and Cantharus cecillei, while less-disturbed islets were characterized by Chl. rusticum, Thais luteostoma, and Turbinidae. Economically valuable gastropods showed signs of miniaturization under intensive harvesting. Biodiversity indices correlated with RSEI, and ABC curve analysis indicated moderate disturbance overall, with the greatest impact at the Donglvhua Bridge site. These findings indicate that a daily subsistence harvest of 100–150 kg resulted in a 31.82% decline in the Shannon-Wiener index, altering the community structure. RSEI provides a cost-effective complement to field monitoring and should be integrated into management frameworks to support both ecological conservation and community livelihoods. Full article

Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region in Italy is presented, which experienced intense flooding in 2023. To understand flood-induced changes in the short term, we quantified the differences in net primary productivity (NPP) and above-ground biomass (AGB) before and after flood events. Short-term analysis of NPP and AGB revealed substantial localized losses within flood-affected areas. NPP showed a net deficit of 7.0 × 10³ g C yr⁻¹, and AGB a net deficit of 0.5 × 10³ Mg C. While the wider region gained NPP (6.7 × 10⁵ g C yr⁻¹), it suffered a major AGB loss (3.3 × 10⁵ Mg C), indicating widespread biomass decline beyond the flood zone. Long-term ecological assessment using the Remote Sensing Ecological Index (RSEI) showed accelerating degradation, with the “Fair” ecological class shrinking from 90% in 2014 to just over 50% in 2024, and the “Poor” class expanding. “Good” and “Very Good” classes nearly disappeared after 2019. High-hazard flood zones were found to contain 9.0 × 10⁶ Mg C in AGB and 1.1 × 10⁷ Mg C in soil organic carbon, highlighting the vulnerability of carbon stocks. This study underscores the importance of integrating flood modeling with ecosystem monitoring to inform climate-adaptive land management and carbon conservation strategies. It represents a clear, quantifiable carbon loss that should be factored into regional carbon budgets and post-flood ecosystem assessments. Full article

In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article

The Qinghai Lake Basin (QLB), as a key component of the ecological security barrier on the Tibetan Plateau, is crucial for regional sustainable development due to the stability of its alpine agro-pastoral ecosystems. This study aims to systematically analyze the spatiotemporal evolution patterns and underlying driving mechanisms of eco-environmental quality (EEQ) in the QLB from 2001 to 2022. Based on Google Earth Engine (GEE) and long-term MODIS data, we constructed a Remote Sensing Ecological Index (RSEI) model to evaluate the EEQ dynamics. Geodetector (GD) was applied to quantitatively identify key driving factors and their interactions. The findings reveal: (1) The mean RSEI value increased from 0.46 in 2001 to 0.51 in 2022, showing a fluctuating improvement trend with significant transitions toward higher ecological quality grades; (2) spatially, a distinct “high-north-south, low-center” pattern emerged, with excellent-grade areas (4.77%) concentrated in alpine meadows and poor-grade areas (5.10%) mainly in bare rock regions; (3) 47.81% of the region experienced ecological improvement, whereas 16.34% showed degradation, predominantly above 3827 m elevation; and (4) GD analysis indicated natural factors dominated EEQ differentiation, with temperature (q = 0.340) and elevation (q = 0.332) being primary drivers. The interaction between temperature and precipitation (q = 0.593) exerted decisive control on ecological pattern evolution. This study provides an efficient monitoring framework and a spatially explicit governance paradigm for maintaining differentiated management and ecosystem stability in alpine agro-pastoral regions. Full article

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) to quantify spatiotemporal ecosystem dynamics within the Baiyangdian watershed from 1990 to 2023, utilizing the Remote Sensing Ecological Index (RSEI). The primary drivers influencing the watershed’s ecological and environmental quality were subsequently analyzed. The results show that the ecological quality of the Baiyangdian Basin showed fluctuating changes from 1990 to 2023. Overall, the northwestern part of the Baiyangdian Basin improved significantly, while the southeastern part was slightly degraded, and the intensity of the change between different RSEI grades was low, mainly fluctuating between poor, medium, and good grades. Both anthropogenic and natural factors have high explanatory power for the ecological quality of the Baiyangdian watershed, and the land use type in particular is the main driver of changes in the RSEI area. The explanatory power of these factors was significantly enhanced by the interaction between them, especially the interaction between the land use type and other drivers. Within the drivers of the land use type, the cropland area, woodland area, shrub area, and grassland area have a significant influence. In summary, the area change in different land use types is the main factor influencing the ecological quality of the Baiyangdian watershed. This study has demonstrative value and implications for large-scale shallow lakes and wetlands, ecological barriers in rapidly urbanizing regions, the integrated management of cross-administrative watersheds, and the use of the GEE platform for long time-series and large-scale ecological monitoring and assessment. Full article

The long-term sustainability of conservation efforts in critical reforestation regions requires timely, spatiotemporal assessments of ecological quality. In alignment with China’s environmental initiatives, this study integrates Google Earth Engine (GEE) and MODIS data to construct an enhanced Remote Sensing Ecological Index (RSEI) for two decades of ecological monitoring. Hotspot analysis (Getis-Ord Gi*) revealed concentrated high-quality zones, particularly in Xinjiang’s Altay Prefecture, with ‘Good’ and ‘Excellent’ areas increasing from 21.64% in 2000 to 31.30% in 2020. To uncover driving forces, partial correlation and geographic detector analyses identified a transition in the Three-North Shelter Forest Program (TNSFP) from climate–topography constraints to land use–climate synergy, with land use emerging as the dominant factor. Socioeconomic influences, shaped by policy interventions, also played an important but fluctuating role. This progression—from natural constraints to active human regulation—underscores the need for climate-adaptive land use, balanced ecological–economic development, and region-specific governance. These findings validate the effectiveness of current conservation strategies and provide guidance for sustaining ecological progress and optimizing future development in the TNSFP. Full article

Assessing ecological quality in mining areas is critical for environmental protection and sustainable resource management. However, most previous studies concentrate on large-scale analysis, overlooking fine-scale assessment in mining areas. To address this issue, this study proposed a novel analysis framework for mining areas by integrating high-resolution Landsat data, the Remote Sensing Ecological Index (RSEI), and the Random Forest regression method. Based on the framework, four decades of spatiotemporal dynamics and drivers of ecological quality were revealed in Youjiang River Valley. Results showed that from 1986 to 2024, ecological quality in Youjiang River Valley exhibited a fluctuating upward trend (slope = 0.004/year), with notable improvement concentrated in the most recent decade. Spatially, areas with a significant increasing trend in RSEI (48.71%) were mainly located in natural vegetation regions, whereas areas with a significant decreasing trend (9.11%) were concentrated in impervious surfaces and croplands in northern and central regions. Driver analysis indicates that anthropogenic factors played a crucial role in ecological quality changes. Specifically, land use intensity, precipitation, and sunshine duration were main determinants. These findings offer a comprehensive understanding of ecological quality evolution in subtropical karst mining areas and provide crucial insights for conservation and restoration efforts in Youjiang River Valley. Full article

This study investigates the spatio-temporal characteristics and driving mechanisms of ecological quality in the mountain–river–sea regional system using the Remote Sensing Ecological Index (RSEI) model, moderate-resolution imaging spectroradiometer (MODIS) data, and the Google Earth Engine (GEE) platform. The analysis, conducted at both the grid and county scales using spatial autocorrelation and geodetector, showed a notable improvement in ecological quality, with the average RSEI value rising from 0.549 in 2000 to 0.627 in 2022. The distribution pattern reveals superior quality in the northwest and inferior quality in central urban cores and coastal zones. Ecological quality exhibited significant spatial clustering, with high–high clusters in karst mountains and low–low clusters in urban and industrial zones. Geodetector analysis identified GDP and population density as dominant factors at the grid scale, and GDP and elevation at the county scale. By quantifying spatio-temporal variations and driving mechanisms of ecological quality across scales, this study provides a solid scientific foundation for regional ecological conservation and sustainable development. Full article

The water source protection areas of the Yangtze-to-Huaihe Water Diversion Project (YHWDP) in Anhui Province serve as crucial ecological barriers to water quality protection. Quantifying their eco-environmental quality (EEQ) dynamics and driving mechanisms is critical for sustainable management. This paper calculated the Remote Sensing Ecological Index (RSEI) for the study area using Landsat satellite data (2015–2024). Temporal and spatial variation characteristics were analyzed using the Theil–Sen estimator, Mann–Kendall test, and coefficient of variation. Future trends were predicted using the Hurst exponent. Finally, the Geodetector model was applied to assess the impact of driving factors. EEQ exhibited a declining trend (p < 0.05), with significant intra-regional heterogeneity. Mean RSEI values ranked as follows: (1) Yangtze River–Huaihe River Connection < Yangtze River Water Northward Conveyance < Yangtze River–Chaohu Lake Water Diversion. (2) From 2015 to 2024, eco-environmental quality improved significantly, showing a spatial pattern of “south > north, east > west.” (3) Overall EEQ changes were characterized by slight to moderate fluctuations. Stability rankings: Yangtze River–Huaihe River Connection > Yangtze River–Chaohu Lake Water Diversion > Yangtze River Water Northward Conveyance. (4) Geodetector analysis identified precipitation, impervious area, and vegetation coverage as the primary factors influencing EEQ in the YHWDP’s water source protection areas. This study reveals ecological changes in the YHWDP region and validates the effectiveness of the comprehensive evaluation method. The findings provide actionable insights for ecological protection in large-scale water diversion projects. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

The Dabie Mountain area in Anhui Province is an essential ecological security barrier and a critical protected area in East China. It is very important to assess its ecological environment quality and identify its key driving forces. Five indicators, including Greenness, Wetness, Dryness, Heat, and Biological Richness, were used to construct an improved remote sensing ecological Index (IRSEI) to assess ecological environment quality. The weights of the five indicators were determined by coupling the analytic hierarchy process (AHP) and the entropy weight method (EWM). The optimal parameters-based geographical detector (OPGD) was used to recognize driving factors. The main conclusions were as follows: (1) the overall rank of ecological environment quality was mainly good and excellent. The ecological quality of forest land was excellent, that of farmland was good, and that of built-up areas was poor. (2) The change in ecological environment quality was mainly stable from 2000 to 2020. The ecological quality of some forests and farmlands improved, with a deteriorating trend in the built-up areas. (3) The Moran’s Index of ecological quality ranged from 0.77 to 0.85, indicating high spatial agglomeration. (4) The OPGD indicated that the DEM had the most explanatory power for ecological quality, and the interactive relationship between the DEM and population density had the most significant impact. (5) In comparison to the conventional remote sensing ecological Index (RSEI), the IRSEI exhibited higher congruence with observed circumstances and improved ecological interpretability. Full article

Arid and semi-arid regions serve as crucial ecological barriers in China, making the spatiotemporal evolution of their ecological environmental quality (EEQ) scientifically significant. This study developed a Modified Remote Sensing Ecological Index (MRSEI) by innovatively integrating the Comprehensive Salinity Indicator (CSI) into the Remote Sensing Ecological Index (RSEI) and applied it to systematically evaluate the spatiotemporal evolution of EEQ (2014–2023) in Yinchuan City, a typical arid region of northwest China along the upper Yellow River. The study revealed the spatiotemporal evolution patterns through the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test, and adopted the Light Gradient Boosting Machine (LightGBM) combined with the Shapley Additive Explanation (SHAP) to quantify the contributions of ten natural and anthropogenic driving factors. The results suggest that (1) the MRSEI outperformed the RSEI, showing 0.41% higher entropy and 5.63% greater contrast, better characterizing the arid region’s heterogeneity. (2) The EEQ showed marked spatial heterogeneity. High-quality areas are concentrated in the Helan Mountains and the integrated urban/rural development demonstration zone, while the core functional zone of the provincial capital, the Helan Mountains ecological corridor, and the eastern eco-economic pilot zone showed lower EEQ. (3) A total of 87.92% of the area (7609.23 km²) remained stable with no significant changes. Notably, degraded areas (934.52 km², 10.80%) exceeded improved zones (111.04 km², 1.28%), demonstrating an overall ecological deterioration trend. (4) This study applied LightGBM with SHAP to analyze the driving factors of EEQ. The results demonstrated that Land Use/Land Cover (LULC) was the predominant driver, contributing 41.52%, followed by the Digital Elevation Model (DEM, 18.26%) and Net Primary Productivity (NPP, 12.63%). This study offers a novel framework for arid ecological monitoring, supporting evidence-based conservation and sustainable development in the Yellow River Basin. Full article