This study investigated the effects of traditional pond culture (CT) and the cage-in-pond system (JY) on the muscle nutritional composition and fatty acid profiles of Spinibarbus sinensis. The results showed that crude protein, crude lipid, total amino acids, essential amino acids, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and n-3 polyunsaturated fatty acids (n-3 PUFAs) in the muscle were significantly higher in the CT group than in the JY group (P < 0.05), whereas ash content was significantly lower. Fatty acid analysis revealed that the CT group exhibited a broader fatty acid spectrum, with significantly higher levels of total saturated fatty acids (∑SFA), total monounsaturated fatty acids (∑MUFA), and total polyunsaturated fatty acids (∑PUFA), particularly n-3 PUFAs and long-chain polyunsaturated fatty acids (LC-PUFAs), especially EPA and DHA. These improvements contributed to markedly lower IA, IT, and HI indices in the CT group, indicating a greater potential for reducing cardiovascular risks. The superior nutritional quality of the CT group may be attributed to the availability of natural food organisms in pond environments, whereas the JY group was limited by higher stocking density and a reliance on formulated diets with relatively simple lipid sources. Overall, the pond culture system enhanced the nutritional quality of S. sinensis muscle, while the cage-in-pond system offered advantages in environmental sustainability and management efficiency. Further improvements in the cagein-pond system could be achieved by optimizing stocking density and dietary lipid composition, thereby enhancing fatty acid deposition and overall nutritional value. These findings provide new insights into the optimization of aquaculture models for improving the flesh quality of cultured fish.
Environmental resource management has gained global traction, yet it remains underexplored within the unique socio-ecological context of sub-Saharan Africa (SSA). To address this gap, this systematic review evaluates the setbacks of environmental resource management in SSA and proposes tangible strategic frameworks. Following the Preferred Reporting Items for Systematic Reviews and MetaAnalyses guidelines, a comprehensive literature search for original articles published between 2015 and 2025 was conducted across PubMed, Web of Science, Scopus, and ScienceDirect, supplemented by an additional search on Google Scholar. This combined search strategy culminated in the final analysis of 22 studies. The findings reveal three primary socio-ecological challenges: (1) the socio-ecological imbalance caused by industrial and mining activities; (2) escalating land use, water management, and socio-economic pressures; and (3) the complexities of biodiversity conservation and human-wildlife conflicts. This study advocates for a ‘One Health’ environmental strategy, recommending the integration of localized environmental education into school curricula and the fostering of community-based co-management to harmonize human livelihoods with ecological conservation in SSA.
Ruminant animals are nutritionally, economically, and culturally very important. However, they produce large amounts of methane (CH4) gas which contributes to climate change, and it is also a loss of energy to the animal. Climate change directly and adversely impacts ruminant livestock production in terms of reduced quantity and quality of water and feeds, and increased animal health and husbandry challenges. Thus, it is important to mitigate CH4 production. This literature review focuses on strategies for reducing CH4 production from ruminant animals. It is concluded that inclusion of plant secondary metabolites like tannins in ruminant livestock feed is promising. Tannins inhibit ammonia and CH4 production through their ability to form complexes with carbohydrates and proteins, and reduce the number of microbes responsible for methanogenesis. However, effects of tannin inclusion in diets on CH4 reduction in ruminant animals are variable, possibly related to the molecular weight of tannins, diet factors, etc. Therefore, further in vivo studies are suggested to determine the effects of tannins on reducing CH4 production, and hence improving animal performance.
Limiting global warming to the critical 2 °C threshold necessitates the development of effective policies and practices for mitigating atmospheric greenhouse gases (GHGs). Agriculture, a major contributor to non-CO2 emissions, particularly methane (CH4) and nitrous oxide (N2O), poses dual challenges to climate stability and food security. Moreover, agriculture’s potential as a carbon sink, through judicious agroecosystem management, offers a crucial tool for combating anthropogenic climate change. China and Bangladesh reported 662.23 MtCO2e and 90.61 MtCO2e, respectively, for agriculture (IPCC category), accounting for over 10% and below 2% of global emissions for the year 2020. CH4 and N2O remain prominent culprits in both countries. Both countries have unwavering commitment to GHGs emission reduction, employing multifaceted, nuanced strategies, regulatory frameworks, and economic instruments. Own to commendable efforts, agricultural emissions of CH4 and N2O in China peaked in 2016, while their emissions continued to rise in Bangladesh. However, significant challenges persist, such as China’s reliance on conventional energy sources and Bangladesh’s need for infrastructural development and financial resources. Both countries share the ambition and necessity of achieving carbon neutrality. China has developed a strategic framework encompassing sustainable agricultural practices, innovative approaches to mitigate livestock emissions, optimized rice cultivation techniques, and robust strategies for soil carbon sequestration. In contrast, Bangladesh’s efforts are centred on enhancing energy efficiency and advocating for organic fertilizer usage. The adoption of collaborative strategies offers a multifaceted approach to addressing the intricate challenge of balancing agricultural production with emissions reduction.
Floods are among the most common and destructive natural disasters, posing a great threat to socioeconomic development and the environment. Distributed hydrological models can simulate hydrological processes in a basin under conditions of uneven spatial distribution of rainfall, underlying surface (landform, agrotype and land-use type) and initial conditions (soil water content and groundwater level), and they have achieved good results in flood prediction. Based on a summary and analysis of relevant literature, this paper describes the structural principles and development process of distributed hydrological models; summarises research progress in reservoir flood, watershed flood and urban stormwater forecasting; and outlines future development trends for distributed hydrological models. This review notes that integrating machine learning with meteorological/hydrodynamic models, enhancing bidirectional feedback mechanisms and incorporating real-time monitoring will considerably improve flood forecasting accuracy.
Stand mingling degree is a key spatial-structure indicator for forest ecosystem functioning. Its optimization presents a high-dimensional, dynamic sequential decision-making problem due to the dynamic neighborhood reconstruction induced by selective cutting. This study proposes a negotiation-based Multi-Agent Reinforcement Learning (MARL) framework, which synergistically generates robust harvesting plans through distributed exploration and a contribution-weighted negotiation fusion mechanism. Experiments on multi-scale permanent sample plots in the Wuyunjie National Nature Reserve, Changde City, Hunan Province, China, demonstrate that in large-scale scenarios, the framework performs comparably to the optimal Tabu Search (TS) algorithm, with a minor performance gap of 0.9%, while achieving the highest stability (coefficient of variation = 5.73%). In small-and medium-scale scenarios, it performs on par with Tabu Search (TS), the Artificial Bee Colony (ABC) algorithm, and other benchmarks, and consistently outperforms Single-agent Q-learning (S-QL). Furthermore, the advantage of our framework over S-QL increases systematically with problem complexity (effect size delta:0.3 -> 0.80), and the relationship between the number of agents and performance gain exhibits diminishing marginal returns. This research provides a highly robust and scalable swarm intelligence decision-making paradigm for dynamic-neighborhood forest optimization.
Frequent algal blooms in freshwater lakes are a major environmental concern. These are caused by eutrophication resulting from climate change and elevated nutrient levels. However, previous studies have not disentangled the effects of nutrient inputs, wind and wave-driven sediment resuspension, algal uptake and release, and hydrological and climatic conditions on nutrient level fluctuations in freshwater lakes. To understand the causes and characteristics of fluctuation patterns of total phosphorus(TP) in the Huayang Lake Group during the dry season, lake water quality, water level, and wind speed data over the past 10 years were analyzed using methods such as violin plots and regression analysis. The results revealed no substantial improvement of TP fluctuations in the Huayang Lake Group during the dry season. Among the four lakes, Longgan and Huanghu lakes exhibited the largest fluctuations, followed by Daguan Lake; meanwhile, Pohu Lake exhibited relatively small fluctuations. During the dry season, wind speed variations corresponded with fluctuations in turbidity and TP concentrations. A binary regression model for relating water level and wind speed to TP concentration (R2 = 0.45) in Longgan Lake performed notably better than a univariate model relating only wind speed to TP concentration (R2 = 0.39), indicating that dry season TP levels were influenced by both factors. Prevailing northeasterly winds during the dry season intensified over the lakes due to restriction by mountains on either side, causing strong disturbances at low water levels and driving TP fluctuations. Differences in lake morphology (area, location, etc.) and wind exposure led to variable TP fluctuations across the lakes during the dry season. Huayang Lake Group should control phosphorus by maintaining ecological water levels and restoring aquatic vegetation to address frequent weather extremes.
Using panel data for 30 Chinese provinces (autonomous regions and municipalities) from 2000 to 2023, this study constructs composite indices via the entropy weight method, measures coupling coordination using a coupling coordination degree model, identifies regional typologies through cluster analysis, and explores heterogeneous drivers with stochastic gradient boosting (SGB) regression. The results indicate that the national coupling coordination degree increases steadily over time, with an overall shift from imbalance toward coordination. Nevertheless, substantial regional disparities persist, with higher coordination in eastern provinces and lower levels in western regions. Provinces can be classified into high,medium-, and low-coordination groups, forming a core-periphery structure. Driver importance varies across groups: high-coordination regions are primarily influenced by ecological efficiency and technological innovation; medium-coordination regions depend more on open development and market-oriented mechanisms; and low-coordination regions are particularly sensitive to industrial-structure upgrading and openness. These findings document the spatiotemporal evolution and differentiated mechanisms underlying coordinated development and provide policy implications for tailoring regional strategies and accelerating green, low-carbon transformation.
Microorganisms play a pivotal role in the material and energy cycles of lake ecosystems. However, seasonal variations in the ecological processes of planktonic bacterial communities (PBC), benthic bacterial communities (BBC), and epiphytic bacterial communities (EBC) remain poorly understood. This study employed high-throughput sequencing of the 16S rRNA gene to analyze 12 water, 12 sediment, and 12 epiphytic biofilm samples collected from Caohai, Guizhou, China, in July and Novemberof 2020. We conducted a comparative investigation of the bacterial community composition, co-occurrence networks, and community assembly processes across the three different media. The results indicated various dominant bacterial groups across the three media. The BBC exhibitedthe highest diversity, as indicated by the highest richness (Chao1 index) and Shannon diversity index. The seasonal changes in the beta-diversity of EBC and PBC were evident but were not significant for BBC. The deterministic processes predominantly govern the assembly of the lake bacterial community. The significance of deterministic processes in benthic bacterial community assembly was higher in summer than in winter, whereas the opposite was observed for EBC. Conversely, the PBC assembly demonstrated no seasonal variation. These findings can provide new insights into the ecological dynamics of bacterial communities in different lake environments.
Research on the influence of sowing date, hybrids, and irrigation regime on the productive and qualitative characteristics of sweet corn was conducted during two growing seasons (2022 and 2023). The experiment was set up according to a randomized complete block design in four replications, with three fixed factors: date of sowing (D: first: end of April/beginning of May and second: mid-July), hybrid (H: Enterprise F1 and Union F1) and irrigation regime (I: control - natural moistening, 50% and 100% of full norm) and a random factor of year (Y). The hybrid Enterprise achieved the highest total ear weight (392.47 g), shelling percentage (up to 70.44%) and protein content (4.12%), while the Union hybrid had the highest dry matter content (up to 40.54%). At the full irrigation norm the highest values are as follows total ear weight up to 392.47 g, shelling percentage up to 70.44%, kernel depth up to 1.27 cm, dry matter up to 40.54% and protein content up to 4.12%. The best results were achieved with the first sowing date and the Enterprise hybrid at the full irrigation norm in the more favorable year of 2022.
This study aims to explore the structural characteristics of multilevel collaborative networks in high-end manufacturing supply chains, the spatial synergy level of service flow and ecological risk, the causal effects of collaborative network participation, and the dynamic correlation and early warning logic of core variables. An integrated framework of 'multi-level network-spatial collaboration-causal identification-dynamic correlation' was constructed based on panel data of 326 enterprises (including specialized, sophisticated, and innovative Small and Medium-sized Enterprises (SMEs) in high-end manufacturing) in the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei industrial clusters in China from 2018 to 2023. The study employs methods including the Multilevel Exponential Random Graph Model (Multilevel ERGM), spatial coupling coordination degree model, Propensity Score Matching combined with Difference-in-Differences (PSM-DID) with heterogeneity analysis, and Bayesian Vector Autoregression (BVAR) model. The results show that the multilevel collaborative network is dominated by the service flow layer and features cross-layer linkage; core enterprises in the service flow layer can significantly drive the collaborative upgrading of other levels. Spatial collaboration levels vary regionally, with the Yangtze River Delta being a high-collaboration core that exhibits a significant positive spatial spillover effect. Collaborative network participation can significantly improve service flow and reduce ecological risks. Dual-carbon policies can strengthen this effect, with core enterprises, the semiconductor industry, and regions with strong policies exhibiting more pronounced effects. Dynamic correlations exist between variables, and network density has a risk-buffering effect. This study constructs a multi-method fusion analysis framework, enriching the statistical methodology system for the ecological research of high-end manufacturing supply chains and providing a quantitative basis for enterprise operation, government governance, and risk early warning.
Long-term land use and land cover changes are fundamental to understanding regional and global transformations, while studying key periods and regions provides crucial insights into large-scale human- environment relationships. This study examines the Hehuang Valley on the northeastern edge of the Qinghai-Tibet Plateau in China in the year 2 AD during the Western Han Dynasty, focusing on the spatial distribution of settlements and the reconstruction of cropland patterns. Using historical records and methods such as site domain analysis and grid-based allocation modeling, the results show: (1) a total population of 149,815 and a cropland area of 958.816 km(2); (2) 102 reconstructed settlements covering 4,545.358 km(2) with a density of 0.23 per km(2) and 77.67% domain overlap; (3) cropland mainly concentrated along both sides of the Huangshui River Basin, with an average reclamation rate of 23.53% and a maximum of 30.73%. The findings demonstrate the significant influence of the Western Han tuntian system on frontier agriculture and ethnic integration of China, offering key data for understanding historical human-environment relationships.
In this study, 50 typical public buildings (Office Building, Hotel, Shopping Mall, School, Hospital) in Jiangsu Province, China, were selected as the objects. Based on the built drawings, bill of materials, and envelope parameters, a refined model was established to calculate the carbon emissions for the LCA. A dynamic prediction model was built to simulate and predict the time nodes of carbon neutralization of various buildings by adjusting the proportion of energy-saving transformation, the proportion of roof photovoltaic power generation, the proportion of green power procurement, and CCER carbon trading. All building types can basically achieve carbon neutrality through technology combination before 2060, and the Office building is expected to be the first to achieve carbon neutrality. The economic analysis shows that a comprehensive transformation oriented toward carbon neutrality has significant longterm economic benefits. According to the prediction model, by 2060, Office buildings will be 15872.97-22467.68 CNY & centerdot;m(-2), hotel buildings will be 20729.36-59572.14 CNY & centerdot;m(-2), Shopping malls will be 14620.78-48440.39 CNY & centerdot;m(-2), school buildings will be 12196.93-33317.86 CNY & centerdot;m(-2), and hospital buildings will be 43792.95-81986.61 CNY & centerdot;m(-2). Energy-saving transformation proves that low-carbon transformation can be transformed into a strategic investment to enhance the long-term competitiveness of assets.
Soil respiration is a key pathway of carbon loss from forest ecosystems, yet its response to increasing nitrogen (N) deposition remains poorly understood in high-altitude alpine forests of the Xizang Plateau. We conducted a simulated N deposition experiment in a forest dominated by Abies georgei var. smithii in the Sejila Mountains, southeastern Xizang, China. Four N addition treatments were established: control (CK, 0 kg N & centerdot;hm(-2)& centerdot;a(-1)), low N (LN, 10 kg N & centerdot;hm(-2)& centerdot;a(-1)), medium N (MN, 15 kg N & centerdot;hm(-2)& centerdot;a(-1)), and high N (HN, 20 kg N & centerdot;hm(-2)& centerdot;a(-1)). Soil respiration (Rs), soil temperature, and soil moisture were measured during the 2020 growing season to examine the seasonal and diurnal dynamics, temperature sensitivity, and hydrothermal regulation of Rs. Simulated N deposition significantly suppressed Rs and growing-season soil CO2 emissions, with Rs consistently following the order CK > LN > MN > HN. Compared with CK, cumulative CO2 emissions decreased by 32.59%, 56.56%, and 73.13% under LN, MN, and HN, respectively. Rs generally increased with soil temperature, but this temperature dependence was weakened under high N input. The Q Q10 value declined from 2.46 in CK to 1.35 in HN, indicating reduced apparent temperature sensitivity of Rs under N enrichment. Two-factor response surface models showed that Rs was jointly regulated by soil temperature and soil moisture, with higher Rs values occurring under relatively high temperature and moderate soil moisture. These results suggest that increasing N deposition may suppress soil respiratory carbon loss and reduce the apparent temperature sensitivity of Rs in alpine forests, while hydrothermal interactions remain important controls on Rs dynamics.
Urban congestion imposes substantial economic burdens and exacerbates adverse environmental externalities, critically necessitating the development of intelligent, low-carbon transportation networks. Confronting the limitations of conventional traffic management paradigms in mitigating vehicular emissions, this study proposes a novel, prediction-driven framework integrating deep learning and metaheuristic optimization to dynamically minimize the ecological footprint of urban mobility. We architect a synergistic system comprising a Spatio-Temporal Graph Neural Network (STGNN) with a decoupled spatio-temporal masking pre-training mechanism for high-fidelity traffic flow forecasting, coupled with an Enhanced Balanced Whale Optimization Algorithm (EBWOA) for anticipatory, ecocentric signal timing adjustments. This integrated model proactively minimizes vehicular idling and delay-primary contributors to urban transport emissions-through dynamically optimized traffic signal timing schemes. Large-scale simulations utilizing real-world traffic datasets demonstrate that our STGNN achieves state-of-the-art prediction accuracy evidenced by average MAE reductions of 4.5% at 60-min horizons. Integrating a validated Comprehensive Modal Emission Model, we demonstrate that the proposed EBWOA-driven optimization significantly reduces average vehicular delays by 15.4% relative to conventional methods. This improvement directly translates to an estimated 13.8% reduction in total CO2 emissions. Collectively, this research advances a computationally efficient framework for realizing sustainable urban eco-mobility, offering a transferable paradigm for carbon emission mitigation in intelligent transportation systems and contributing substantively to smart city decarbonization objectives.
In order to explore the effects of shading and fertilization on the growth and photosynthetic characteristics of Polygonatum cyrtonema, and provide theoretical basis for standardized artificial cultivation. In this study, two-year-old P. cyrtonema tubers were used as experimental material. Three light levels (L1 = 100%, full light, L2 = 65% +/- 5%, moderate shading, L3 = 30% +/- 5%, heavy shading) and two fertilization gradients (D1 = 2 g/plant, D2 = 4 g/plant) were set. The results showed that the plant height and tuber net growth of L2D2 and L3D1 treatments were the largest, respectively. Shading increased the chlorophyll a, b, (a + b), net photosynthetic rate (P-n), stomatal conductance (G(s)), and transpiration rate (T-r) of P. cyrtonema, while reducing chlorophyll a/b and intercellular CO2 concentration (C-i). D2 fertilization gradients increased chlorophyll content and reduce the G(s) and Tr, the highest P-n was observed in the L2D1. In summary, P. cyrtonema exhibited a strong photosynthetic capacity under shading treatments, and the moderate shading combined with 2 g/plant compound fertilizer was the most conducive treatment for enhancing the photosynthetic performance of P. cyrtonema.
Pepper plants are often attacked by numerous pest species, particularly the green peach aphid (Myzus persicae Sulzer), which causes significant damage to plant growth and productivity. Chemical insecticides are frequently used to control M. persicae, but their application results in several environmental and health problems. The use of novel natural substances that can reduce M. persicae populations while minimizing environmental impacts offers a promising solution for organic farming systems. A greenhouse study was conducted to evaluate the insecticidal effects of Salvadora persica aqueous extract (SAE) and essential oil (SEO) against M. persicae on pepper plants, as well as their impact on the total acidity, total sugars, and vitamin C content of pepper fruits. The aphid population was monitored before treatment, and the active ingredients of S. persica were identified. Three concentrations of SEO (C1: 1 mL/L, C2: 1.5 mL/L, C3: 2 mL/L) and SAE (C1: 50 mL/L, C2: 100 mL/L, C3: 150 mL/L) were tested. The results demonstrated that M. persicae has a short generation time of 10.29 days on pepper plants under greenhouse conditions. S. persica contains three active ingredients: benzyl nitrile, 1H-pyrrole, and benzene. All of which exhibit insecticidal effects against M. persicae. The highest concentration (C3) of both SEO and SAE was the most effective against M. persicae followed by C2 and C1, compared to control. Additionally, foliar application of all tested SEO and SAE concentrations preserved the total acidity, total sugars, and vitamin C content of pepper fruits. In this study, SEO (2 mL/L) and SAE (150 mL/L) were shown to be effective natural insecticides for controlling M. persicae populations while preserving fruit nutrient quality. These findings provide promising prospects for farmers in organic or low-input systems, where managing aphid outbreaks can be particularly challenging.
Modified fillers can enhance the efficiency of runoff infiltration and the adsorption for bioretention cells, however, there is a lack of quantitative research on the pollutants seasonal succession patterns during the leaching-accumulation alternating process. This study evaluated the leaching characteristics and risks of heavy metals (HMs), nitrogen, phosphorus, and Dissolved Organic Matter (DOM) from typical solid waste amendments through dynamic leaching experiments. The results indicate that the modifiers exhibit good adsorption effects on NH4+-N, while other pollutants show varying degrees of leaching phenomena. The additive fly ash and recycled aggregate construction waste (RACW) are at high-risk levels when discharge in surface water. The organic components in the leachate of the ameliorant mainly consist of humic-like acids and soluble microbial metabolites. A bioretention site facility has been designed and constructed. The monitoring results indicated that the range of water volume reduction is 14.91%similar to 56.78% (mean = 28.65%). The reduction of pollutant loads are as follows: TP (Total Phosphorus) > NH4+-N > Zn > Cu > TN (Total Nitrogen) > COD (Chemical Oxygen Demand) > Cd > NO3--N. There is a significant negative correlation between water reduction rates and rainfall levels (p < 0.05), while showed a positive correlation with rainfall interval days (p < 0.05). An effective DRAINMOD model (R-2 >0.9) was established. The water reduction rates were observed as follows: Winter (62.84%) >Spring (15.09%) > Autumn (11.59%) > Summer (9.69%). The seasonal variation of NNO3--N and NH4+-N is not significant, with annual concentration reduction rates of 37.06% similar to 48.85% (mean = 44.13%) and 73.95% similar to 83.01% (mean = 74.76%), respectively.
To scientifically assess the comprehensive impact of large-scale photovoltaic power plant construction on the ecological environment and identify key driving factors and differentiated management paths, this study selected typical photovoltaic power plant clusters across different climate zones and landform types in China as research objects and constructed an evaluation index system covering four dimensions: vegetation restoration, soil quality, biodiversity, and microclimate regulation. An empirical study was conducted using a combination of methods: one-way ANOVA + LSD multiple comparisons, hierarchical multiple linear regression, and factor analysis + hierarchical cluster analysis. The results show that large-scale photovoltaic power plant construction has a positive overall effect on the ecological environment; however, there are significant differences among different indicators and different types of power plants. Agro-photovoltaic complementary power plants have better ecological performance than desert-type power plants. Human intervention measures (vegetation restoration, operation, and maintenance management) contributed 18% to the incremental impact, making them the core positive driving factor. Factor analysis extracted three common factors (cumulative variance contribution rate of 82.3%), and the power plants were classified into three categories: eco-friendly, eco-neutral, and eco-risky. The research conclusions provide a scientific basis for ecological site selection, construction and operation optimization, and differentiated management policy formulation for photovoltaic power plants.
