Search Results (8,596)

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. Full article

Background:Pinellia ternata is a major medicinal herb widely utilized in traditional medicine, but is sensitive to high temperature, which often triggers a severe “sprout tumble” phenomenon. Methods: To elucidate the molecular mechanisms of heat tolerance in P. ternata, we screened two contrasting germplasms: the heat-tolerant JBX1 and the heat-sensitive XBX4. In the present study, a combined analysis of physiology, transcriptome, and metabolome was performed on JBX1 and XBX4 under heat stress at 40 °C. Results: JBX1 exhibited significantly greater leaf thickness, higher basal chlorophyll content, more stable antioxidant enzyme activities, and lower oxidative damage than XBX4 under heat stress. Transcriptomically, JBX1 maintained elevated basal expression of genes encoding key enzymes in carbon fixation, amino acid metabolism, and phenylpropanoid biosynthesis, as well as those encoding heat shock transcription factors (HSFs), heat shock proteins (HSPs), and the thermosensor Thermo-With ABA-Response 1 (TWA1). Metabolomically, JBX1 accumulated higher levels of key primary metabolites, antioxidants, and protective phenylpropanoids under both control and heat conditions. Notably, a “polarity reversal” emerged in nitrogen metabolism, where core amino acids accumulated in JBX1 but were depleted in XBX4. Integrated analysis revealed a more coordinated gene–metabolite network in JBX1 involving the phenylpropanoid, ATP-binding cassette (ABC) transporter, and glutathione pathways. Conclusions: Our findings demonstrate that JBX1 possessed stronger basal thermotolerance, which is derived from coordinated establishment of higher constitutive metabolic reserves and efficient dynamic metabolic reprogramming. This study provides insights into the molecular mechanisms of heat stress in P. ternata. Full article

Grasslands are key terrestrial ecosystems in which root dynamics regulate soil carbon and nutrient cycling. Although grazing constitutes the predominant land use practice in grassland ecosystems, its impacts on root dynamics remain inadequately elucidated, particularly across a gradient of grazing intensities. In this two-year field experiment, an improved root window method was applied to investigate the effects of four grazing intensities (no grazing, light grazing, moderate grazing, heavy grazing) on root production, root mortality, root standing crop, root turnover, and root lifespan in the saline–alkaline grassland in northern China. The results showed that root production and root mortality exhibited pronounced seasonal dynamics, with peaks in June and August for root production and in September for root mortality. These seasonal patterns were primarily driven by precipitation and were not significantly altered by grazing intensity. Moderate grazing significantly increased root production by 51.2% through changes in soil bulk density and selective livestock grazing, supporting the intermediate disturbance hypothesis. Root turnover was predominantly shaped by plant community composition and interannual precipitation, as opposed to grazing intensity. Overall, these findings indicate that moderate grazing promotes root growth, providing important insights into the sustainable utilization of saline–alkali grassland resources. In other words, appropriate measures must be taken to effectively manage grazing activities in the fragile saline–alkaline grasslands of northern China. Full article

The Lower Permian M Formation marine carbonate gas reservoir in Block X of the Sichuan Chongqing exploration area has extreme working conditions with moderate H₂S content (0.57–0.97%), moderate CO₂ content (2.59–5.59%), and high formation pressure (70–80 MPa). Gas wells face challenges such as multi medium synergistic corrosion, large productivity differences, and limited economic viability. This article addresses the above issues for the first time by analyzing the dual corrosion mechanism, selecting corrosion-resistant pipes (nickel-based alloys/nickel–tungsten alloy coatings), evaluating the adaptability of corrosion inhibitor processes, and real-time monitoring and warning of corrosion risks. A collaborative anti-corrosion technology system of “mechanism material process monitoring” is constructed, and the first successful field implementation was carried out in this block. The experiment shows that the uniform corrosion rate of nickel–tungsten alloy coating under extreme working conditions (122 °C/85 MPa) is only 0.004 mm/a, which is more economical than traditional nickel-based alloys (cost reduction of 69%); CT2 series corrosion inhibitors can selectively inhibit the corrosion rate of gas wells with different water contents (efficiency > 82%). The combination of electromagnetic flaw detection and multi arm wellbore logging technology has achieved dynamic monitoring of downhole pipe corrosion. This system has been successfully applied in seven gas wells in Block X, achieving controllable corrosion risks, cost reduction and efficiency improvement, and providing a replicable technical paradigm for the safe and economic development of marine high-sulfur gas reservoirs. Full article

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

To investigate how steam-explosion pretreatment affects humification during sawdust composting, an aerobic composting experiment was conducted using sawdust, chicken manure, and spent mushroom substrate as feedstocks. Two treatments were established—a steam-explosion-pretreated sawdust group (SEW) and an untreated sawdust control (CK)—each with three replicate reactors. Samples were collected dynamically at five key composting stages (initial, heating, thermophilic, cooling, and maturation) for physicochemical, enzymatic, and microbial community analyses. Linear mixed-effects model analysis revealed that enzyme activities were significantly affected by treatment, composting time, and their interaction. SEW significantly enhanced cellulase and polyphenol oxidase activities, and increased laccase and peroxidase activities at specific stages. Compared with CK (humic substances, 75.30 g/kg), SEW promoted higher humic substance accumulation (120.80 g/kg) and altered the dynamics of dissolved organic carbon. Microbial co-occurrence networks in SEW (50 nodes, 602 edges) were more complex than CK (49 nodes, 464 edges), indicating tighter microbial interactions. Path analysis revealed that HS in CK was mainly influenced by DOC and temperature, while HS in SEW was associated with enzyme activities, microbial diversity, and Pseudogracilibacillus. These results suggest that steam-explosion pretreatment enhances substrate transformation and humic substance formation during composting. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

Volatile halogenated hydrocarbons (VHHs) are critical air toxic pollutants, with some ozone-depleting substances (ODSs) strictly regulated by the Montreal Protocol. However, current understanding of the pollution characteristics, sources, and health risks of atmospheric VHHs in Southwest China remains insufficient. This study performed field observations of atmospheric VHHs in summer in Mianyang, a medium-sized industrial city in the Sichuan Basin. Freon-12 (563 ± 20 ppt) and Freon-11 (264 ± 15 ppt) were the most abundant chlorofluorocarbons (CFCs); chloromethane (785 ± 261 ppt) and methylene chloride (563 ± 505 ppt) dominated among VSLSs. The mean concentration of regulated ODSs (1037 ± 33 pptv) was notably lower than unregulated very short-lived chlorinated substances (1887 ± 745 pptv), reflecting effective ODSs phase-out locally, yet enhancements relative to Northern Hemisphere background implied potential leakage from residual tanks. Methylene chloride and trichloroethylene concentrations exceeded global background levels by over 10 times, indicating strong anthropogenic industrial influences. Phased-out CFCs displayed negligible diurnal variation due to stringent emission controls, whereas unregulated VSLSs exhibited a distinct U-shaped diurnal cycle, with peaks driven by morning boundary layer dynamics and evening accumulation. Positive matrix factorization revealed that industrial sources, including electronic solvents (28.6%), industrial processes (27.8%), and solvent usage (23.7%), accounted for 80.1% of total VHHs. The total carcinogenic risk (2.3 × 10⁻⁵) surpassed the acceptable threshold (1 × 10⁻⁶), dominated by 1,2-dichloroethane, chloroform, carbon tetrachloride, and 1,2-dichloropropane. All individual compounds exhibited mean hazard quotients (HQs) below the non-carcinogenic risk threshold. The cumulative hazard index reached 1.5, suggesting combined non-carcinogenic risks to the local population. These results support VHHs health risk management and ODSs control in Southwest Chinese industrial cities. Full article

Thismia species are non-photosynthetic plants entirely dependent on fungal partners for carbon and nutrients. While their arbuscular mycorrhizal associations are well-documented, bacterial symbiont roles remain unexplored. Using 16S rRNA gene amplicon sequencing, we investigated endophytic bacterial communities in T. gardneriana, T. javanica, and T. mirabilis from geographically distinct locations in Thailand. Despite geographic separation, Thismia spp. consistently harbored bacterial compositions taxonomically and functionally distinct from surrounding soil microbiomes. Root endospheres were significantly enriched in Pseudomonadota and Bacteroidota, particularly Puia, while showing reduced compositional dynamics of Acidobacteriota and Planctomycetota. Bacterial communities in Thismia roots were markedly distinct from surrounding soil, while root endosphere communities from geographically distinct habitats clustered together regardless of spatial separation. Mantel and partial Mantel tests confirmed that host species identity, not geographical location, was the primary predictor of root bacterial community structure. Functional prediction analyses suggested root-associated communities were enriched for nitrogen cycling pathways, particularly nitrogen fixation and nitrate reduction. The selective enrichment of Bacteroidota, known for nitrogen fixation and phosphate mobilization, suggests these bacteria provide critical nutritional support in nutrient-poor forest floor environments. Isolated root strains belonged exclusively to Bacillota, including Neobacillus with plant growth-promoting traits. Our findings highlight the importance of tripartite plant–fungal–bacterial interactions in Thismia nutritional ecology. Full article

Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. Among the three models, PIO-SVM produced the highest numerical accuracy. For the training dataset, it obtained an R² of 0.85 and an RMSE of 46.12 t/hm². For the testing dataset, it achieved an R² of 0.73 and an RMSE of 62.19 t/hm², compared with 0.72 and 66.25 t/hm² for the standard SVM model and 0.70 and 65.19 t/hm² for the RF model. The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. Overall, the results suggest that PIO-based parameter optimization can improve SVM performance for AGB estimation in mountainous forests. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics. Full article

Understanding how land management influences soil carbon (C) and nitrogen (N) dynamics is critical for improving ecosystem resilience and carbon sequestration potential in semiarid rangelands. This study used classical field- and laboratory-based methods to assess soil organic carbon (SOC), organic matter (OM), and N content at 13 sites across four ecological provinces in eastern Oregon, USA. Treated sites—where traditional rangeland restoration and management practices had been applied to them (i.e., juniper removal, sagebrush removal, post-fire grass seeding, and land conversion to pasture)—were paired with adjacent untreated control sites. Soil samples were collected at two depths, 0 to 10 cm and 15 to 25 cm and analyzed for C, N, OM, bulk density (BD), soil volumetric water content (SVWC), porosity, and texture. Soil C and N stocks were calculated on an area basis (t ha⁻¹), and statistical analyses were conducted using one-way ANOVA and correlation tests. Treated sites generally exhibited higher soil C, N, and OM content compared to untreated sites, particularly in the upper 10 cm of soil. Data obtained from the two soil depths (0 to 10 cm and 15 to 25 cm) were averaged and assumed to represent the top 30 cm of the soil profile, corresponding to the effective rooting zone at each field. The site where sagebrush removal was followed by grass seeding exhibited the highest soil C and N stocks (115.8 t C ha⁻¹ and 9.2 t N ha⁻¹, respectively). This site also had the highest OM content (9.53%), which was observed in the topsoil layer (0 to 10 cm) across all sites and depths. Strong positive correlations between C and N were detected across all sites (mean r = 0.92), while negative correlations were observed between soil C and bulk density at several locations. Results suggest that vegetation management practices such as woody plant removal and grass establishment can enhance soil C storage and nutrient retention in semiarid rangeland ecosystems. These findings provide baseline data to inform land management strategies aimed at improving soil health and carbon sequestration potential in the Pacific Northwest region in the USA. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

Molten salt chlorination is a key industrial route for producing titanium tetrachloride (TiCl₄), yet the atomistic catalytic role of iron (Fe) in the carbothermic chlorination of titanium oxides remains unclear. Here, the chlorination behavior of the NaCl–C–Cl₂–FeTiO₃ system was investigated by combining thermodynamic calculations with Ab Initio Molecular Dynamics (AIMD) and Deep Potential Molecular Dynamics (DPMD) simulations. AIMD results show that carbon adjacent to Fe exhibits enhanced reactivity, and that Fe-C synergistic electron transfer promotes both titanium oxide reduction and subsequent titanium chlorination. DPMD results further reveal that Fe not only accelerates these transformations, but also improves interfacial contact among carbon, titanium oxides, and molten salt, thereby enhancing mass transfer and shortening the formation time of TiCl₄. Temperature-dependent analysis indicates that Fe-C and C-O coordination numbers remain high near 1073 K, where TiCl₄ formation is efficient and relatively stable. Although increasing temperature can further enhance diffusion, its effect on reaction acceleration is limited, while excessively high temperatures weaken Fe-C interactions and reduce catalytic efficiency. These findings clarify the catalytic mechanism of Fe in molten salt chlorination at the atomic scale and provide theoretical support for process optimization. Full article

As a core decarbonization technology, the scaling up of Near-Zero Energy Building (NZEB) technologies under the “dual carbon” goal necessitates collaboration among governments, technology suppliers, and construction enterprises. However, high research and development (R&D) costs coupled with low market acceptance impede widespread adoption. This study develops a tripartite evolutionary game model to analyze strategic interactions among stakeholders. Using MATLAB 2022B simulations, we simulate the strategy sets for the government (subsidize/no subsidy), suppliers (R&D/no R&D), and enterprises (procure/no purchase). The results identify two Evolutionary Stable Strategies (ESS): a market-driven ESS (0, 1, 1) emerges when the green premium (Pm) exceeds the incremental cost (Cb); while a policy-driven ESS (1, 1, 1) requires government subsidies (S) to offset R&D gaps, specifically when $S>Cr/\alpha -Pmz$. These findings provide a theoretical basis for understanding the synergistic mechanisms underlying NZEB adoption and highlight the dynamic interplay between policy incentives and market forces. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article