Search Results (157)

In arid and semi-arid regions, severe salinity imposes strong abiotic constraints on farmland restoration. This study evaluated the one-season effects of organic amendments on soil quality, maize productivity, and short-term ecosystem multifunctionality (EMF) responses under severe salinity stress. We conducted a field experiment with biofertilizer (targeting plant–soil biological regulation) and composted manure (targeting direct soil amelioration) applied at different rates. The high-rate composted manure treatment (T6) showed the largest short-term improvements: compared with the chemical-fertilizer-only control under the same irrigation and plastic-mulch management (CK; soil quality index (SQI) = 0.716, yield = 6.657 t ha⁻¹, EMF_a = 0.456), SQI increased by 165%, maize yield increased by 41%, and EMF_a increased by 104% relative to CK within one growing season. Partial least squares path modeling (PLS-PM) suggested that under biofertilizer treatments, a plant-related association pathway was observed but relatively weak (β = 0.215), whereas under composted manure treatments, EMF variation was mainly associated with soil quality improvement (β = 0.992). Overall, these short-term results suggest that, under the tested application rates and severe salinity stress, high-rate composted manure can more effectively improve baseline soil conditions than biofertilizers during the initial season. These findings offer a preliminary conceptual perspective for a phased management strategy, serving strictly as a preliminary hypothesis where priority is given to soil amelioration in the initial phase and gradual integration of biologically oriented interventions as baseline conditions improve. However, future multi-year and multi-site studies are strictly required to validate the long-term viability of this proposed framework and to test whether these association patterns persist across longer time scales and broader regional contexts. Full article

Soil extracellular enzymes serve as critical drivers in the cycling of nutrients within ecosystems, and their stoichiometry can effectively reveal the metabolic resource limitations of soil microorganisms. However, extracellular enzyme activities, microbial metabolic characteristics, and their influencing factors in different grassland types in the Qilian Mountains have rarely been studied. This study focuses on alpine meadows (TJs), swampy meadows (HBs), and temperate desert grasslands (DLHs) in the Qilian Mountains. Extracellular enzyme activity and stoichiometric characteristics in the 0–30 cm soil layer were analyzed to explore the limiting factors on microbial metabolism and clarify the main driving factors affecting nutrient limitation. Compared with swampy meadows and temperate desert grasslands, alpine meadows exhibited greater extracellular enzyme activity, as revealed by the results. Statistical analysis revealed that enzyme activity exhibited a significant positive correlation with nitrate nitrogen (NO₃⁻-N), total phosphorus (TP), total potassium (TK), available potassium (AK), and dissolved organic carbon (DOC), while showing a significant negative correlation with soil moisture content (SWC) (p < 0.05). Vector analysis of soil enzymes showed that soil microorganisms in the three grassland types are limited by carbon (C) and phosphorus (P). Among them, DLH microorganisms are highly restricted by carbon, while HB microorganisms are highly restricted by phosphorus. Random forest results showed that total phosphorus (TP), available potassium (AK), nitrogen-to-phosphorus ratio (N: P), nitrate nitrogen (NO₃⁻-N), and readily oxidizable carbon (ROC) contribute significantly to vector length, while total potassium (TK), soil organic carbon (SOC), particulate organic carbon (POC), bulk density (BD), and carbon–nitrogen ratio (C: N) contribute significantly to vector angle. A partial least squares path model (PLS-PM) revealed that although microbial metabolic limitation is influenced by specific soil factors, the comprehensive effect of soil physicochemical properties is the dominant factor regulating microbial carbon and phosphorus limitation. This study provides valuable data and insights that elucidate the metabolic characteristics of soil microorganisms across different grassland types in the Qilian Mountains, thereby improving the mechanistic understanding of soil nutrient cycling and supporting evidence-based strategies for the sustainable management and conservation of these fragile ecosystems. Full article

To elucidate the effects of nitrogen (N) addition on soil carbon (C), N, and phosphorus (P) cycling in high-altitude orchards on the Qinghai–Tibet Plateau, a three-year field experiment was conducted at an altitude of 3000 m with four N application rates (0, 150, 300, and 450 kg N ha⁻¹, designated as CK, N150, N300, and N450, respectively). We determined soil physicochemical properties, 12 soil enzyme activities, and metagenomic characteristics, and further adopted partial least squares path modeling (PLS-PM) for data analysis and mechanism exploration. The results were as follows: (1) The N300 treatment yielded the maximum C-hydrolase activities and soil organic carbon content, with a 40.6% increase in soil organic carbon compared with the CK group. (2) The N450 treatment resulted in a 365.4% increase in soil nitrate content and significantly reduced the soil pH (from 6.32 to 5.86). Such environmental filtering significantly decreased the relative abundance of Nitrospirota and its core denitrification genes, including nosZ and narI. (3) Continuous N input induced secondary soil P limitation, leading to a more than 90% increase in phosphatase activities under the N450 treatment. Pseudomonadota activated soil P sources by enriching the functional potential of the phn gene cluster. Furthermore, the PLS-PM analysis revealed a significant negative statistical association between P-cycling enzymes and N-cycling functional potential (p < 0.01). This statistical linkage supports the observation of divergent metabolic responses among different element cycles. In conclusion, under the specific experimental conditions tested, an optimal N application rate of 300 kg N ha⁻¹ is recommended to balance agricultural productivity and soil ecological health. The microbiome of alpine apple orchards responds to elevated N input through metabolic trade-offs, namely reducing the functional potential for denitrification and enhancing the P recycling system. These findings provide vital molecular evidence to guide fertilizer reduction, optimize nutrient management, and promote the sustainable development of high-altitude agroecosystems. Full article

Microbial functional traits play a central role in regulating carbon mineralization efficiency (CME) in peatlands, yet how they respond to concurrent warming and atmospheric nitrogen deposition remains unclear. In this study, peat soils from three vegetation types (sedge, reed, and shrub) were subjected to controlled microcosm incubations simulating warming and nitrogen addition gradients. Microbial community composition and functional profiles were characterized using 16S rRNA high-throughput sequencing and Functional Annotation of Prokaryotic Taxa (FAPROTAX) functional prediction, while dissolved organic matter (DOM) composition was analyzed via excitation–emission matrix fluorescence spectroscopy with parallel factor analysis (EEM-PARAFAC) and fluorescence indices. Integrating correlation analysis, Random Forest, and partial least squares path modeling (PLS-PM) modeling, we identified microbial functional traits as key factors linking environmental changes to soil CME, with DOM serving as a substrate-mediated pathway. External nitrogen input primarily drove shifts in microbial functional composition, whereas warming modulated substrate utilization preferences and DOM turnover. The interaction between warming and nitrogen selectively reshaped microbial functional profiles, thereby jointly determining CME. Functional traits explained more variation in CME than taxonomic composition, indicating a “structure–function decoupling” under environmental change. These findings highlight the central role of microbial functional traits in peatland carbon transformation and suggest that the net response of peatland carbon emissions to future environmental change will depend critically on the balance between warming magnitude and nitrogen deposition levels. Full article

Optimizing orchard mulching regimes is a pivotal strategy for mitigating the detrimental effects of water scarcity and soil degradation on kiwifruit productivity in the Guanzhong Plain, China. To characterize the integrated effects of varying mulching patterns, a two-year field study was conducted in a kiwifruit (Actinidia deliciosa) orchard, evaluating four treatments: (1) FG: intra-row fabric with inter-row grass (multiple mulch); (2) FN: intra-row fabric with inter-row bare soil; (3) NG: intra-row bare soil with inter-row grass; and (4) NN: intra-row bare soil with inter-row bare soil. Understanding the impacts of these regimes on the edaphic environment, photosynthetic performance, and sugar metabolism is essential for improving kiwifruit production under semi-arid conditions. The results demonstrated that the FG treatment significantly improved soil water storage (SWS), with an increase of 1.83–55.16 mm, and enhanced the soil nutrient content (NH₄⁺-N, NO₃⁻-N, and soil organic matter), thereby optimizing the rhizosphere environment. During the critical phenological stages, the FG treatment increased the leaf photosynthetic parameters, such as the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs), while reducing the intercellular CO₂ concentration (Ci). Specifically, grass mulching (FG and NG) elevated the chlorophyll a content during early growth and carotenoids levels throughout reproduction, whereas fabric mulching (FG and FN) enhanced the chlorophyll b content throughout the entire reproductive period. Collectively, these improvements bolstered photosynthetic efficiency and may have contributed to improved carbon allocation and sugar accumulation. All three mulching treatments (FG, FN, and NG) significantly improved the fruit yield-related parameters, including the total fruit number per plant (PFN), single fruit weight (SFW), and yield (Y), as well as the fruit sugar-related indices, such as soluble solids content (TSS), total soluble sugar content (TS), reducing sugar (TRS), and the sugar–acid ratio (SAR). The partial least squares path modeling (PLS-PM) revealed that these improvements were primarily driven by the synergistic optimization of SWS and photosynthetic productivity. Notably, the model identified a physiological trade-off between yield formation and sugar accumulation, while the overall fruit quality exerted a strong positive influence on sugar metabolism. The correlation analysis indicated that the higher fruit sucrose accumulation under the FG and FN treatments were associated with increased sucrose phosphate synthase (SPS) and sucrose synthase (SS) activities, suggesting a potential link between mulching-induced improvements in plant physiological status and sucrose metabolism. These findings suggest that the combined use of intra-row fabric and inter-row grass mulching (FG) provides a sustainable strategy for enhancing soil conditions and fruit quality in water-limited kiwifruit orchards. Full article

Animal manure represents a critical reservoir that facilitates the dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). However, the current understanding of ARG evolution during extensive composting remains insufficient. This study systematically investigated two common aerobic composting techniques: push-flow trough composting (FC) and membrane-covered composting (FM). Results indicated that both processes demonstrated substantial antibiotic removal capacities, achieving total removal rates of 88.89% (FC) and 79.20% (FM). Nevertheless, their effectiveness in removing ARGs varied considerably. During the 31 days of composting, the total removal rates of ARGs were 59.97% (FC) and 76.11% (FM), while the removal rates for class 1 integron (intI1) were 2.31% (FC) and 69.13% (FM). With the exception of tetX, tetG, and tetW, all other ARGs exhibited a rebound during the later stage of the FC process. In contrast, the FM process effectively reduced the risk of ARG rebound during this phase, which can be attributed to its extended thermophilic period and the physical barrier effect of the semi-permeable membrane. Network analysis indicated that ARGs were primarily associated with Bacillota and Pseudomonadota. The Partial Least Squares Path Model (PLS-PM) revealed that the bacterial community was the main factor influencing ARG dynamics in FC, while in FM, both the bacterial community and intI1 were the primary drivers. This study provides critical insights for optimizing composting strategies to prevent the dissemination of antibiotic resistance. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

PM_2.5 is a major air pollutant characterized by complex sources and strong spatiotemporal heterogeneity. However, accurately quantifying the relative contributions of different factors remains difficult due to the lack of long-term datasets and the strong correlations between meteorological factors and emissions. To address this problem, the study utilizes the China long-term particulate matter (CLPM) dataset developed in previous research to investigate the dominant drivers and regional disparities of PM_2.5 concentration variations from 1980 to 2022. The analysis employs Gaussian Convolution (GC) to model pollutant diffusion, Partial Least Squares (PLS) regression to address multicollinearity, and the Lindeman-Merenda-Gold (LMG) method to quantify the relative contributions of each driver. The results reveal that as the convolution scale increased from 0.25° to 10°, dominant PM_2.5 sources shifted from local anthropogenic emissions to regional biomass burning and large-scale dust transport, highlighting the scale-dependent transition of pollution drivers. Furthermore, PM_2.5 concentrations are predominantly explained by emissions, which account for over 60% of the total variance and exceed 80% in eastern China, while meteorological factors are associated with 12–26%. Among these, total precipitation and downward surface solar radiation have the strongest influences on pollutants. It is important to note that these results reflect the statistical explanatory power of emissions and meteorological variables within the regression model. Overall, this research provides a method for separating the statistical influences of emissions and meteorological factors, offering methods for multi-scale explanatory power of PM_2.5 and other atmospheric pollutants. Full article

Cascade dams fragment river habitats, but how seasonal hydrology modulates the biogeography and assembly of fish gut microbiota remains unclear. We surveyed gut bacterial communities of the omnivorous fish Saurogobio punctatus across 10 reaches separated by cascade dams in the Qijiang River during the wet (summer) and dry (winter) seasons using 16S rRNA gene amplicon sequencing. Sampling was synchronized among reaches to minimize temporal variability. Winter exhibited stronger differentiation among reaches and a steeper distance–decay pattern, and reach-scale environmental heterogeneity (especially dissolved inorganic nitrogen) was more stable under weak hydrodynamics. Null model analyses showed that stochastic processes dominated in summer, with dispersal-related processes and drift being prominent under high connectivity, whereas deterministic assembly increased in winter and was mainly associated with homogeneous selection. Compositionality-aware differential abundance analysis (ANCOM-BC2) identified 409 genera with a significant seasonal differential abundance after adjusting for reach (FDR q < 0.05). Random forest classification, used as a complementary prediction-oriented feature-ranking analysis, indicated higher reach discriminability in winter, with Nitrospirota ranking among the top features. PLS-PM indicated that α-diversity had the strongest direct association with β-diversity in the specified model, whereas spatial and environmental effects were linked to β-diversity mainly through indirect, α-diversity-mediated pathways. Biologically, α-diversity may reflect an integrative summary of the within-gut taxon pool shaped by host filtering and environmentally derived inputs (e.g., diet- and habitat-associated sources), which can influence the magnitude of between-reach compositional turnover. Together, these results show that seasonal hydrological regimes tune spatial turnover and assembly of fish gut microbiota in cascade-regulated rivers. Full article

The succession of plant communities and soil-driven mechanisms triggered by wetland degradation are central issues in global ecology. To investigate the effects of Deyeuxia purpurea wetland degradation on plant community characteristics and its key soil regulatory factors, this study selected D. purpurea wetlands with different degradation degrees in the Sanjiang Plain as research objects and analyzed the characteristics of plant communities, soils, and their relationships. The results indicated that wetland degradation was significantly associated with turnover in plant community composition, with hydrophytic species progressively replaced by mesophytic and xerophytic species. As degradation intensified, Simpson’s diversity index, the Shannon–Wiener index, Pielou’s evenness index, and Patrick’s richness index all increased significantly. The non-degraded wetland exhibited significantly higher aboveground, belowground, and total biomass than the degraded wetlands. Aboveground and total biomass showed a significant negative correlation with the diversity index. Soil pH, water content (WC), total phosphorus (TP), dissolved organic nitrogen (DON), and ammonium nitrogen (NH₄⁺-N) were key factors associated with changes in plant community diversity and biomass. Partial least squares path modeling (PLS-PM) and variance partitioning analysis (VPA) further quantified potential association pathways, showing that wetland degradation exerted both direct and indirect effects on key soil physicochemical factors and plant community characteristics. Specifically, wetland degradation was directly associated with decreases in soil pH, WC, and TP, while positively affecting soil dissolved organic nitrogen (DON) and plant diversity. It also indirectly influenced plant species composition and biomass through changes in soil pH, WC, DON, and TP. TP was negatively correlated with plant diversity and biomass, whereas ammonium nitrogen had a direct positive effect on species composition. Dissolved organic nitrogen directly negatively affected species composition. Overall, this study systematically elucidates plant community response patterns and the synergistic driving mechanisms of multiple soil factors during D. purpurea wetland degradation, providing an important scientific basis for wetland conservation and ecological restoration in the Sanjiang Plain. Full article

Agricultural land-use conversion in high-altitude cold-arid inland river basins profoundly affects soil ecosystems. This study investigates the middle and lower reaches of the Bayin River Basin (Qaidam Basin, China) at approximately 3000 m elevation. We examined a continuous, reversible gradient of land-use intensity ranging from intensively managed cultivated land and orchards to marginal farmland abandoned owing to salinisation and low fertility. Using a multi-model fusion framework combining geostatistics, random forest regression and partial least-squares path modelling, we quantified the spatial patterns of soil properties and the drivers of soil organic carbon (SOC). Compared with marginal farmland, both cultivated land and orchards showed markedly higher SOC content (10.7–41.1% increase), elevated total nitrogen (TN) and clay content, and reduced electrical conductivity and sand fraction. These changes demonstrate that abandonment of marginal farmland impairs SOC accumulation while accelerating soil degradation and salinisation. SOC and TN exhibited strong spatial autocorrelation over distances exceeding 27 km, largely controlled by broad-scale factors such as topography and climate. The Random Forest and Partial Least Squares Path Modeling consistently reveal a close synergistic variation between Total Nitrogen (TN) and Soil Organic Carbon (SOC). TN exerts a direct positive driving effect on SOC, while land use intensity positively affects SOC through an indirect pathway: “sand content drives land use → enhances vegetation cover → increases TN.” Reverse modeling has validated a similar driving effect of SOC on TN. This study offers practical pathways for the sustainable management of marginal farmland and the enhancement of carbon sinks, addressing a common issue in China and other developing countries. Full article

Straw and cattle manure are common agricultural wastes, and their composting plays a critical role in regional nutrient cycling and organic carbon management. During composting, the structural evolution and humification processes of dissolved organic matter (DOM), fulvic acid (FA), and humic acid (HA) are regulated by environmental factors such as temperature and pH. However, systematic studies on the multi-component fluorescence characteristics of DOM in straw–manure systems and their coupling with environmental variables remain limited. In this study, maize straw and cattle manure were used as raw materials, with four mixing ratios (T1–T4: 2:8, 4:6, 6:4, and 8:2), to investigate the effects of raw material proportions on the structural evolution of DOM, fulvic acid (FA), and humic acid (HA) during composting. Three-dimensional fluorescence spectroscopy combined with parallel factor analysis (EEMs-PARAFAC) was applied to characterize organic components, their transformation patterns, and their relationships with environmental factors. The EEMs-PARAFAC identified 3, 2, and 3 components for DOM, FA, and HA, respectively. Moderate straw–cow manure ratios (T2 and T3) maintained high microbial activity while promoting humic-like component accumulation and FA-to-HA conversion. Fluorescence indices indicated mixed substrate-derived and microbial sources for DOM, predominantly microbial origins for FA, and a shift in HA from substrate-derived to mixed sources. Overall, humification remained low (humification coefficient < 1.5), reflecting an early composting stage. Mantel analysis and partial least squares path modelling (PLS-PM) revealed temperature as the dominant factor associated with HA formation, whereas an alkaline pH inhibited humification. These findings clarify how substrate ratios regulate humification via environmental microhabitats, providing a theoretical basis for optimizing straw–manure co-composting and enhancing compost stability and soil carbon sequestration. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

Digital twin (DT) technology is recognized for its transformative potential to enhance efficiency in the construction process. However, the full potential of DT in construction practices remains largely unrealised. Moreover, few studies explore how DT success factors affect efficiency improvement (EI), energy optimization (EO), and cost control (CC) in the context of economic strength (ES). The study applied a hybrid research method to examine the impact of key DT success factors on EI, EO, and CC under the moderation of ES. After a critical literature review, five key DT success factors were identified. Then, 490 valid questionnaires were analyzed with the Partial Least Squares Structural Equation Model (PLS-SEM) to assess how success factors affect DT effectiveness. This is complemented using extreme gradient boosting (XGBoost) to assess prediction accuracy and understand which factors most influenced EI, EO, and CC. Research shows that ES exerts a significant positive influence on the relationships between most success factors and performance outcomes. High levels of ES enhance the contribution of success factors to performance in EI, EO, and CC. Resource management (RM) has a strong influence on EI and EO, but a weaker influence on CC; process optimization (PO) has the strongest influence on EO, a moderate influence on CC, and the weakest influence on EI; real-time monitoring (R-Tm) primarily affects EI; sustainable design (SD) has a comprehensive and significant regulatory effect on EI, EO, and CC; and predictive maintenance (PM) has a strong influence on both EI and CC. In practice, it offers practical guidance for implementing DT and supports policy and resource planning for building stakeholders. Full article

Microalgal amendments can improve soil structure by regulating extracellular polymeric substances (EPSs). However, the mechanisms underlying this process in red soils (characterized by high clay content and susceptibility to acidification) under different farming practices remain unclear. This study examined how Chlorella vulgaris (C. vulgaris) amendment influences EPS composition to enhance soil aggregate stability under arable land and rice paddy farming. A five-month pot experiment using a completely randomized design was conducted to investigate the effects of Chlorella vulgaris amendment on soils cultivated with Pennisetum × sinese and rice, two economically important crops commonly grown in South China. At the end of the experiment, Chlorella vulgaris amendment substantially increased both the mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates under both farming systems. Excitation–emission matrix (EEM) fluorescence spectroscopy revealed distinct changes in soil EPS components between the two farming types. Under arable land farming, humic-like and protein-like EPSs were dominant in Chlorella vulgaris-amended treatments, with fluorescence intensities more than doubling compared to the control. Conversely, under rice paddy farming, soil fulvic acid was the main component and showed a moderate increase. Partial least squares path modeling (PLS-PM) demonstrated that protein-like and humic-like EPSs had the strongest direct effects on aggregate stability in arable land red soil, while fulvic acid was the key factor in rice paddy red soil. The present study demonstrates that Chlorella vulgaris amendment improves aggregate stability in red soils through farming-specific, EPS-mediated pathways, providing a quantitative framework for researchers and land managers seeking to apply microalgal amendments for red soil enhancement and sustainable land management. Full article