Search Results (7,761)

As a critical link between regional economic development and ecological security, understanding the dynamics of water retention is essential for sustainable water resource management in the Huaihe River Economic Belt. This study explores the spatio-temporal evolution and spatial explanatory factors of water retention across five temporal snapshots (2003, 2008, 2013, 2018, and 2023). Based on the InVEST model, we assessed water retention capacity at both grid and spatial development levels, thereby obtaining the retention characteristics of different land-use types and their responses to land-use transitions. Furthermore, a parameter-optimized geographical detector was employed to quantify the relative contributions of climatic-environmental and social-economic factors to the spatial variance of the modeled water retention index. Results indicate that the total water retention capacity exhibited significant interannual fluctuations, with the net capacity in 2023 being lower than the initial level in 2003. Retention values displayed obvious spatial heterogeneity, with high levels concentrated in the southwest and north and low levels distributed in the central area, closely mirroring precipitation distribution. While forest land exhibited the strongest unit water retention capacity, cropland contributed the most to the total volume (50.49%) due to its predominant areal proportion (73.92%). Notably, the conversion of forest to cropland was spatially associated with the most substantial loss in the modeled retention capacity. Soil saturated hydraulic conductivity and land-use type were identified as the dominant factors explaining the spatial variance of water retention. These findings underscore the methodological utility of coupling the InVEST model with a parameter-optimized geographical detector. For practical ecosystem management, the results suggest that spatial planning policies should strictly limit the conversion of ecological lands to agricultural use and prioritize targeted soil hydrological improvements in the central plains to secure long-term water resources. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

Despite recent advances in polycyclic aromatic hydrocarbon (PAH) research on the Tibetan Plateau (TP), studies investigating the transport potential and accumulation dynamics of these contaminants in the Hengduan Mountains, especially in forest soils which are important sinks for atmospheric PAHs, remain scarce. In the present study, soil and lichen samples (partially located under the forest canopy) were concurrently collected from 62 sampling sites across the Hengduan Mountains to characterize the occurrence, spatial distribution patterns, and underlying controlling factors of PAHs. The total concentrations of the 16 US EPA priority PAHs (∑₁₆PAHs) in soils and lichens ranged from 59.8 to 1163 ng/g and 174 to 3362 ng/g, respectively—values consistently higher than those reported in corresponding matrices from the northern and northwestern TP. Further, concentrations of PAHs in both soil and lichen under the forest canopy are significantly higher than those on the leeward slope without forest. Compositional fractionation of PAHs along the longitudinal and latitudinal gradients of sampling locations indicates significant modulation of PAH distribution by both the Indian monsoon and East Asian monsoon, a pattern further corroborated by air mass backward trajectory analysis. Our results confirm that PAHs can be transported to the southeastern TP slope via long-range atmospheric transport (LRAT). Notably, the combined effects of mountain cold-trapping and forest filtering jointly govern the deposition and spatial distribution of PAHs in this region. Full article

This study aimed to characterize the spatiotemporal dynamics of soil respiration (CO₂ efflux), soil moisture, and soil temperature across different land-use systems in a semi-arid environment through in situ monthly monitoring and to evaluate the potential of UAV-based imagery combined with Random Forest modeling to spatialize these variables within the agroforestry system. The variables were monitored monthly using an Infrared Gas Analyzer (IRGA) over 9 months, and UAV imagery was acquired at two distinct time points. The 11-month experimental campaign enabled evaluation of seasonal and spatial variability and of soil physical and hydraulic properties. Soil CO₂ efflux ranged from 1.0 to 6.7 μmol m⁻² s⁻¹, with higher values observed during the rainy period, closely following soil moisture dynamics. Soil moisture and temperature exhibited clear seasonal patterns driven by rainfall variability. The pasture system showed higher CO₂ efflux in most months, while AFS2 presented more stable fluxes over time. In contrast, AFS1 exhibited lower CO₂ efflux, likely associated with its soil characteristics. Despite these patterns, no significant differences were observed among land-use systems for most soil physical properties. UAV-derived data combined with machine learning techniques proved effective for modeling soil CO₂ efflux, soil temperature, and soil moisture, demonstrating their potential for monitoring soil processes in semi-arid environments. Overall, agroforestry systems did not significantly differ from other land uses in terms of CO₂ efflux, likely due to their early stage of development. These findings indicate that the effects of agroforestry systems on soil processes occur gradually and highlight the importance of long-term monitoring to fully capture system dynamics. Full article

Soiling in photovoltaic systems is a recurring problem that reduces energy generation and demands efficient operation and maintenance (O&M) strategies. In this context, this paper proposes a machine learning-based approach to identify dirt levels and generate cleaning alerts using operational and weather data. Initially, the models were evaluated with a decision threshold ranging from 0.5 to 0.7, using only operational features. Subsequently, the inclusion of weather features was tested, which improved the models’ performance and enabled the selection of the best models for the exhaustive features search step. The models analyzed in this step were Extra Trees, Histogram-based Gradient Boosting, Extreme Gradient Boosting, and Random Forest. Exhaustive analysis further improved model performance, as indicated by global metrics and ROC curves. The Extra Trees model with a threshold of 0.5 showed the best performance and was selected as the final configuration, achieving an accuracy of 0.9884 and an AUC-ROC of 0.9957. Finally, the selected model was applied to determine daily soiling levels and trigger alerts based on temporal persistence, indicating its potential to support predictive O&M decisions and cleaning actions in PV systems. Full article

Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) are two operationally defined fractions frequently used in studies related to soil organic carbon (SOC) dynamics. However, the changes and governing mechanisms of these fractions, particularly along a restoration chronosequence, remain poorly understood. Here, we investigated changes in SOC fractions, soil properties, and microbial communities across a restoration chronosequence (1, 5, 7, 13, and 20 years) of alpine meadows using a space-for-time substitution approach on the Qinghai–Tibet Plateau. We quantified the contributions of biotic and abiotic drivers using Spearman correlation analysis, linear regression and random forest analysis. The results revealed a unimodal pattern in SOC, POC, and MAOC contents, peaking at 7, 5, and 7 years, respectively, with no further increase thereafter. Restoration duration strongly shaped microbial community structure and observed species richness, but had no significant effect on Shannon index and Pielou index. Random forest analysis identified soil water content (SWC) and total nitrogen (TN) as the primary predictors of SOC. The microbial community composition dominated the variation in POC while enzyme activity was the key driver of MAOC. Our findings highlight that soil carbon accumulation during alpine meadow restoration is a nonlinear process with a temporal threshold, and POC and MAOC are regulated by distinct biotic and abiotic mechanisms. This study provides a theoretical basis for understanding carbon sequestration mechanisms during alpine meadow restoration and developing sustainable grassland management strategies. Full article

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

Enhanced rock weathering (ERW) is a promising carbon dioxide removal strategy that accelerates silicate mineral dissolution to generate alkalinity and sequester carbon in soils and aquatic systems. The frequency and severity of fires are increasing globally, and fire-prone regions such as agricultural lands, forests, and grasslands overlap substantially with potential ERW deployment areas. However, fire–ERW interactions remain unexamined. This perspective synthesizes the literature on fire effects on soil properties to develop a conceptual framework for predicting fire impacts on ERW performance. An assessment of the available literature reveals that the effects of fire on soil pH and inorganic carbon are nonlinear with respect to severity, complicating both dissolution kinetics and carbon verification. Base cation pulses from ash are temporary and subject to rapid export. Fire-induced soil water repellency and erosion may dominate chemical effects in controlling ERW material fate, particularly during the first year post-fire. Pyrogenic carbon and thermally altered minerals create novel soil‒rock interactions with unknown consequences for weathering rates. The authors concluded that fire history must be incorporated as a covariate in ERW deployment planning and monitoring, reporting, and verification design. Full article

Successive cropping frequently causes a decline in Chinese Fir (Cunninghamia lanceolata) biomass, a problem intricately tied to soil nutrient shifts and microbial processes. This research investigates the mechanisms governing biomass carbon partitioning and soil nutrient shifts in these plantations. This study investigated five Chinese Fir clones (‘ck’, ‘b44’, ‘K13’, ‘F13’, and ‘kt13’) across two cultivation regimes: continuous cropping (second-generation plantation, G2) and first-generation plantation (G1). The focus was on their biomass and soil nutrient status. The results showed that: (1) The biomass of different Chinese Fir clones at 25 years of age decreased significantly with increasing generations of continuous cultivation. Tree height showed no significant differences among clones within the same generation; however, the G2 cultivation significantly inhibited diameter at breast height (DBH). (2) The changes in soil nutrients and microbial activity under different successive generations (G1, G2) was closely linked to the decline in Chinese Fir biomass carbon. Analysis revealed that the decreases in dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and Catalase (CAT) activity were significantly positively correlated with the reduction in biomass carbon. Concurrently, the decrease in soil pH showed a significant negative correlation with microbial biomass carbon (MBC) and Sucrase (SUC) activity. (3) Regarding growth traits, although tree height showed no significant differences among clones within the same generation, DBH was generally and significantly inhibited under G2 cultivation. An exception was the ‘K13’ clone, which remained largely unaffected. In terms of carbon accumulation, G2 cultivation led to a universal decline in biomass carbon across clones; however, the magnitude of reduction in different components (leaf, branch, stem, root) and total biomass carbon varied clone-specifically. Notably, ‘K13’ exhibited the strongest tolerance, with a significantly smaller decrease in tree biomass carbon compared to the other four clones, which showed substantially lower tree carbon stocks across all components relative to G1 plantations. This indicates that successive cropping of Chinese Fir likely constrains the carbon sequestration capacity of plantations by altering soil nutrient properties, thereby suppressing tree DBH growth and biomass carbon accumulation, likely through reduced net primary productivity. Among the five clones, ‘K13’ was the least affected, demonstrating its high potential for adaptation to continuous cultivation. These findings provide implications for sustainable forest management by guiding clone selection to mitigate productivity decline under successive cropping. Full article

This study investigates plant species diversity, regeneration patterns, and the ecological drivers influencing endangered plant species in the Than Sa–Phuong Hoang Nature Reserve, Thai Nguyen Province, Vietnam. Although tropical forest ecosystems in Southeast Asia are known for their high biodiversity, there is still a lack of site-specific studies that integrate species diversity, regeneration dynamics, and environmental drivers at the reserve scale. A total of 15 standard plots (20 × 50 m) were established across three main forest types (limestone forests, soil mountain forests, and transitional forests) to assess species composition, community structure, and regeneration patterns. Multivariate analyses, including principal component analysis (PCA) and cluster analysis, were applied to identify key ecological factors shaping species distribution and regeneration. The results recorded 1234 plant species belonging to 171 families, confirming the high biodiversity of the study area. Regeneration capacity differed significantly among forest types and was strongly influenced by environmental variables such as canopy cover, soil moisture, topography, and human disturbance. Multivariate results revealed clear ecological differentiation among forest types, highlighting the role of environmental filtering in structuring plant communities. The three target species (Curculigo orchioides Gaertn, Parashorea chinensis, and Paphiopedilum hirsutissimum Stein) exhibited strong dependence on stable microhabitat conditions and showed limited regeneration under disturbed environments, indicating high sensitivity to ecological changes and anthropogenic pressure. This study provides new insights into species–environment relationships at a local scale and highlights key ecological drivers of endangered plant distribution and regeneration, contributing to more effective conservation planning and biodiversity management in tropical forest ecosystems. Full article

Intensive nitrogen (N) fertilization in Phyllostachys edulis (Carrière) J.Houz. forests increases productivity but also accelerates nitrous oxide (N₂O) emissions, posing a challenge to balancing forest yield with environmental sustainability. Silicon (Si), a beneficial element for bamboo, has emerged as a potential regulator of soil nitrogen (N) cycling, but its role in controlling N₂O emissions in forest ecosystems is not fully understood. In this study, we conducted a factorial pot experiment using P. edulis forest soil, with data collected over two years, but only the second-year results were analyzed, with controlled N (0, 80, and 160 mg kg⁻¹) and Si (0, 25, and 50 mg kg⁻¹) additions. The experiment lasted two years, but only the second-year data were used for analysis. We investigated how Si affected soil inorganic N dynamics, enzyme activities, plant growth, and cumulative N₂O emissions. Si addition significantly reduced N-induced N₂O emissions by up to 53%, with the strongest mitigation observed under moderate N input (p < 0.05, two-way ANOVA). This effect was associated with lower activities of AMO, NaR, and NiR, together with reduced availability of oxidized N substrates, indicating that Si mitigated N₂O emissions mainly by constraining upstream N transformation processes rather than by directly suppressing N₂O fluxes. Si addition also tended to promote plant biomass accumulation. These findings suggest that integrating Si fertilization into bamboo forest management may help improve nutrient use efficiency while mitigating greenhouse gas emissions. Full article

This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection, and identifies the temporal patterns of research, the most frequently used algorithms, the prominent remote sensing technologies, and the geographical distribution of research output. Increased research output during the period of 2013–2025 is attributed to the availability of high-level computing, satellites, and UAV imagery. The earlier studies in machine learning primarily involved the use of the Random Forest and Support Vector Machine algorithms, whereas in the past few years, deep learning, and especially Convolutional Neural Networks, have become more dominant. The most widely used data sources in remote sensing are the imagery from UAVs and the Sentinel satellite missions. The evaluation revealed that most of the geographical research activity was centered in the United States and China, but there is a trend of increasing research activity in most of the other developed countries. Research in Africa and South America remains particularly underdeveloped. Considering the rapid development of research, data fusion of optical and radar satellite imagery, UAV imagery, weather and soil datasets are expected to further improve the representation of agricultural systems. Full article

Compound Drought and Heatwave (CDH) events increasingly threaten terrestrial carbon uptake, yet the spatiotemporal heterogeneity of Gross Primary Productivity (GPP) responses in urban agglomerations remains unclear. This study analyzed CDH impacts in China’s three major urban agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, using ERA5 and satellite GPP data (GOSIF and FluxSat) for representative CDH years (2007 for BTH; 2022 for YRD and PRD). CDH conditions exhibited a coherent hot–dry coupling, with temperature anomalies of 0.46–1.26 K and soil moisture deficits of −0.042 to −0.169 m³ m⁻³, accompanied by enhanced atmospheric dryness. Pronounced spatial heterogeneity in GPP responses aligned with regional climatic regimes and ecosystem types. The water-limited BTH region exhibited significant GPP deficits, with anomalies of −1.13 Standard Deviations (STD) and −0.96 STD for GPP_FluxSat and GPP_GOSIF, respectively. Conversely, the energy-limited regions showed positive anomalies: the YRD recorded +0.32 and +1.79 STD, while the PRD reached +1.86 and +1.06 STD for GPP_FluxSat and GPP_GOSIF, respectively. Mechanistically, the north–south contrast suggests a transition from water-limited vulnerability to energy-limited resilience, with vegetation traits and management (e.g., potential irrigation buffering in croplands and deeper water access in forests) modulating sensitivity to atmospheric dryness. These findings provide quantitative benchmarks for improving regional carbon-cycle assessments and adaptation planning under increasing compound extremes. Full article

Atmospheric nitrogen (N) deposition is an important global change driver in forest ecosystems, yet its long-term effects on belowground microbial communities in cold-temperate coniferous forests remain insufficiently understood. In this study, endpoint shotgun metagenomic sequencing was used to evaluate bulk soil microbial communities after 12 years of experimental N addition in a Larix gmelinii-dominated forest in the Greater Khingan Mountains of northeastern China. Four treatments were included: control (0 kg N ha⁻¹ yr⁻¹), low N (25 kg N ha⁻¹ yr⁻¹), medium N (50 kg N ha⁻¹ yr⁻¹), and high N (75 kg N ha⁻¹ yr⁻¹). Microbial alpha diversity did not differ significantly among treatments, although moderate N addition showed a tendency to maintain relatively higher richness and diversity. In contrast, beta-diversity analysis indicated clear shifts in community composition along the N addition gradient. Pseudomonadota, Acidobacteriota, and Actinomycetota dominated the microbial communities, with Pseudomonadota tending to increase under N enrichment, whereas some oligotrophic groups showed reduced relative abundance. Functional annotation showed that metabolism-related genes remained dominant across treatments, and carbohydrate-active enzyme profiles suggested altered microbial potential for complex carbon decomposition under long-term N input. Nitrogen addition also modified the abundance patterns of some antibiotic resistance genes and mobile genetic elements, although overall resistome differentiation among treatments remained limited. These results provide endpoint metagenomic evidence that long-term N addition can reshape bulk soil microbial community composition and selected functional potentials in cold-temperate coniferous forest soils, even when overall alpha diversity remains relatively stable. Full article

Trees in arid mountainous forests adapt to seasonal water variability through dynamic eco-physiological adjustments. This study investigated the spatiotemporal dynamics and environmental drivers of such adaptations in Picea schrenkiana Fisch. et Mey, a keystone conifer in China’s Tianshan Mountains. We monitored key indicators—including osmoregulatory substances, antioxidant enzyme activities, and stoichiometric traits—across three regions (eastern, central, western) and three seasons (spring, summer, autumn) during the 2023 growing season. The results revealed significant seasonal shifts in all the measured traits (p < 0.05). Spring was characterized by high carbon allocation toward soluble sugars and starch, supporting growth; summer triggered elevated antioxidant enzyme activities to mitigate oxidative stress; and autumn favored nitrogen accumulation and proline synthesis, indicating preparatory storage for winter. Soil factors were primarily positively associated with antioxidant enzyme activity (path coefficient = 0.51; p < 0.001), whereas microenvironmental factors were more complex and often negatively correlated. The partial least squares path model confirmed that osmoregulatory substances centrally link stoichiometric adjustments with antioxidant defense, revealing an integrated physiological strategy. These findings elucidate the mechanism underlying the resilience of P. schrenkiana in arid highlands and provide a framework for its conservation under environmental change. Full article