Search Results (45)

Global CO₂ concentrations are gradually increasing, and forests, as the main terrestrial carbon pool, are attracting growing attention in mitigating climate change. However, the impacts of forest types, species diversity, structural diversity, and environmental factors on the carbon sequestration mechanisms of subtropical forests remain unclear. This study established 45 forest plots (20 m × 20 m) in Lishui City, aiming to investigate the relationships between forest diversity, environmental factors, and carbon storage of subtropical forests among different forest types. Results showed that coniferous forests had the lowest species diversity (0.86), which exhibited extremely significant differences from broad-leaved forests (1.47, p < 0.01) and coniferous broad-leaved mixed forests (1.58, p < 0.01). The carbon storage of broad-leaved forests was 97.50 t·ha⁻¹, which was higher than that of coniferous broad-leaved mixed forests (77.08 t·ha⁻¹) and coniferous forests (75.57 t·ha⁻¹). The carbon storage of coniferous forests was significantly positively affected by species diversity (p < 0.05). Tree height was the most significant structural diversity factor affecting forest carbon storage (p < 0.05). The results of the structural equation model (SEM) showed that the proportion of broad-leaved trees in forests and structural diversity had a significant positive effect on carbon storage (p < 0.01). Species diversity had a non-linear relationship with carbon storage. The ecological niche complementarity effect and selection effect interacted with changes in species diversity. When the species diversity was lower than 1.12 (Shannon–Wiener index), the ecological niche complementarity effect dominated and promoted carbon sequestration; when it was above this threshold, the selection effect dominated and weakened carbon sequestration. This study recommends prioritizing the planting of broad-leaved tree species during afforestation and paying attention to the current status of forest diversity. Full article

A method based on a statically indeterminate planar frame model was developed for the analysis and evaluation of box-type subgrade structures in high-speed railways. The objective of this study is to establish a concise and mechanically rigorous framework capable of quantifying the effects of key geometric parameters on bending moments, shear forces, and slab deflection, thereby providing guidance for structural refinement. Symbolic derivation and structural mechanics theory are combined to formulate the analytical model, and finite element simulations in Abaqus are used to verify the theoretical predictions under the design loads of the Quzhou–Lishui railway section located between Quzhou City and Lishui City in Zhejiang Province, China. Key findings show the maximum bending moments at the slab center and web-slab junction, reaching 14,818 kN·m, and the maximum shear forces of 16,934 kN at the web-slab junction. The top slab center showed the maximum deflection, approximately 7.5 × 10⁻² mm. Simulation errors were below 5%. The optimization results recommend a web spacing of 4.5–5 m and a web height of 5–8 m. In an engineering case, reducing the web spacing from 6 m to 5 m and adjusting the web height from 7 m to 6.5 m dropped the top-slab mid-span bending moment from 10,628 kN·m to 5603 kN·m (an 89.7% reduction). Concrete use fell by 2.61% (from 24,900 to 24,250 m³/km), and overall costs dropped by about 5%. These findings demonstrate that the proposed analytical method provides an effective basis for rational parameter selection and preliminary structural design of box-type railway subgrades. Full article

Atmospheric turbulence is a macroscopic fluid motion caused by random atmospheric movement. The atmospheric refractive index structure constant ($C_n^2$) is an important parameter for characterizing the intensity of atmospheric optical turbulence. Quantitative analysis of the impact of near-surface atmospheric optical turbulence on photovoltaic system performance in typical complex environmental regions holds significant theoretical and practical value. We used a meteorological tower equipped with temperature pulsation meters to conduct long-term monitoring of near-surface atmospheric turbulence in Lishui District, Nanjing City, and analyzed the diurnal variability, vertical distribution characteristics, and impact of various underlying surface types on turbulence intensity. Our results show that the $C_n^2$ values near the surface typically range from ${10}^{-17}$ to ${10}^{-13}{\mathrm{m}}^{-2/3}$, exhibiting significant diurnal variations with peak values around noon. The vertical structure demonstrates a pronounced diurnal switching mechanism: during daytime convection, turbulence intensity decreases markedly with height, following a strong power-law profile with an exponent of approximately $-0.505$ (${\mathrm{R}}^2\approx 0.99$); in contrast, the vertical coupling weakens during the stable nighttime. Moreover, surface characteristics significantly influence turbulence variation patterns. Our study provides observational data support for understanding physical processes in the near-surface boundary layer under complex environmental conditions. Full article

Yangjiatang Village traces its origins to the late Ming and early Qing dynasties. It has evolved over more than 400 years of history. There are 78 rammed-earth buildings left, making it one of the most complete and largest rammed-earth building complexes in East China. This study investigated the traditional rammed-earth houses in Yangjiatang Village, Songyang County, Zhejiang Province. By combining field investigation, microscopic characterization, and experimental simulation, we systematically revealed the erosion resistance of rammed earth in a subtropical humid climate was systematically revealed. Using a combination of advanced techniques including drone aerial photography, X-ray diffraction (XRD), microbial community analysis, scanning electron microscopy (SEM), and soil leaching simulations, we systematically revealed the anti-erosion mechanisms of rammed-earth surfaces in Yangjiatang Village. The study found that (1) rammed-earth walls are primarily composed of Quartz, Mullite, lepidocrocite, and Nontronite, with quartz and lepidocrocite being the dominant minerals across all orientations. (2) Regulating the community structure of specific functional microorganisms enhanced the erosion resistance of rammed-earth buildings. (3) The surface degradation of rammed-earth walls is mainly caused by four factors: structural cracks, surface erosion, biological erosion and roof damage. These factors work together to cause surface cracking and peeling (depth up to 3–5 cm). (4) This study indicates that the microbial communities in rammed-earth building walls show significant differences in various orientations. Microorganisms play a dual role in the preservation and deterioration of rammed-earth buildings: they can slow down weathering by forming protective biofilms or accelerating erosion through acid production. Full article

This paper addresses the scientific needs for investment decision-making in distribution networks against the backdrop of new power systems, constructing a three-tier decision-making system that includes investment scale decision-making, investment structure allocation, and investment project prioritization. Initially, it systematically analyzes the new requirements imposed by the new power systems on distribution networks and establishes an investment index system encompassing four dimensions: “capacity, self-healing, interaction, and efficiency”. Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. Furthermore, distribution network projects are categorized into ten classes, and an investment direction decision-making model is constructed to determine the investment scale for each attribute. Then, for the shortcomings of the traditional project comparison method, the investment project decision-making model is established with the attribute investment amount as the constraint and the maximisation of project benefits under the attribute as the goal. Finally, the effectiveness of the decision-making system is verified by taking the Lishui distribution network as a case study. The results show that the system keeps the investment scale prediction error within 5.9%, the city’s total investment deviation within 0.3%, and the projects are synergistically optimized to provide quantitative support for distribution network investment decision-making in the context of a new type of electric power system, and to achieve precise regulation. Full article

Integrated rice–fish farming, crucial for sustainable agriculture, relies on the judicious use of pesticide. This study evaluates the toxicity of six common rice-field pesticides on Procypris merus (rice flower carp), a key species in these systems. We conducted acute and chronic toxicity tests, assessing survival, growth, oxidative stress (SOD, CAT, MDA, 8-OHdG), and neurotoxicity (AChE). Results revealed a spectrum of toxicity: abamectin and trifloxystrobin were highly toxic; pretilachlor was moderately so; and glufosinate-ammonium, triflumezopyrim, and thiazole zinc were low. Notably, triflumezopyrim induced significant oxidative stress and DNA damage, while all three low-toxicity pesticides inhibited AChE activity, indicating potential neurotoxicity. Despite these effects, all observed toxicities were reversible within 7–14 days. Considering that the tested concentrations exceeded typical field application rates, glufosinate-ammonium, triflumezopyrim, and thiazole zinc are deemed relatively safe for P. merus at recommended dosages. Our findings provide critical insights for optimizing pesticide selection in rice–fish farming, balancing pest control with ecological safety, thereby informing sustainable agricultural practices. Full article

Under intensifying climate change impacts, accurate quantification of population exposure to urban flooding has become an imperative component of risk mitigation strategies, particularly when considering the dynamic nature of human mobility patterns. Previous assessments relying on neighborhood block-scale population estimates derived from conventional census data have been constrained by significant spatial aggregation errors. This study presents methodological advancements through the integration of social sensing data analytics, enabling unprecedented spatial resolution at the building scale while capturing real-time population dynamics. We developed an agent-based simulation framework that incorporates (1) building-based urban environment, (2) hydrodynamic flood modeling outputs, and (3) empirically grounded human mobility patterns derived from multi-source geospatial big data. The implemented model systematically evaluates transient population exposure through spatiotemporal superposition analysis of flood characteristics and human occupancy patterns across different urban functional zones in Lishui City, China. Firstly, multi-source points of interest (POIs) data are aggregated to acquire activated time of buildings, and an urban environment system at the building scale is constructed. Then, with population, buildings, and roads as the agents, and population behavior rules, activity time of buildings, and road accessibility as constraints, an agent-based model in an urban flood scenario is designed to dynamically simulate the distribution of population. Finally, the population dynamics of urban flood exposure under a flood scenario with a 50-year return is simulated. We found that the traditional exposure assessment method at the block scale significantly overestimated the exposure, which is four times of our results based on building scale. The proposed method enables a clearer portrayal of the disaster occurrence process at the urban local level. This work, for the first time, incorporates multi-source social sensing data and the triadic relationship between human activities, time, and space in the disaster process into flood exposure assessment. The outcomes of this study can contribute to estimate the susceptibility to urban flooding and formulate emergency response plans. Full article

Phosphorus (P) is an essential nutrient for rice growth, and the presence of phosphate-solubilizing bacteria (PSB) is an effective means to increase soil P content. However, the direct application of PSB may have minimal significance due to their low survival in soil. Biochar serves as a carrier that enhances microbial survival, and its porous structure and surface characteristics ensure the adsorption of Bacillus megaterium. Inoculating rice husk biochar-immobilized with Bacillus megaterium (BMB) resulted in dissolved inorganic and organic P levels of 39.55 and 31.97 mL L⁻¹, respectively. Subsequently, rice pot experiments were conducted to investigate the response of soil microbial P mobilization and P uptake in rice to fertilizer inputs. The organic fertilizer (OF) combined with BMB treatment (MOF) showed the highest soil available phosphorus (AP) at 38 days, with a value of 7.83 mg kg⁻¹, as well as increased the pqqC abundance while decreasing the abundance of phoD bacterial communities compared with the control. Furthermore, the bioavailable P reservoir (H₂O–Pi and NaHCO₃–Pi) in soil was greatly increased through the fertilizer input and microbial turnover, with the highest H₂O–Pi (3.66 mg kg⁻¹) in OF treatment and the highest NaHCO₃–Pi (52.65 mg kg⁻¹) in MOF treatment. Additionally, carbon utilization analysis was applied using the commercial Biolog system, revealing that the MOF treatment significantly increased the utilization of carbohydrates, polymers, and amino acid carbon sources. Moreover, compared to the control, MOF treatment significantly increased the shoot (0.469%) and root P (0.516%) content while promoting root development and thereby supporting rice growth. Our study demonstrates that the MOF treatment displayed higher P levels in both soil and rice plants, providing a theoretical basis for further understanding the role of biochar-based bacterial agents in rice P management. Full article

Background: China has a high incidence rate of varicella yet a low coverage rate of the varicella vaccine (VarV), with safety concerns being a leading cause of the lack of vaccination willingness. This study aimed to describe VarV-related adverse events following immunization (AEFIs) and analyze their characteristics in Zhejiang, China, 2020–2022. Methods: VarV-related AEFIs in Zhejiang Province from 1 January 2020 to 31 December 2022 were collected through the Chinese National AEFI Information System (CNAEFIS) for a descriptive epidemiological analysis. Results: From 2020 to 2022, a total of 1477 VarV-related AEFI cases were reported (incidence rate: 34.79/100,000). The three most frequently reported clinical symptoms of common adverse reactions were fever, redness, and induration at the vaccination site. The distribution of VarV-related AEFIs varied significantly by age, dose, severity, and season. VarV-related AEFIs were more likely to be non-severe adverse events that occurred in the summer and winter seasons following the first dose of vaccine and among those under 3 years old. The top three regions with the highest incidence rates were Lishui City (59.53/100,000), Quzhou City (41.05/100,000), and Jinhua City (40.43/100,000). Most of the cases achieved full recovery without treatment (96.21%), and the rest were successfully treated without any sequelae. Conclusions: VarV demonstrates a safe profile in Zhejiang Province. Most VarV-related AEFIs are common reactions without requiring treatment, and the rates of rare and severe AEFIs remain low. Consistent monitoring, investigation, and diagnosis are needed to guide future VarV research and vaccination strategy adjustment. Our findings call for more policy changes, such as integrating VarV into China’s National Immunization Program and conducting more trials to evaluate the safety and effectiveness of VarV. Full article

The optimization of urban multi-source water supply systems is essential for addressing the growing challenges of water allocation, cost management, and system resilience in modern cities. This study introduces a graph-theory-based optimization model to analyze the structural and operational dynamics of urban water supply systems, incorporating constraints such as water quality, pressure, and system connectivity. Using Lishui City as a case study, the model evaluates three water allocation plans to meet the projected 2030 water demand. Advanced algorithms, including Floyd’s shortest path algorithm and the GA-COA-SA hybrid optimization algorithm, were employed to address constraints such as pipeline pressure, water quality attenuation, and nonlinear flow dynamics. Results indicate a 1.4% improvement in cost-effectiveness compared to the current allocation strategy, highlighting the model’s capability to enhance efficiency. Among the evaluated options, Plan 2 emerges as the most cost-effective solution, achieving a supply capacity of 4.5920 × 10⁵ m³/d with the lowest annual cost of 5.7015 × 10⁷ yuan, highlighting the model’s capability to improve both efficiency and resilience. This study prioritizes cost-efficiency tailored to regional challenges, distinguishing itself from prior research that emphasized redundancy and water quality analysis. The findings demonstrate the potential of graph-theoretic approaches combined with advanced optimization techniques to enhance decision-making for sustainable urban water management. Full article

The evaluation of site quality for mixed forests is a comprehensive approach to analyzing forest site conditions and tree species growth performance. Accurate site quality assessment is crucial for understanding and enhancing the ecological functions and productivity potential of forests. This study focuses on mixed forests in Lishui City, Zhejiang Province. Using the Two-way Indicator Species Analysis (TWINSPAN) method, coniferous mixed forest, broadleaved mixed forest, and mixed coniferous–broadleaved forests in the region were classified into 15 forest types. Site form models for each type were then constructed using the Algebraic Difference Approach (ADA) to categorize site quality levels. Subsequently, a site quality classification model was developed by integrating site and climatic factors, employing four machine learning algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost. This model effectively facilitated the evaluation of site quality in mixed forests. The results showed that, across the 15 forest types, the site form models based on the ADA method achieved R² values greater than 0.634, indicating accuracy in capturing tree height growth trends in mixed forests. For site quality classification, all four models (RF, KNN, SVM, and XGBoost) achieved overall accuracies above 0.77. Among these, the machine learning models ranked in effectiveness for site quality classification as follows: XGBoost > RF > SVM > KNN. These findings suggest that the site form model is a suitable criterion for classifying site quality in mixed forests in Lishui City, Zhejiang Province, and that the XGBoost-based model demonstrates strong classification accuracy. This study provides a scientific basis for site-adapted tree selection and advances information on mixed forest management. Full article

Traditional gross ecosystem product (GEP) accounting methods often operate at macro scales, failing to reflect the localized and nuanced values of wetland ecosystems. This study addresses these challenges by introducing a fine-grained classification system based on a localized adaptation of international standards. The framework integrates high-precision national land surveys and remote sensing quantitative analysis while incorporating fisheries resource models, climate regulation beneficiary mapping, and visitor interpolation to address data scarcity related to human activities. This approach refines the spatial calculation methods for functional quantity accounting at fine scales. The results demonstrate that the refined classification maintains consistency with traditional methods in total value while adapting to multi-scale accounting, filling gaps at small and medium scales and providing a more accurate representation of localized wetland characteristics. Additionally, the study highlights the dominance of cultural services in GEP, emphasizing the need to balance cultural and regulatory services to ensure fairness in decision-making. Finally, a village-scale decision-support model is proposed, offering actionable guidance for wetland management and sustainable development planning. Full article

The traditional process for selecting urban gas station sites often emphasizes economic benefits and return on investment, frequently overlooking mandatory and guiding constraints established by territorial spatial planning regulations. This neglect can compromise the effective layout and future growth of cities, potentially affecting their long-term development. To address this issue, this study develops a systematic framework for urban gas station site selection that integrates both mandatory and guiding constraints. By conducting detailed analyses of feasible construction areas and fuel demand, the framework quantifies relevant indicators and establishes a comprehensive index system for site selection. A multi-objective optimization model employing genetic algorithms was utilized to maximize fuel demand coverage, minimize inter-station redundancy, and achieve optimal site coverage. This framework was applied to the central urban area of Lishui City, China, as a case study. The site selection schemes achieved a coverage rate exceeding 90%, an inter-station redundancy rate around 30%, and a demand coverage rate surpassing 90%, optimizing the key objectives. Compared to traditional methods that often ignore territorial spatial planning constraints, this framework effectively avoids conflicts with urban planning and regulatory requirements. It enhances infrastructure coordination, supports environmental sustainability, and exhibits strong adaptability to diverse urban contexts, thus offering valuable support for practical decision-making. Full article

Grasping how scale influences the interactions among ecosystem services (ESs) is vital for the sustainable management of multiple ESs at the regional level. However, it is currently unclear whether the actual ES interactions and their driving mechanisms are consistent across different spatial and temporal scales. Therefore, using the Lishui River Basin of China as a case study, we analyzed the spatial and temporal distribution of five key ESs across three scales (grid, sub-watershed, and county) from 2010 to 2020. We also innovatively used Pearson correlation analysis, Self-organizing Mapping (SOM), and random forest analysis to assess the dynamic trends of trade-offs/synergies among ESs, ecosystem service bundles (ESBs), and their main socio-ecological drivers across different spatiotemporal scales. The findings showed that (1) the spatial distribution of ESs varied with land use types, with high-value areas mainly in the western and northern mountainous regions and lower values in the eastern part. Temporally, significant improvements were observed in soil conservation (SC, 3028.23–5023.75 t/hm²) and water yield (WY, 558.79–969.56 mm), while carbon sequestration (CS) and habitat quality (HQ) declined from 2010 to 2020. (2) The trade-offs and synergies among ESs exhibited enhanced at larger scales, with synergies being the predominant relationship. These relationships remained relatively stable over time, with trade-offs mainly observed in ES pairs related to nitrogen export (NE). (3) ESBs and their socio-ecological drivers varied with scales. At the grid scale, frequent ESB flows and transformations were observed, with land use/land cover (LULC) being the main drivers. At other scales, climate (especially temperature) and topography were dominant. Ecosystem management focused on city bundles or downstream city bundles in the east of the basin, aligning with urban expansion trends. These insights will offer valuable guidance for decision-making regarding hierarchical management strategies and resource allocation for regional ESs. Full article

Fall armyworm (FAW, Spodoptera frugiperda) has now spread to more than 26 Chinese provinces. The government is working with farmers and researchers to find ways to prevent and control this pest. The use of biochar is one of the economic and environmentally friendly strategies to increase plant growth and improve pest resistance. We tested four v/v combinations of bamboo charcoal with coconut bran [BC1 (10:1), BC2(30:1), BC3(50:1)] against a control (CK) in maize. We found that plant height, stem thickness, fresh weight and chlorophyll content were significantly higher in BC2, in addition to the lowest FAW survival %. We then compared the metabolome and transcriptome profiles of BC2 and CK maize plants under FAW herbivory. Our results show that the levels of flavonoids, amino acids and derivatives, nucleotides and derivatives and most phenolic acids decreased, while terpenoids, organic acids, lipids and defense-related hormones increased in BC-grown maize leaves. Transcriptome sequencing revealed consistent expression profiles of genes enriched in these pathways. We also observed the increased expression of genes related to abscisic acid, jasmonic acid, auxin and MAPK signaling. Based on these observations, we discussed the possible pathways involved in maize against FAW herbivory. We conclude that bamboo charcoal induces anti-herbivory responses in maize leaves. Full article