Intensive Human Activities Research Articles

Occurrence of per- and polyfluoroalkyl substances in wheat, maize, rice, and soybean from chinese major grain producing regions.

Dietary exposure to per- and polyfluoroalkyl substances (PFAS) has been a concern for many years. Previous studies demonstrated that foods such as meat, milk, fish, vegetables, and tuber crops could be potential sources of human exposure to PFAS. However, research on PFAS contamination in grains remains limited. Therefore, this study conducted a comprehensive investigation of PFAS contamination in raw grains from major grain-producing regions in China. Among all grain species, soybeans exhibited the highest Σ16PFAS level (1.01ng/g dw), followed by rice (0.570ng/g dw), wheat (0.542ng/g dw), and maize (0.245ng/g dw). Short-chain perfluoroalkyl carboxylates (PFCAs) displayed higher detection frequencies compared to their long-chain homologues and perfluoroalkyl sulfonates (PFSAs). Perfluorobutanoic acid (PFBA) and perfluorooctanoic acid (PFOA) were dominant in wheat, maize, and soybean samples, while sodium p-perfluorous nonenoxybenzenesulfonate (OBS) was predominant in rice. OBS contributed approximately 70% to Σ16PFAS concentrations in rice. The concentration difference of OBS was not significant between indica rice and japonica rice, however, a disparity was observed between early indica rice and mid-late indica rice, suggesting that the growing period potentially affects OBS level in rice grains. Compared to other regions, east China displayed relatively higher PFAS concentrations in grain samples. This finding is consistent with previous studies and may be attributed to the intensive industrial and human activities. The estimated daily intake (EDI) of Σ16PFAS in grains from this study ranged from 0.0829 to 3.32ng/kg bw/day. The dietary health risks associated with PFOA in wheat and OBS in rice warrant further attention.

Effects of reduced flow gradient on benthic biofilm communities' ecological network and community assembly.

The intensification of human activities has led to flow reduction and cut-off in most global rivers, seriously affecting riverine organisms and the biogeochemical processes. As key indicators of river ecosystems' structure and function, benthic biofilms play a critical role in driving primary production and material cycling in rivers. This research aimed to investigate the characteristics of microbial communities' complexity and stability during river flow reduction. Benthic biofilms were grown in artificial channels and subjected to eight gradients of flow reduction (represented by flow velocity from 0.4 to 110 cm/s). Biofilms' biodiversity, ecological networks and community assembly of bacteria, fungi and algae were investigated by high-throughput sequencing. Results showed significant differences in community composition and structure under different flow conditions. The eight flow gradients' microbial communities were divided into three groups: low, medium and high flows. The flow reduction led to significant decreases in bacterial and fungal communities' Chao1 index. Low flow conditions enriched the bacterial phyla Oxyphotobacteria, Alphaproteobacteria and Mollicutes, but significantly decreased the fungal phylum Chytridiomycota. Lowering flow reduced the fungal network's number of nodes and increased the algal network's number of edges. Cross-domain interactions network analysis showed a gradual increase in node and edge numbers with decreasing flow, while decreasing average path length. The neutral model predicted stochastic processes primarily drove biofilm community assembly, and that model's explanations decreased as the flow gradient decreased. The null model analysis revealed diffusion limitation as the most common stochastic ecological process for bacterial and algal communities, with reduced flow reducing heterogeneous selection and increasing diffusion-limited processes. This study provides an in-depth analysis of flow reduction's effects on biofilm communities' ecological networks and community assembly.

Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City

Abstract Coastal zones have intense human activity and are ecologically fragile. Assessing ecological risks in coastal zones is crucial to promoting ecological safety and sustainable development for coastal cities. However, the relationship between ecological risk assessment and distance from the coastline is indeterminate in current research. To solve this problem, this study constructs the sea–land gradient and reconstructs the landscape pattern index and landscape ecological risk index (ERI) considering the sea–land gradient paradigm. This study aims to analyze the spatial–temporal variation characteristics and effects of the sea–land gradient on landscape pattern and ecological risk in Dalian City from 2000 to 2015. The results show the following: (1) between 2000 and 2015, a significant effect of coastline on the coastal landscape pattern in Dalian is reducing landscape fragmentation. The extent of this influence gradually diminished along the sea–land gradient. The landscape aggregation of urban building sites near the sea was relatively high and gradually increased over the years; (2) in Dalian, the landscape ERI has decreased near the sea and increased near the inland year by year; (3) the dominant pattern of landscape ecological risk was characterized by a transition from high to low levels within the [0, 2.5] km zone, while a transition from low to high levels predominated within the [2.5, Max] km zone; and (4) the northern region in Dalian primarily goes from high to low risk in the buffer zone near the landside, whereas the southern area has this changing pattern in the buffer zone near the seaside.

Source apportionment of organic carbon and black carbon in the surface sediments of the Pearl River Estuary and its adjacent South China Sea: insight from stable carbon and nitrogen isotopes

Coastal estuaries and adjacent continental shelf seas constitute vital global carbon reservoirs, and the sources and transformations of organic carbon in these regions are crucial to global biogeochemical cycles and climate change. This study investigated the total organic carbon (TOC), total nitrogen (TN), black carbon (BC), and their stable carbon and nitrogen isotopes (δ15NTN, δ13CTOC, δ13CBC) in the surface sediments of the Pearl River Estuary (PRE) and its adjacent northern South China Sea (NSCS) aiming to assess the impact of human activities on organic carbon dynamics in these areas. Results showed that the highest TOC concentrations occurred in the inner PRE due to intense human activities, and decreased seaward. The west side of the PRE exhibited higher TOC levels than the east side, which was attributed to differences in hydrodynamic processes and human activities. The westward flow of the Pearl River diluted water, which carried terrestrial organic matter inputs due to the influence of the Coriolis effect and intense local human activities, was a primary contributor to higher TOC levels on the west side (terrestrial source). In contrast, increased productivity and intensive mariculture activities on the east side predominated as sources of organic matter (marine source). Similar to the TOC, BC and TN sources were mainly influenced by human activities. δ15NTN distribution shows that TN in the east side of PRE mainly originated from industrial wastewater input from the Pearl River, while in the east side TN was mainly from domestic sewage discharge. Additionally, BC sources have shifted from primarily biomass combustion in the 1990s to fossil fuel emissions presently. Isotopic analysis revealed that over 70% of BC originated from fossil fuel inputs and C3 plant combustion, highlighting the significant influence of human activities in the PRE and adjacent NSCS, and underscoring the need for effective management and protection of the eco-environment in these regions.

From nature to urbanity: exploring phyllosphere microbiome and functional gene responses to the Anthropocene.

The Anthropocene exerts various pressures and influences on the stability and function of the Earth's ecosystems. However, our understanding of how the microbiome responds in form and function to these disturbances is still limited, particularly when considering the phyllosphere, which represents one of the largest microbial reservoirs in the terrestrial ecosystem. In this study, we comprehensively characterized tree phyllosphere bacteria and associated nutrient-cycling genes in natural, rural, suburban, and urban habitats in China. Results revealed that phyllosphere bacterial community diversity, richness, stability, and composition heterogeneity were greatest at the most disturbed sites. Stochastic processes primarily governed the assembly of phyllosphere bacterial communities, although the role of deterministic processes (environmental selection) in shaping these communities gradually increased as we moved from rural to urban sites. Our findings also suggest that human disturbance is associated with the reduced influence of drift as increasingly layered environmental filters deterministically constrain phyllosphere bacterial communities. The intensification of human activity was mirrored in changes in functional gene expression within the phyllosphere microbiome, resulting in enhanced gene abundance, diversity, and compositional variation in highly human-driven disturbed environments. Furthermore, we found that while the relative proportion of core microbial taxa decreased in disturbed habitats, a core set of microbial taxa shaped the distributional characteristics of both microbiomes and functional genes at all levels of disturbance. In sum, this study offers valuable insights into how anthropogenic disturbance may influence phyllosphere microbial dynamics and improves our understanding of the intricate relationship between environmental stressors, microbial communities, and plant function within the Anthropocene.

Quantification of streamflow response to climate change and human activities within upstream mountainous areas of the Daqing River Basin, Northern China

The Daqinghe River Basin is located in the North China Plain. In recent years, however, climate warming, drying, and intense human activities have led to declining ecosystem functions and shrinking wetlands in the region. Understanding streamflow changes in the upstream mountainous areas of the Daqinghe River Basin in this changing environment and identifying the driving factors can provide a scientific basis for water resources management and optimization in these areas. This study focuses on the Beihedian River watershed, the Xidayang Reservoir watershed, and the Wangkuai Reservoir watershed in the upstream mountainous areas of the Daqinghe River. It is based on hydro-meteorological data collected between 1963 and 2019. The methods used in the study include the linear tendency estimation method, the non-parametric Mann-Kendall trend test, the elasticity coefficient method, and hydrological simulation methods. The results of this study suggest that the streamflow, precipitation, and potential evapotranspiration (PET) in the three watersheds showed an overall decreasing trend. The minimum precipitation decrease rate ranged from −1.09 to −0.55 mm/a, and the minimum streamflow decreasing rate at the Beihedian Hydrological Station was −1.32 mm/a, with a minimum range of 0–176.03 mm. Change-point analysis revealed that the streamflow in the Beihedian River and Xidayang Reservoir watersheds experienced a significant change point around 1999, with a significant level of α=0.05. As for the Wangkuai Reservoir watershed, a significant change point was observed around 1980, which is likely attributable to land system reforms and protective forest projects. The attribution analysis which combined both climate change and human activities using the elasticity coefficient method and hydrological simulation methods indicated that climate change contributed an average of 32.93%, 34.50%, and 35.12% to the reduction in streamflow in the three watersheds, respectively. Human activities accounted for an average contribution of 67.07%, 65.50%, and 64.88%, respectively. Water conservancy projects, afforestation, and other human activities were identified as the primary factors contributing to streamflow decreases.

Multi-Scale Effects of Supply–Demand Changes in Water-Related Ecosystem Services Across Different Landscapes in River Basin

To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted Regression (MGWR) are used to analyze the factors influencing changes in WESs supply–demand. The findings indicate that (1) at the macroscale, population size, and economic activity are the main driving factors, while at the microscale, precipitation becomes the primary factor influencing fluctuations in WESs supply–demand. (2) Furthermore, over time, the influence of social factors becomes increasingly significant. (3) The explanatory power of a single factor typically increases as it interacts with other factors. (4) Abundant precipitation helps in the generation and maintenance of WESs, but intense human activities may have negative impacts on them. Therefore, we have made significant progress in identifying and analyzing the natural and human-induced driving forces affecting changes in WESs by deeply integrating long-term multi-source remote sensing data with the OPGD and MGWR models.

Significant negative impact of human activities on carbon storage in the Yellow River Delta over the past three decades

With the increasing intensification of human activities, significant changes in land use and land cover (LULC) have posed a severe threat to the carbon storage capacity of wetland ecosystems. A deep understanding of this impact is crucial for protecting regional ecosystems and promoting sustainable development. This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the human activity intensity (HAI) index to conduct detailed grid analysis and global analysis of carbon storage through creating fishnet system and explored the complex relationship between carbon storage and HAI in the Yellow River Delta (YRD), China. The results indicated that over the past 30 years, natural wetlands such as meadow wetlands and salt marshes in the study area had undergone significant degradation due to escalating human activities, while artificial wetlands and non-wetland areas expanded. Concurrently, the total regional carbon storage had declined by 2.08 Tg, representing a significant drop of 8.22 % in the YRD from 1990 to 2020. Among them, dry land, as the primary land type, served as the most crucial carbon pool. Additionally, the human activity intensity of land surface (HAILS) increased significantly, with a growth rate of 37.27 %. HAI mapping revealed a continuous expansion of areas with high HAI. In contrast, the Yellow River Delta National Nature Reserve (YRDNNR) maintained relatively low HAI. Correlation analysis further showed the significant negative correlation (p < 0.01) between carbon storage and HAI, with r values of grid analysis ranging from −0.1395 to −0.0344, while that for global analysis was −0.9643, respectively. This reflected the spatial heterogeneity and agglomeration effects of data analysis across different scales. This study provides valuable insights for achieving the “dual carbon” goals and supporting the conservation and management of wetland ecosystems.

Modeling the Spatial Flows of Nitrogen: The Case of Xiamen

Rapid city expansion and intensive human activities have remarkably affected nitrogen flow, leading to increasingly intricate spatial heterogeneity of nitrogen flow. Focused on the temporal characteristics of nitrogen flow at certain city scales, the existing research has missed comprehensive grid-scale spatial models for nitrogen flow. To address this gap, this study develops a comprehensive spatial model for nitrogen flow by incorporating both natural and anthropic processes. Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. The results of spatial characteristics of Xiamen in 2015 revealed that hotspots of nitrogen input were primarily located in the surrounding areas outside and east of Xiamen, with the maximum nitrogen input reaching 20.07 × 104 kg/ha. However, the hotspots of nitrogen load in the atmosphere were concentrated in the urban center (i.e., Xiamen Island) and the nearby sea areas. The maximum nitrogen outputs can reach 18.32 × 104 kg/ha, which is 18 times the total nitrogen output to the water environment. Additionally, it was found that a significant gradient correlation exists between nitrogen flow and population density. These findings provide support for low-nitrogen spatial planning and emission reduction policymaking.

Impact of Forest Landscape Patterns on Ecological Quality in Coastal Cities of Fujian, China, from 2000 to 2020

The Fujian coastal zone, a key region in China’s coastal belt, has experienced significant landscape and ecological changes due to intense human activities. Understanding the relationship between landscape patterns and ecological quality is critical for sustainable development and ecological protection. Taking the coastal cities, including Fuzhou, Xiamen, and Ningde in Fujian Province of China, as a case, the spatio–temporal changes in landscape patterns and the remote sensing-based ecological index (RSEI) during 2000 and 2020 were explored by the Google Earth Engine (GEE) cloud platform, and then their spatial relationships were identified through Pearson correlation analysis and bivariate spatial autocorrelation analysis. The findings reveal that (1) forest land was the dominant landscape in Fuzhou and Ningde, while cropland prevailed in Xiamen. Significant changes occurred in the land use landscape patterns of the three cities, mainly due to a substantial increase in the built-up land and varying degrees of reduction in arable and forest land. At the landscape level, both Fuzhou and Xiamen exhibited increased landscape fragmentation, while Ningde showed a trend of landscape aggregation; at the class level, forest land in Fuzhou and Xiamen exhibited increased fragmentation, whereas in Ningde, it showed an aggregation trend. (2) Between 2000 and 2020, the ecological–environmental quality of Fuzhou and Ningde continuously improved, while the improvement in Xiamen was less significant. Poor and fair ecological environments in the three cities were mainly concentrated in city centers and coastal zones, and areas of ecological quality degradation were primarily concentrated in coastal zones. (3) Correlation analysis indicates that, whether at the landscape level or the class level, the ecological quality of the three cities is significantly negatively correlated with the fragmentation index and significantly positively correlated with the aggregation index. Moreover, the positive correlation between ecological quality and the forest landscape aggregation index, as well as the negative correlation with the forest landscape fragmentation index, are both significantly stronger than those at the landscape level. As urbanization progresses, forest landscape fragmentation intensifies, especially in city centers and coastal areas, having a significant negative impact on ecological quality. These results highlight the importance of landscape pattern management in maintaining ecological quality. This paper provides insights for coastal cities on balancing urban development with ecological preservation in the context of rapid urbanization.

Composite Risk Assessment of HNS Discharged from Marine Industrial Facilities: A Case Study on Incheon Port, South Korea

This study conducted a composite risk assessment to evaluate the environmental impacts of phenol, a Hazardous and Noxious Substance (HNS) released or leaked from port facilities. The study area was designated as the vicinity of Incheon Port, South Korea, where the volume of petrochemical-related materials is substantial and various industrial facilities are located. For the composite risk assessment, various vulnerability maps were developed, incorporating the dispersion range of phenol calculated through numerical modeling. The vulnerability maps were generated by classifying socio-environment, legally protected areas, habitats, and species, followed by integrating these individual vulnerability maps to construct an integrated vulnerability map. The composite risk assessment was conducted by considering both the integrated vulnerability map and the dispersion range of phenol. The assessment results indicated that the highest risk by depth was observed in the lower layers due to the settling characteristics of phenol. Spatially, areas where islands and coastlines converge exhibited relatively higher risks. This was attributed to the high concentrations of phenol released from industrial facilities, such as crude oil refineries, petrochemical plants, and organic compound manufacturers, in regions characterized by intense human activity, sensitive habitats, and legally protected areas. Continuous monitoring of these high-risk areas is crucial for assessing the environmental impacts of HNS substances like phenol.

Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Ecological Environmental Quality (EEQ) assessment is a crucial method for understanding regional ecological conditions and identifying ecological problems. Understanding how EEQ responds to various natural and anthropogenic factors under different landform conditions is extremely important to improve the accuracy and usefulness of the assessment. Landforms, as a key natural geographic element, influence not only the spatial distribution of EEQ, but also the nature and intensity of human activities. However, we lack a comprehensive understanding of how different landform types affect the ecological environment. In this paper, we quantitatively assessed the spatial heterogeneity characteristics of EEQ in Myanmar based on the Remote Sensing Ecological Index (RSEI) model, and used Geodetector to reveal the driving mechanisms of EEQ in terms of the whole region and specific landform types. The results showed that: (1) The overall EEQ in Myanmar showed a fluctuating downward trend from 2000 to 2020, and the spatial heterogeneity is obvious. The plains and hills had poor EEQ, while the mountains, highlands and most of the northern regions have better EEQ. (2) The EEQ of different landform types differed significantly. The EEQ was best in middle relief mountains and poorer in plains and high relief mountains. (3) Land use, human footprint and net primary productivity (NPP) were the main factors affecting EEQ in Myanmar, and there are significant spatial and temporal dynamic interactions between different factors. (4) There were significant differences in the factors affecting EEQ between landform types. For example, EEQ in plains, hills, low relief mountains, and middle relief mountains was influenced by a combination of natural and anthropogenic factors. In contrast, upland mountain areas were mainly influenced by natural factors alone. As the degree of landform fluctuation increases, the influence of each factor generally showed a downward trend. This study reveals the spatio-temporal variation characteristics of Myanmar’s EEQ, key driving factors, and differential response mechanisms of ecological environment evolution under different landform types, and provides a scientific basis for formulating ecosystem protection and restoration strategies based on local conditions.

Spatiotemporal Variation Characteristics and Source Identification of Nitrogen in the Baiyangdian Lake Water, China

To study the characteristics and sources of nitrogen in the Baiyangdian Lake, this research conducted water quality monitoring during three hydrological periods (normal period, flood period, and dry period), and 165 pieces of routine water quality monitoring data were collected from the three national control sections for Baiyangdian Lake and its inflow rivers. By integrating water chemical analysis with multivariate statistical techniques, the study comprehensively investigated the spatiotemporal variation patterns of nitrogen in Baiyangdian Lake and identified the sources of nitrogen pollution. The results showed that the concentration of total nitrogen (TN) was highest during the dry period, reaching an average of 0.924 mg/L, and 31.3% of the sites exceeded the national Grade III surface water quality standard, reflecting a potential risk of nitrogen pollution. Based on the ion ratio method and principal component analysis (PCA), the main sources of nitrogen pollution in Baiyangdian Lake were identified as manure and domestic sewage, with agricultural fertilizers also having a certain impact on water nitrogen pollution. In addition, the study also compared the nitrogen concentration in Baiyangdian Lake with several important lakes in China. The results showed that the concentrations of TN and ammonium nitrogen (NH4+-N) in Baiyangdian Lake are lower than those in lakes in areas with similar human activity intensity, indicating that the water quality of Baiyangdian is relatively good. This study can provide a scientific basis for water quality management and pollution prevention for Baiyangdian Lake.

Using Advanced InSAR Techniques and Machine Learning in Google Earth Engine (GEE) to Monitor Regional Black Soil Erosion—A Case Study of Yanshou County, Heilongjiang Province, Northeastern China

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil.

Identification of subsurface archaeology at Mutaredh, United Arab Emirates, using ground penetrating radar

The United Arab Emirates holds great historical importance, as evidenced by many archaeological sites, such as the Jebel Hafit Tombs and the Hili Archeological Park in Al Ain. At the western edge of Mutaredh Oasis, a major new archaeological site was discovered in 2023 during a construction project. Several important archeological features have been documented, including an earthen mosque and boundary walls, Iron Age irrigation systems, and a circular stone tomb dating to the Bronze Age. However, the eastern edge of the Mutaredh site has remained unexplored to date. Ground Penetrating Radar (GPR) has been proven to be a successful method in mapping archaeological remains. Accordingly, a high-resolution GPR survey was carried out to identify the continuity of the unearthed archeological elements and delineate new unexplored features. A comprehensive 3D model of buried archaeological features was constructed using the acquired high-resolution GPR data in the eastern part of the Mutaredh site. Several selected anomalies have been observed, similar to the uncovered features in the western part of the Mutaredh site and structures documented in other nearby archaeological sites. The geometry and extension of these anomalies have enabled the possible identification of a further two Bronze Age circular tombs, as well as the delineation of a system of water channels (falaj), and irrigation networks with tree pits from the Iron Age. Moreover, walls probably belonging to the Late Islamic Age are identified. These findings suggest that Mutaredh has been a site of intensive human activity from the Bronze Age through to the Late Islamic period. Given the density of identified anomalies, further significant features are anticipated to lie buried in the immediate surrounding areas, promising continued insights into the area’s rich archaeological heritage. The findings of this study may guide archaeologists to specific locations and assist in selecting the most appropriate excavation techniques for the verification stage.

An Assessment of High Natural Manganese Concentration in the Groundwater Upstream of a Non-Hazardous Solid Waste Plant in the Southern Latium Region

Manganese (Mn) is one of the most abundant metals naturally present in the environment, but currently, it also represents an important factor of environmental contamination due to intense human activity. To investigate the nature of the presence of concentrations above the limits (CSC) established by Legislative Decree 152/06 for manganese in the groundwater underlying a non-hazardous solid waste plant, a study was carried out on the geochemical characteristics of the aquifers present in the study area. The study aimed to determine the natural background value (NBV) of Mn in the groundwater underlying the study area, according to the “Guidelines for Determining the Background Values of Soils and Groundwater” of ISPRA. Indeed, NBV assessment and site-specific considerations can help identify any specific sources of contamination in an area. In the study area, the chemical–physical and geochemical parameters of the water of 11 piezometers were analyzed. Subsequently, statistical tests were applied to detect an NBV identified as 192.3 µg/L, therefore much higher than the CSC, due to possible ongoing local phenomena linked to the geogenic conditions of the soil, which could determine high manganese values. In fact, in the study area, some lithologies favor establishing anaerobic environments and releasing manganese in the groundwater as hydroxides. Consequently, this process can lead to high manganese concentrations, even exceeding legal limits.

Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China.

The stability of ecosystems in high mountain canyon areas is poor, and the interaction between humans and the land is complex, making these ecosystems more vulnerable to destruction. Quantitatively assessing the ecosystem service value (ESV) in high mountain canyon areas and revealing its spatiotemporal evolution patterns and driving factors play a crucial role in the construction of regional ecological barriers and the assurance of ecological security. This study focuses on the Upper Minjiang River as the research area, using the InVEST model and the Equivalent Factor Method to estimate ESV. This combination aims to address the inadequacy of the Equivalent Factor Method in reflecting the variability of ESV across different regions, and the sensitivity of the InVEST model to data changes that results in insufficient accuracy of ESV assessments. By harnessing spatial au-tocorrelation and the geodetector method, we unravel the spatiotemporal evolution characteristics and driving factors of ESV. The results show that: (1) From 2000 to 2020, the ESVs estimated by the two estimations increased by 31.28% and 22.47%, respectively, both indicating that the eco-environment quality of the upper Minjiang River has been continuously improved. (2) When Moran's I was greater than 0.5 (p < 0.05), the spatial clustering of "High-High" and "Low-Low" ESV was obvious. It is clear that the ESV varies geographically. High values are primarily found in the study area's center and southern regions, as well as on both banks of the Minjiang River, whereas low values are more common in the region's northern region. (3) Slope and human activity intensity (HAI) are the principal contributors to the spatial differentiation of the ESV, more than 60% of the interaction types between the two factors were classified as dual-factor enhancement. The synergistic reinforcing effects of HAI, slope, elevation, and temperature collectively shape the shifts in ESV spatial distribution. This study offers a novel evaluative lens on the ESV of the Upper Minjiang River area, supplying a sturdy data support for crafting specific ecological preservation and rejuvenation strategies in the coming years.

Matching and driving mechanism analysis of the supply and demand relationships of soil conservation services in karst peak-cluster depression basin in Southwest Guangxi, China

The karst peak-cluster depression basin in southwestern Guangxi, characterized by a fragile ecological environment and intensive human activities, is characterized by rocky desertification and ecological function degradation. This degradation not only impairs the region’s ecological integrity but also limits its ecological and welfare benefits. Traditionally, the estimation of soil conservation service supply and demand in karst areas relies on internationally recognized models, which often exclude the critical factor of karst desertification. This omission introduces significant uncertainty in the assessment of soil supply and demand in karst terrains. To address this gap, our study employs an adapted revised universal soil loss equation model tailored for karst regions to conduct a quantitative analysis of the soil supply–demand relationship in southwestern Guangxi. We explore its spatiotemporal evolution and spatial matching characteristics utilizing geographic detectors to identify the driving factors influencing the ecological supply-to-demand ratio (ESDR). Our findings revealed the following: (1) From 2000 to 2020, the soil conservation supply–demand ratio in the study area showed a fluctuating upward trend, with an overall increase of approximately 63.29%. Specifically, there was a fluctuating increase from 2000 to 2008 and from 2013 to 2020, while a downward trend was observed from 2009 to 2012. (2) The predominant matching pattern of the soil conservation services in the research area was “low supply, low demand”, which was primarily located in the southeastern part of southwestern Guangxi. (3) The supply–demand relationship of soil conservation services is significantly influenced by factors such as vegetation coverage, slope, soil type, and lithology, with each factor exhibiting a marked interactive effect on the ESDR. This study evaluates and reveals the supply–demand relationship and driving mechanisms of soil conservation services in the karst region of southwestern Guangxi, which contributes to the precise formulation of strategies for rocky desertification prevention and ecological restoration.

Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p &gt; 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

Impact of urbanization on water-energy-carbon nexus system: The case of Zhengzhou, China

Exploring the impact of urbanization on water-energy‑carbon nexus system is helpful to discover the inherent interaction mechanism and co-evolutionary pattern of water-energy‑carbon nexus in urban expansion. Based on the calculation of water-energy‑carbon of different land use types, the impact of urbanization on water-energy‑carbon nexus system in Zhengzhou was analyzed, and the future evolution of water-energy‑carbon nexus system under different urbanization scenarios was simulated. The results showed that land use change during urbanization was affected by the industrial pattern and the intensity of human activities, which further caused the increases in water and energy consumption and carbon emissions. There was a positive correlation between urbanization level and coupling coordination degree of water-energy‑carbon nexus system. Especially, the changes in water and energy consumption caused by land use change during urbanization largely determined the coupling coordination level of water-energy‑carbon nexus system. Additionally, different urbanization scenarios would lead to different growth rates of water and energy consumption and carbon emissions, in which the impact of land urbanization on urban water-energy‑carbon nexus system was greater than that of the economic and population urbanization. In the future, urban expansion control and land use regulation based on collaborative optimization of water-energy‑carbon should be adopted.