Diversity Patterns of Eukaryotic Phytoplankton in the Medog Section of the Yarlung Zangbo River.

As one of the important biodiversity conservation areas in China, the ecosystem in the lower reaches of the Yarlung Zangbo River is fragile, and is particularly sensitive to global changes. To reveal the diversity pattern of phytoplankton, the metabarcode sequencing was employed in the Medog section of the lower reaches of the Yarlung Zangbo River during autumn 2019 in present study. The phytoplankton assemblies can be significantly divided into the main stem and the tributaries; there are significant differences in the phytoplankton biomass, alpha and beta diversity between the main stem and the tributaries. While both the main stem and the tributaries are affected by dispersal limitation, the phytoplankton assemblages in the entire lower reaches are primarily influenced by heterogeneous selection. Community dissimilarity and assembly process were significantly correlated with turbidity, electrical conductivity, and nitrogen nutrition. The tributaries were the main source of the increase in phytoplankton diversity in the lower reaches of the Yarlung Zangbo River. Such diversity pattern of phytoplankton in the lower reach may be caused by the special habitat in Medog, that is, the excessive flow velocity, and the significant spatial heterogeneity in physical and chemical factors between stem and tributaries. Based on the results and conclusions obtained in present study, continuous long-term monitoring is essential to assess and quantify the impact of global changes on phytoplankton.

Mitigation Measures Could Aggravate Unbalanced Nitrogen and Phosphorus Emissions from Land-Use Activities.

Socioeconomic factors and mitigation potentials are essential drivers of the dynamics of nutrient emissions, yet these drivers are rarely examined at broad spatiotemporal scales. Here, we combine material flow analysis and geospatial analysis to examine the past and future changes of nitrogen and phosphorus emissions in China. Results show that anthropogenic nitrogen and phosphorus emissions increased by 17% and 32% during 2000-2019, respectively. Meanwhile, many regions witnessed decreasing nitrogen emissions but rising phosphorus discharged to waterbody, leading to a 20% decrease in the nitrogen/phosphorus ratio. In addition to many prominent factors like fertilizer use, the increasing impervious land area around cities is a notable factor driving the emissions, indicating the urgency to limit building expansion, especially in North China Plain and other less-developed regions. Improving land-use efficiency and consuming behaviors could reduce nitrogen and phosphorus emissions by 65-77% in 2030, but the nitrogen/phosphorus ratio will increase unintendedly due to larger reduction potentials for phosphorus, which may deteriorate the aquatic ecosystem. We highlight that nitrogen and phosphorus emissions should be reduced with coordinated but differentiated measures by prioritizing nitrogen reduction through cropland and food-system management.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years.

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Internal transformation and damming regulate the longitudinal variation of DOM bioavailability in a large river.

Understanding the spatial patterns of dissolved organic matter (DOM) and factors that influence them is crucial for maintaining river ecosystem functions and riverine health, considering the significant role of DOM in water quality and aquatic ecosystems. Nevertheless, there is limited knowledge regarding the spatial variation of DOM bioavailability and the factors driving them in large river systems. This study involved 39 sampling locations along the main stem of the Changjiang River, spanning its entire length (>5000 km) during a dry season. Spatial patterns of DOM were assessed by measurements of DOC concentrations and eight fluorescence DOM indices, namely fluorescence index (FI-A and FI-B), Trytophan/Tyrosine, Humic A, Humic C, humification indices (HIX-A and HIX-B), and Freshness index (ß/α). The results revealed that the water DOM in the main stem of the Changjiang River primarily originated from terrestrial sources. A decline in DOM bioavailability was observed from the upper to the lower basin, aligning with the carbon processing prediction rather than the river continuum concept (RCC). The pure effect of physicochemical factors (25.30%) was greater than that of geographic factors (9.40%). The internal transformation processes determined the significant longitudinal decreases of DOM bioavailability. While no significant difference in DOM bioavailability was observed between reaches before and after the dams, the construction of dams was found to improve DOM bioavailability at the subsection scale and reduce the spatial autocorrelation of DOM bioavailability across the entire basin.

Exploring China's water scarcity incorporating surface water quality and multiple existing solutions.

Water scarcity has threatened the sustainability of human life, ecosystem evolution, and socio-economic development. However, previous studies have often lacked a comprehensive consideration of the impact of water quality and existing solutions, such as inter-basin water transfer and unconventional water resources, on water scarcity. In this paper, an improved approach was proposed to quantify water scarcity levels by comprehensively considering surface water quality and multiple solutions. China's water scarcity was first assessed at a high spatial resolution on a monthly basis over the 5-year period from 2014 to 2018. Then, the driving factors including water quality and solutions were identified by a geographic detector model. Finally, an in-depth investigation was conducted to unravel the effects of water quantity solutions (i.e., inter-basin water transfer and unconventional water use), and water quality solutions (i.e., improving surface water quality) on alleviating water scarcity. Based on monthly assessments considering water quality and multiple existing solutions, the results showed that over half of the national population (∼777 million) faced water scarcity for at least one month of the year. Agricultural water use and inadequate water quality were the main driving factors responsible for China's water scarcity. Over four-fifths of the national population (∼1.10 billion) could benefit from alleviated water scarcity through a combination of water quantity and quality solutions. However, the existing solutions considered were insufficient to completely resolve water scarcity in China, especially in Northern China, persisting as a challenging issue. The results obtained from this study provided a better understanding of China's water scarcity, which could contribute to guiding future efforts aimed at alleviating water scarcity and ensuring water security in China.

Deciphering the key factors affecting pesticide residue risk in vegetable ecosystem.

Soil contamination, particularly from pesticide residues, presents a significant challenge to the sustainable development of agricultural ecosystems. Identifying the key factors influencing soil pesticide residue risk and implementing effective measures to mitigate their risks at the source are essential. Here, we collected soil samples and conducted a comprehensive survey among local farmers in the Three Gorges Reserve Area, a major agricultural production region in Southwest China. Subsequently, employing a dual analytical approach combining structural equation modeling (SEM) and random forest modeling (RFM), we examined the effects of various factors on pesticide residue accumulation in vegetable ecosystems. Our SEM analysis revealed that soil characteristics (path coefficient 0.85) and cultivation factor (path coefficient 0.84) had the most significant effect on pesticide residue risk, while the farmer factors indirectly influenced pesticide residues by impacting both cultivation factors and soil characteristics. Further exploration using RFM identified the three most influential factors contributing to pesticide residue risk as cation exchange capacity (CEC) (account for 18.84%), cultivation area (account for 14.12%), and clay content (account for 13.01%). Based on these findings, we carried out experimental trials utilizing Integrated Pest Management (IPM) technology, resulting in a significant reduction in soil pesticide residues and notable improvements in crop yields. Therefore, it is recommended that governmental efforts should prioritize enhanced training for vegetable farmers, promotion of eco-friendly plant protection methods, and regulation of agricultural environments to ensure sustainable development.

Recent advance of microbial mercury methylation in the environment.

Methylmercury formation is mainly driven by microbial-mediated process. The mechanism of microbial mercury methylation has become a crucial research topic for understanding methylation in the environment. Pioneering studies of microbial mercury methylation are focusing on functional strain isolation, microbial community composition characterization, and mechanism elucidation in various environments. Therefore, the functional genes of microbial mercury methylation, global isolations of Hg methylation strains, and their methylation potential were systematically analyzed, and methylators in typical environments were extensively reviewed. The main drivers (key physicochemical factors and microbiota) of microbial mercury methylation were summarized and discussed. Though significant progress on the mechanism of the Hg microbial methylation has been explored in recent decade, it is still limited in several aspects, including (1) molecular biology techniques for identifying methylators; (2) characterization methods for mercury methylation potential; and (3) complex environmental properties (environmental factors, complex communities, etc.). Accordingly, strategies for studying the Hg microbial methylation mechanism were proposed. These strategies include the following: (1) the development of new molecular biology methods to characterize methylation potential; (2) treating the environment as a micro-ecosystem and studying them from a holistic perspective to clearly understand mercury methylation; (3) a more reasonable and sensitive inhibition test needs to be considered. KEY POINTS: • Global Hg microbial methylation is phylogenetically and functionally discussed. • The main drivers of microbial methylation are compared in various condition. • Future study of Hg microbial methylation is proposed.

Identification of factors driving the spatial distribution of molybdenum (Mo) in topsoil in the Longitudinal Range-Gorge Region of Southwestern China using the Geodetector model.

As a key component of plant nitrogen-fixing enzymes and a variety of human coenzyme factors, molybdenum (Mo) plays an essential role in supporting both plant growth and human health. Soil is a key medium for the cycling of Mo in the biosphere. However, the driving anthropogenic and natural factors governing the spatial distribution of Mo in soil and their interactions are not well understood. To determine the factors that affect the spatial patterns of Mo in topsoil, 6980 samples were collected from the Longitudinal Range-Gorge Region (Linshui County, Sichuan Province, China). In this area, tall mountains are adjacent to deep valleys. Topsoil with enriched Mo is mostly distributed in mountainous areas. The most important factors influencing Mo in topsoil are soil parent materials (q = 0.482), altitude (q = 0.256), and soil type (q = 0.259). There are synergistic effects among the various driving factors [q(X1 âˆ© X2) > Max[q(X1), q(X2)]]. The Geodetector model was used to validate the magnitude of the interaction effects. The contribution to interacting factors is nonlinearly enhanced when the contribution of a single factor was low (any two factors of aspect, road distance, land use type, and S). The contribution to interacting factors is enhanced bidirectionally when the contribution of a single factor was high (any two factors of altitude, soil type, soil parent material, OM, and TFe2O3). When the contribution of one factor is high and the other is low, the contributing to interacting factors is mostly enhanced bidirectionally and a few are nonlinearly enhanced.

The relative impacts of vegetation, topography and weather on landscape patterns of burn severity in subtropical forests of southern China.

Understanding the landscape patterns of burn severity is vital for managing fire-prone ecosystems. Relatively limited research has been done about fire and burn severity patterns in subtropical forests. Here, we derived the pre-fire forest type data from a global land-cover product at 30 m resolution based on time-series Landsat imageries. Using Landsat 8 OLI remote sensing imagery and field-based composite burn index (CBI), this study spatially mapped the burn severity of 27 forest fires in the subtropical forest ecosystems in southern China from 2017 to 2021. The landscape pattern of patches with different burn severity was quantified using landscape indices. In addition, factors influencing the patterns of burn severity across the landscape were determined using the Geodetector model. Burn severity of patches varied significantly over space. High burn severity was common in forest patches with low fragmentation, low patch density, and regular shape. In contrast, moderate and low burn severity was prevalent in patches with smaller patch size, high patch density, and complex shapes. Extensively burned forest patches were located at higher elevations, while more fragmented patches were located in gently sloping areas. Topographic factors were the most significant factors influencing variances in burn severity across the forest patches, followed by weather conditions. Compared to low elevation areas, vegetation types at the high elevation areas (dominated by Masson pine) are more singular, with higher fuel loads, thus resulting in a more regularly-shaped distribution of highly severe burning patches. A detailed understanding of burn severity patterns and driving factors in a landscape can help develop sustainable forest management and restoration strategies. Practically, fire managers should conduct mechanical fuel treatments or thinning of forests at high-elevation areas to reduce the potential of severe fire behavior and the continuity of fire spread.

Global trade-driven transfer of atmospheric polycyclic aromatic hydrocarbon emissions and associated human inhalation exposure risk.

Polycyclic aromatic hydrocarbons (PAHs) are of significant public concern because of their toxicity and long-range transport potential. Extensive studies have been conducted to explore the source-receptor relationships of PAHs via atmospheric transport. However, the transfer of trade-driven regional and global PAHs is poorly understood. This study estimated the virtual PAHs emission transfer embodied in global trade from 2004 to 2014 and simulated the impact of international trade on global contamination and associated human inhalation exposure risk of PAHs. Results show that trade-driven PAHs flowed primarily from developed to less-developed regions, particularly in those regions with intensive heavy industries and transportation. As the result, international trade resulted in an increasing risk of lung cancer induced by exposure to PAHs (27.8% in China, 14.7% in India, and 11.3% in Southeast Asia). In contrast, we found decreasing risks of PAHs-induced lung cancer in Western Europe (63.2%) and the United States (45.9%) in 2004. Our findings indicate that final demand and emission intensity are the key driving factors contributing to rising and falling consumption-based PAHs emissions and related health risk respectively. The results could provide a useful reference for global collaboration in the reduction of PAHs pollution and related health risks.

Characterization of driving factors for the long-term succession of bloom-forming cyanobacterial genera in Lake Erhai, southwest China.

Cyanobacterial blooms pose a global environmental concern, with various genera contributing to their formation. The harmfulness of cyanobacterial blooms varies depending on the specific genus, yet the factors triggering their formation remain incompletely understood. This study conducted qPCR of sediment DNA in Lake Erhai to reconstruct the historical succession of three common bloom-forming cyanobacterial genera (i.e., Microcystis, Dolichospermum, and Aphanizomenon). The driving factors and their corresponding thresholds were identified, and human activities related to driving factors were evaluated. The results revealed two successions in the past century. The first succession transitioned from Aphanizomenon (1902-1978) to Microcystis and Dolichospermum (1978-1999), driven by TN:TP and TP. The second succession shifted from Microcystis and Dolichospermum (1978-1999) to Microcystis (1999-2010), driven by TP, TN:TP, and temperature. The thresholds of TP and TN:TP for the Microcystis bloom were 0.023 mg/L and 17, respectively. TN:TP was significantly influenced by domestic pollution and crop farming in both successions, while TP was significantly impacted by domestic pollution in the first succession and by pollution from crop and dairy farming in the second succession. These results shed light on the underlying mechanism responsible for the blooms of various cyanobacterial genera and could serve as a valuable reference for effectively preventing and controlling nutrient input in the watershed.

Improvements in the InVEST water purification model for detecting spatio-temporal changesintotal nitrogen and total phosphorus discharges in the Huai river watershed, China.

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980-2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980-2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP.

The shift in the spatiotemporal relationship between supply and demand of ecosystem services and its drivers in China.

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010-2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development.

Understanding air pollution reduction from the perspective of synergy with carbon mitigation in China from 2008 to 2017.

In response to the dual challenges of air pollution control and carbon mitigation, China has strategically shifted its focus towards the synergistic reduction of air pollutants and CO2 emissions. This study identifies the potential areas and specific air pollutant species (including CO, NOx, and SO2) for co-reduction with carbon mitigation. We also reveal the driving forces behind the emissions of each air pollutant at both the national and regional scales. Our findings are as follows: (1) The potential for synergistic reduction of CO and SO2 with CO2 emissions has diminished in economically developed areas. There is a significant opportunity for co-reduction of SO2 and CO2 in the western and northern regions of China, particularly within Heilongjiang Province. (2) NOx is the key species for synergistic reduction with CO2 emissions across China, especially in the Chengyu Plain. (3) Cleaner production and the synergistic reduction effect are the primary contributors to national air pollutant reduction in China from 2008 to 2017. Conversely, efforts in economic development and energy efficiency have led to emission increases. Energy and industrial structures have only made limited contributions to emission reductions, and carbon mitigation shows an inhibition effect on emission reductions. These results offer valuable insights for developing targeted regional strategies for deeper air pollution control, considering the specific characteristics and needs of each region. Additionally, our findings highlight the importance of addressing policy misalignments and strengthening mutual-influence mechanisms between air pollution control and carbon mitigation, ensuring that policies for carbon reduction also effectively contribute to air quality improvements.

Navigating the path to dual carbon goals: Understanding the driving forces of energy transition welfare performance.

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006-2014 and 2015-2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed.

Influencing factors and spatiotemporal heterogeneity of livestock greenhouse gas emission: Evidence from the Yellow River Basin of China.

Livestock is one of major sources of greenhouse gas (GHG) emissions in China. Clarifying spatiotemporal characteristics of GHG emissions from livestock and exploring influencing factors can provide reference for grasping regional changes of GHG emission and formulate strategies of carbon reduction for livestock industry. However, existing literatures considered both spatial and temporal impacts and dynamic evolution trend of these factors seldomly. This paper used the life cycle assessment (LCA) method to estimate GHG emissions of livestock in 114 cities of the YRB from 2000 to 2021. On this basis, spatiotemporal heterogeneity of influencing factors was analyzed by using geographically and temporally weighted regression (GTWR) model. Finally, future evolution trend of GHG emissions from livestock was predicted by combining traditional and spatial Markov chain. Four main results were listed as follows. Firstly, GHG emission in the life cycle of livestock industry increased from 57.202 million tons (Mt) carbon dioxide equivalent (CO2e) in 2000 to 77.568 Mt CO2e in 2021. Secondly, structure of livestock industry, labor flow and mechanization were vital factors that led to increase of GHG emissions from livestock. Positive effects of labor flow and mechanization were increasing year by year, while negative effect of urbanization and positive effect of economic development were decreasing year by year. Markov chain analysis shown that probability of keeping high level of GHG emissions of livestock in the YRB unchanged were 96% (T = 1) and 90% (T = 5), and there also existed a Matthew effect. In addition, probability of level transfer of GHG emission in urban livestock was spatially dependent. Government should formulate strategies for livestock development and optimize low-carbon transformation of energy structure for livestock and poultry husbandry based on local conditions and key driving factors in the future. Meanwhile, boundaries of administrative divisions should be broken to promote reduction of GHG emissions in livestock comprehensively.

Dynamic analysis of carbon emissions from construction and demolition activities in Japan: Revealed by high-resolution 4D-GIS modeling.

To meet the 2050 decarbonization target of the global buildings and construction sector, more attention is needed to reduce carbon emissions from construction and demolition. However, current national carbon accounting studies for these activities remain limited in spatial granularity and localized applicability. This study developed a bottom-up spatiotemporal database of carbon emissions from building construction and demolition in Japan via integrating a geographic information system-based building stock model, statistical data, and survey information. Focusing on municipal-level emissions, the Logarithmic Mean Divisia Index approach was used to decompose spatiotemporal variations and identify the contributing factors. Results indicate that carbon emissions from Japan's construction and demolition activities fell by more than 50% between 2005 and 2020, largely due to declining new/demolished-to-stock ratio, suggesting a transition to a stock-based society. Central cities' reliance on carbon-intensive buildings positively contributed to spatial variations in their construction emissions, underscoring the importance of sustainable materials and timber designs. Differences between prefectures in demolition emission intensity highlighted the strategic placement of recycling facilities in key regions to curb transportation-related emissions. Overall, these findings provided data reference for local governments to devise tailored policies for managing construction and demolition emissions.

Scale effects and spatial heterogeneity of driving factors in ecosystem services value interactions within the Tibet autonomous region.

Widespread land development, deforestation, and wetland degradation have disrupted the physical integrity and functional capacity of ecosystems, leading to a reduction in ecosystem service values (ESV). However, comprehensive research addressing ESV interactions that represent various ecosystem services from multifaceted angles is limited. Moreover, the relative significance and spatiotemporal diversity of natural and socio-economic variables influencing ESV demand further investigation. This study conducts both quantitative and qualitative assessments of the spatiotemporal dynamics and interrelationships of ESV in the Tibet autonomous region from 2000 to 2020. Geographical detector and geographically weighted regression models are applied to ascertain the relative importance and spatial heterogeneity of diverse ESV determinants. The findings reveal the following key insights: (1) Barren lands experienced the most substantial expansion from 2000 to 2020, indicating an exacerbation of desertification in the Tibet autonomous region. (2) Over the two decades, ESV exhibited an overall upward trajectory, with regulation of water flows, water bodies, and forests making the most significant contributions to ESV and its growth. (3) The quantitative and qualitative assessment of ESV interrelations has identified the number of trade-offs and synergies, along with spatial occurrences, offering a detailed foundation for the scientific management of ecosystems. Specifically, quantitative results portray ESV correlations as positive or negative, qualitative spatial mapping elucidates intricate local interactions among ESV. (4) The primary driver of ESV in the Tibet autonomous region is NDVI (0.072), with elevation following closely behind, underscoring the predominant influence of natural factors relative to socio-economic variables. This research serves as a scientific underpinning for the development of ecological conservation policies and the execution of ecological restoration initiatives.

Combating land degradation through human efforts: Ongoing challenges for sustainable development of global drylands.

Drylands, as highly vulnerable ecosystems, support environmental functions and human well-being. Nevertheless, widespread land degradation and desertification present significant global and regional environmental challenges, with limited consensus on their area and degree. This study used time-series vegetation productivity and meteorological data from 2000 to 2020 to quantify global land degradation trends and driving factors in drylands. The results show a notable restoration of land degradation in drylands worldwide, with the area of improved land exceeding the degraded area by 1.4 times, although the threat of degradation persists. India and China emerge as pioneers in effective land improvement strategies, offering valuable experiences for other regions. Combined effects, as quantitatively distinguished by our established model, dominate the degradation and improvement processes. Notably, human activities play a decisive role in influencing land degradation trends, with the potential for either exacerbation or reversal. This study provides new perspectives on environmental health and human activities from global and regional observations. Finally, our research provides scientific support for desertification control and contributes to the overall advancement of the SDGs globally.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces.

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000-2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.