Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in...Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine,particularly in the context of the 2022 Russia-Ukraine conflict.We provide the first comprehensive assessment of both conflict-and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023.In addition,we propose a novel method using machine learning models to isolate the impact of conflict on cropland.Our findings reveal that,prior to the conflict,the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate,soil properties,and elevation—whereas socio-economic factors(e.g.,GDP and population size)exerted a weaker influence.Interannual dynamics in productive cropland area were largely driven by climate variability.The onset of conflict in 2022 dramatically altered this landscape,with nearly half of the cropland grid cells experiencing a conflict-induced reduction.Notably,almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change.Remarkably,in 2023,the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation.Despite this,the total area of productive cropland in 2023 remained below expected levels,due to ongoing conflict and localized droughts.Finally,we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.展开更多
Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establi...Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establish the driving force model of utilized change of cultivated land. Driving factors, action mechanism and process of utilized change of cultivated land were analyzed, and the differences during all factors were compared. The study provides some decision basis for sustainable utilization and management of land resources in Qinghai Lake Area.展开更多
Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoti...Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.展开更多
As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegeta...As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.展开更多
This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data fr...This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.展开更多
The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ec...The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.展开更多
Based on regional paleoclimate sequences,records of human activities,paleoclimate simulations,and detailed environmental historical records,we discuss the impacts of Holocene climate change and human activities on the...Based on regional paleoclimate sequences,records of human activities,paleoclimate simulations,and detailed environmental historical records,we discuss the impacts of Holocene climate change and human activities on the evolution of the Shule River in the western Qilian Mountains,China.The results indicate that during the early to mid-Holocene,the river evolution of the Shule River alluvial fan was closely related to regional climate fluctuations.In the late Holocene,flood agriculture began to emerge along the Shule River.During the historical period,population growth and the expansion of arable land led to increased river water usage,resulting in decreased access to the expected distribution of water resources in other regions,which in turn has caused imbalances in the regional hydrological ecosystem.展开更多
Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable developm...Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable development.In this study,based on the data of kNDVI in Jiangsu Province(an economically developed coastal region in eastern China)from 2002 to 2022,the spatial and temporal dynamics of vegetation in the province were systematically analyzed by using the Theil-Sen slope estimation and Mann-Kendall trend test methods.The results indicate that vegetation coverage in Jiangsu Province generally followed a trend of"fluctuation in the early period and improvement in the later period"from 2002 to 2022.Spatially,kNDVI changes exhibited clear heterogeneity,with an overall pattern of"decline in the south,increase in the north,and stability in the central region".Based on the 21-year mean of kNDVI,it is found that vegetation conditions were relatively better in northern and central Jiangsu,while lower mean of kNDVI was observed in southern Jiangsu(e.g.,Suzhou,Wuxi,and Changzhou),reflecting the pressure of accelerating urbanization on green space coverage.Further investigation into the driving factors of changes in vegetation reveals that social factors had the strongest influence,with a path coefficient of-0.86,followed by topographic and climatic factors.This spatial differentiation pattern and the identified driving factors highlight ongoing conflicts between the economic development and ecological conservation in Jiangsu Province.In the future,land use structure should be optimized based on local conditions,and coordinated development between ecological restoration and urban expansion should be strengthened.展开更多
Integrating the supply and demand of ecosystem services(ESs)across various scales is crucial for regional sustainable development.However,the relationships between ESs supply and demand,along with their determinants,h...Integrating the supply and demand of ecosystem services(ESs)across various scales is crucial for regional sustainable development.However,the relationships between ESs supply and demand,along with their determinants,have not been thoroughly investigated from a multi-spatial perspective.In this study,we quantified four ESs(carbon sequestration,water yield,food supply,and soil conservation)at six spatial scales(pixel,10 km,50 km,county,municipality and watershed scale)in China's Loess Plateau(LP),characterized by fragile ecological environment and high human activity.The ESs supply-demand matches and their trade-offs or synergies as well as the dominant influencing factors at different scales were identified.There was significant spatial heterogeneity in the distribution of ESs supply and demand across the LP.The balance between ESs supply and demand became obvious from pixel to watershed(municipality)scale,with the area proportion increased by 66.78%,57.85%,and 17.89% for carbon sequestration,water yield and food supply,respectively.The supply-demand match of paired ESs was dominated by synergistic effects at the grid scales and county scale,and their trade-offs mainly occurred in municipality and watershed scales.Population and GDP emerged as the primary factors influencing the supply-demand matches for carbon sequestration,water yield,and food supply,whereas soil conservation was primarily shaped by natural factors.Furthermore,the influence of dominant factors strengthened as the spatial scale increases.The load coefficient of GDP,land use degree and human activities index increased by 0.5057,0.6985 and 0.6705 from pixel scale to watershed scale,respectively.Thus,implementation of specific management measures should consider both the overall situation of ESs at large scale and influencing factors at small scale.This multi-scale study sheds light on understanding the interactions between supply and demand in different ESs,and provides new insights for hierarchical ecosystem management.展开更多
Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and...Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and water eutrophication,making them some of the most endangered ecosystems worldwide.In the Yellow Sea and Bohai Sea regions,seagrass bed assessment and monitoring have been largely overlooked.Thus,strengthening research efforts is necessary to identify current distribution patterns and long-term changes in seagrass bed resources.This study focused on a seagrass bed in Sanggou Bay,Rongcheng,using remote sensing(RS)and geographic information system technologies to analyze multisource satellite data from the US Landsat and Chinese resource satellite series.By combining RS indexes with historical survey data,large-scale temporal and geographic distribution data for seagrass beds were obtained in the study area from 1985 to 2022.The spatial distribution and evolution trends of the seagrass bed were analyzed using a water depth inversion model,and the factors driving its degradation were identified.Results indicated that the seagrass bed area in Sanggou Bay fluctuated between 100 and 140 km^(2) from 1985 to 2010.During 2010–2013,dynamic changes in the seagrass bed area increased,with a considerable decrease in its overall size.After 2014,changes were minimal,indicating a notably stable state.Seagrass bed degradation in Sanggou Bay is influenced by high-intensity human activities,pollution from coastal land sources,raft cultures,underwater terrain conditions,and sedimentary environmental factors.The findings offer essential insights for developing seagrass restoration and protection strategies in Sanggou Bay and contribute to the broader scientific efforts for coastal ecosystem conservation and rehabilitation.展开更多
The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow Rive...The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River(UYR).However,the spatiotemporal patterns and driving mechanisms of vegetation growth status(VGS)in the region remain poorly understood.Based on the hydrological model PLS,an innovative WEP-CHC model was developed by integrating regional environmental and vegetation growth characteristics.Furthermore,combined with the PLS-SEM model and other methods,this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR.The results indicated that:①VGS exhibited significant spatiotemporal variation trends within the study area.In the study period of 1970–2020,the GPP onset time was significantly advanced(p<0.05)while the GPP peak value was significantly increased.Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region.②Soil freeze-thaw conditions significantly influenced VGS(p<0.05).The complete thawing time of permafrost was closely coincided with the GPP onset time,with a correlation coefficient exceeding 0.84.After controlling soil freeze-thaw effects using partial correlation analysis,it was found that better initial soil hydrothermal conditions would lead to better VGS;③The model constructed with annual hydrothermal conditions(AHC),soil freeze-thaw period(SFTP),vegetation growth season(VGS),initial soil hydrothermal conditions(ISHC),and annual solar radiation conditions(ASRC),demonstrated good explanatory power for vegetation growth.The R^(2)values of PLS-SEM were above 0.76 in all five subregions.However,their effects on VGS varied significantly across subregions.Overall,AHC and SFTP were the dominant factors in all subregions.Furthermore,the impacts of ISHC and VGC were statistically insignificant,whereas the effects of ASRC exhibited high complexity.This study not only provides new insights into the current state of hydrological-ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.展开更多
Quantifying grassland changes and their drivers is essential to ensure the stability of grassland resources in China.We established a research framework with two primary objectives:to evaluate grassland degradation an...Quantifying grassland changes and their drivers is essential to ensure the stability of grassland resources in China.We established a research framework with two primary objectives:to evaluate grassland degradation and restoration over the past 30 years,and to quantify the contributions of climate change and anthropogenic activities to these changes across different grassland cover types.The results revealed that despite a net loss of 6.87×10^(4)km^(2)in China's total grassland area from 1990 to 2020,the proportion of high-coverage grassland increased by 2.45%,demonstrating an improvement in productivity per unit area.Conversion of grassland to cropland was the dominant land change type,with 80.83%occurring in the western part of the Northwest Ecological Region.Although the total degraded grassland area reached 3.33×10^(5)km^(2)during 1990-2020,this degradation was overwhelmingly dominated by the mild level(94.98%),with severe degradation accounting for only 5.02%.A comparison of the periods 2000-2010 and 1990-2000 revealed that grassland restoration became enhanced in the northeastern part of the Qinghai-Tibet Plateau Ecological Region but degradation intensified in the southwestern part.Moreover,mobile grazing emerged as the primary anthropogenic driver of grassland changes.These new findings provide an important scientific basis for adaptable grassland resource protection and grassland-livestock balanced management.展开更多
As a major worldwide issue,desertification poses significant threats to ecosystem stability and long-term socioeconomic growth.Within China,the Mu Us Sandy land represents a crucial region for studying desertification...As a major worldwide issue,desertification poses significant threats to ecosystem stability and long-term socioeconomic growth.Within China,the Mu Us Sandy land represents a crucial region for studying desertification phenomena.Comprehending how desertification risks are distributed spatially and what mechanisms drive them remains fundamental for implementing effective strategies in land management and risk mitigation.Our research evaluated desertification vulnerability across the Mu Us Sandy land by applying the MEDALUS model,while investigating causal factors via geographical detector methodology.Findings indicated that territories with high desertification vulnerability extend across 71,401.7 km^(2),constituting 76.87%of the entire region,while zones facing extreme desertification hazard cover 20,578.9 km^(2)(22.16%),primarily concentrated in a band-like pattern along the western boundary of the Mu Us Sandy land.Among the four primary indicators,management quality emerged as the most significant driver of desertification susceptibility,followed by vegetation quality and soil quality.Additionally,drought resistance,land use intensity,and erosion protection were identified as the key factors driving desertification sensitivity.The investigation offers significant theoretical perspectives that can guide the formulation of enhanced strategies for controlling desertification and promoting sustainable land resource utilization within the Mu Us Sandy land region.展开更多
CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotempora...CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.展开更多
[Objective] The aim was to provide theoretical basis for the study of underground water dynamic changes in Songnen Plain in Jilin Province.[Method] The dynamic changes and driving factors for the underground water in ...[Objective] The aim was to provide theoretical basis for the study of underground water dynamic changes in Songnen Plain in Jilin Province.[Method] The dynamic changes and driving factors for the underground water in Songnen Plain in Jilin Province was expounded.[Result] Since 1960s,the temperature in the Songnen Plain in Jilin Province increased gradually.The average temperature increased 2℃;precipitation reduced gradually.Especially,the trend of precipitation reduction in west area was more distinct;in the meantime,the development of underground water augmented gradually and reached 2 800 million m3 in 2008.Driven by many factors,regional underground water level had distinct changes.Potential water position reduced greatly in northwest fan-shaped area.The one in other places were stable and even increased in certain parts;confined water position decreased quickly in general and it increased in certain parts.[Conclusion] The general deterioration trend of underground water environment was inevitable.But,the deterioration process can be eased through scientific planning and regional underground water resources so as to realize sustainable utilization of regional underground water resources.展开更多
Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving efflue...Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving effluent discharge standards often requires considerable energy and chemical consumption during operation,resulting in significant carbon footprints.In this study,GHG emissions are systematically accounted for,and the driving factors of carbon footprint growth in China’s MWWTPs are explored.In 2020,a total of 41.9 million tonnes(Mt)of carbon dioxide equivalent(CO_(2)-eq)were released by the sector,with nearly two-thirds being indirect emissions resulting from energy and material usage.The intensity of electricity,carbon source,and phosphorus removing agent consumption increasingly influence carbon footprint growth over time.Through statistical inference,benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions,and the potential for mitigation through more efficient energy and material utilization is calculated.The results suggest that many MWWTPs offer significant opportunities for emission reduction.Consequently,empirical decarbonization measures,including intelligent device control,optimization of aeration equipment,energy recovery initiatives,and other enhancements to improve operational and carbon efficiency,are recommended.展开更多
Due to the multiple impacts of global climate change and anthropogenic disturbances,regional ecological landscapes have been developing towards fragmentation.How to quantitatively measure regional ecological landscape...Due to the multiple impacts of global climate change and anthropogenic disturbances,regional ecological landscapes have been developing towards fragmentation.How to quantitatively measure regional ecological landscape fragmentation and identify its key drivers is an important foundation for regional biodiversity conservation and ecosystem restoration.Taking the Guanzhong Plain Urban Agglomeration(GPUA),China as the research object,this paper proposes a comprehensive framework that integrates landscape pattern index,principal component analysis,random forest(RF)and other methods to quantitatively analyze the spatial and temporal evolution of ecological landscape fragmentation and its driving factors.The results show that:1)cropland,forestland and grassland showed significant spatial differentiation in the landscape pattern index,and the change of their mean values indicated that cropland and forestland show a trend of‘little decrease-continuous increase’.Spatially,the northwestern and southeastern regions showed significant fragmentation and prominent spatial heterogeneity.2)From 2010 to 2020,the landscape fragmentation of cropland and forestland increased by 71%and 20%,respectively,while that of grassland decreased by 33%,indicating that the degree of landscape fragmentation of cropland changed more drastically than that of other ecological land.3)It was found that slope was the most important factor affecting landscape fragmentation of ecological land.In addition,road density had a significant effect on landscape fragmentation of cropland and forestland,but the min-distance between patches and the county center had an important effect on landscape fragmentation of grassland.This study can provide theoretical references for urban agglomeration planning and sustainable landscape management on a regional scale.展开更多
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
基金supported in part by the National Natural Science Foundation of China(Grants No.41971284 and 42371321)the Key Research and Development Program of Hubei Province(Grant No.2025BAB024).
文摘Ukraine,as one of the world’s largest agricultural producers and exporters,plays a critical role in global food security.It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine,particularly in the context of the 2022 Russia-Ukraine conflict.We provide the first comprehensive assessment of both conflict-and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023.In addition,we propose a novel method using machine learning models to isolate the impact of conflict on cropland.Our findings reveal that,prior to the conflict,the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate,soil properties,and elevation—whereas socio-economic factors(e.g.,GDP and population size)exerted a weaker influence.Interannual dynamics in productive cropland area were largely driven by climate variability.The onset of conflict in 2022 dramatically altered this landscape,with nearly half of the cropland grid cells experiencing a conflict-induced reduction.Notably,almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change.Remarkably,in 2023,the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation.Despite this,the total area of productive cropland in 2023 remained below expected levels,due to ongoing conflict and localized droughts.Finally,we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.
基金Supported by The Regional Sustainable Development of the Qing-TibetPlateau(2004)~~
文摘Using path analysis, correlation analysis, partial correlation analysis and system dynamics method to study the driving force of cultivated land in Qinghai Lake Area, and using gradually regression analysis to establish the driving force model of utilized change of cultivated land. Driving factors, action mechanism and process of utilized change of cultivated land were analyzed, and the differences during all factors were compared. The study provides some decision basis for sustainable utilization and management of land resources in Qinghai Lake Area.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0102)the China Scholarship Council Program(202406190114)。
文摘Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.
文摘As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U21A2027)the New Cornerstone Science Foundation through the XPLORER PRIZE(2023-1033).
文摘This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.
基金funded by the Central University D Project(HFW230600022)National Natural Science Foundation of China(71973021)+1 种基金National Natural Science Foundation Youth Funding Project(72003022)Heilongjiang Province University Think Tank Open Topic(ZKKF2022173).
文摘The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.
基金The National Natural Science Foundation of China(Grant 42371159)。
文摘Based on regional paleoclimate sequences,records of human activities,paleoclimate simulations,and detailed environmental historical records,we discuss the impacts of Holocene climate change and human activities on the evolution of the Shule River in the western Qilian Mountains,China.The results indicate that during the early to mid-Holocene,the river evolution of the Shule River alluvial fan was closely related to regional climate fluctuations.In the late Holocene,flood agriculture began to emerge along the Shule River.During the historical period,population growth and the expansion of arable land led to increased river water usage,resulting in decreased access to the expected distribution of water resources in other regions,which in turn has caused imbalances in the regional hydrological ecosystem.
文摘Vegetation not only plays a critical role in regulating regional climate,hydrological cycles,carbon sequestration,and oxygen release,but also is directly linked to ecosystem stability and regional sustainable development.In this study,based on the data of kNDVI in Jiangsu Province(an economically developed coastal region in eastern China)from 2002 to 2022,the spatial and temporal dynamics of vegetation in the province were systematically analyzed by using the Theil-Sen slope estimation and Mann-Kendall trend test methods.The results indicate that vegetation coverage in Jiangsu Province generally followed a trend of"fluctuation in the early period and improvement in the later period"from 2002 to 2022.Spatially,kNDVI changes exhibited clear heterogeneity,with an overall pattern of"decline in the south,increase in the north,and stability in the central region".Based on the 21-year mean of kNDVI,it is found that vegetation conditions were relatively better in northern and central Jiangsu,while lower mean of kNDVI was observed in southern Jiangsu(e.g.,Suzhou,Wuxi,and Changzhou),reflecting the pressure of accelerating urbanization on green space coverage.Further investigation into the driving factors of changes in vegetation reveals that social factors had the strongest influence,with a path coefficient of-0.86,followed by topographic and climatic factors.This spatial differentiation pattern and the identified driving factors highlight ongoing conflicts between the economic development and ecological conservation in Jiangsu Province.In the future,land use structure should be optimized based on local conditions,and coordinated development between ecological restoration and urban expansion should be strengthened.
基金supported by the National Natural Science Foundation of China(Grants No.W2412141,U2243231,and 42301323)the Youth Innovation Promotion Association CAS(Grant No.Y202013)。
文摘Integrating the supply and demand of ecosystem services(ESs)across various scales is crucial for regional sustainable development.However,the relationships between ESs supply and demand,along with their determinants,have not been thoroughly investigated from a multi-spatial perspective.In this study,we quantified four ESs(carbon sequestration,water yield,food supply,and soil conservation)at six spatial scales(pixel,10 km,50 km,county,municipality and watershed scale)in China's Loess Plateau(LP),characterized by fragile ecological environment and high human activity.The ESs supply-demand matches and their trade-offs or synergies as well as the dominant influencing factors at different scales were identified.There was significant spatial heterogeneity in the distribution of ESs supply and demand across the LP.The balance between ESs supply and demand became obvious from pixel to watershed(municipality)scale,with the area proportion increased by 66.78%,57.85%,and 17.89% for carbon sequestration,water yield and food supply,respectively.The supply-demand match of paired ESs was dominated by synergistic effects at the grid scales and county scale,and their trade-offs mainly occurred in municipality and watershed scales.Population and GDP emerged as the primary factors influencing the supply-demand matches for carbon sequestration,water yield,and food supply,whereas soil conservation was primarily shaped by natural factors.Furthermore,the influence of dominant factors strengthened as the spatial scale increases.The load coefficient of GDP,land use degree and human activities index increased by 0.5057,0.6985 and 0.6705 from pixel scale to watershed scale,respectively.Thus,implementation of specific management measures should consider both the overall situation of ESs at large scale and influencing factors at small scale.This multi-scale study sheds light on understanding the interactions between supply and demand in different ESs,and provides new insights for hierarchical ecosystem management.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2022QNLM 050302-4)the Geological Survey Project of the China Geological Survey(No.DD20230071)+1 种基金the China Geological Survey Project‘Investigation and Monitoring of the Coastal Geological Environment of the Yangtze River Estuary’(No.DD20242714)the cooperation fund of Collaborative Research on Marine Geological Environment and Hazards in the Yangtze River Delta and Red River Delta.
文摘Seagrass beds are crucial coastal ecosystems,functioning as vital blue carbon sinks and natural ecological barriers.However,these ecosystems are increasingly threatened by global climate events,coastal development,and water eutrophication,making them some of the most endangered ecosystems worldwide.In the Yellow Sea and Bohai Sea regions,seagrass bed assessment and monitoring have been largely overlooked.Thus,strengthening research efforts is necessary to identify current distribution patterns and long-term changes in seagrass bed resources.This study focused on a seagrass bed in Sanggou Bay,Rongcheng,using remote sensing(RS)and geographic information system technologies to analyze multisource satellite data from the US Landsat and Chinese resource satellite series.By combining RS indexes with historical survey data,large-scale temporal and geographic distribution data for seagrass beds were obtained in the study area from 1985 to 2022.The spatial distribution and evolution trends of the seagrass bed were analyzed using a water depth inversion model,and the factors driving its degradation were identified.Results indicated that the seagrass bed area in Sanggou Bay fluctuated between 100 and 140 km^(2) from 1985 to 2010.During 2010–2013,dynamic changes in the seagrass bed area increased,with a considerable decrease in its overall size.After 2014,changes were minimal,indicating a notably stable state.Seagrass bed degradation in Sanggou Bay is influenced by high-intensity human activities,pollution from coastal land sources,raft cultures,underwater terrain conditions,and sedimentary environmental factors.The findings offer essential insights for developing seagrass restoration and protection strategies in Sanggou Bay and contribute to the broader scientific efforts for coastal ecosystem conservation and rehabilitation.
基金funded by the National Key R&D Program(2021YFC3200203,2023YFC3206303)the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)National Natural Science Foundation of China(52394233,52122902).
文摘The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River(UYR).However,the spatiotemporal patterns and driving mechanisms of vegetation growth status(VGS)in the region remain poorly understood.Based on the hydrological model PLS,an innovative WEP-CHC model was developed by integrating regional environmental and vegetation growth characteristics.Furthermore,combined with the PLS-SEM model and other methods,this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR.The results indicated that:①VGS exhibited significant spatiotemporal variation trends within the study area.In the study period of 1970–2020,the GPP onset time was significantly advanced(p<0.05)while the GPP peak value was significantly increased.Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region.②Soil freeze-thaw conditions significantly influenced VGS(p<0.05).The complete thawing time of permafrost was closely coincided with the GPP onset time,with a correlation coefficient exceeding 0.84.After controlling soil freeze-thaw effects using partial correlation analysis,it was found that better initial soil hydrothermal conditions would lead to better VGS;③The model constructed with annual hydrothermal conditions(AHC),soil freeze-thaw period(SFTP),vegetation growth season(VGS),initial soil hydrothermal conditions(ISHC),and annual solar radiation conditions(ASRC),demonstrated good explanatory power for vegetation growth.The R^(2)values of PLS-SEM were above 0.76 in all five subregions.However,their effects on VGS varied significantly across subregions.Overall,AHC and SFTP were the dominant factors in all subregions.Furthermore,the impacts of ISHC and VGC were statistically insignificant,whereas the effects of ASRC exhibited high complexity.This study not only provides new insights into the current state of hydrological-ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.
基金The Postgraduate Research Innovation Project of Department of Education of Inner Mongolia Autonomous Region,No.KC2024029BThe Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23100201The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0608。
文摘Quantifying grassland changes and their drivers is essential to ensure the stability of grassland resources in China.We established a research framework with two primary objectives:to evaluate grassland degradation and restoration over the past 30 years,and to quantify the contributions of climate change and anthropogenic activities to these changes across different grassland cover types.The results revealed that despite a net loss of 6.87×10^(4)km^(2)in China's total grassland area from 1990 to 2020,the proportion of high-coverage grassland increased by 2.45%,demonstrating an improvement in productivity per unit area.Conversion of grassland to cropland was the dominant land change type,with 80.83%occurring in the western part of the Northwest Ecological Region.Although the total degraded grassland area reached 3.33×10^(5)km^(2)during 1990-2020,this degradation was overwhelmingly dominated by the mild level(94.98%),with severe degradation accounting for only 5.02%.A comparison of the periods 2000-2010 and 1990-2000 revealed that grassland restoration became enhanced in the northeastern part of the Qinghai-Tibet Plateau Ecological Region but degradation intensified in the southwestern part.Moreover,mobile grazing emerged as the primary anthropogenic driver of grassland changes.These new findings provide an important scientific basis for adaptable grassland resource protection and grassland-livestock balanced management.
基金the National Natural Science Foundation of China(Grant No.42301336)the Open Research Fund of Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security(Grant No.HWWSF202302).
文摘As a major worldwide issue,desertification poses significant threats to ecosystem stability and long-term socioeconomic growth.Within China,the Mu Us Sandy land represents a crucial region for studying desertification phenomena.Comprehending how desertification risks are distributed spatially and what mechanisms drive them remains fundamental for implementing effective strategies in land management and risk mitigation.Our research evaluated desertification vulnerability across the Mu Us Sandy land by applying the MEDALUS model,while investigating causal factors via geographical detector methodology.Findings indicated that territories with high desertification vulnerability extend across 71,401.7 km^(2),constituting 76.87%of the entire region,while zones facing extreme desertification hazard cover 20,578.9 km^(2)(22.16%),primarily concentrated in a band-like pattern along the western boundary of the Mu Us Sandy land.Among the four primary indicators,management quality emerged as the most significant driver of desertification susceptibility,followed by vegetation quality and soil quality.Additionally,drought resistance,land use intensity,and erosion protection were identified as the key factors driving desertification sensitivity.The investigation offers significant theoretical perspectives that can guide the formulation of enhanced strategies for controlling desertification and promoting sustainable land resource utilization within the Mu Us Sandy land region.
基金supported by the National Natural Science Foundation of China(No.52200120)the R&D Program of Beijing Municipal Education Commission(No.KM202310011003).
文摘CO_(2) emissions(CEs)pose a growing threat to environmental changes and global warming,attracting extensive attention.Here,we leveraged near-real-time monitoring data spanning 2019 to 2022 to investigate spatiotemporal heterogeneity,sectoral contributions,provincial spatial correlation,and driving factors influencing CEs at the provincial level in China.Our analysis,integrating Moran’s Index analysis,Spearman correlation analysis,and the Geographically Weighted Regression model,unveiled China’s consistent world-leading CEs,surpassing 10,000 Mt over the study period.Spatially,CEs exhibited a heterogeneous distribution,with markedly higher emissions in eastern and northern regions compared to western and southern areas.Temporally,CEs displayed significant fluctuations,peaking in the fourth quarter before declining in subsequent quarters.Chinese NewYear and COVID-19 had the biggest effects on CEs,with average daily reductions of-20.8%and-18.9%,respectively,compared to the four-year average and the same period in 2019.Sectoral analysis highlighted the power and industry sectors as primary contributors to CEs in China,jointly accounting for 37.9%-40.2%and 43.5%-46.4%of total CEs,respectively.Spatial clustering analysis identified a distinct High-High agglomeration region,predominantly encompassing provinces such as Inner Mongolia,Shandong and Jiangsu.Furthermore,total energy consumption and electricity consumption emerged as significant drivers of CEs,exhibiting correlation coefficients exceeding 0.9,followed by exhaust emissions,population size,and gross domestic product.Moreover,the influence of drivers on provincial CEs exhibited notable spatial heterogeneity,with regression coefficients displaying a decreasing gradient from north to south.These findings provide scientific and technological support to realize the provincial dual-carbon goals in China.
基金Supported by Chinese Geographic Investigation Bureau Financial Support Project(1212010813093)~~
文摘[Objective] The aim was to provide theoretical basis for the study of underground water dynamic changes in Songnen Plain in Jilin Province.[Method] The dynamic changes and driving factors for the underground water in Songnen Plain in Jilin Province was expounded.[Result] Since 1960s,the temperature in the Songnen Plain in Jilin Province increased gradually.The average temperature increased 2℃;precipitation reduced gradually.Especially,the trend of precipitation reduction in west area was more distinct;in the meantime,the development of underground water augmented gradually and reached 2 800 million m3 in 2008.Driven by many factors,regional underground water level had distinct changes.Potential water position reduced greatly in northwest fan-shaped area.The one in other places were stable and even increased in certain parts;confined water position decreased quickly in general and it increased in certain parts.[Conclusion] The general deterioration trend of underground water environment was inevitable.But,the deterioration process can be eased through scientific planning and regional underground water resources so as to realize sustainable utilization of regional underground water resources.
基金supported by the National Natural Science Foundation of China(52200228 and 72022004)the National Key Research and Development Program of China(2021YFC3200205 and 2022YFC3203704).
文摘Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving effluent discharge standards often requires considerable energy and chemical consumption during operation,resulting in significant carbon footprints.In this study,GHG emissions are systematically accounted for,and the driving factors of carbon footprint growth in China’s MWWTPs are explored.In 2020,a total of 41.9 million tonnes(Mt)of carbon dioxide equivalent(CO_(2)-eq)were released by the sector,with nearly two-thirds being indirect emissions resulting from energy and material usage.The intensity of electricity,carbon source,and phosphorus removing agent consumption increasingly influence carbon footprint growth over time.Through statistical inference,benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions,and the potential for mitigation through more efficient energy and material utilization is calculated.The results suggest that many MWWTPs offer significant opportunities for emission reduction.Consequently,empirical decarbonization measures,including intelligent device control,optimization of aeration equipment,energy recovery initiatives,and other enhancements to improve operational and carbon efficiency,are recommended.
基金Under the auspices of National Natural Science Foundation of China(No.42271214)Key Research Program of Gansu Province(No.23ZDKA0004)Natural Science Foundation of Gansu Province(No.25JRRA212,21JR7RA281,22JR11RA149,24JRR A250)。
文摘Due to the multiple impacts of global climate change and anthropogenic disturbances,regional ecological landscapes have been developing towards fragmentation.How to quantitatively measure regional ecological landscape fragmentation and identify its key drivers is an important foundation for regional biodiversity conservation and ecosystem restoration.Taking the Guanzhong Plain Urban Agglomeration(GPUA),China as the research object,this paper proposes a comprehensive framework that integrates landscape pattern index,principal component analysis,random forest(RF)and other methods to quantitatively analyze the spatial and temporal evolution of ecological landscape fragmentation and its driving factors.The results show that:1)cropland,forestland and grassland showed significant spatial differentiation in the landscape pattern index,and the change of their mean values indicated that cropland and forestland show a trend of‘little decrease-continuous increase’.Spatially,the northwestern and southeastern regions showed significant fragmentation and prominent spatial heterogeneity.2)From 2010 to 2020,the landscape fragmentation of cropland and forestland increased by 71%and 20%,respectively,while that of grassland decreased by 33%,indicating that the degree of landscape fragmentation of cropland changed more drastically than that of other ecological land.3)It was found that slope was the most important factor affecting landscape fragmentation of ecological land.In addition,road density had a significant effect on landscape fragmentation of cropland and forestland,but the min-distance between patches and the county center had an important effect on landscape fragmentation of grassland.This study can provide theoretical references for urban agglomeration planning and sustainable landscape management on a regional scale.
