环境科学最新文献

{"title":"[Effects of Long-term Nitrogen Application on Soil Bacterial Community Structure， Functional Groups， and Assembly Processes in Tea Gardens].","authors":"Yu-Zhen Chen, Yun-Ni Chang, Jun Sun, Feng Wang, Wen-Quan Yu","doi":"10.13227/j.hjkx.202507019","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507019","url":null,"abstract":"<p><p>Nitrogen fertilizer application is an essential aspect of tea plantation management to improve the yield and quality of tea， and the nitrogen fertilizer constitutes the major artificial factor affecting the soil bacterial community structure. The field experiment （small cement pond） was set up in the base of the Tea Research Institute of Fujian Province （beginning in 2011）， and four nitrogen levels were applied： N0 （0 kg·km<sup>-2</sup>）， N1 （112.5 kg·hm<sup>-2</sup>）， N2 （225 kg·hm<sup>-2</sup>）， and N3 （450 kg·hm<sup>-2</sup>， all measured by N）， and each treatment was repeated four times. We used 16S rRNA high-throughput sequencing to analyze the effects of long-term nitrogen applications on the soil bacterial diversity， functional groups， and community assembly. Further， the Pearson correlation analysis and redundancy analysis （RDA） were also used to examine the soil property factors that drive the community structure. The results showed that： ① The soil bacterial diversity followed a unimodal trend with increasing N concentration. Compared with that in the N0 treatment， the soil bacterial diversity index （Ace， Chao1， and Shannon） of N2 and N3 treatments significantly decreased by 6.4%-18.74%， 5.93%-19.72%， and 3.99%-6.61% in the spring sampling period， and the soil bacterial diversity index of N2 and N3 treatments significantly decreased by 10.94%-5.95%， 11.02%-25.30%， and 3.61%-8.62% in the autumn sampling period. ② The results of principal coordinates analysis （PCoA） and permutational multivariate analysis of variance （PERMANOVA） showed that long-term nitrogen application drastically changed the community structures of soil bacteria in tea plantations. Compared with N0， N2 and N3 significantly increased the relative abundance of Firmicutes in two seasons， but N3 significantly decreased the relative abundance of Acidobacteria and Myxococcota， and the most important factors affecting the differences in bacterial communities were ammonium nitrogen， acid phosphatase， and nitrate nitrogen. ③ The functional prediction with FAPROTAX showed that chemoheterotrophy （33.01%）， aerobic chemoheterotrophy （32.34%）， and cellulolysis （11.76%） were the main ecological functions in tea garden soils. Long-term nitrogen application significantly changed the ecological functions of bacteria in tea garden soils， and the relative abundances of several functional genes associated with N cycling were reduced by medium-high nitrogen treatments （N2 and N3）. Redundancy analysis （RDA） showed that the ammonium nitrogen， available potassium， and total nitrogen were the most important factors to explain the differences in bacterial ecological functions. ④ Network analysis revealed that the N1 treatment increased the number of edges， average degree， average clustering coefficient， and network density of bacterial networks， and the N2 and N3 treatments significantly decreased the number of edges， average degree， and network density of bacterial networks compared with ","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6530-6544"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Land Use Conflict Identification and Multi-scenario Simulation Evolution in Henan Province Based on PLUS Model].","authors":"Bao-Rong Qu, Jun-Chang Huang, Ling Li, Shan-Shan Guo, Tai-Wei Hou, Zi-Jun Ma, Li-Ming Ai, Yi Zhang","doi":"10.13227/j.hjkx.202507025","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507025","url":null,"abstract":"<p><p>Accurate identification and dynamic measurement of land use conflicts is an important basis for analyzing regional land use dynamics， optimizing spatial layout， and promoting sustainable development. Taking Henan Province as an example， this study utilizes five periods of land use data from 2000 to 2020 to construct a land use conflict identification model based on land use landscape pattern， systematically analyzes the spatial and temporal evolution characteristics of land use conflicts at both county and grid scales， and employs the PLUS model to simulate conflict patterns under different development scenarios for Henan Province by 2035. The results show that： ① During the study period， cultivated land area decreased from 108 531.03 km² to 103 327.43 km²， representing a decline of 4.79%， and construction land expanded significantly， increasing from 17 262.72 km² to 22 152.55 km²， an increase of 28.33%. ② From 2000 to 2020， land use conflicts in Henan Province were predominantly controllable. The average conflict index at the grid scale fluctuated downward from 0.41 to 0.40. However， the proportion of severely uncontrolled areas increased from 0.71% to 8.51%， indicating a significant intensification of localized conflicts. In terms of spatial distribution， controllable conflict zones were concentrated in areas with minimal human activity， such as forested regions and mountainous areas， exhibiting stable patterns. Conversely， uncontrollable conflict zones showed significant changes. Since 2005， driven by industrialization and urbanization， both the extent and severity of land use conflicts have markedly increased in urban centers， densely populated areas， and their surrounding regions. ③ Simulations of multiple land use conflict scenarios in Henan Province for 2035 indicated that the proportion of uncontrolled units remained below 50% across all four scenarios. Among these， the urban development scenario exhibited the most severe conflicts， with uncontrolled units accounting for 47.11%. The ecological protection and arable land priority scenarios exhibited lower conflict levels. The ecological protection scenario had 59.7% controllable units， while the arable land priority scenario had 70.89% controllable units； only 1.15% of units were severely uncontrollable， and the combined proportion of units with basic uncontrollable and severely uncontrollable was minimal. Therefore， the arable land priority scenario represents the optimal development path for Henan's future land use. The results of the study can provide scientific reference for Henan Province to coordinate arable land conservation with urbanization development and formulate scientifically sound land use policies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6199-6211"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Cost-benefit Analysis of Synergistic Pollutant and Carbon Reduction Through Watershed Interventions].","authors":"Chen-Yang Wang, Qi-Meng Jia, Hai-Feng Jia, Yin Wang, Jin-Cheng Liu","doi":"10.13227/j.hjkx.202507403","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507403","url":null,"abstract":"<p><p>Quantitative analysis of the environmental benefits， carbon emissions， and economic costs of watershed management interventions is crucial for meeting pollution and carbon reduction targets. However， existing watershed studies mainly focus on assessing the environmental benefits and financial costs of pollution control facilities. Few evaluate their carbon emissions， and those that do typically focus on single facility types， missing a comprehensive and systematic analysis of emissions across entire watershed pollution control systems. This study creates a watershed cost-benefit assessment framework for water interventions， demonstrated in the Dianchi watershed. By comparing the characteristics of interventions and their effects on water quality and quantity， along with carbon emission and economic cost data from facility construction and operation， this analysis examines the cost-benefit performance under various scenarios. The results revealed notable synergistic effects between urban low impact development （LID） implementation and other watershed facilities. Urban LID and rural best management practices （BMP） measures showed lower marginal carbon abatement costs per unit pollutant reduction， while the inter-basin ecological water diversion involved higher costs. Conversely， water diversion and combined sewer storage capacity expansion （SCE） exhibited lower marginal economic costs， whereas urban LID implementation incurred higher costs. This framework uncovers synergistic interactions among water environment control measures， providing a methodological foundation for systematic cost-benefit assessment of watershed intervention systems.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6047-6056"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Ecological Risk Assessment and Source Analysis of Soil Heavy Metals in Different Land Use Patterns in Ningxia].","authors":"Ke-Lin Chen, Hong Li, Jian-Jun Ma, Ya-Qi Zhang, Yu-Rong Li, Ling Yang, Le-Ying Feng, Rui-Ming Dai, Kun Ma, Dan-Bo Pang, Lin Chen","doi":"10.13227/j.hjkx.202504084","DOIUrl":"https://doi.org/10.13227/j.hjkx.202504084","url":null,"abstract":"<p><p>Taking the soil with different land use patterns in Ningxia as the research object， based on the analysis and determination of the content characteristics of As， Hg， Cr， Ni， Cu， Zn， Cd， and Pb in the samples， the geoaccumulation index and the potential ecological risk index were used to evaluate the risk of metal pollution， and the traceability analysis of the sources of heavy metal pollution was carried out by combining correlation analysis， principal component analysis， and the absolute principal component score-multiple linear regression （APCS-MLR） model. The average contents of Zn and Cd， as well as the average contents of Hg， Cu， Zn， Cd， and Pb in irrigated and paddy field soils exceeded the soil background values in Ningxia. The eight heavy metals in the soil under different land use patterns were lower than the risk screening and control values. The land accumulation index and potential risk index showed that Hg and Cd were the most polluted compared to other elements in these three types of land and were the main soil pollution factors of the three different land use methods. The analysis of the APCS-MLR model showed that the pollution sources of dryland included agriculture， nature， industry， and their mixtures， accounting for 36.6%， 20.1%， 14.1%， and 29.2%， respectively. The pollution sources of irrigated land were nature， transportation-agriculture mixed sources， agricultural sources， industrial sources， and nature-transportation-agriculture-industry mixed sources， accounting for 50.08%， 31.32%， 6.65%， and 11.94%， respectively. The pollution sources of paddy fields included natural-agricultural composite sources， agricultural sources， industrial sources， and natural-agricultural-industrial mixed sources， accounting for 46.81%， 21.24%， 12.65%， and 19.30%， respectively. Through in-depth analysis of pollution sources and influencing factors， this study provides an important basis for formulating targeted pollution prevention and control measures and promotes the process of sustainable agricultural development and ecological environmental protection.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6422-6432"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Dynamic Assessment and Zonal Control of Ecological Environment Quality in the Upstream Region of the Honghe River Basin Based on MRSEI].","authors":"Kun Yang, Yi-Zhong Huang, Chuan-Shu Huang","doi":"10.13227/j.hjkx.202507391","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507391","url":null,"abstract":"<p><p>The Honghe River is an important international river originating in Yunnan， serving as an ecological barrier for the province， and is a meeting point for the dry-hot valley's fragile zone and ecological corridor. Within the region， biodiversity coexists with ecological issues such as soil erosion and rocky desertification. A systematic analysis assesses the spatial differentiation and temporal changes in the ecological environment quality of the watershed and the driving factors， thereby proposing zoning control measures， which is significant for establishing Yunnan as a national leader in ecological civilization and can provide foundational information for decision-making in the development， protection， and collaborative management of the Honghe River. This study， focusing on the ecological environment characteristics of the region， explores constructing an improved remote sensing ecological index （MRSEI） that integrates the normalized difference vegetation index （NDMVI）， humidity （WET）， aridity （NDBSI）， and temperature （LST） as core parameters for evaluating ecological environment quality and analyzes its applicability， establishing MRSEI based on five periods of Landsat remote sensing data from 2000 to 2020 for evaluation， obtaining the spatial distribution and dynamic changes of ecological environment quality in the upper reaches of the Honghe River Basin， employing Sen-MK trend analysis， Hurst index， and partial derivative methods to reveal its evolution trends and driving mechanisms. Based on the characteristics of ecological environment quality evolution， the five types of control units with appropriate control measures were proposed. The results indicate that： ① Compared to the traditional RSEI， MRSEI could more accurately reflect changes in vegetation cover， showing better applicability in the study area. ② There was a significant spatial disparity in ecological environment quality in the study area； from 2000 to 2020， the overall trend of ecological environment quality in the watershed improved， with MRSEI mean increasing from 0.298 7 to 0.322 1. The vegetation in the southern part noticeably improved， while the rocky desertification area in the north continued to degrade. ③ It was predicted that 10.12% of the area in the future study region will continue to improve ecologically， as well as 26.18% of the improved areas at risk of reversal. ④ Precipitation was a major positive influencing factor， significantly contributing to the improvement of ecological environment quality， whereas human activities were negative influencing factors， serving as major driving forces for ecological environment quality degradation and fluctuations. ⑤ The watershed was classified into five types of control units： ecological protection core area， monitoring area， optimization area， restoration area， and potential governance area， with corresponding control measures proposed.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6251-6263"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial-temporal Evolution and Driving Factors of Soil Organic Carbon in Karst and Non-karst Areas of Guangxi Based on Random Forest].","authors":"Xiao-Yu Linghu, Xing-Ping Wei, Jun-Bing Pu, Fan Yan, Shi-Die Chen, Hui Li, Rou-Man Huang, Yun-Li Cai","doi":"10.13227/j.hjkx.202507378","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507378","url":null,"abstract":"<p><p>The difference in soil organic carbon density （SOCD） between karst and non-karst areas affects the accurate assessment of soil carbon sinks at the provincial scale， making it a hot and difficult topic in soil carbon pool research. Taking the karst and non-karst regions of Guangxi as an example， and based on soil profile and environmental variable data from 1980 and 2010， a random forest model was constructed to analyze the spatiotemporal differentiation characteristics of topsoil SOCD in karst and non-karst region. Furthermore， SHAP values and partial dependence plots were employed to reveal the response characteristics of different driving factors to topsoil SOCD in the two regions. The results show that： ① The mean SOCD in the karst region was higher than that in the non-karst region in both 1980 and 2010. The areas with a decrease in the annual mean change rate of SOCD in the karst region showed a high spatial consistency with the expansion areas of rocky desertification. ②Total nitrogen content and slope were both key driving factors affecting SOCD in the two regions. In addition， silt content， soil types， and normalized difference vegetation index significantly influenced SOCD in the karst region， while mean annual temperature， elevation， and total precipitation exerted important impacts on SOCD in the non-karst region. ③ The relationships between SOCD and the driving factors were characterized by diversity. SOCD showed a nonlinear positive correlation with total nitrogen content， silt content， normalized vegetation index， and elevation； a nonlinear negative correlation with mean annual temperature and total annual precipitation； and a nonlinear relationship with slope that was initially negative and then positive. Moreover， there were distinct thresholds in all these relationships. These research findings provide a scientific basis for systematically assessing the potential of soil carbon sinks， formulating measures for regulating soil carbon pools， and developing carbon management zoning strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6223-6236"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial Patterns and Drivers of Carbon Density in Terrestrial Ecosystems in Southwest China].","authors":"Fan Yang, Xing-Ping Wei, Xiao-Yu Linghu, Rou-Man Huang, Hui Li, Shi-Die Chen, Yun-Li Cai","doi":"10.13227/j.hjkx.202507339","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507339","url":null,"abstract":"<p><p>The complexity of the spatial distribution pattern of carbon density in terrestrial ecosystems in Southwest China and its influencing factors determines its key position in regional carbon cycle research and makes it a crucial point for accurate carbon sink assessment and ecosystem management. Based on the 2010s Chinese terrestrial ecosystem carbon density dataset and soil profile data from soil monographs， this study employs random forest and piecewise structural equation modeling to explore the spatial distribution characteristics and the contribution of influencing factors to the three major carbon pools （aboveground biomass carbon pool， belowground biomass carbon pool， and soil organic carbon pool） in terrestrial ecosystems in Southwest China. ① The results showed that the carbon density （C） of terrestrial ecosystems in Southwest China during the 2010s was （16.42±3.07） kg·m^-2， with a total carbon storage of 31.58 Pg. The contributions of the soil carbon pool， aboveground vegetation carbon pool， and belowground vegetation carbon pool were 73.69% （23.27 Pg）， 20.77% （6.56 Pg）， and 5.54% （1.75 Pg）， respectively. ② The vegetation carbon pool showed a spatial pattern of higher values in the south and lower values in the north， while the soil carbon pool generally had higher values in the west and lower values in the east. ③ The piecewise structural equation model indicated that climate change， vegetation cover， and soil physicochemical properties directly affected the biophysical processes of terrestrial ecosystems， while geographical environment and human activities mainly influenced carbon density indirectly by altering land use and vegetation cover. Therefore， accurately assessing the carbon storage of terrestrial ecosystems in Southwest China， clarifying their spatial distribution characteristics， and exploring the interaction mechanisms among influencing factors are vital for evaluating the carbon sink/source feedback mechanisms under climate change and predicting future carbon cycle trends.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5831-5847"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Effects and Mechanisms of Artificial Intelligence Driving Agricultural Carbon Emission Reduction： A Synergistic Perspective of Green Finance and Industrial Structure Upgrading].","authors":"Jie Hu, Jian-Chao Luo","doi":"10.13227/j.hjkx.202512128","DOIUrl":"https://doi.org/10.13227/j.hjkx.202512128","url":null,"abstract":"<p><p>As China advances its carbon peaking and carbon neutrality goals， agricultural low-carbon transition has become a critical priority for reconciling ecological security and food security. While artificial intelligence （AI） offers transformative potential to address bottlenecks in agricultural green transformation， its carbon reduction mechanisms， particularly the synergistic roles of green finance and industrial structure upgrading， remain underexplored in existing literature， which often lacks integrated analytical frameworks and long-term dynamic evidence. Based on the dual perspectives of green finance and industrial structure upgrading， this study uses the panel data of 31 provinces in China from 2000 to 2023 and constructs the two-way fixed effect model to empirically test the effect and mechanism of AI driving agricultural carbon emission reduction. The results show that： ① AI had a significant and robust inhibitory effect on agricultural carbon emissions， and this conclusion remained robust after a series of robustness tests. ② The mechanism test confirmed that AI could indirectly and synergistically promote agricultural carbon emission reduction by promoting the development of green finance and the upgrading of industrial structure. ③ Heterogeneity analysis showed that the effect of AI on agricultural carbon emission reduction was more prominent in the eastern region， regions with a medium level of urbanization， a low level of agricultural modernization， and non-major grain-producing areas. The study suggests promoting the application of AI technology in the agricultural field at the comprehensive policy level， build a coordinated path between green finance and agricultural structure transformation， and implementing differentiated support and key breakthrough strategies to help AI to empower agricultural green development and fully release its carbon emission reduction efficiency.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5961-5973"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatio-temporal Evolution and Driving Forces of Landscape Ecological Risk in the Yangtze River Economic Belt].","authors":"Jia-He Li, Wei-Yue Li, Yao-Hui Liu","doi":"10.13227/j.hjkx.202504187","DOIUrl":"https://doi.org/10.13227/j.hjkx.202504187","url":null,"abstract":"<p><p>Conducting landscape ecological risk assessment and exploring driving factors can provide scientific support for ecological protection and high-quality development. The Yangtze River Economic Belt was selected as the study area， and multi-temporal remote sensing land-use data from 2010， 2015， and 2020 was utilized to construct a landscape ecological risk assessment model employing landscape pattern indices. We conducted a land type distribution analysis to explore the temporal and spatial fluctuations in landscape ecological risks. The Moran index was utilized to examine the spatial correlation and clustering patterns of landscape ecological risks， while the optimal parameter Geodetector was applied to investigate the driving forces of impact factors. The results indicate：① Arable land， forests， and grasslands constituted the principal categories of land. Between 2010 and 2015， the region experiencing changes in land use constituted 0.77% of the whole research area； however， from 2015 to 2020， this region represented 29.80% of the total study area， signifying substantial variations in land use patterns. ② The principal levels of landscape ecological risk were low risk， lower risk， and medium risk. High-risk regions were predominantly located in the Yangtze River Delta， the Su-Wan Floodplain， the Jianghan Plain， the Dongting Lake Plain， the Poyang Lake Plain， and the foothills of the Qinghai-Tibet Plateau in northwestern Sichuan Province. The ecological hazards in the middle and lower reaches of the Yangtze River exceeded those found in the upper reaches. ③ From 2010 to 2015， landscape ecological risk alterations were predominantly observed in lower-risk areas， which evolved into low ecological risk zones. The magnitude of these alterations was more pronounced in the higher reaches than in the middle and lower reaches. It was evident that between the years 2015 and 2020， the region exhibiting elevated risk levels surpassed that of reduced risks， and the magnitude of alterations in risk levels was more pronounced. The distribution of landscape ecological risk demonstrated a positive spatial association， characterized by a generally consistent spatial clustering pattern. Initially， high-high clustering rose before declining， whereas low-low clustering persisted in its decline. ④ The level of human disturbance， land use intensity， NDVI， and slope have been identified as significant determinants of landscape ecological risk in the Yangtze River Economic Belt. The explanatory power of pairwise interactions between driving factors was enhanced. The results of the research are of significance for the optimization of ecological compensation and sustainable development planning in the study area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6317-6327"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatiotemporal Distribution Characteristics of Soil Organic Carbon and Carbon Sequestration Potential in Cultivated Land in Hebei Province in the Past 40 Years].","authors":"Chen-Yu Lin, Dong-Heng Yao, Ru-Qian Zhang, Liang Li, En-Yi Xie, Wen-Cai Zhang, Zhen-Ting Zhao, Jing Zhao, Xiang-Bin Kong","doi":"10.13227/j.hjkx.202506324","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506324","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Exploring the spatiotemporal distribution characteristics， influencing factors， and carbon sequestration potential of soil organic carbon density （SOCD） and soil organic carbon stock （SOCS） in the whole profile （0-120 cm） of cultivated land in major grain-producing areas is significant for regional agricultural sustainable development and farmland carbon management. Based on the data of the second soil survey in Hebei Province （1980） and the measured soil profile data of cultivated land in 2020， we estimated the SOCD and SOCS in cultivated land in Hebei Province at different soil depths and under different soil types. We revealed the spatiotemporal distribution characteristics of soil organic carbon （SOC） in cultivated land in Hebei Province， analyzed the influencing factors of SOC using a geographic detector model， and estimated the carbon sequestration potential of cultivated land soil using the saturation value method. The conclusions are as follows： ① From 1980 to 2020， the average values of SOCD in surface soil （0-20 cm）， middle soil （20-70 cm）， and deep soil （70-120 cm） in Hebei Province increased by 0.511， 0.340， and 0.599 kg·m&lt;sup&gt;-2&lt;/sup&gt;， respectively. In terms of spatial distribution， the SOCD in the northern， southern， and western regions of central-southern Hebei showed an increasing trend， while SOCD in the southern part of Tangshan City， Zhangjiakou City， and the northern part of Chengde City showed a decreasing trend during the 40 years. From the perspective of each soil type， the SOCD increased in fluvo-aquic soils， cinnamon soils， castanozems， and castano-cinnamon soils and decreased in brown earths in the whole soil profile. In terms of influencing factors， the explanatory power of annual precipitation， annual temperature， normalized difference vegetation index， pH， and total nitrogen varied across different layers. The interaction between any two influencing factors had a stronger explanatory power for changes in SOCD than either factor alone， exhibiting non-linear enhancement or two-factor enhancement characteristics. ② From 1980 to 2020， the SOCS in the middle soil accounted for approximately 40%-45% of total SOCS in the whole profile. During the 40 years， the increases in SOCS of the surface layer and deep layer accounted for 35.24% and 41.31% of the total increase in SOCS of the whole soil profile， respectively. As the main soil types in Hebei Province， the sum of SOCS in fluvo-aquic soils and cinnamon soils accounted for 73.35% and 75.65% of total SOCS in the two periods， respectively. ③ Cultivated land in Hebei Province was a \"carbon sink\" area. The soil carbon sequestration potential of cultivated land in the whole profile of Hebei Province was 148.762 Tg， with the carbon sequestration potentials of the surface， middle， and deep layers being 46.740， 38.117， and 63.905 Tg， respectively， accounting for 31.42%， 25.62%， and 42.96% of the total. In the future， we should further enhance the SOC in cultivated land in Heb","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6212-6222"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}