环境科学最新文献

{"title":"[Decoupling Effect and Interactive Relationship Among Transportation Infrastructure, Economic Growth, and Carbon Emissions in China].","authors":"Zhi-Guo Shao, Ke-Xin Li, Meng-di Li","doi":"10.13227/j.hjkx.202408256","DOIUrl":"https://doi.org/10.13227/j.hjkx.202408256","url":null,"abstract":"<p><p>The construction of transportation infrastructure boosts economic growth while facing the challenge of carbon emissions pressure. Clarifying the relationship among transportation infrastructure, economic growth, and carbon emissions is important in order to promote the realization of the goal of \"dual-carbon.\" Based on the panel data of 30 provinces in China from 2002 to 2021, the research period was divided into four stages （2002-2006, 2007-2011, 2012-2016, and 2017-2021）. The Tapio decoupling model was used to analyze the decoupling state between carbon emissions and transportation infrastructure, as well as between carbon emissions and economic growth, and the panel vector autoregression （PVAR） model was used to study the dynamic relationship and internal influence mechanism among the three in each region. The results showed that： ① The overall decoupling relationship between carbon emissions and transportation infrastructure in China showed the changing trend of \"weak decoupling → strong negative decoupling → strong decoupling → weak decoupling.\" ② The decoupling relationship between carbon emissions and economic growth in 30 provinces only showed four states in the four stages： strong decoupling, weak decoupling, expansive coupling, and expansive negative decoupling. During the research period, the decoupling index between carbon emissions and economic growth decreased in most provinces of China, and the overall decoupling state improved, but the carbon emissions decoupling situation was unstable. ③ Transportation infrastructure had a positive impact on economic growth in each region, and both transportation infrastructure and economic growth had a positive impact on carbon emissions in each region, but the degree of impact varied by region. The results of the research can provide low-carbon development strategies for the construction of transportation infrastructure and help promote the stable, healthy, and sustainable development of China's economy.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"4813-4825"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856597","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 Sensitivity Evaluation of Three-River-Source National Park Based on CRITIC Objective Weighting Method].","authors":"Ying He, Zhong-Qiu Zhao, Zhen-Ran Mei, Hang Bai","doi":"10.13227/j.hjkx.202407221","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407221","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Ecological sensitivity assessment is a probability assessment that intuitively reflects the potential ecological and environmental risks in a region under ecological imbalance. Utilizing ecological sensitivity assessment enables the precise identification of sites and areas that require priority environmental construction and protection, allowing for targeted conservation efforts in these regions. To effectively measure the comprehensive impact of environmental changes and human activities on the ecosystem of the Three-Rivers-Source National Park, as well as to explore the key areas where regional ecological issues occur, the following steps are taken： First, based on the location characteristics and ecological background of the Three-Rivers-Source National Park, a multi-dimensional evaluation index system was quantitatively constructed, including topography, climate, soil, vegetation, species, water resources, and human activities. Second, the CRITIC objective weighting method was used to comprehensively evaluate the ecological sensitivity of the Three-Rivers-Source. The CRITIC objective weighting method fully considers the magnitude of indicator variations and the correlations between indicators. Compared to subjective weight assignment methods, it is better able to reflect the influence of evaluation factors on comprehensive weights, thereby further enhancing the accuracy of the data, which is also an innovative aspect of the study. Finally, the GeoDa software was used to conduct spatial autocorrelation verification analysis on the evaluation factors of ecological sensitivity and to discuss the zoning of protection and management areas. The results showed： ① The buffering distance of wildlife habitats, land use types, and elevation were the three factors with the highest weights in evaluating the ecological sensitivity of the Three-Rivers-Source, with weights of 7.501%, 7.38%, and 7.189%, respectively. ② The Three-Rivers-Source National Park was categorized as having a moderately sensitive ecological status. The comprehensive sensitivity of the study area increased from the northwest to the southeast. The composite sensitivity index ranged between 1.65 and 4.00, with areas of high and very high sensitivity accounting for more than 50% of the total area. These regions were predominantly characterized by the presence of lakes, rivers, and vegetation conservation areas, as well as high-altitude areas, which were frequent sites for ecological issues. ③ The evaluation factors of ecological sensitivity in the Three-Rivers-Source National Park showed significant spatial autocorrelation and high spatial clustering, distributed in a contiguous and cohesive pattern on the spatial level. Hotspots that require priority ecological management were mainly concentrated in the Lancang River and Yellow River source areas. The purpose of the study was to explore the theory and methods of ecological environment protection in the Three-Rivers-Source National","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5156-5168"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856600","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":"[Prediction of China's Carbon Emission Intensity Based on a Grey Breakpoint Model with Inverse Accumulation].","authors":"Hui-Ping Wang, Zhun Zhang","doi":"10.13227/j.hjkx.202407216","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407216","url":null,"abstract":"<p><p>Given the escalating challenges posed by global climate change, as the world's largest carbon emitter, China is facing a huge challenge in achieving its \"dual carbon\" goals. Therefore, reasonable prediction of China's carbon emission intensity is crucial for formulating effective emission reduction strategies. Considering the external shocks faced by the economic system, the time breakpoint is introduced into the traditional grey prediction model. The model is optimized from two aspects： accumulation method and background value, and a new grey breakpoint model with inverse accumulation is constructed. Based on the calculation of China's carbon emissions, the carbon emission intensity from 2023 to 2030 was predicted. The following conclusions were drawn： ① By adding time breakpoints, the new model achieved accurate prediction of the future trend of the system under external shocks, further reflecting the principle of information priority in the modeling process. ② Under the external impact of the COVID-19, the growth rate of China's GDP further slowed down, and the carbon emissions showed different characteristics in the four regions. The carbon emissions in the northeast began to decline gradually, while the carbon emissions in the eastern and western regions accelerated. ③ From 2023 to 2030, China's carbon emission intensity will considerably decrease. Compared with that in 2020, the carbon emission intensity is expected to decrease by 13.2% in 2025 and by 22.6% in 2030, with the highest decline in the northeast and the lowest in the east. However, under current conditions, China still finds it difficult to fully achieve its 2025 and 2030 emission reduction targets, with the eastern and western regions facing enormous pressure to reduce carbon emissions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"4765-4777"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856608","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":"[Source Apportionment of Heavy Metals in Soils Based on Machine Learning Algorithms and Receptor Model].","authors":"Jie Ma, Ming-Sheng Li, Xue Feng","doi":"10.13227/j.hjkx.202408149","DOIUrl":"https://doi.org/10.13227/j.hjkx.202408149","url":null,"abstract":"<p><p>To analyze the source apportionment and influence factors of heavy metals in soils surrounding a coal gangue heap in Chongqing, three machine learning algorithms （decision tree （DT）, random forest （RF）, and support vector machine （SVM）） and the absolute principal component scores-multiple linear regression （APCS-MLR） receptor model were used. The surface soil results showed that the average values of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn were 0.44, 0.18, 9.92, 32.3, 129, 100, 72.8, and 148 mg·kg^-1. Combined profile soil data showed that Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn were affected by human activities to varying degrees. Using machine learning algorithms analysis, RF was better than DT and SVM, and <i>R</i>² values of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn were 0.783, 0.728, 0.528, 0.753, 0.753, 0.853, 0.822, and 0.756. \"The number of coal gangue units\" （<i>X</i>₁）, \"the vertical height difference between the sampling point and coal gangue heap\" （<i>X</i>₂）, and \"the distance between the sampling point and the coal gangue heap\" （<i>X</i>₃） were the key driving factors by human activities. Combined with APCS-MLR model analysis, the soil in the study area was affected by natural sources, mining sources, and mixed sources （including atmospheric deposition, agricultural production, life and traffic emissions, etc.）, with contribution rates of 42.5%, 37.1%, and 20.4%, respectively. The combined application of the machine learning algorithms and receptor model can make the results of source apportionment more comprehensive, accurate, and reliable.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5229-5236"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856616","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 Changes and Driving Factors of Carbon Storage in the Middle Reaches of the Yellow River Based on PLUS-InVEST-GeoDetector Model].","authors":"Fan-Fan Bi, Zhi-Tao Wu, Han-Xue Liang, Zi-Qiang Du, Tian-Jie Lei, Bin Sun","doi":"10.13227/j.hjkx.202406176","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406176","url":null,"abstract":"<p><p>Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yellow River is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the \"double carbon\" plan. Based on the five-year interval, the land use data of the middle reaches of the Yellow River from 2000 to 2020 were selected, and the spatio-temporal evolution characteristics of carbon reserves were estimated and analyzed by coupling with the PLUS-InVEST-GeoDetector model, and the driving factors affecting the spatio-temporal differentiation of carbon reserves were discussed. Finally, the carbon reserves of the middle reaches of the Yellow River in 2030 were predicted under four developmental scenarios： natural development, ecological protection, economic development, and cultivated land protection. The findings indicate that： ① The middle reaches of the Yellow River's carbon storage showed a consistent growth trend between 2000 and 2020, exhibiting an increase by 5.75×10⁷ t. The evolution of the spatial distribution was reasonably stable, exhibiting the characteristics of \"southeast is higher than northwest.\" ② The middle reaches of the Yellow River's carbon storage differentiated both spatially and temporally between 2000 and 2020, with two-factor enhancement and nonlinear enhancement observed in the interaction detection of each driving element. The main driving force was the NDVI. ③ From 2020 to 2030, the carbon storage of the four scenarios in the Yellow River's middle reaches showed an increasing trend in comparison to that in 2020. Of them, the carbon storage of the ecological preservation scenario rose the highest at 3.93×10⁷ t, while the carbon storage of the economic growth scenario increased the least at 4.8×10⁶ t. The findings of the study will offer some evidence in favor of the middle reaches of the Yellow River's long-term development and ecological environment management.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"4742-4753"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856629","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":"[Water Quality Analysis and Prediction for the Middle Route of South-to-North Water Diversion Project Based on EDM-LSTM].","authors":"Bing Bai, Fei Dong, Wen-Qi Peng, Xiao-Bo Liu","doi":"10.13227/j.hjkx.202407244","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407244","url":null,"abstract":"<p><p>To deeply analyze the causal relationships among various water quality indicators in the Middle Route of South-to-North Water Diversion Project and achieve high-precision predictions, a method combining empirical dynamic modeling （EDM） and deep learning is proposed. Empirical dynamic modeling is utilized to conduct causal analysis among water quality indicators. Based on this, a dataset is constructed to train long short-term memory （LSTM） neural networks for water quality prediction. The prediction accuracy and computational time of different LSTM structures are compared. The results showed that： ① The water quality of the Middle Route of South-to-North Water Diversion was stable, with no significant abrupt changes along the route. ② There was a bidirectional causal relationship between total nitrogen and dissolved oxygen, as well as pH, in the Middle Route of South-to-North Water Diversion Project. ③ The neural network trained based on causal analysis results could achieve high-precision water quality predictions for the Middle Route of South-to-North Water Diversion Project, with the Nash efficiency coefficient of the predictions generally exceeding 0.85. This method can deeply analyze the causal relationships among variables and achieve high-precision predictions, providing scientific support for water quality management and subsequent analysis and prediction of water ecological factors in the Middle Route of South-to-North Water Diversion Project.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5103-5111"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856659","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":"[Impact of Climate Change and Human Activities on Net Primary Productivity of Vegetation in the Yunnan-Guizhou Plateau].","authors":"Yi-Fei Zhang, Jun-Ling Zhang","doi":"10.13227/j.hjkx.202407164","DOIUrl":"10.13227/j.hjkx.202407164","url":null,"abstract":"<p><p>Vegetation net primary productivity （NPP） is a key indicator for assessing the carbon budget of terrestrial ecosystems. Studying the impact of climate change and human activities on vegetation NPP is critical for a deeper understanding of carbon cycling mechanisms and promoting sustainable economic development. Based on MOD17A3 NPP data, meteorological data, and land use data, this study explores the spatiotemporal variation characteristics of NPP across different geological backgrounds and vegetation types in the Yunnan-Guizhou Plateau from 2001 to 2020, using the Theil-Sen Median slope estimator and Mann-Kendall significance test. An improved residual analysis method is employed to investigate the relative contributions of climate change and human activities to vegetation NPP in the Yunnan-Guizhou Plateau. The results indicated the following： From 2001 to 2020, the NPP of vegetation in the Yunnan-Guizhou Plateau showed an increasing trend at a rate of 3.39 g·（m²·a）^-1. The multi-year average NPP of vegetation in non-karst areas was 901.42 g·（m²·a）^-1, which was higher than the 837.83 g·（m²·a）^-1 in karst areas. However, the growth rate of vegetation NPP in non-karst areas was 2.56 g·（m²·a）^-1, which was lower than the 3.69 g·（m²·a）^-1 in karst areas. Among different types of vegetation, herbaceous vegetation had the highest multi-year average NPP at 900.26 g·（m²·a）^-1, with a relatively high growth rate of 3.6 g·（m²·a）^-1. Arbor vegetation had a higher multi-year average NPP of 864.54 g·（m²·a）^-1 but the lowest growth rate at only 2.69 g·（m²·a）^-1. Economic vegetation had a lower multi-year average NPP of 809.24 g·（m²·a）^-1 but a higher growth rate of 3.96 g·（m²·a）^-1. Precipitation contributed positively to vegetation NPP in the Yunnan-Guizhou Plateau, with a positive contribution rate of 68.16%, while temperature had a positive contribution rate of 74.5%. Precipitation significantly promoted vegetation growth in the central and eastern regions of the Yunnan-Guizhou Plateau but had a suppressive effect on vegetation in the western regions. From 2001 to 2020, climate change contributed 77.09% to the changes in vegetation NPP in the Yunnan-Guizhou Plateau, which was higher than the 22.91% contribution from human activities. Human activities had a positive contribution rate of 70.76% to vegetation NPP in karst areas, higher than the 60.96% in non-karst areas. Human activities had a larger positive contribution rate to herbaceous vegetation NPP at 73.02% and to shrub vegetation at 71.92%. The findings provide a theoretical basis for formulating tailored ecological restoration and management strategies for the Yunnan-Guizhou Plateau.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5217-5228"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856582","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":"[Occurrence Characteristics, Bioaccumulation, and Ecological Risk of PFASs in Rivers Receiving Different Effluents].","authors":"Si-Yuan Yang, Jian-Chao Liu, Guang-Hua Lu, Jun Hou","doi":"10.13227/j.hjkx.202406018","DOIUrl":"10.13227/j.hjkx.202406018","url":null,"abstract":"<p><p>Perfluorinated and polyfluoroalkyl substances （PFASs） are a type of persistent organic pollutants, which are widely used in leather anti-fouling treatment and fire extinguishing materials and threaten ecological security by entering environmental media in many ways. In this study, the occurrence characteristics of PFASs in river water and fish were investigated and their environmental risks were evaluated in five different types of rivers receiving different effluent. The results showed that PFASs were widely present in the receiving rivers, and the total concentration of PFASs ranged from 37.44 ng·L^-1 to 167.37 ng·L^-1. Urban comprehensive tail water and airport rainwater were the main pollution sources of PFASs, while short-chain PFASs were the main pollution types, with a pollution contribution rate of 58.7%. The accumulation potential of PFASs in carnivorous fish （<i>Carassius auratus</i>）was the highest, with a concentration of 136.87 ng·g^-1, which was 1.5 times higher than that of yellow catfish （<i>Tachysurus fulvidraco</i>） and 9.6 times higher than that of loach （<i>Misgurnus anguillicaudatus</i>）. The brain and liver were the main accumulation organs of PFASs, with contents of 217.49 ng·g^-1 and 166.8 ng·g^-1, followed by the muscle, gill, and intestine. Hexafluoropropylene oxide dimeric acid （GenX） and perfluorooctane sulfonate （PFOS） had a high bioaccumulation risk, and the average bioaccumulation potential （lgBAF） was 4.14 L·kg^-1 and 3.91 L·kg^-1, respectively. PFASs showed a low-to-medium mixed risk （RQ &lt; 0.080）. Perfluorooctanoic acid （PFOA） was the most important risk contributor, with a contribution rate of 75.31%, while fish consumption had a low risk to human health.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5082-5091"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856605","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":"[Progress on the Migration Mechanism and Toxic Effects of Nanoplastics in Terrestrial Plants].","authors":"Xiao-Fei Liu, Yu Zhang, Dong Liang, Qi-Qi Fan, Jiang Yu, Na Zhang","doi":"10.13227/j.hjkx.202406083","DOIUrl":"10.13227/j.hjkx.202406083","url":null,"abstract":"<p><p>Nanoplastics are widely distributed in soil as an emerging environmental contaminant. Recently, the effects of nanoplastics on terrestrial plants have gained significant attention. The mechanisms through which terrestrial plants absorb and transport nanoplastics include surface adsorption, intercellular transport, cleavage uptake, and stomatal uptake. Accumulation of nanoplastics in plants leads to growth retardation, inhibition of photosynthesis, and oxidative damage. At the molecular level, nanoplastics affect plant transcriptomics, metabolomics, and proteomics. The uptake, transport, and adverse effects of nanoplastics in terrestrial plants is a complex process influenced by factors like nanoplastic particle size, surface charge, and physical-chemical properties. We aim to summarize the progress in research on nanoplastics and terrestrial plant interactions and provide future research directions based on existing knowledge gaps.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5295-5302"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856611","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":"[Occurrence Characteristics and Ecological Risk Assessment of Microplastic Pollution in the Yellow River Basin].","authors":"Zheng Yang, Meng-Yuan Li, Zheng-Yun Deng, Xin Gui, Li Ma, Fa-Wen Zhang","doi":"10.13227/j.hjkx.202406206","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406206","url":null,"abstract":"<p><p>The Yellow River, as the \"mother river\" of China, urgently needs research and analysis on the MPs pollution status and ecological risks in the Yellow River Basin. The aim of the study was to explore the spatial distribution and composition characteristics of MPs in the Yellow River Basin, establish a comprehensive investigation and evaluation system for MPs at the watershed scale, and based on a two-dimensional risk assessment matrix, evaluate the absolute ecological risk of MPs at each sampling point within the watershed. The results showed that the abundance range of MPs in the study area was （0.49-350 280.00）×10³ items·m^-3, with an average abundance of （31 050.00±7 740.00）×10³ items·m^-3. In terms of spatial distribution, the abundance of tributaries in the middle reaches of the Yellow River was higher than that of the main stream, and the Yellow River estuary was the area with the highest detected abundance. The size of MPs in the watershed was concentrated between 0-1 000 μm, with fibers and fragments as the main shapes and colored and transparent as the main colors. The risk index range of the two-dimensional risk assessment matrix was 3-20 levels, with 77.78% of the area classified as high-risk. The Liujiaxia Reservoir is a water source area that requires special attention.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5316-5324"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856549","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}