工业工程最新文献

{"title":"[Impact Mechanism of the Spatial Correlation Network of Digital-real Economy Integration on the Synergy of Pollution and Carbon Emission Reduction].","authors":"Zheng Li, Kai Wang, Jia-Xin Tan, Chang Gan","doi":"10.13227/j.hjkx.202508023","DOIUrl":"https://doi.org/10.13227/j.hjkx.202508023","url":null,"abstract":"<p><p>Against the backdrop of digital-intelligent transformation， exploring the effects and mechanisms of the spatial correlation network of digital-real economy integration on the synergy of pollution and carbon emission reduction holds significant practical implications for promoting the coordinated advancement of ecological civilization construction and high-quality development. Using the modified gravity model， social network analysis， and econometric regression model， this study examines the influence mechanism of the spatial correlation network of digital-real economy integration in China's provinces on the synergy of pollution and carbon emission reduction from 2011 to 2022. The results indicate that： ① The spatial correlation of digital-real economy integration in China exhibited an increasingly complex \"multi-center driven\" network structure， presenting a layered differentiation pattern that decreased from east to west and from inner to outer regions. ② The spatial correlation network of digital-real economy integration could significantly promote the synergy of pollution and carbon emission reduction. Compared with that in the eastern region， non-pilot provinces， and service-oriented provinces， the spatial correlation network of digital-real economy integration in the central-western regions， pilot provinces， and production-oriented provinces exerted a stronger promoting effect on the synergy of pollution and carbon emission reduction. ③ The spatial correlation network of digital-real economy integration exerted positive impacts on the synergy of pollution and carbon emission reduction by promoting technological progress， driving economic agglomeration， and reducing energy intensity. Both human capital investment and public environmental attention strengthened the promoting role of the spatial correlation network of digital-real economy integration in the synergy of pollution and carbon emission reduction. The findings provide theoretical support and practical references for formulating collaborative emission reduction strategies and achieving the synergy of pollution and carbon emission reduction.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6070-6081"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012879","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 Characteristics and Influencing Factors of Fossil Energy Carbon Emissions in Sichuan Province].","authors":"Hong-Yang Ren, Bo-Ting Zhang, Yang Gao, Lei Yang, Ruo-Lan Du, Xi Li, Bing Wang","doi":"10.13227/j.hjkx.202503163","DOIUrl":"https://doi.org/10.13227/j.hjkx.202503163","url":null,"abstract":"<p><p>To characterize the spatiotemporal evolution pattern and driving mechanisms of fossil energy carbon emissions in Sichuan Province， a carbon emission accounting framework covering energy extraction， transmission， and consumption stages was established. By integrating local Moran's <i>I</i>， LISA spatiotemporal clustering， and center of gravity migration methods， the study revealed the regional aggregation and dynamic migration features of carbon emissions. Furthermore， the STIRPAT model was combined with Pearson correlation analysis， ridge regression， and random forest methods to identify key driving factors. The results showed that：① Fossil energy carbon emissions in Sichuan Province exhibited a fluctuating upward trend， dominated by the energy consumption stage. ② Significant spatial heterogeneity and temporal differentiation were observed， with the northwest-southeast spatial distribution pattern of carbon emission， and the core of emission migrating northward， reflecting a transition from a \"resource-based\" to an \"industry-based\" regional pattern. Meanwhile， local aggregation intensified， with \"high-high\" clusters expanding from the early Panxi Region to the southern Sichuan Province urban agglomeration， resulting in increasingly prominent regional clustering. ③ From 2011 to 2021， Sichuan generally experienced weak decoupling， with carbon emission growth lagging behind GDP growth and improving emission efficiency. ④ Carbon emissions were strongly positively correlated with urbanization rate， freight turnover， GDP， and energy consumption （|<i>r</i>|0.63-0.94）， with extraction， transmission， and end-use consumption stages primarily driven by energy scale， logistics expansion， and economic and demographic factors， respectively.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6000-6013"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012941","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":"[Analysis of the Spatial-temporal Pattern and Influencing Factors of the Coordinated Development of Carbon Reduction， Pollution Reduction， and Green Expansion in the Yellow River Basin].","authors":"Hong-Hong Liu, Jia-Yun Zhang","doi":"10.13227/j.hjkx.202507113","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507113","url":null,"abstract":"<p><p>The collaborative promotion of carbon reduction， pollution reduction， and green expansion is an important method for achieving the \"dual carbon\" goals and ecological civilization strategy in the Yellow River Basin. Clarifying the spatiotemporal pattern and influencing factors of the synergy level of carbon reduction， pollution reduction， and green expansion is the basis for policy formulation. On the basis of clarifying the synergistic and mutually beneficial relationship between carbon reduction， pollution reduction， and green expansion growth， a comprehensive evaluation system for the carbon reduction， pollution reduction， and green expansion composite system was constructed using 54 prefecture level cities in the Yellow River Basin as the research object. The level of collaborative development was measured using a three-dimensional space vector model， describing the dynamic evolution of collaborative development through kernel density estimation methods， and combining Geodetectors to analyze the influencing factors of collaborative development. The results show that： ① The level of coordinated development in the Yellow River Basin was on the rise， while regional heterogeneity was evident. The level of coordination among upstream cities was higher than that among midstream cities， while downstream cities had the lowest level. The collaborative level of non-resource-based cities was higher than that of resource-based cities. ② The gap in the collaborative level of carbon reduction， pollution reduction， and green expansion in different regions widened， and the polarization of the entire basin was gradually increasing. The coordinated promotion of carbon reduction， pollution reduction， and green expansion in the Yellow River Basin still faces significant structural and fundamental pressures， and the problem of uneven development between regions is more prominent. ③ The urbanization rate and the proportion of the tertiary industry were the core driving factors for the coordinated development of carbon reduction， pollution reduction， and green expansion in the Yellow River Basin， while per capita GDP， the proportion of the secondary industry， and the level of science and technology were auxiliary influencing factors. The article studies and deconstructs the level， dynamic evolution trend， and influencing factors of coordinated development of carbon reduction， pollution reduction， and green expansion in the Yellow River Basin， providing policy basis for ecological protection and high-quality development in the Yellow River Basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6025-6037"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012950","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":"[Social-ecological System Risk Assessment and Management Zoning in the Beijing-Tianjin-Hebei Region from a Spatio-temporal Dynamic Relationship Perspective].","authors":"Lu Zuo, Jun-Jing Lu, Lei-Gang Sun, Qing-Tao Hao, Qin-Yi Wang, Peng-Fei Zhang, Xiao-Qian Ma","doi":"10.13227/j.hjkx.202508085","DOIUrl":"https://doi.org/10.13227/j.hjkx.202508085","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The interaction of multiple risk factors between natural systems and human societies during rapid urbanization poses significant ecological risk to regions. Scientifically assessing and managing these risks is crucial for regional ecological security and sustainable development. This study adopts a multi-source risk spatio-temporal dynamic relationship perspective， incorporating the social-ecological system （SES） framework to assess and analyze the spatio-temporal evolution characteristics of ecosystem risk， social system risk， and comprehensive ecological risk in the Beijing-Tianjin-Hebei （BTH） Region from 2000 to 2020. Based on the spatio-temporal dynamic correlations among different risk types， the study area is categorized into six sub-regions and three macro-regions using bivariate spatial autocorrelation and hot spot-cold spot analysis. Furthermore， optimal parameters-based Geodetector analysis is employed to investigate the driving differences of comprehensive ecological risks across the entire region and within each partitioned zone. The results show that： ① The mean values of ecosystem risk， social system risk， and comprehensive ecological risk in the BTH Region were 0.464， 0.299， and 0.381， respectively. Their distribution exhibited a spatial pattern of low values in the northwest and high values in the southeast. The risk levels of various types along the Yanshan-Taihang Mountains were relatively low， and the risk in plain areas was significantly higher than that in mountainous areas， with ecosystem risk being stronger than social system risk. ② Over the past 20 years， ecosystem risk decreased by 2.55% and social system risk increased by 2.01%， resulting in a slight decrease （0.78%） in comprehensive ecological risk. Comprehensive ecological risk decreased significantly in the northwestern mountainous areas and the coastal wetlands in the eastern plains. Urbanized areas exhibited a ring structure： constant risk levels in the core and increased risk in the peripheral zones. ③ A significant and strengthening positive spatial correlation existed between ecosystem risk and social system risk. The proportions of areas with significantly increased and decreased comprehensive ecological risks were 18.63% and 19.06%， respectively. Emergency control macro-zones were concentrated in urban expansion fringes， key control macro-zones were located within urban core areas， and close monitoring macro-zones were distributed in the urban periphery and coastal belts. Dynamic regulation macro-zones covered the most extensive area， while preventive management macro-zones and conservation macro-zones were primarily situated within the Yanshan-Taihang Mountains Ecological Functional Area. ④Topography， urban construction， and socio-economic activities dominated the spatial differentiation of comprehensive ecological risk across the entire region and within monitoring macro-zones. Urban construction exerted a particularly prominent influence within contro","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6392-6404"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012973","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":"Biotribological Modelling and Performance Analysis of Blood Pumps Using Machine Learning","authors":"Vipin Kumar,&nbsp;Ankit Prakash,&nbsp;Ravi Prakash Tewari,&nbsp;Anubhav Rawat","doi":"10.1049/bsb2.70032","DOIUrl":"https://doi.org/10.1049/bsb2.70032","url":null,"abstract":"<p>The design and operation of blood pumps are very complex and expensive to save lives of patients suffering from heart ailments. Machine learning approaches may be those better affordable ones which can be used to enhance understanding of correlation between operating parameters and performance of the pumps that is between pressure rise in blood pumps and clearance, blade height and rotational speed of the pump. Four types of machine learning algorithms, that is multiple linear regression, polynomial regression, artificial neural network and random forest regression have been employed in the current study to predict pressure rise. Performance evaluation metrics have shown that machine learning approaches are efficiently able to predict the pressure rise in blood pumps using its operational parameters. Among all of the models used, the ANN (3-12-12-1) model has shown the highest accuracy of 99.9% with RELU activation function for input and hidden layers, linear activation for the output layer using ADAMAX as an optimiser. The ANN model is found to be accurate with 0.0012 MSE, 0.023 MAE and 0.999 <i>R</i>², which are significantly lower while predicting the pressure rise in the blood pump. The ANN model is also found to predict the physical behaviour of the blood pump efficiently.</p>","PeriodicalId":52235,"journal":{"name":"Biosurface and Biotribology","volume":"12 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bsb2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial-temporal Characteristics Evolution of Land Use Carbon Emissions in China from 2000 to 2021 and Carbon Emission Prediction].","authors":"Jia-Wei Yu, Zu-Heng Cheng, De-Xing An, Wen-Lin Zhang, Yu-Fen Niu, Jun Tong","doi":"10.13227/j.hjkx.202501138","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501138","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The spatiotemporal evolution of carbon emissions and its future trend prediction， as an important scientific issue in response to global climate change， has become a key area of research in climate science， environmental science， and policy studies. As the world's largest emitter of carbon， China's land use changes have had a profound impact on the dynamic changes in carbon emissions. This study utilizes the annual land use dataset from Wuhan University， socioeconomic data from the China Statistical Yearbook， and MODIS global net primary productivity （NPP） data to examine the carbon emissions and carbon sequestration from various 3land types such as urban areas， arable land， and forest land in different regions. It systematically analyzes the specific impact of land use changes in China on carbon emissions from 2000 to 2021. Additionally， the study employs a Gaussian Process Regression （GPR） model for carbon emission forecasting and analyzes the spatial distribution of carbon emissions in relation to economic development using the Gini coefficient and the TAPIO decoupling model. The results indicate that： ① From 2000 to 2021， construction land expanded by 67.28% （2000-2021）， with an average annual growth rate exceeding 4%. Significant conversion of arable land occurred in the eastern region. Forest land/water body areas increased by 2.12% and 10.38%， respectively， with notable ecological restoration in the western region. Grassland， arable land， and unused land decreased by 1.86%-3.28%， showing clear characteristics of intensive land use. ② From 2000 to 2021， carbon emissions from construction land dominated in proportion， with total emissions increasing by 235% （an annual growth rate of 6.07%） and spatial diffusion extending to the central and western regions. Forest carbon sequestration increased by 16.6% （an annual growth rate of 0.77%）， while carbon absorption by grasslands slightly declined， and the carbon function differentiation between the eastern and western regions intensified. ③ The carbon emission Gini coefficient decreased from 0.592 2 in 2000 to 0.540 7 in 2021， indicating a more even distribution of carbon emissions. The carbon emissions of the top 10% grids increased from 108.9 million tons to 330.4 million tons， showing that high-emission areas were still driving the growth of carbon emissions. The internal differences in the western region were the largest （Gini coefficient of 0.847 3）. ④ From 2000 to 2013， China was in a \"weak decoupling\" state， with economic growth accompanied by an increase in carbon emissions. From 2014 to 2016， China entered a \"strong decoupling\" phase， where GDP growth occurred alongside a reduction in carbon emissions. From 2017 onwards， carbon emissions began to rise again， returning to a \"weak decoupling\" state. ⑤ The GPR model predicted that China's carbon emissions will peak in 2028， and carbon neutrality will be achieved before 2060. The western region has already become a carbon sink， while th","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5907-5924"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012365","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 of Biochar Amendment on Microbial Community Structure in Purple Soils with Varying Acidification Levels].","authors":"Lu Peng, Yu-Xi Guo, Jie Lu, Meng Chen, Juan Huang, Liu-Yan Zhou, Zi-Fang Wang, Wen-Cai Dai, Ming Gao","doi":"10.13227/j.hjkx.202506317","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506317","url":null,"abstract":"<p><p>Biochar， as a soil amendment， plays an important role in increasing soil pH and promoting microbial diversity. An indoor incubation experiment combined with high-throughput sequencing was conducted to investigate the effects of biochar addition on soil pH， organic matter content， and microbial community structure in purple soils with different acidification degrees， aiming to elucidate the response characteristics of purple soils to biochar input under varying acidification conditions and to provide a theoretical basis for improving soil fertility and promoting sustainable utilization of purple soils. Biochar was added at 3% of the dry soil weight and incubated for 63 days under laboratory conditions. The treatments included three acidification levels： T1， severe acidification （pH=4.48）； T2， moderate acidification （pH=4.90）； and T3， slight acidification （pH=5.82）. The results showed that： ① Under equal biochar application， compared with pre-incubation values， soil pH increased significantly in all treatments by 0.09， 0.34， and 0.37 units for T1， T2， and T3， respectively （<i>P</i><0.05）. Organic matter increased by 70.13%-118.98%， with the highest increase observed in moderately acidified soil. ② Microbial community structures differed markedly among the three acidification levels， and greater acidification reduced microbial richness. Both bacterial richness （Chao1） and diversity （Shannon） followed the order T3>T2>T1； fungal richness （Chao1） followed T3>T2>T1， while fungal diversity （Shannon） followed T3>T1>T2. ③ The dominant bacterial phyla in all treatments were Proteobacteria and Chloroflexi， while the dominant fungal phyla were Ascomycota， Basidiomycota， and Mortierellomycota. ④ Biochar addition altered microbial community structures across different acidification levels， with dominant microbial taxa shifting along the soil pH gradient and more complex and stable co-occurrence networks forming under near-neutral conditions. ⑤ Redundancy analysis revealed that pH， TN， AP， C/N， and <i>β</i>-glucosidase were the main factors shaping bacterial community structure， whereas TN， C/N， cellobiohydrolase， and sucrase were the main factors influencing fungal community structure. In conclusion， biochar application improved soil pH， organic matter， and total nitrogen contents； altered microbial community succession and network characteristics； and thereby enhanced soil ecological health.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6504-6515"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012863","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 Evolution and Influencing Factors of Carbon Emissions in China's Four Major Urban Agglomerations].","authors":"Hai-Peng Wu, Qiao-Xing Li","doi":"10.13227/j.hjkx.202506331","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506331","url":null,"abstract":"<p><p>Guided by the national strategic goals of carbon peaking and carbon neutrality， it is of great significance to investigate the spatiotemporal evolution characteristics and driving factors of carbon emissions in China's major urban agglomerations. Based on the integrated nighttime light index and energy statistical data， this study estimates the carbon emissions of the Beijing-Tianjin-Hebei （BTH）， Yangtze River Delta （YRD）， Pearl River Delta （PRD）， and Chengdu-Chongqing （CC） urban agglomerations from 2000 to 2022. By analyzing carbon emission intensity and per capita carbon emissions， the spatiotemporal evolution patterns and regional disparities are explored. Furthermore， the logarithmic mean Divisia index （LMDI） decomposition method is employed to explore the dynamic changes of the influencing factors. The results reveal that total carbon emissions in the four urban agglomerations experienced rapid growth in the early stage， followed by a noticeable slowdown. Spatially， carbon emissions expanded from densely concentrated core cities to surrounding regions， with intra-regional emissions gradually becoming more balanced. Emission intensity generally showed a downward trend， with steady declines in the YRD and PRD regions and a more significant decrease in the BTH Region， although it continued to maintain a relatively high level. Per capita carbon emissions demonstrated diversified trajectories along with economic development： The YRD and BTH regions underwent inter-quartile transitions， while the PRD Region maintained a low and stable level， and the CC region exhibited greater internal variability. Economic growth remained the primary driver of carbon emission increases， whereas the effect of population scale had weakened. Improvements in industrial energy efficiency and the optimization of industrial structure played a major role in curbing emissions， and the contribution of energy structure adjustment had become increasingly prominent. In summary， the evolution of carbon emissions in China's four major urban agglomerations was characterized by decelerated growth， spatial diffusion， and persistent regional heterogeneity. To promote a low-carbon transition， region-specific and coordinated strategies should be strengthened moving forward.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5889-5906"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012864","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 Evolution and Driving Factors of Net Primary Productivity in the Southwest Alpine Canyon Region of China from 2001 to 2020].","authors":"Jin Yang, Ke-Qin Wang, Yi-Ran Li, Zhen Wang, Ling Li, Xiao-Wen Feng","doi":"10.13227/j.hjkx.202507146","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507146","url":null,"abstract":"<p><p>As an important ecologically fragile region and ecological barrier in China， the Southwest Alpine Canyon Region of China plays a critical role in maintaining ecological security and enhancing carbon sink benefits in southwestern China. Investigating the spatiotemporal evolution of its carbon sequestration capacity and the contribution of driving factors is of great significance. Existing regional studies have mostly focused on vegetation cover and soil erosion， while analysis of changes in carbon sequestration capacity and its driving factors remains insufficient. Therefore， this study took vegetation net primary productivity （NPP） as an entry point and utilized the MOD17A3HGF （Version 6.1） dataset， along with Theil-Sen trend analysis， Mann-Kendall significance test， multiple partial correlation analysis， and residual analysis， to examine the spatiotemporal evolution of NPP and its driving factors in the Southwest Alpine Canyon Region of China from 2001 to 2020. The results indicated that： ① During the study period， the NPP （in terms of C） in the Southwest Alpine Canyon Region of China showed a non-significant decreasing trend [changing rate=-0.1 g·（m²·a）^-1， <i>P</i>=0.92]. Spatially， it exhibited a distribution pattern of \"faster in the south than in the north， and faster in the east than in the west.\" Significant increasing trends were concentrated in the Water Conservation and Soil Retention Zone of the Northern Yunnan-Western Sichuan Alpine Gorge （zone Ⅲ）， while significant decreasing trends were mainly distributed in the Ecological Conservation Zone of the Southeastern Tibet Alpine Gorge Region （zone Ⅰ）. ② The response of NPP changes to meteorological variations showed significant differences， with temperature being the key driving factor， followed by solar radiation > precipitation > wind speed. ③ Changes in NPP were influenced by both climate change （accounting for approximately 50.1% of the area） and human activities （accounting for about 44.0% of the area）. Specifically， the increase in regional NPP was primarily driven by climatic factors （accounting for about 32.7% of the area）， while the decrease in NPP was mainly caused by human activities （accounting for approximately 19.3% of the area）. The findings reveal the spatiotemporal patterns and main driving factors of NPP in the Southwest Alpine Canyon Region of China， providing references for assessing the carbon sink development potential and implementing differentiated regional ecological management strategies in the area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6151-6161"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012876","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":"[Priority Control Screening of Organic Micropollutants in the Water Body of the Lower Reaches of the Yangtze River from Wuhu to Nanjing].","authors":"Mo-Shan Cen, Yan Zeng, Yu-Chun Wang, Jing Zhang, Ying An, Tian Lin, Jing-Fu Wang, Jing-An Chen","doi":"10.13227/j.hjkx.202507139","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507139","url":null,"abstract":"<p><p>The types and emissions of chemicals are constantly increasing， posing a huge threat to aquatic ecosystems. Given the complexity and large quantity of pollutants， it is difficult to achieve comprehensive monitoring and control. Therefore， screening out the priority pollutants that pose a significant threat to aquatic ecosystems is the key to formulating pollution control measures. The section of the Yangtze River's main stream from Wuhu to Nanjing is a core component of the Yangtze River Economic Belt. The water pollution problems caused by economic growth may lead to an increase in organic micropollutants （OMPs） in the water bodies， thereby threatening the ecological security. This study detected 197 OMPs in the mainstream of the Yangtze River from Wuhu to Nanjing， with 150 contaminants identified. Priority screening was conducted based on hazard effects， occurrence levels， and risk prioritization. The hazard potential （HP） was determined based on the environmental persistence， bioaccumulation， and ecotoxicity of pollutants. The exposure potential （EP） was determined by the median concentration and detection rate of pollutants， and the priority index （PI） was calculated. The results showed that polybrominated diphenyl ethers （PBDEs） and polychlorinated biphenyls （PCBs） had high HP values， while sweeteners （SAs） had low HP values. SAs exhibited the highest EP values， whereas PBDEs had the lowest EP values. Risk assessment indicated that 34 pollutants posed a threat to the ecosystem. Based on the PI index， a list of 11 high-priority pollutants was generated. Among the high-priority pollutants， PFASs accounted for as high as 54.5%. PFASs mainly originated from industrial emissions. For future ecological environment monitoring and regulation in the Wuhu-Nanjing section of the Yangtze River mainstream， special attention should be paid to PFOA， PFNA， PFOS， 10∶2 FTCA， HFPO-TA， 8∶2 FTCA， BDE194， BDE209， BDE206， DEHP， and DBDPE. Pollutants with medium priority also require attention. It is suggested that stricter supervision and treatment measures should be implemented for the wastewater from fluorine chemical， electroplating， and textile enterprises along the river， in order to control the emission of PFASs.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"6104-6115"},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012977","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}