工业工程最新文献

{"title":"[China's Provincial Horizontal Carbon Compensation Mechanism Based on Power Carbon Transfer].","authors":"Fu-Gui Dong, Ming-Xia Shi, Wan-Ying Li, Pei-Jun Wang","doi":"10.13227/j.hjkx.202405192","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405192","url":null,"abstract":"<p><p>Establishing a provincial carbon compensation mechanism is essential to achieving balanced and sustainable regional development and contribute to carbon peaking and neutrality goals. To establish a fair and reasonable horizontal provincial carbon compensation mechanism， this study took 31 provinces （cities and autonomous regions） in China as the research object. First， we measured the carbon transfer volume caused by power generation and consumption in 2021 and identified the carbon compensation subject and object. Second， we used the CO₂ shadow price calculated by the slack measurement model Slacks-Based Measure （SBM） as the unit price of carbon to determine the carbon compensation standard. Then， carbon emission responsibility allocation adopted a shared responsibility system， using the economic contribution coefficient and ecological carrying coefficient to modify the Pearl growth curve model， determining the carbon compensation coefficient and using its normalized value as the basis for carbon emission responsibility allocation. Finally， the provincial carbon compensation was realized through the fiscal transfer theory. The results showed that the national average CO₂ shadow price was 1 944.84 yuan·t^-1， and the shadow price was related to the level of economic development， industrial structure effect， and other factors. The total amount of carbon compensation in each province was 54.202 billion yuan in 2021， and 12 regions were compensated provinces， such as Shanxi， and compensation provinces included 19 regions， such as Guangdong. The overall spatial distribution of carbon compensation showed a state of \"paying from east to west.\" In the end， suggestions for constructing the provincial horizontal carbon compensation system were made from multiple perspectives.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4065-4074"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660575","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 and Challenges in Remote Sensing Monitoring of Water Quality Parameters in Inland Waters].","authors":"Han-Bo Zhang, Shi-Qing Dou, Ying Wen","doi":"10.13227/j.hjkx.202406125","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406125","url":null,"abstract":"<p><p>In recent years， pollution of inland water bodies has become a major problem jeopardizing aquatic ecosystems， water resources， and human health. Accurate and dynamic monitoring of the water quality status of inland water bodies is important for taking the most effective and urgent pollution protection measures. Remote sensing technology is widely used in water quality monitoring of inland water bodies because of its spatial and temporal advantages， which can realize long-term， rapid， and dynamic monitoring of water quality and help to reveal the characteristics of pollutants and pollutant migration that are difficult to detect by conventional methods. However， remote sensing monitoring studies of water quality pose serious challenges in terms of data synchronization， clarity of inversion mechanisms， spatial and temporal limitations of inversion algorithms， robustness of inversion models， and accuracy of atmospheric corrections. Therefore， the evolution patterns and development trends of remote sensing monitoring of inland water quality parameters will be explored by taking data sources， monitoring methods， monitoring indicators， etc.， as breakthrough points. We sort out the frequently used remote sensing data sources and remote sensing water quality monitoring methods， clarify the basic principles and application scenarios of each method， analyze the strengths and weaknesses of each method， and reflect on the opportunities and current challenges. On the basis of the above analysis， we propose that future research on remote sensing monitoring of water quality parameters in inland water bodies should be centered on the refinement of remote sensing data， integration of monitoring platforms， standardization of monitoring techniques， and integration of water quality models. In the future， we should deeply participate in the research and practice in water quality monitoring using remote sensing， accelerate the integration of new technologies and methods， and provide strong scientific support to promote the development of this field.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4211-4226"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660596","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":"[Carbon Emission Difference and Influencing Factors Identification in Different Functional Areas of Resource-based Cities].","authors":"Ya-Ping Zhang, Jian-Jun Zhang, Jia-Jia Su, Yi-Xin Dai, Sen Liang, Tian-Yi Chen","doi":"10.13227/j.hjkx.202407017","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407017","url":null,"abstract":"<p><p>Resource-based cities， as an important strategic support base for energy resources in China， are currently in a critical period of transformation and development. Reasonable control of carbon emissions is conducive to achieving regional sustainable development. Taking Wuan， a typical resource-based city in Hebei Province， as an example， the differences in urban functional areas were identified based on the combination of multi-source data. Three-dimensional spatial surface interpolation method was used to identify the spatial characteristics of urban carbon emissions. Random forest （RF） model was used to determine the impact of different functional areas on carbon emissions. The differences in carbon emissions within the land structure were determined using a non-parametric testing method. The results showed that： ① All types of construction land in Wu'an showed a multi-center irregular distribution， and the land use for transportation facilities， public management and public service， and commercial-residential mixed land accounted for the largest proportion， with contributions of 35.3%， 24.57%， and 20.46%， respectively. ② The spatial pattern of carbon emissions was higher in the middle and lower areas in the west of the city， as a \"mountain-like\" spatial distribution. The areas with high carbon emissions were mainly concentrated in the areas with diverse types of construction land and wide distribution of industrial land. ③ Industrial land was an important source of carbon emissions， with the importance as high as 66.4%. The carbon emissions of chimneys and smelting areas in industrial land were significantly higher than those in other areas， and green space played a role in carbon emission reduction. To effectively reduce the carbon emission of resource-based cities， long-term and effective carbon emission reduction policies should include reasonable planning of transportation land， paying attention to urban space layout around industrial land， avoiding the concentration of high-carbon emission projects， and focusing on reasonable optimization of the proportion of land used in industrial land and real-time dynamic monitoring of corporate carbon emissions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4110-4118"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660571","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 Antibiotics in Suspended Particulate Matter in the Yellow River Delta].","authors":"Zhen Yuan, Shu-Xiang Zhang, Xin-Bin Li, Chun-Guang Che, Qi-Yuan Wang, Zheng-Qi Xia, Kun Wang, Ying-Jie Sun","doi":"10.13227/j.hjkx.202406028","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406028","url":null,"abstract":"<p><p>To investigate the distribution and ecological risks of antibiotics in the aquatic environment of the Yellow River Delta （YRD）， the pollution characteristics of 45 typical antibiotics， including fluoroquinolones， sulfonamides， tetracyclines， and macrolides were determined in water and suspended particulate matter （SPM） samples collected from the lower reaches of the YRD， YRD Nature Reserves， and the YRD Estuary. This study use a high performance liquid chromatography tandem triple quadrupole mass spectrometry to measure the concentrations. The results showed that 12 and 18 antibiotics were detected at concentration levels in the range of N.D.-4 249.79 ng·L^-1 and N.D.-8 535.39 ng·g^-1 in the water and SPM samples， respectively. Among them， norfloxacin， enoxacin， doxycycline， pefloxacin， and sarafloxacin， belonging to fluoroquinolones and tetracyclines， were mostly detected； for the sulfonamides， the detection rate and concentration levels were relatively low， and macrolides were not detected in any samples. Moreover， due to the significant influences of human activities， the levels of antibiotic pollution were generally higher downstream than those upstream in the YRD water bodies. The levels of antibiotic pollution were positively correlated with the salinity of the waters in the nearby estuary. Conversely， the total concentrations of target antibiotics in the suspended particulate matter （SPM） were higher upstream than those downstream， which were consistent with the content of SPM along the sampling sites. Furthermore， the partition coefficient （<i>K</i>_d） of the detected antibiotics between water and SPM were calculated， showing that the <i>K</i>_d（L·kg^-1）values were in the range of 1.2×10³ （doxycycline）-8.7×10³ （sarafloxacin）. These results suggested that the detected antibiotics tended to be associated with SPM once entering the investigated area. Of particular note， it was observed that the <i>K</i>_d values followed the rank order： fluoroquinolones&gt;tetracycline&gt;sulfonamides， and there was a significant spatial variability. The correlation analysis of <i>K</i>_d and water environment factors showed that the partition of target antibiotics in the water-SPM interface was largely dependent on the properties of organic matter. In addition， the microstructure and composition of SPM also played an important role in the interaction between the antibiotics and SPM. The ecological risk assessment results showed that norfloxacin in water presented a high risk to aquatic organisms， doxycycline presented a moderate risk to algae and invertebrates， chlortetracycline and sulfadimethoxine had a low risk to invertebrates with a low trophic level， and other antibiotics had a small risk. However， when considering the multiple risk of simultaneous pollutants， all sites appeared as high risk and require further attention.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4273-4284"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660593","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 Antibiotic Sources and Ecological Risk Assessment in Inland Rivers of Northwest China].","authors":"Yun-Zhou Tan, Lei-Lei Lu, Dan Qi, Sheng-Hu Zhang, Dan Wu, Guo-Dong Kang, Rong-Rong Lei, Ling Wang","doi":"10.13227/j.hjkx.202405173","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405173","url":null,"abstract":"<p><p>Rivers play vital roles as sources and sinks for antibiotics； however， current studies often overlook the antibiotic contamination in northwest China， where water is scarce. Therefore， this study assessed antibiotic contamination in a typical inland river Y and its tributary P in northwest China. We used high-performance liquid chromatography-tandem mass spectrometry （HPLC-MS/MS） to detect the concentrations of 39 antibiotics across five classes. Then， we used the positive matrix factorization （PMF） model to analyze the sources of antibiotics and evaluate the ecological risks. The results indicated that sulfadoxine （SDX） dominated （mean concentration： 3 734.87 ng·L^-1） in all samples with a peak of 30 120.38 ng·L^-1. Oytetracycline （OXY） and sulfamonomethoxine （SMM） were also detected （17.02-71.32 ng·L^-1）. The PMF model revealed that antibiotics mainly originated from aquaculture and domestic sewage. Ecological risk assessment showed high ecological risks and antibiotic resistance development potential for SDX at multiple points in Y and P， warranting attention. Additionally， OXY exhibited low ecological risk and moderate resistance development potential， while SMM showed low ecological risk. Other antibiotics temporarily posed no risk to the ecological environment. This study provides a theoretical basis for antibiotic pollution prevention and risk management in the northwest inland region.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4264-4272"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660503","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 Carbon Emission Characteristics and Influencing Factors of Different Land Use Types in Hunan Province].","authors":"Ling-Feng Xu, Wan-Ling Chen, Hong-Mei Zhu, Wei-Lin Wang, Fei-Xue Luo","doi":"10.13227/j.hjkx.202406034","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406034","url":null,"abstract":"<p><p>It is important to explore the development path of urban low-carbon economies and formulate climate control policies to analyze the spatial and temporal evolution characteristics of land use carbon emissions and their influencing factors by accounting for carbon emissions from different land use types. Our study measured the carbon emissions of different land-use types in Hunan Province in 2010 and 2019. We used the OLS model to determine the influencing factors of land use carbon emissions based on the framework for land use carbon emissions using land use and cover and statistical data. The results showed that： ① Over 64% of the cities in Hunan Province had varying degrees of growth in total carbon emissions， with an east-high and west-low distribution. Specifically， the high-carbon emission cities were mainly located in the central part of Hunan Province with Loudi and Xiangtan as the center， spreading towards the north-south direction. ② Carbon emissions from energy consumption in construction land were the main source of carbon emissions in Hunan Province. More than 90% of the carbon emissions from construction land in cities accounted for 85% of urban carbon emissions， with some cities exceeding 95%. ③ The secondary and tertiary industries had consistently been the most significant contributors to the increase in carbon emissions， with their coefficients becoming larger despite minimal changes. In contrast， the primary industry consistently exerted a carbon-suppressing effect on emissions， with its coefficients also increasing but exhibiting substantial changes.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4136-4148"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660504","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 Driving Factors for Spatiotemporal Changes in Carbon Sources/Sinks in Taihang and Yanshan Mountains].","authors":"Peng-Fei Tian, Feng Yan, Zong-Chao Wang, Yuan-Hang Li, Ze-Hua Wen, Ya-Heng Chen","doi":"10.13227/j.hjkx.202406227","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406227","url":null,"abstract":"<p><p>As an extremely important characteristic of ecosystems， clarifying the spatial and temporal patterns and driving mechanisms of net ecosystem productivity （NEP） is of great significance for the protection and restoration of the Taihang and Yanshan Mountains ecosystem. The net ecosystem productivity （NEP） was estimated using MODIS remote sensing data. By integrating natural and human-related data， we used methods such as trend analysis， Hurst exponent， optimal parameters-based geographical detector， and multi-scale geographically weighted regression to estimate regional NEP and analyzed its spatiotemporal variation characteristics and the impact of driving factors on this change. The results showed that： ① In terms of time， the NEP in the Taihang and Yanshan Mountains Region showed a fluctuating upward trend from 2002 to 2020， with a growth rate of 4.96 g·（m²·a）^-1. In terms of space， the Taihang Mountains Region was characterized by \"low surrounding areas and high central areas\"， while the Yanshan Mountains Region was characterized by \"high northern areas and low southern areas\". ② The factor detection results showed that temperature， GDP density， and elevation were the main influencing factors for the spatial differentiation of the Taihang and Yanshan Mountains NEP. The degree of explanation of NEP by each factor after an interaction was much higher than that of a single factor， and the interaction between temperature and fractional vegetation cover was the largest. ③ There were significant differences in the effects of various factors on NEP. Among them， temperature， GDP density， and nighttime light intensity had a negative impact on NEP as a whole； vegetation coverage had a positive effect on NEP； and precipitation， elevation， slope， and population density had bidirectional effects on NEP.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4403-4415"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660505","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 Patterns of Carbon Flows and Scenario Simulation in Resource-based Cities in Dry Areas].","authors":"Xiao-Min Wang, Xiao-Yan Bu, Ling-Na Du, Zhi-Chang Hong, Jia-Rui Wang","doi":"10.13227/j.hjkx.202406092","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406092","url":null,"abstract":"<p><p>A study on carbon storage and emission in dryland resource cities under land use change was carried out to provide a scientific basis for low-carbon development in dryland resource cities. Taking Shizuishan City， a resource city in the dry zone， as an example， we integrated multi-source data such as station observation， sample testing， remote sensing monitoring， and statistical data. Furthermore， we coupled the InVEST and PLUS-Markov models to analyze the spatial and temporal evolution of land use patterns， carbon emission， carbon storage， and carbon flow in Shizuishan City under the four scenarios of natural development， arable land protection， ecological protection， and economic development in the past 15 years and the future and to identify the driving factors of carbon flow using geoprobes. The results showed that： ① From 2005 to 2020， the change in land use in Shizuishan City caused an increase in carbon emissions， a decrease in carbon stock， and a negative net carbon flow， and changes in carbon emissions， carbon stock， and carbon flow were similar to the spatial characteristics of land use. ② From 2005 to 2035， the net carbon flow under the four scenarios in Shizuishan City was negative， and the ecological protection scenario was more obvious in suppressing the decline of net carbon flow. ③ Slope， DEM， distance to the road， mean annual temperature， and mean annual precipitation were the main factors affecting the spatial differentiation of carbon flow， and the interactions between mean annual temperature and slope as well as slope and DEM were significant. The results of the study provide a reference for the green and low-carbon transformation and sustainable development of resource cities in the \"several bends\" of the Yellow River and provide scientific support for the construction of low-carbon cities in resource areas in dry zones.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4119-4135"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660555","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":"[Carbon Peak Prediction of Energy Consumption in China： Based on STIRPAT Extended Model].","authors":"Bao-Guo Shan, Li Yao, Cheng-Long Zhang, Xian-Dong Tan","doi":"10.13227/j.hjkx.202406016","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406016","url":null,"abstract":"<p><p>Achieving carbon peak before 2030 and carbon neutrality in 2060 is the solemn commitment of the Chinese government to the international community. It is important to scientifically identify the key influencing factors and accurately predict carbon peak value and time for achieving the dual carbon targets in China. On the basis of the economic and energy consumption data from 1980 to 2022， a STIRPAT extended multivariate-nonlinear model was built， which was fitted by a ridge regression to examine the relationships between carbon emissions of energy consumption and seven influencing factors， including population， GDP per capita， energy intensity， secondary industry proportion， fossil energy proportion， electrification rate， and urbanization rate. Based on the proposed STIRPAT extended model， predictions of carbon emissions of energy consumption were made for the period from 2023-2035 under three different scenarios. The results showed that： ① There were five factors that increased the carbon emissions including population， urbanization rate， secondary industry proportion， GDP per capita， and electrification rate. The degree of influence decreased in turn. Fossil energy proportion and energy intensity were the two factors that restrained the carbon emissions. The influencing degree of fossil energy proportion was the biggest， and that of energy intensity was the smallest. ② During different stages of economic development， the roles and contributions of the seven factors changed significantly. In particular， the effects of energy intensity， secondary industry proportion， and fossil energy proportion resulted in the turning changes， which reflected the periodical characteristics of carbon emissions in different stages. ③ Carbon emissions of energy consumption will achieve a peak during 2028-2032 in China. The peak was 11.66-12.75 billion tons. Under the baseline scenario， the peak was 12.04 billion tons， which will be fulfilled in 2030. The peak of the low-carbon scenario was 11.66 billion tons in 2028， which was 3.16% lower than that of the baseline scenario. The peak of the high-carbon scenario was 12.75 billion tons in 2032， which was 5.90% higher than that of the baseline scenario. Based on the research results， reasonable suggestions such as accelerating renewable energy development， increasing the electrification rate， optimizing the economic structure， and improving energy efficiency are put forward to ensure that China will achieve its carbon peak target before 2030.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4052-4064"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660572","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 Analysis of Different Buffer Scale Landscape Patterns on Water Quality of the Fourth Drainage Ditch in Ningxia Yellow River Irrigation Area].","authors":"Ji-Long Ma, Kun Ma, Tie-Na Xie, Jian-Jun Ma, Hong Li, Xiang Yue, Lin-Pu Han, Yong-Jie Qi, Jia-Qi Gao, Biao Jia","doi":"10.13227/j.hjkx.202407049","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407049","url":null,"abstract":"<p><p>Landscape patterns at specific spatial scales can respond to the sources of non-point source pollutants in water bodies and the surface landscape's ability to absorb them. To investigate the influence of landscape patterns on the water quality of the drainage ditch at different circular buffer scales， the fourth drainage ditch of the Ningxia Yellow River Irrigation Area （YDIA） was taken as the research object， and based on the land-use data in 2022 and the dynamic water quality monitoring data of 26 sampling points from 2021 to 2022， five circular buffer scales， including 100， 200， 500， 1 000， and 2 000 m， were set up， and three methods. Landscape pattern analysis， Spearman rank correlation analysis， and redundancy analysis were used to filter the water quality of the drainage ditch. Landscape pattern analysis， Spearman rank correlation analysis， and redundancy analysis were used to screen out the optimal buffer scales and corresponding key landscape indicators to explain the changes in water quality and to analyze the response relationship between the water quality of the drainage ditch and landscape features. The results showed that： ① The fourth drainage ditch in Ningxia Huang Irrigation Area was mostly polluted by high nitrogen， and more than 70% of the TN indexes exceeded the standard limit for Class V water throughout the year， while the TP indexes did not exceed the standard limit for Class III water， which was less polluted. ② The landscape characteristics at the scale of the 200 m circular buffer zone had a close relationship with the water quality indexes of the two periods， and the explanation rates of the fallow period and the cultivation period at that scale were the highest， 45.76% and 44.14%， respectively， and the explanation rate was the highest， 47.63% and 45.83%， respectively， which was the best buffer scale for discriminating the water quality of the fourth drainage ditch. ③ Cultivated land was the main 'source' land use type within the optimal buffer zone， which was the main factor affecting the change of pollutants in the drainage ditch. Followed by the spreading index， aggregation index， scattering and juxtaposition index， and fragrance uniformity， which were the main landscape factors affecting the change of water quality. Therefore， when optimizing the landscape pattern method for monitoring and controlling water pollution in drainage ditches， it is recommended to strictly control the proportion of arable land and other 'source' land-use types within the 200 m circular buffer zone， and at the same time， improve the diversity， connectivity， and aggregation of the landscape. The results of this study can provide new ideas and solutions for monitoring and analyzing the water quality of drainage ditches in Ningxia's Yellow River Irrigation District.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4251-4263"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660589","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}