Neutron Activation Dose Assessment Based on a Human Head Phantom Post-BNCT Guanchao Wu1,2, Zuokang Lin2, Zijian Zhang1,2, Zhiyuan Lin1,2, Yinan Zhu2, Ye Dai2 and Zhimin Dai2(1.ShanghaiTech University, Shanghai, 201210...Neutron Activation Dose Assessment Based on a Human Head Phantom Post-BNCT Guanchao Wu1,2, Zuokang Lin2, Zijian Zhang1,2, Zhiyuan Lin1,2, Yinan Zhu2, Ye Dai2 and Zhimin Dai2(1.ShanghaiTech University, Shanghai, 201210, China;2.Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China)展开更多
39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.Ar...39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,...Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,and Wei-hao Qin1(1.National Institute for Radiological Protection,Chinese Center for Disease Control and Prevention,Beijing,100088,China.)展开更多
Rapid developments in the electronic information industry drive the increased energy usage and carbon emission of data center buildings,prompting the focus on the energy efficiency and environmental sustainability.Exp...Rapid developments in the electronic information industry drive the increased energy usage and carbon emission of data center buildings,prompting the focus on the energy efficiency and environmental sustainability.Expanded operation envelopes of tropical data centers is assessed to analyze the potential for the building energy savings and carbon emission reduction through collaborative analysis of operation modes(OMs),supply air temperature(SAT),and outdoor air temperature(OAT).The OMs of compression vary with the setpoints of SAT,in which the average exergy efficiency of compressors at alternate operation mode is 6.8%and 8.0%lower than that of double and single compression operations.As SAT rises from 20℃to 32℃,the system exergoeconomic factor increases from 5.4%to 8.0%,and the average carbon cost decreases by 36.5%.Additionally,with just an 8.5%increase in exergy cost(i.e.,Case 8)at OAT rising from 30 to 34℃,the high SAT and low refrigerant charges provide considerable exergy cost advantages versus resisting the OAT fluctuations.Dynamic operation strategies are also proposed and compared to cope with the impacts of tropical environments.Compared to the 26℃SAT baseline,the average energy savings are 9.1-14.7%,indicating the ability to fully utilize outdoor and indoor conditions.展开更多
Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This st...Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This study evaluates the carbon footprint(CF)and economic viability of a liquefied natural gas(LNG)-fueled fishing vessel,using real engine operation simulations to provide precise and dynamic evaluation of fuel consumption and GHG emissions.Operational profiles are obtained through the utilization of onboard monitoring systems,whereas engine performance is simulated using the 1D/0D AVL Boost^(TM)model.Life cycle assessment(LCA)is conducted to quantify the environmental impact,whereas life cycle cost assessment(LCCA)is performed to analyze the profitability of LNG as an alternative fuel.The potential impact of the future fuel price uncertainties is addressed using Monte Carlo simulations.The LCA findings indicate that LNG has the potential to reduce the CF of the vessel by 14%to 16%,in comparison to a diesel power system configuration that serves as the baseline scenario.The LCCA results further indicate that the total cost of an LNG-powered ship is lower by 9.5%-13.8%,depending on the share of LNG and pilot fuels.This finding highlights the potential of LNG to produce considerable environmental benefits while addressing economic challenges under diverse operational and fuel price conditions.展开更多
Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analys...Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.展开更多
Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;...Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.展开更多
Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in mariti...Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.展开更多
The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face ...The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face limitations in accuracy,with the prediction rates of most models ranging from 80%to 90%.This study introduces a new hybrid machine learning framework,termed the Subtractive Clustering Method-based Adaptive Neural Network Fuzzy Inference System(SCM-ANFIS),and evaluates its performance in the Wenchuan earthquake region.This region features distinctive geology(e.g.,Longmenshan Fault-governed complex tectonics)and abundant fundamental data;additionally,the 2008 Wenchuan Earthquake provides a pertinent case for earthquakeinduced landslide model evaluation.Based on a literature review and correlation analysis,this study systematically identified 12 key influencing factors that collectively characterize the region's high landslide susceptibility,shaped by intense seismic activity,complex terrain,and fragmented rock masses.Positive and negative samples were extracted as target variables through buffer sampling to calculate earthquake-induced landslide susceptibility.The susceptibility zoning map was then calibrated and generated by incorporating the regional landslide area percentage.The study concludes the following:(1)Compared to traditional machine learning approaches,the model demonstrates strong performance and stability,achieving a prediction accuracy of 98.5%.Approximately 97.89%of historically documented landslides in the Wenchuan region were located within areas identified as having high susceptibility,which aligns well with observed spatial distributions.(2)Increase in the buffer distance contributes to enhance prediction accuracy while a larger sample size improves model stability.(3)The model exhibits superior performance and possesses scalability for application in other regions,such as Jiuzhaigou and Luding.(4)Nonetheless,limitations remain regarding uncertainty,sample composition,algorithmic design,and practical implementation.Future research should focus on improving data precision and optimizing algorithmic frameworks.Overall,this study provides valuable support for landslide susceptibility assessments and contributes essential data for disaster risk mitigation efforts.展开更多
The construction sector is facing significant challenges in transitioning to a defossilised system.While wood-based products have considerable potential,reliance on adhesives derived from fossil fuels poses significan...The construction sector is facing significant challenges in transitioning to a defossilised system.While wood-based products have considerable potential,reliance on adhesives derived from fossil fuels poses significant sustainability concerns.Tannin-based adhesives present a compelling bio-based alternative,offering advantageous bonding properties with the potential to reduce toxicity,minimise fossil resource use,and enhance end-of-life scenarios.Despite extensive research demonstrating the technical potential of tannin-based adhesives,industrial adoption remains limited—partly due to the paucity of studies addressing their environmental impacts.The present study investigates the use of tannin-based adhesives in the production of interior-grade plywood,employing urea-formaldehyde(UF)adhesive as reference.The evaluated formulations incorporate quebracho tannin with hexamine or novel protein-containing ingredients,namely soy protein isolate,soy flour,and tara germ powder.Technical tests assessed bonding quality,bending strength,and modulus of elasticity in five-layer plywood.A cradle-to-grave life cycle assessment(LCA)was conducted,with the novelty of using plywood as the functional unit.One formulation,combining tannin and hexamine,exhibited performance comparable to UF-bonded plywood,meeting EN 310 and EN 314 Class 1 standards.Environmental benefits were notable,with carcinogenic human toxicity reduced by 47%,even without accounting for formaldehyde emissions during the use stage.Fossil resource depletion decreased by up to 13%,and global warming potential from fossil sources fell by 10%,in accordance with EN 15804:2012+A2:2019.These findings provide a foundation for further optimisation,broader application in wood-based panels,and enhanced sustainability in construction.展开更多
Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspect...Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.展开更多
Microplastics(MPs)are ubiquitous and pose an environmental risk.This review examined MP pollution in terrestrial ecosystems from a myriad of poorly understood sources.Knowledge regarding the occurrence sources,migrati...Microplastics(MPs)are ubiquitous and pose an environmental risk.This review examined MP pollution in terrestrial ecosystems from a myriad of poorly understood sources.Knowledge regarding the occurrence sources,migration behaviors,ecotoxicology,absorption mechanisms,and effects of MPs has also been fully summarized.Microplastics interact with contaminants,such as antibiotics,pesticides,heavy metals,etc.,and may act as vectors for contaminant transfer in terrestrial ecosystems.The transportation and retention of MPs in soil are governed by interactions among their inherent properties,such as size,shape,surface charge,and density.Interestingly,MP migration into soil is lacking research.The MPs and nanoplastics were also found in edible fruits and vegetables.The MP contamination in soil affects ecosystems,causing soil structure changes,fertility reduction,and pollutant leaching into groundwater.The MP concentration lies in the range of 43-2443 and 40-43000 items kg-1in agricultural and urban soils,respectively.This review provides a comprehensive roadmap for future research and a framework for soil MP risk assessment.Future studies on the uptake,accumulation,and translocation of MPs and their associated toxins by plants are essential for evaluating their risks to food security and human health.Research on MPs in terrestrial habitats lacks comprehensive data on their long-term persistence,degradation pathways,and interactions with soil components under varying environmental conditions.Additionally,limited understanding exists regarding MP impacts on soil biodiversity,pollutant mobility,and plant uptake,highlighting the need for innovative detection methods and effective pollution abatement strategies.展开更多
Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and ev...Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.展开更多
An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performanc...An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,post...This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,postoperative follow-up data,and a review of the pathological mechanisms of ocular injury caused by bee venom,this study comprehensively assesses the injury characteristics,treatment course,and visual outcomes.Bee venom induces severe complications such as corneal damage,uveitis,cataract,and secondary glaucoma through multiple mechanisms including direct cytotoxicity,immune-inflammatory responses,and enzymatic hydrolysis.Despite interventions including anterior chamber irrigation,phacoemulsification with intraocular lens implantation,and antiglaucoma surgery,the affected eye ultimately lost light perception.Forensic examination confirmed the absence of light perception in the left eye and abnormal visual pathway function,consistent with clinical observations.According to the relevant Chinese disability assessment standard(JR/T 0083-2013,Article 4.2.2),the injury was classified as grade 7 disability.This study provides an in-depth discussion of the mechanisms and key forensic identification points in bee-sting-induced blindness,offering a scientific reference for similar forensic clinical cases.展开更多
Hydrological extremes,such as floods,droughts,and compound events,are extremely dangerous to human societies,ecosystems,and infrastructures,whose frequency and severity are affected by climate change more and more.Eff...Hydrological extremes,such as floods,droughts,and compound events,are extremely dangerous to human societies,ecosystems,and infrastructures,whose frequency and severity are affected by climate change more and more.Effective disaster preparedness,water resource management,and climate adaptation have to do with accurate prediction and extensive risk assessment.This review sums up recent progress in predictive modeling and risk assessment systems in the framework of hydrological extremes in the changing climatic conditions.Statistical and empirical techniques,including extreme value theory and nonstationary frequency analysis,give probabilistic information using historic records,whereas process-based models give an understanding of physical hydrological processes at different climate and land-use conditions.New information-based and hybrid methods that use machine learning and high-resolution data take advantage of the complexity and nonlinearities and enhance the predictive power.Hazard,exposure,vulnerability,and adaptive capacity risk assessment models allow predictive output to be translated into actionable decision support,with socio-economic aspects and analysis of the scenario.Case studies of various regions across the globe show the use of these techniques to address floods,droughts,and compound events,with success and current problems.The review also addresses current trends such as compound hazard,multi-hazard integration,AI-enabled modelling,and cross-sectoral decision support,and outlines research priorities of improving predictive capability and resilience.This review will inform researchers,policymakers,and practitioners by offering a synthesis of all the effects of the hydrological extremes in climate change to formulate sound strategies for alleviating these effects.展开更多
Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai C...Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.展开更多
文摘Neutron Activation Dose Assessment Based on a Human Head Phantom Post-BNCT Guanchao Wu1,2, Zuokang Lin2, Zijian Zhang1,2, Zhiyuan Lin1,2, Yinan Zhu2, Ye Dai2 and Zhimin Dai2(1.ShanghaiTech University, Shanghai, 201210, China;2.Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China)
文摘39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
文摘Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,and Wei-hao Qin1(1.National Institute for Radiological Protection,Chinese Center for Disease Control and Prevention,Beijing,100088,China.)
基金supported by the National Research Foundation,Singapore,funded under Energy Research Testbed and Industry Partnership Funding Initiative,part of the Energy Grid(EG)2.0 programme.
文摘Rapid developments in the electronic information industry drive the increased energy usage and carbon emission of data center buildings,prompting the focus on the energy efficiency and environmental sustainability.Expanded operation envelopes of tropical data centers is assessed to analyze the potential for the building energy savings and carbon emission reduction through collaborative analysis of operation modes(OMs),supply air temperature(SAT),and outdoor air temperature(OAT).The OMs of compression vary with the setpoints of SAT,in which the average exergy efficiency of compressors at alternate operation mode is 6.8%and 8.0%lower than that of double and single compression operations.As SAT rises from 20℃to 32℃,the system exergoeconomic factor increases from 5.4%to 8.0%,and the average carbon cost decreases by 36.5%.Additionally,with just an 8.5%increase in exergy cost(i.e.,Case 8)at OAT rising from 30 to 34℃,the high SAT and low refrigerant charges provide considerable exergy cost advantages versus resisting the OAT fluctuations.Dynamic operation strategies are also proposed and compared to cope with the impacts of tropical environments.Compared to the 26℃SAT baseline,the average energy savings are 9.1-14.7%,indicating the ability to fully utilize outdoor and indoor conditions.
文摘Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This study evaluates the carbon footprint(CF)and economic viability of a liquefied natural gas(LNG)-fueled fishing vessel,using real engine operation simulations to provide precise and dynamic evaluation of fuel consumption and GHG emissions.Operational profiles are obtained through the utilization of onboard monitoring systems,whereas engine performance is simulated using the 1D/0D AVL Boost^(TM)model.Life cycle assessment(LCA)is conducted to quantify the environmental impact,whereas life cycle cost assessment(LCCA)is performed to analyze the profitability of LNG as an alternative fuel.The potential impact of the future fuel price uncertainties is addressed using Monte Carlo simulations.The LCA findings indicate that LNG has the potential to reduce the CF of the vessel by 14%to 16%,in comparison to a diesel power system configuration that serves as the baseline scenario.The LCCA results further indicate that the total cost of an LNG-powered ship is lower by 9.5%-13.8%,depending on the share of LNG and pilot fuels.This finding highlights the potential of LNG to produce considerable environmental benefits while addressing economic challenges under diverse operational and fuel price conditions.
文摘Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.
基金The National Natural Science Foundation of China(No.52470211)Special Foundation of Jiangsu Province Science and Technology Plan(No.BZ2024017)RECLAIM Network Plus Project(No.EP/W034034/1).
文摘Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.
基金supported by the National Key R&D Program of China(No.2022YFC3701500)the Key R&D Plan Projects of Zhejiang Province(No.2024SSYS0072)Zhejiang Provincial Natural Science Foundation(No.LDT23E0601).
文摘Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.
基金financial support from the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant No.ZDJ2021-12)。
文摘The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face limitations in accuracy,with the prediction rates of most models ranging from 80%to 90%.This study introduces a new hybrid machine learning framework,termed the Subtractive Clustering Method-based Adaptive Neural Network Fuzzy Inference System(SCM-ANFIS),and evaluates its performance in the Wenchuan earthquake region.This region features distinctive geology(e.g.,Longmenshan Fault-governed complex tectonics)and abundant fundamental data;additionally,the 2008 Wenchuan Earthquake provides a pertinent case for earthquakeinduced landslide model evaluation.Based on a literature review and correlation analysis,this study systematically identified 12 key influencing factors that collectively characterize the region's high landslide susceptibility,shaped by intense seismic activity,complex terrain,and fragmented rock masses.Positive and negative samples were extracted as target variables through buffer sampling to calculate earthquake-induced landslide susceptibility.The susceptibility zoning map was then calibrated and generated by incorporating the regional landslide area percentage.The study concludes the following:(1)Compared to traditional machine learning approaches,the model demonstrates strong performance and stability,achieving a prediction accuracy of 98.5%.Approximately 97.89%of historically documented landslides in the Wenchuan region were located within areas identified as having high susceptibility,which aligns well with observed spatial distributions.(2)Increase in the buffer distance contributes to enhance prediction accuracy while a larger sample size improves model stability.(3)The model exhibits superior performance and possesses scalability for application in other regions,such as Jiuzhaigou and Luding.(4)Nonetheless,limitations remain regarding uncertainty,sample composition,algorithmic design,and practical implementation.Future research should focus on improving data precision and optimizing algorithmic frameworks.Overall,this study provides valuable support for landslide susceptibility assessments and contributes essential data for disaster risk mitigation efforts.
文摘The construction sector is facing significant challenges in transitioning to a defossilised system.While wood-based products have considerable potential,reliance on adhesives derived from fossil fuels poses significant sustainability concerns.Tannin-based adhesives present a compelling bio-based alternative,offering advantageous bonding properties with the potential to reduce toxicity,minimise fossil resource use,and enhance end-of-life scenarios.Despite extensive research demonstrating the technical potential of tannin-based adhesives,industrial adoption remains limited—partly due to the paucity of studies addressing their environmental impacts.The present study investigates the use of tannin-based adhesives in the production of interior-grade plywood,employing urea-formaldehyde(UF)adhesive as reference.The evaluated formulations incorporate quebracho tannin with hexamine or novel protein-containing ingredients,namely soy protein isolate,soy flour,and tara germ powder.Technical tests assessed bonding quality,bending strength,and modulus of elasticity in five-layer plywood.A cradle-to-grave life cycle assessment(LCA)was conducted,with the novelty of using plywood as the functional unit.One formulation,combining tannin and hexamine,exhibited performance comparable to UF-bonded plywood,meeting EN 310 and EN 314 Class 1 standards.Environmental benefits were notable,with carcinogenic human toxicity reduced by 47%,even without accounting for formaldehyde emissions during the use stage.Fossil resource depletion decreased by up to 13%,and global warming potential from fossil sources fell by 10%,in accordance with EN 15804:2012+A2:2019.These findings provide a foundation for further optimisation,broader application in wood-based panels,and enhanced sustainability in construction.
文摘Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.
文摘Microplastics(MPs)are ubiquitous and pose an environmental risk.This review examined MP pollution in terrestrial ecosystems from a myriad of poorly understood sources.Knowledge regarding the occurrence sources,migration behaviors,ecotoxicology,absorption mechanisms,and effects of MPs has also been fully summarized.Microplastics interact with contaminants,such as antibiotics,pesticides,heavy metals,etc.,and may act as vectors for contaminant transfer in terrestrial ecosystems.The transportation and retention of MPs in soil are governed by interactions among their inherent properties,such as size,shape,surface charge,and density.Interestingly,MP migration into soil is lacking research.The MPs and nanoplastics were also found in edible fruits and vegetables.The MP contamination in soil affects ecosystems,causing soil structure changes,fertility reduction,and pollutant leaching into groundwater.The MP concentration lies in the range of 43-2443 and 40-43000 items kg-1in agricultural and urban soils,respectively.This review provides a comprehensive roadmap for future research and a framework for soil MP risk assessment.Future studies on the uptake,accumulation,and translocation of MPs and their associated toxins by plants are essential for evaluating their risks to food security and human health.Research on MPs in terrestrial habitats lacks comprehensive data on their long-term persistence,degradation pathways,and interactions with soil components under varying environmental conditions.Additionally,limited understanding exists regarding MP impacts on soil biodiversity,pollutant mobility,and plant uptake,highlighting the need for innovative detection methods and effective pollution abatement strategies.
基金Research on Problems and Countermeasures in Building the Capacity of the Grassroots International Chambers of Commerce in the Context of High-Quality Development (W2024H03841)a key research project of the China Council for the Promotion of International Trade in 2025。
文摘Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.
基金supported in part by National Key R&D Program of China(2022YFF0610600).
文摘An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
文摘This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,postoperative follow-up data,and a review of the pathological mechanisms of ocular injury caused by bee venom,this study comprehensively assesses the injury characteristics,treatment course,and visual outcomes.Bee venom induces severe complications such as corneal damage,uveitis,cataract,and secondary glaucoma through multiple mechanisms including direct cytotoxicity,immune-inflammatory responses,and enzymatic hydrolysis.Despite interventions including anterior chamber irrigation,phacoemulsification with intraocular lens implantation,and antiglaucoma surgery,the affected eye ultimately lost light perception.Forensic examination confirmed the absence of light perception in the left eye and abnormal visual pathway function,consistent with clinical observations.According to the relevant Chinese disability assessment standard(JR/T 0083-2013,Article 4.2.2),the injury was classified as grade 7 disability.This study provides an in-depth discussion of the mechanisms and key forensic identification points in bee-sting-induced blindness,offering a scientific reference for similar forensic clinical cases.
文摘Hydrological extremes,such as floods,droughts,and compound events,are extremely dangerous to human societies,ecosystems,and infrastructures,whose frequency and severity are affected by climate change more and more.Effective disaster preparedness,water resource management,and climate adaptation have to do with accurate prediction and extensive risk assessment.This review sums up recent progress in predictive modeling and risk assessment systems in the framework of hydrological extremes in the changing climatic conditions.Statistical and empirical techniques,including extreme value theory and nonstationary frequency analysis,give probabilistic information using historic records,whereas process-based models give an understanding of physical hydrological processes at different climate and land-use conditions.New information-based and hybrid methods that use machine learning and high-resolution data take advantage of the complexity and nonlinearities and enhance the predictive power.Hazard,exposure,vulnerability,and adaptive capacity risk assessment models allow predictive output to be translated into actionable decision support,with socio-economic aspects and analysis of the scenario.Case studies of various regions across the globe show the use of these techniques to address floods,droughts,and compound events,with success and current problems.The review also addresses current trends such as compound hazard,multi-hazard integration,AI-enabled modelling,and cross-sectoral decision support,and outlines research priorities of improving predictive capability and resilience.This review will inform researchers,policymakers,and practitioners by offering a synthesis of all the effects of the hydrological extremes in climate change to formulate sound strategies for alleviating these effects.
基金supported by the Northeast Geological Science and Technology Innovation Center of China Geological Survey(Grant NO.QCJJ2022-43)the Natural Resources Comprehensive Survey Project(Grant Nos.DD20230470,DD20230508)the National Groundwater Monitoring Network Operation and Maintenance Program(Grant No.DD20251300109).
文摘Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.
