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
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
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
With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Tradition...With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.展开更多
Dear Editor,On July 25,2024,an Oncologic Drugs Advisory Committee meeting was held to discuss the design of the AEGEAN trial,which evaluated the perioperative use of durvalumab for resectable non-small cell lung cance...Dear Editor,On July 25,2024,an Oncologic Drugs Advisory Committee meeting was held to discuss the design of the AEGEAN trial,which evaluated the perioperative use of durvalumab for resectable non-small cell lung cancer(NSCLC)[1].The Food and Drug Administration(FDA)stated that the two-arm trial was not designed to isolate contributions of neoadjuvant and adjuvant phases.Since the clinical benefit of adjuvant durvalumab remains uncertain,this limitation raises concerns about potential over-treatment.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
UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vest...UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.展开更多
Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in a...Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.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
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly deve...Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.展开更多
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me...Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.展开更多
Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described...Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described plant species are at risk of extinction(Bachman et al.,2024).The number of plant extinctions has increased by 60%in the last 100 years(Di Marco et al.,2017).Over the past 250 years,571 plant species have gone extinct—more than twice the combined total of extinct birds,mammals,and amphibians(217 species)(Briggs,2019).展开更多
This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the L...This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the Life Cycle Assessment(LCA)methodology.The treatments are either in-situ or exsitu,the latter requiring an initial dredging to transport the contaminated sediments to the management site.More in detail,four ex-situ remediation technologies based on landfilling,bioremediation,electrokinetic technique and soil washing were identified.These technologies are compared to an in-situ strategy currently under validation for enhancing bioremediation of the polluted sediments of the Bagnoli-Coroglio site.Our results indicate that the disposal in landfilling site is the worst option in most categories(e.g.,650 kg CO_(2) eq./t of treated sediment,considering the nearest landfilling site),followed by the bioremediation,mainly due to the high energy demand.Electrokinetic remediation,soil washing,and innovative in-situ technology represent the most sustainable options.In particular,the new in-situ technology appears to be the least impacting in all categories(e.g.,54 kg CO_(2) eq./t of treated sediment),although it is expected to require longer treatment time(estimated up to 12 months based on its potential efficiency).It can reduce the impact on climate change more than 12 times compared to the disposal and 7 times compared to bioremediation in addition to the possibility to avoid/reduce the dredging operations and the consequent dispersion of pollutants.The results open relevant perspectives towards more eco-sustainable and costly effective actions for the reclamation of contaminated marine sediments.展开更多
Despite the widespread presence and frequent detection of polycyclic aromatic hydrocarbons(PAHs)in various aspects of life,there is limited research on their exposure levels in pregnant women and cumulative exposure f...Despite the widespread presence and frequent detection of polycyclic aromatic hydrocarbons(PAHs)in various aspects of life,there is limited research on their exposure levels in pregnant women and cumulative exposure from the living environment.This study included 1311 women in late pregnancy from the Zunyi birth cohort and measured the urinary concentrations of 10 hydroxylated PAH metabolites(OH-PAHs).Risk assessment was conducted based on the estimated daily intake to calculate the hazard quotient and hazard index(HI).A linear regression model was used to analyze the relationship between creatinine-adjusted OH-PAHs concentrations and living environment and lifestyle factors,while principal component analysis was applied to trace the sources of PAHs exposure.1-OHPYR was detected in all participants’urine,with naphthalene metabolites having the highest concentrations among creatinine-adjusted PAHs.OH-PAHs concentrations were associated with housing type,room number,cooking frequency,household size,exercise frequency,fuel type,distance from main road,and drinking water source.Pregnant women using traditional fuels and living in bungalows had higher health risks than those using clean energy and living in buildings.Those living within 100 m of a main road had higher HI than those farther away.Coal combustion was identified as the primary source of PAHs exposure.The study emphasizes the importance of reducing PAHs exposure,especially for pregnant women living in polluted environments.It recommends public health interventions such as improving indoor ventilation and providing clean energy to reduce related health risks.展开更多
The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current t...The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current trends in environmental analytical chemistry whereby,rather than targeted quantification,an integrated pollutant assessment,which upholds chemical discovery,interpretability,and real-world relevance,is desired.We initially explain the conceptual change between preset sets of analytes to the chemical space exploration made possible by exploring the chemical space using high-resolution mass spectrometry,multidimensional separations,and rapid/direct analysis technologies.We next mention how the new classes of contaminants and transformation products,as well as the complexity of mixtures,reveal the long-standing gaps in sensitivity,selectivity,and confidence of the identification,especially in the non-targeted workflows.In response to such limitations,we now mention changes that combine chemical measurement with biological and data-informed aspects,such as effect-based assays,exposure-oriented metrics,chemometrics,and machine learning feature prioritization and structure annotation.We also look at the transformation of higher orders of analytical products into clean-up programs and decision programs,which should focus on continuous and in-place sensing,tiered monitoring designs,and risk-based prioritization plans that more closely reflect the changing realities of the environment.Lastly,we determine future research requirements in harmonization,open data infrastructure,and reproducibility,and the development of autonomous and intelligent analytical systems that can perform adaptive monitoring and provide insights quickly.All these changing frontiers transform environmental analysis into a detection instrument into an actionable environmental intelligence that can be used to proactively manage and protect the ecosystems and human health.展开更多
Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general contr...Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.展开更多
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
Caohai Lake,a typical freshwater lake on the southwestern plateau of China,is threatened by drainage projects,domestic sewage,and agricultural pollution,which jeopardize its ecological functions.This study aimed to as...Caohai Lake,a typical freshwater lake on the southwestern plateau of China,is threatened by drainage projects,domestic sewage,and agricultural pollution,which jeopardize its ecological functions.This study aimed to assess the ecological health of Caohai Lake and to develop a phytoplankton-based Index of Biotic Integrity(P-IBI)adapted to its ecological characteristics.From July to November 2024,fourteen sampling sites were established across the lake,and samples were collected monthly to analyze physicochemical water parameters and phytoplankton community structure,followed by the construction of the P-IBI.A total of 88 phytoplankton species belonging to six phyla were identified,with Chlorophyta being the most species-rich group(46 species,52.27%)and Cyanophyta as the dominant group.Nutrient concentrations,including total nitrogen(TN)and total phosphorus(TP),were higher in July-August and gradually decreased from September to November.The mean P-IBI of Caohai Lake was 3.851,corresponding to a“sub-healthy”ecological status.Key metrics,including the percentage of Bacillariophyta cell density(M16),percentage of cell density contributed by the three dominant species(M18),Shannon-Wiener diversity index(M19),and the overall P-IBI,were significantly correlated with water temperature(WT),chlorophyll-a(Chl.a),and nutrient concentrations.These results provide a scientific basis for the environmental management and ecological protection of Caohai Lake.展开更多
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.
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.
基金funded in part by the Fundamental Research Funds for the Central Universities under Grant NS2023052in part by the Natural Science Foundation of Jiangsu Province of China under Grants No.BK20231439 and No.BK20222012.
文摘With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.
基金supported by the Beijing Nova Program (20230484277)the National Natural Science Foundation of China (82303955)。
文摘Dear Editor,On July 25,2024,an Oncologic Drugs Advisory Committee meeting was held to discuss the design of the AEGEAN trial,which evaluated the perioperative use of durvalumab for resectable non-small cell lung cancer(NSCLC)[1].The Food and Drug Administration(FDA)stated that the two-arm trial was not designed to isolate contributions of neoadjuvant and adjuvant phases.Since the clinical benefit of adjuvant durvalumab remains uncertain,this limitation raises concerns about potential over-treatment.
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the Postdoctoral Science Foundation Funded Project of China with grant No.2021M701687Introduction and Education Plan for Young Innovative Talents in Colleges and Universities of Shandong Province.
文摘UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0208)the National Natural Science Foundation of China(Nos.42171148 and 42330512)the Key R&D Project from the Science and Technology Department of Tibet(No.XZ202501ZY0030).
文摘Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.
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.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF1303405).
文摘Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.
基金Science and Technology Innovation 2030 Major Project,Grant/Award Number:2023ZD0508506。
文摘Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.
基金support of the ORG.one project of Oxford Nanopore Technologies(ONT),the Rufford Grants(45249-1)the Idea Wild Grants(Project ID-KJOSINDI0125-00)the Mohamed Bin Zyed Species Conservation(MBZ)(GEF Grant no-240535253)Funds in our efforts to conserve threatened trees in the Western Ghats Biodiversity Hotspot Forest regions.
文摘Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described plant species are at risk of extinction(Bachman et al.,2024).The number of plant extinctions has increased by 60%in the last 100 years(Di Marco et al.,2017).Over the past 250 years,571 plant species have gone extinct—more than twice the combined total of extinct birds,mammals,and amphibians(217 species)(Briggs,2019).
基金support in the literature analysis.This study has been carried out in the framework of the project funded by EU entitled“Bioremediation of contaminated sediments in coastal areas of exindustrial sites-LIFE SEDREMED”(No.LIFE20 ENV/IT/000572).
文摘This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the Life Cycle Assessment(LCA)methodology.The treatments are either in-situ or exsitu,the latter requiring an initial dredging to transport the contaminated sediments to the management site.More in detail,four ex-situ remediation technologies based on landfilling,bioremediation,electrokinetic technique and soil washing were identified.These technologies are compared to an in-situ strategy currently under validation for enhancing bioremediation of the polluted sediments of the Bagnoli-Coroglio site.Our results indicate that the disposal in landfilling site is the worst option in most categories(e.g.,650 kg CO_(2) eq./t of treated sediment,considering the nearest landfilling site),followed by the bioremediation,mainly due to the high energy demand.Electrokinetic remediation,soil washing,and innovative in-situ technology represent the most sustainable options.In particular,the new in-situ technology appears to be the least impacting in all categories(e.g.,54 kg CO_(2) eq./t of treated sediment),although it is expected to require longer treatment time(estimated up to 12 months based on its potential efficiency).It can reduce the impact on climate change more than 12 times compared to the disposal and 7 times compared to bioremediation in addition to the possibility to avoid/reduce the dredging operations and the consequent dispersion of pollutants.The results open relevant perspectives towards more eco-sustainable and costly effective actions for the reclamation of contaminated marine sediments.
基金supported by the National Key R&D Program of China(Nos.2018YFC1004300 and 2018YFC1004302)the Science&Technology Program of Guizhou Province(Nos.QKHHBZ[2020]3002,QKHPTRC-GCC[2022]039-1 and QKHPTRCCXTD[2022]014)the Scientific Research Program of Guizhou Provincial Department of Education(No.QJJ[2023]019).
文摘Despite the widespread presence and frequent detection of polycyclic aromatic hydrocarbons(PAHs)in various aspects of life,there is limited research on their exposure levels in pregnant women and cumulative exposure from the living environment.This study included 1311 women in late pregnancy from the Zunyi birth cohort and measured the urinary concentrations of 10 hydroxylated PAH metabolites(OH-PAHs).Risk assessment was conducted based on the estimated daily intake to calculate the hazard quotient and hazard index(HI).A linear regression model was used to analyze the relationship between creatinine-adjusted OH-PAHs concentrations and living environment and lifestyle factors,while principal component analysis was applied to trace the sources of PAHs exposure.1-OHPYR was detected in all participants’urine,with naphthalene metabolites having the highest concentrations among creatinine-adjusted PAHs.OH-PAHs concentrations were associated with housing type,room number,cooking frequency,household size,exercise frequency,fuel type,distance from main road,and drinking water source.Pregnant women using traditional fuels and living in bungalows had higher health risks than those using clean energy and living in buildings.Those living within 100 m of a main road had higher HI than those farther away.Coal combustion was identified as the primary source of PAHs exposure.The study emphasizes the importance of reducing PAHs exposure,especially for pregnant women living in polluted environments.It recommends public health interventions such as improving indoor ventilation and providing clean energy to reduce related health risks.
文摘The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current trends in environmental analytical chemistry whereby,rather than targeted quantification,an integrated pollutant assessment,which upholds chemical discovery,interpretability,and real-world relevance,is desired.We initially explain the conceptual change between preset sets of analytes to the chemical space exploration made possible by exploring the chemical space using high-resolution mass spectrometry,multidimensional separations,and rapid/direct analysis technologies.We next mention how the new classes of contaminants and transformation products,as well as the complexity of mixtures,reveal the long-standing gaps in sensitivity,selectivity,and confidence of the identification,especially in the non-targeted workflows.In response to such limitations,we now mention changes that combine chemical measurement with biological and data-informed aspects,such as effect-based assays,exposure-oriented metrics,chemometrics,and machine learning feature prioritization and structure annotation.We also look at the transformation of higher orders of analytical products into clean-up programs and decision programs,which should focus on continuous and in-place sensing,tiered monitoring designs,and risk-based prioritization plans that more closely reflect the changing realities of the environment.Lastly,we determine future research requirements in harmonization,open data infrastructure,and reproducibility,and the development of autonomous and intelligent analytical systems that can perform adaptive monitoring and provide insights quickly.All these changing frontiers transform environmental analysis into a detection instrument into an actionable environmental intelligence that can be used to proactively manage and protect the ecosystems and human health.
基金supported by the National Natural Science Foundation of China(grant numbers 42171085)and the National Key R&D Program of China(Grant No.2024YFF1307801,2024YFF1307804).
文摘Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
文摘Caohai Lake,a typical freshwater lake on the southwestern plateau of China,is threatened by drainage projects,domestic sewage,and agricultural pollution,which jeopardize its ecological functions.This study aimed to assess the ecological health of Caohai Lake and to develop a phytoplankton-based Index of Biotic Integrity(P-IBI)adapted to its ecological characteristics.From July to November 2024,fourteen sampling sites were established across the lake,and samples were collected monthly to analyze physicochemical water parameters and phytoplankton community structure,followed by the construction of the P-IBI.A total of 88 phytoplankton species belonging to six phyla were identified,with Chlorophyta being the most species-rich group(46 species,52.27%)and Cyanophyta as the dominant group.Nutrient concentrations,including total nitrogen(TN)and total phosphorus(TP),were higher in July-August and gradually decreased from September to November.The mean P-IBI of Caohai Lake was 3.851,corresponding to a“sub-healthy”ecological status.Key metrics,including the percentage of Bacillariophyta cell density(M16),percentage of cell density contributed by the three dominant species(M18),Shannon-Wiener diversity index(M19),and the overall P-IBI,were significantly correlated with water temperature(WT),chlorophyll-a(Chl.a),and nutrient concentrations.These results provide a scientific basis for the environmental management and ecological protection of Caohai Lake.
