Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial het...Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosyste...Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosystems.However,in the context of global warming,WUE evolution and its primary drivers on the Tibetan Plateau remain unclear.This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001–2020.Results indicated an annual mean WUE of 0.8088 gC/mm·m^(2)across the plateau,with a spatial gradient reflecting decrease from the southeast toward the northwest.Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64%and 9.69%of the total,respectively.Remarkably,66.67%of the region exhibited trend reversals,i.e.,39.94%of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease,and 26.73%of the area demonstrated a shift from a trend of decrease to a trend of increase.Environmental factors accounted for 70.79%of the variability in WUE.The leaf area index and temperature served as the major driving forces of WUE variation.展开更多
Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose a...Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.展开更多
This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data fr...This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.展开更多
Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental chall...Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.展开更多
Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored ...Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainabl...The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly e...As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.展开更多
Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air...Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air quality.BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities,necessitating the assessment of atmospheric concentrations and their spatial variation.This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India.The study investigates the correlation of BTEX with other criteria pollutants andmeteorological parameters,aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions.Additionally,the paper categorizes various regions in India according to the Air Quality Index(AQI)based on BTEX pollution levels.The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central,eastern,and western regions.In contrast,the southern zone experiences the least pollution with BTEX.Seasonal analysis indicates that winter and postmonsoon periods,characterized by lower temperatures,are associated with higher BTEX levels due to the accumulation of localized emissions.When comparing the different zones in India,high traffic emissions and localized activities,such as solvent use and solvent evaporation,are found to be the primary sources of BTEX.The findings of the current study aid in source characterization and identification,and better understanding of the region’s air quality problems,which helps in the development of focused BTEX pollution reduction and control strategies.展开更多
Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates th...Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates the spatiotemporal variation and diversity of microplastic on the coastal beaches in Xiamen City,China,considering the combined effects of seasons,human activities,and physicochemical properties of sediments.It is detected that the abundance of microplastics in Xiamen beaches was 0.271±0.01 items/g.The abundance of microplastics in dry season was significantly higher than in rainy season.In terms of spatial variation,the beaches that attracted a larger number of tourists exhibited significantly higher microplastic abundance.The temporal pattern of microplastic distribution on different beaches varied greatly due to region-specific human activities(e.g.,mangrove restoration project)and sedimentary properties(e.g.,bulk density).When the assemblage of microplastics in the coastal area was viewed as a biological community,the Shannon-Wiener index and Pielou's index were higher in rainy season and in the beaches with high intensity of tourist activities,which suggests that the human activities and the surface runoff may contribute to the diversity of microplastics on coastal beaches.Future investigations are encouraged to combine controlled experiments and long-term monitoring at multiple scales to elucidate the underlying mechanisms and factors associated with microplastic pollution in coastal zone.展开更多
Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic ...Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.展开更多
Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ...Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ecological resilience(ER)and economic level(EL)of development,which poses a notable social threat.Currently,the link between ER and EL in China remains unclear,especially in terms of spatial dislocation(SD),referring to the disconnect between the locations where environmental impacts occur and those where economic benefits or activities are concentrated.Therefore,this paper aims to provide theoretical support and an empirical basis for policy-based solutions to address this gap.Based on the SD theory,this study systematically discusses the temporal changes,spatial patterns,and SD characteristics of China’s ER and EL using spatial auto-correlation and barycentric analysis to analyze data from 30 provinces covering the period 2011-2021.The key results are as follows.China’s ER shows a general trend of growth;however,its distribution is uneven.The spatial pattern generally decreases from the southeastern coastal provinces to the northwest.Moreover,a gradually increasing positive correlation is observed between the ER and EL,but this correlation varies by region,with some showing regional linkages and others developing independently.Finally,the dislocation index of ER and EL presents divergent results based on region-the eastern and central regions primarily show a high level of dislocation,whereas the western and northeastern regions show a low level of dislocation.The results provide a comprehensive overview of the spatiotemporal patterns in the association between ER and EL in China.The results emphasize that to balance sustainable regional development and ecological governance,a region-specific approach must be employed,prioritizing innovation-driven strategies for high ER in more developed regions and market-oriented strategies in less developed regions.展开更多
Understanding the local ecological security status and its underlying drivers can be used as an effective reference for balancing ecosystem development with societal needs. This study assesses the ecological security ...Understanding the local ecological security status and its underlying drivers can be used as an effective reference for balancing ecosystem development with societal needs. This study assesses the ecological security of the Loess Plateau(LP) by integrating ecosystem health and ecosystem services, explores the varying impacts of ecosystem structure, quality, and services on ecological security index(ESI), and identifies the key driving factors of ESI using the Geodetector model. The results show that:(1) the average ESI indicates a relatively safe ecological status in LP with a significant increase in ESI observed in 50.21% of the region, largely due to the ecological restoration programs.(2) Natural factors predominantly influence ESI, although human factors play a significant role in the earthy-rocky mountain region and plateau wind-sand region.(3) The interactions between driving factors have a much greater impact on ESI than any single factor, with the interactions between precipitation and human factors being the most influential combination. This study provides a novel perspective on assessing ecological security in LP. We recommend that future ecological restoration efforts should consider the varying roles of ecosystem structure, quality, and services in ESI while tailoring strategies to the primary driving factors based on local conditions.展开更多
Objective This study aimed to identify high-risk areas for type 2 diabetes mellitus(T2DM)mortality to provide relevant evidence for interventions in emerging economies.Methods Empirical Bayesian Kriging and a discrete...Objective This study aimed to identify high-risk areas for type 2 diabetes mellitus(T2DM)mortality to provide relevant evidence for interventions in emerging economies.Methods Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality.The relationships between economic factors,air pollutants,and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model.Results A coastal district in East Guangdong,China,had the highest risk(Relative Risk[RR]=4.58,P<0.01),followed by the 10 coastal districts/counties in West Guangdong,China(RR=2.88,P<0.01).The coastal county in the Pearl River Delta,China(RR=2.24,P<0.01),had the third-highest risk.The remaining risk areas were two coastal counties in East Guangdong,16 districts/counties in the Pearl River Delta,and two counties in North Guangdong,China.Mortality due to T2DM was associated with gross domestic product per capita(GDP per capita).In pilot assessments,T2DM mortality was significantly associated with carbon monoxide.Conclusion High mortality from T2DM occurred in the coastal areas of East and West Guangdong,especially where the economy was progressing towards the upper middle-income level.展开更多
Objective Human brucellosis is a serious public health concern in the Xilingol League,Inner Mongolia;however,the epidemic trends are unclear.Method In this study,Joinpoint regression analysis and spatiotemporal analys...Objective Human brucellosis is a serious public health concern in the Xilingol League,Inner Mongolia;however,the epidemic trends are unclear.Method In this study,Joinpoint regression analysis and spatiotemporal analysis were applied to investigate the epidemic evolution of human brucellosis.Result From 2004 to 2023,a total of 35,747 cases were reported,with an annual average of 1787.35cases and an annual average incidence rate of 176.04/100,000.The incidence increased from 173.96/100,000 in 2004 to 500.71/100,000 in 2009 and fluctuated to 61.43/100,000 in 2023.Three epidemic join points were observed in which the disease experienced an alternative rise and fall,peaking in 2009(APC=21.73,P>0.001)and 2020(APC=21.51,P>0.001).The disease showed a persistent decline trend in lentitude(AAPC=–5.30,P>0.001),suggesting challenges in disease control and a higher risk of rebound.The most cases were reported in Xilinhot City(n=4,777),followed by 4,391 in Sonid Left Banner,and 4,324 in Abaga Banner.Spatiotemporal analysis revealed two high clusters(CI and CII)from 2005 to 2012,the high cluster encompassing eight counties and shifting from north to south.Conclusion The present analysis highlights that human brucellosis has decreased significantly in the Xilingol League,but the epidemic is still severe;further implementation of a strict control program is necessary.展开更多
The genetic regulation of hair density in animals remains poorly understood.The Dazu black goat,characterized by its black coarse hair and white skin,provides a unique model for dissecting coarse hair density(CHD).Usi...The genetic regulation of hair density in animals remains poorly understood.The Dazu black goat,characterized by its black coarse hair and white skin,provides a unique model for dissecting coarse hair density(CHD).Using high-resolution micro-camera imaging,this study analyzed 905 skin images,33 skin transcriptomes,272 whole-genome sequences,and 182 downloaded transcriptomes.Morphological assessment from juvenile to adult stages revealed the thickening of hair shafts accompanied by a progressive decline in density,largely attributable to rapid surface expansion of the trunk skin.Transcriptomic comparison between high-and low-CHD individuals identified 572 differentially expressed genes(DEGs).A genome-wide association study detected 25 significant single nucleotide polymorphisms(P<9.07e-8)and mapped 48 annotated genes,with the most prominent association signal located near GJA1 on chr9.15931585-18621011.Literature review and Venn analysis highlighted six genes(GJA1,GPRC5D,CD1D,CD207,TFAM,and CXCL12)with documented roles in skin and hair biology,and three genes(GJA1,GPRC5D,and ATP6V1B1)overlapped with DEGs.Multiple-tissue transcriptomic profiling,western blotting,immunohistochemical staining,and skin single-cell RNA sequencing confirmed that GJA1 and GPRC5D were highly and specifically expressed in skin,particularly within hair follicles.Expression was localized predominantly to follicular stem cells and dermal papilla cells,suggesting a significant role in folliculogenesis and structural maintenance.Cross-validation using four public datasets further demonstrated positive correlations between GJA1 and GPRC5D expression and hair follicle density.The innovative micro-camera application allowed the elucidation of spatiotemporal patterns and genes associated with CHD,thereby addressing a significant knowledge gap in animal hair density.展开更多
基金supported by the National Natural Science Foundation of China(Nos.42305147 and 42405138)the Natural Science Foundation of Jiangsu Province(No.BK20230428).
文摘Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金National Nonprofit Institute Research Grant of CAF,No.CAFYBB2018ZA004,No.CAFYBB2023ZA009Fengyun Application Pioneering Project,No.FY-APP-ZX-2023.02。
文摘Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosystems.However,in the context of global warming,WUE evolution and its primary drivers on the Tibetan Plateau remain unclear.This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001–2020.Results indicated an annual mean WUE of 0.8088 gC/mm·m^(2)across the plateau,with a spatial gradient reflecting decrease from the southeast toward the northwest.Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64%and 9.69%of the total,respectively.Remarkably,66.67%of the region exhibited trend reversals,i.e.,39.94%of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease,and 26.73%of the area demonstrated a shift from a trend of decrease to a trend of increase.Environmental factors accounted for 70.79%of the variability in WUE.The leaf area index and temperature served as the major driving forces of WUE variation.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(NO.SML2021SP201)the National Natural Science Foundation of China(Grant No.42306200 and 42306216)+2 种基金the National Key Research and Development Program of China(Grant No.2023YFC3008100)the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311021004)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Project No.SL2021ZD203)。
文摘Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U21A2027)the New Cornerstone Science Foundation through the XPLORER PRIZE(2023-1033).
文摘This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.
文摘Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.
基金supported by the National Key Research and Development Program of China (Nos.2022YFC3702000 and 2022YFC3703500)the Key R&D Project of Zhejiang Province (No.2022C03146).
文摘Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金supported by the funding provided by the State Key Laboratory of Hydraulics and Mountain River Engineering(SKHL2210)National Natural Science Foundation of China(42171304)+1 种基金the Sichuan Science and Technology Program(2023YFS0380)Natural Science Foundation of Jiangsu Province of China(BK20242018)。
文摘The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金support for this research from the Natural Science Foundation of Henan Province(252300421005).
文摘As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.
文摘Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air quality.BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities,necessitating the assessment of atmospheric concentrations and their spatial variation.This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India.The study investigates the correlation of BTEX with other criteria pollutants andmeteorological parameters,aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions.Additionally,the paper categorizes various regions in India according to the Air Quality Index(AQI)based on BTEX pollution levels.The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central,eastern,and western regions.In contrast,the southern zone experiences the least pollution with BTEX.Seasonal analysis indicates that winter and postmonsoon periods,characterized by lower temperatures,are associated with higher BTEX levels due to the accumulation of localized emissions.When comparing the different zones in India,high traffic emissions and localized activities,such as solvent use and solvent evaporation,are found to be the primary sources of BTEX.The findings of the current study aid in source characterization and identification,and better understanding of the region’s air quality problems,which helps in the development of focused BTEX pollution reduction and control strategies.
基金Supported by the Natural Science Foundation of Fujian Province(No.2022J05278)the Marine and Fishery Development Special Fund of Xiamen(No.23YYST064QCB36)。
文摘Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates the spatiotemporal variation and diversity of microplastic on the coastal beaches in Xiamen City,China,considering the combined effects of seasons,human activities,and physicochemical properties of sediments.It is detected that the abundance of microplastics in Xiamen beaches was 0.271±0.01 items/g.The abundance of microplastics in dry season was significantly higher than in rainy season.In terms of spatial variation,the beaches that attracted a larger number of tourists exhibited significantly higher microplastic abundance.The temporal pattern of microplastic distribution on different beaches varied greatly due to region-specific human activities(e.g.,mangrove restoration project)and sedimentary properties(e.g.,bulk density).When the assemblage of microplastics in the coastal area was viewed as a biological community,the Shannon-Wiener index and Pielou's index were higher in rainy season and in the beaches with high intensity of tourist activities,which suggests that the human activities and the surface runoff may contribute to the diversity of microplastics on coastal beaches.Future investigations are encouraged to combine controlled experiments and long-term monitoring at multiple scales to elucidate the underlying mechanisms and factors associated with microplastic pollution in coastal zone.
基金supported by the Project of National Natural Science Foundation of China(22102095,21773153)the National Key Basic Research and Development Program(2018YFB1502001)financial support from the program of China Scholarships Council(No.202306230242).
文摘Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.
基金funded by the National Natural Science Foundation of China[Grant No.71963030]a subproject of China’s third comprehensive scientific expedition to Xinjiang[Grant No.SQ2021xjkk01800]+1 种基金a major science and technology project in the Xinjiang Uygur Autonomous Region[Grant No.2022A01003]a scientific research innovation project for excellent doctoral students of Xinjiang University[Grant No.XJU2022BS010].
文摘Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ecological resilience(ER)and economic level(EL)of development,which poses a notable social threat.Currently,the link between ER and EL in China remains unclear,especially in terms of spatial dislocation(SD),referring to the disconnect between the locations where environmental impacts occur and those where economic benefits or activities are concentrated.Therefore,this paper aims to provide theoretical support and an empirical basis for policy-based solutions to address this gap.Based on the SD theory,this study systematically discusses the temporal changes,spatial patterns,and SD characteristics of China’s ER and EL using spatial auto-correlation and barycentric analysis to analyze data from 30 provinces covering the period 2011-2021.The key results are as follows.China’s ER shows a general trend of growth;however,its distribution is uneven.The spatial pattern generally decreases from the southeastern coastal provinces to the northwest.Moreover,a gradually increasing positive correlation is observed between the ER and EL,but this correlation varies by region,with some showing regional linkages and others developing independently.Finally,the dislocation index of ER and EL presents divergent results based on region-the eastern and central regions primarily show a high level of dislocation,whereas the western and northeastern regions show a low level of dislocation.The results provide a comprehensive overview of the spatiotemporal patterns in the association between ER and EL in China.The results emphasize that to balance sustainable regional development and ecological governance,a region-specific approach must be employed,prioritizing innovation-driven strategies for high ER in more developed regions and market-oriented strategies in less developed regions.
基金National Natural Science Foundation of China,No.42371103Natural Science Basic Research Plan in Shaanxi Province of China,No.2023-JC-YB-229。
文摘Understanding the local ecological security status and its underlying drivers can be used as an effective reference for balancing ecosystem development with societal needs. This study assesses the ecological security of the Loess Plateau(LP) by integrating ecosystem health and ecosystem services, explores the varying impacts of ecosystem structure, quality, and services on ecological security index(ESI), and identifies the key driving factors of ESI using the Geodetector model. The results show that:(1) the average ESI indicates a relatively safe ecological status in LP with a significant increase in ESI observed in 50.21% of the region, largely due to the ecological restoration programs.(2) Natural factors predominantly influence ESI, although human factors play a significant role in the earthy-rocky mountain region and plateau wind-sand region.(3) The interactions between driving factors have a much greater impact on ESI than any single factor, with the interactions between precipitation and human factors being the most influential combination. This study provides a novel perspective on assessing ecological security in LP. We recommend that future ecological restoration efforts should consider the varying roles of ecosystem structure, quality, and services in ESI while tailoring strategies to the primary driving factors based on local conditions.
基金Medical Research Ethics Review Committee of the Guangdong Provincial Center for Disease Control and Prevention,China(No.W96-027E-202307).
文摘Objective This study aimed to identify high-risk areas for type 2 diabetes mellitus(T2DM)mortality to provide relevant evidence for interventions in emerging economies.Methods Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality.The relationships between economic factors,air pollutants,and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model.Results A coastal district in East Guangdong,China,had the highest risk(Relative Risk[RR]=4.58,P<0.01),followed by the 10 coastal districts/counties in West Guangdong,China(RR=2.88,P<0.01).The coastal county in the Pearl River Delta,China(RR=2.24,P<0.01),had the third-highest risk.The remaining risk areas were two coastal counties in East Guangdong,16 districts/counties in the Pearl River Delta,and two counties in North Guangdong,China.Mortality due to T2DM was associated with gross domestic product per capita(GDP per capita).In pilot assessments,T2DM mortality was significantly associated with carbon monoxide.Conclusion High mortality from T2DM occurred in the coastal areas of East and West Guangdong,especially where the economy was progressing towards the upper middle-income level.
基金supported by the National Key Research and Development Program of China(Grant number:2019YFC1200700[No.32095])National Natural Science Foundation of China(L2124006,Institute internal number:90100)Central Government-Guided Local Science and Technology Development Funding Projects(Grant number:2024ZY0053)。
文摘Objective Human brucellosis is a serious public health concern in the Xilingol League,Inner Mongolia;however,the epidemic trends are unclear.Method In this study,Joinpoint regression analysis and spatiotemporal analysis were applied to investigate the epidemic evolution of human brucellosis.Result From 2004 to 2023,a total of 35,747 cases were reported,with an annual average of 1787.35cases and an annual average incidence rate of 176.04/100,000.The incidence increased from 173.96/100,000 in 2004 to 500.71/100,000 in 2009 and fluctuated to 61.43/100,000 in 2023.Three epidemic join points were observed in which the disease experienced an alternative rise and fall,peaking in 2009(APC=21.73,P>0.001)and 2020(APC=21.51,P>0.001).The disease showed a persistent decline trend in lentitude(AAPC=–5.30,P>0.001),suggesting challenges in disease control and a higher risk of rebound.The most cases were reported in Xilinhot City(n=4,777),followed by 4,391 in Sonid Left Banner,and 4,324 in Abaga Banner.Spatiotemporal analysis revealed two high clusters(CI and CII)from 2005 to 2012,the high cluster encompassing eight counties and shifting from north to south.Conclusion The present analysis highlights that human brucellosis has decreased significantly in the Xilingol League,but the epidemic is still severe;further implementation of a strict control program is necessary.
基金supported by the National Key Research and Development Program of China(2022YFD1300202)Collection,Utilization,and Innovation of Animal Resources by Research Institutes and Enterprises of Chongqing(Cqnyncw-kqlhtxm),Chongqing Modern Agricultural Industry Technology System(CQMAITS202413)National Training Program of Innovation and Entrepreneurship for Undergraduates(S202310635040)。
文摘The genetic regulation of hair density in animals remains poorly understood.The Dazu black goat,characterized by its black coarse hair and white skin,provides a unique model for dissecting coarse hair density(CHD).Using high-resolution micro-camera imaging,this study analyzed 905 skin images,33 skin transcriptomes,272 whole-genome sequences,and 182 downloaded transcriptomes.Morphological assessment from juvenile to adult stages revealed the thickening of hair shafts accompanied by a progressive decline in density,largely attributable to rapid surface expansion of the trunk skin.Transcriptomic comparison between high-and low-CHD individuals identified 572 differentially expressed genes(DEGs).A genome-wide association study detected 25 significant single nucleotide polymorphisms(P<9.07e-8)and mapped 48 annotated genes,with the most prominent association signal located near GJA1 on chr9.15931585-18621011.Literature review and Venn analysis highlighted six genes(GJA1,GPRC5D,CD1D,CD207,TFAM,and CXCL12)with documented roles in skin and hair biology,and three genes(GJA1,GPRC5D,and ATP6V1B1)overlapped with DEGs.Multiple-tissue transcriptomic profiling,western blotting,immunohistochemical staining,and skin single-cell RNA sequencing confirmed that GJA1 and GPRC5D were highly and specifically expressed in skin,particularly within hair follicles.Expression was localized predominantly to follicular stem cells and dermal papilla cells,suggesting a significant role in folliculogenesis and structural maintenance.Cross-validation using four public datasets further demonstrated positive correlations between GJA1 and GPRC5D expression and hair follicle density.The innovative micro-camera application allowed the elucidation of spatiotemporal patterns and genes associated with CHD,thereby addressing a significant knowledge gap in animal hair density.
