As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to ...Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to climate change.Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022,this paper constructs a framework consisting of spatiotemporal evolution,synergy effect measurement,differences in contributions across regions,and influencing factors analysis to reveal the relationship between agricultural PC and CR.The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85%and 0.79%,respectively.However,the annual decline rates of their emission intensities were 3.14%and 4.32%,respectively.This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results,that the effect of PC was weaker than that of CR and had an obvious“policy node effect.”Simultaneously,the synergy between PC and CR evolved from“basic coordination”to“basic imbalance.”The contribution of inter-regional differences was relatively large,while intra-regional differences were smaller,highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR.The basic conditions,industrial structure,input intensity,and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR,and the influence of these significant factors exhibited clear spatiotemporal heterogeneity.These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.展开更多
Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and...Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
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
Shorebirds migrate long-distances along the East Asian-Australasian Flyway(EAAF),exhibiting distinct spatiotemporal fluctuations in population dynamics.Because of habitat degradation and population declines at key sto...Shorebirds migrate long-distances along the East Asian-Australasian Flyway(EAAF),exhibiting distinct spatiotemporal fluctuations in population dynamics.Because of habitat degradation and population declines at key stopover sites along the EAAF,the South Korea's coastal wetlands have gained increasing attention for their ecological value.This study analyzed the shorebird population dynamics across 35 coastal wetlands in South Korea from 2016 to 2024 using data from the National Marine Ecosystem Monitoring Program.For the timeseries analysis,we employed three indicators:seasonal chan ges in abundance,short-term fluctuations(Fi),and long-term trends,assessed using the TRends and Indices for Monitoring data(TRIM)model.Abundance,species richness,and Shannon diversity indices were assessed across the regions during spring and autumn.The TRIM results revealed significant population increases in both seasons("Strong increase"in spring and"Moderate increase"in autumn).Species-level trends indicated notable increases in large-bodied shorebirds,including globally threatened species such as the Far Eastern Curlew(Numenius madagascariensis).Eurasian Curlew(N.arquata),and Eurasian Oystercatcher(Haematopus ostralegus),whereas other species showed variable responses.The Yellow Sea region(Gyeonggi,Chungcheong,and Western Jeolla)showed high biodiversity indices in spring,which may be associated with time-minimization strategies,whereas autumn patterns were characterized by more flexible and selective stopover use,possibly related to energy-minimization strategies.The East Coast and Jeju regions showed the lowest biodiversity indices.Furthermore,community-level analyses using Non-metric Multidimensional Scaling(NMDS)and PERMANOVA revealed distinct clustering of bird assemblages by macro-region and season,confirming significant spatial differentiation in community composition.These findings contrast with the broader declining trends reported across the EAAF and suggest that South Korea's coastal wetlands may serve as stable alternative stopover habitats,potentially supporting the redistribution or recovery of some species.This study highlights the importance of transboundary cooperation and region-specific habitat management that reflects local ecological contexts for effective conservation.展开更多
Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-de...Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.展开更多
The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha...The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.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
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
The shear behavior of intact loess is intricately linked to the spatiotemporal evolution of its mesoscopic characteristics.Understanding this relationship is crucial for comprehending and preventing loess landslides.T...The shear behavior of intact loess is intricately linked to the spatiotemporal evolution of its mesoscopic characteristics.Understanding this relationship is crucial for comprehending and preventing loess landslides.To systematically investigate this connection,our study conducted triaxial shear tests on both Malan loess and Lishi loess,encompassing variations in confining pressures.Additionally,nondestructive,real-time CT observations were employed to track the dynamic evolution of loess mesostructures.The experimental findings illuminate significant insights.The Malan loess exhibits strain hardening during shearing,with the degree of hardening exhibiting an increase in tandem with rising confining pressure.Conversely,the Lishi loess manifests a transition from strain softening to strain hardening as confining pressure increases.Under elevated confining pressure,the specimen undergoes structural damage while concurrently forming a denser configuration through particle friction and rearrangement,leading to strain hardening and volume reduction.In contrast,the central portion of the specimen exhibits heightened sensitivity to deformation under low confining pressures.Gradual crack expansion,emanating from the center and progressing towards the ends,results in progressive specimen destruction and a concomitant reduction in stress.On a macroscopic level,the specimen undergoes expansion at its center while contracting at its ends.The findings of this study unveil the intricate mechanisms governing loess deformation in the presence of varying confining pressures,thereby contributing significantly to our understanding of loess landslide formation and providing a robust theoretical framework for preventive measures.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
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
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
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
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
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
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
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
基金National Social Science Fund of China,No.22BGL182。
文摘Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to climate change.Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022,this paper constructs a framework consisting of spatiotemporal evolution,synergy effect measurement,differences in contributions across regions,and influencing factors analysis to reveal the relationship between agricultural PC and CR.The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85%and 0.79%,respectively.However,the annual decline rates of their emission intensities were 3.14%and 4.32%,respectively.This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results,that the effect of PC was weaker than that of CR and had an obvious“policy node effect.”Simultaneously,the synergy between PC and CR evolved from“basic coordination”to“basic imbalance.”The contribution of inter-regional differences was relatively large,while intra-regional differences were smaller,highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR.The basic conditions,industrial structure,input intensity,and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR,and the influence of these significant factors exhibited clear spatiotemporal heterogeneity.These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.
基金supported by the National Natural Science Foundation of China(82574173,82003516)Jiangsu Provincial Natural Science Foundation(BK20251958)+2 种基金Jiangsu Provincial Medical Key Discipline(ZDXK202250)Top Talent Awards Project Fund(RDF-TP-0023,RDF-TP-0030)Postgraduate Research Fund(PGRS2112022)at Xi'an Jiaotong-Liverpool University.
文摘Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
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.
文摘Shorebirds migrate long-distances along the East Asian-Australasian Flyway(EAAF),exhibiting distinct spatiotemporal fluctuations in population dynamics.Because of habitat degradation and population declines at key stopover sites along the EAAF,the South Korea's coastal wetlands have gained increasing attention for their ecological value.This study analyzed the shorebird population dynamics across 35 coastal wetlands in South Korea from 2016 to 2024 using data from the National Marine Ecosystem Monitoring Program.For the timeseries analysis,we employed three indicators:seasonal chan ges in abundance,short-term fluctuations(Fi),and long-term trends,assessed using the TRends and Indices for Monitoring data(TRIM)model.Abundance,species richness,and Shannon diversity indices were assessed across the regions during spring and autumn.The TRIM results revealed significant population increases in both seasons("Strong increase"in spring and"Moderate increase"in autumn).Species-level trends indicated notable increases in large-bodied shorebirds,including globally threatened species such as the Far Eastern Curlew(Numenius madagascariensis).Eurasian Curlew(N.arquata),and Eurasian Oystercatcher(Haematopus ostralegus),whereas other species showed variable responses.The Yellow Sea region(Gyeonggi,Chungcheong,and Western Jeolla)showed high biodiversity indices in spring,which may be associated with time-minimization strategies,whereas autumn patterns were characterized by more flexible and selective stopover use,possibly related to energy-minimization strategies.The East Coast and Jeju regions showed the lowest biodiversity indices.Furthermore,community-level analyses using Non-metric Multidimensional Scaling(NMDS)and PERMANOVA revealed distinct clustering of bird assemblages by macro-region and season,confirming significant spatial differentiation in community composition.These findings contrast with the broader declining trends reported across the EAAF and suggest that South Korea's coastal wetlands may serve as stable alternative stopover habitats,potentially supporting the redistribution or recovery of some species.This study highlights the importance of transboundary cooperation and region-specific habitat management that reflects local ecological contexts for effective conservation.
基金funding from Grant No. HIDSS-0002 DASHH (Data Science in Hamburg-Helmholtz Graduate School for the Structure of Matter)partially supported by the Helmholtz Imaging platform through the project “Smart Phase.”
文摘Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008202)the National Natural Science Foundation of China(No.42406019)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202353066)。
文摘The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.
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.
基金financially supported by the Second Tibetan Plateau Scientific Expedition and Research Program of China(Grant No.2019QZKK0905)the Youth Fund of Shanxi Provincial Science and Technology Department of China(GrantNo.202103021223200)the Natural Science Foundation of Shaanxi Province,China(Grant No.2024JC-YBMS-314).
文摘The shear behavior of intact loess is intricately linked to the spatiotemporal evolution of its mesoscopic characteristics.Understanding this relationship is crucial for comprehending and preventing loess landslides.To systematically investigate this connection,our study conducted triaxial shear tests on both Malan loess and Lishi loess,encompassing variations in confining pressures.Additionally,nondestructive,real-time CT observations were employed to track the dynamic evolution of loess mesostructures.The experimental findings illuminate significant insights.The Malan loess exhibits strain hardening during shearing,with the degree of hardening exhibiting an increase in tandem with rising confining pressure.Conversely,the Lishi loess manifests a transition from strain softening to strain hardening as confining pressure increases.Under elevated confining pressure,the specimen undergoes structural damage while concurrently forming a denser configuration through particle friction and rearrangement,leading to strain hardening and volume reduction.In contrast,the central portion of the specimen exhibits heightened sensitivity to deformation under low confining pressures.Gradual crack expansion,emanating from the center and progressing towards the ends,results in progressive specimen destruction and a concomitant reduction in stress.On a macroscopic level,the specimen undergoes expansion at its center while contracting at its ends.The findings of this study unveil the intricate mechanisms governing loess deformation in the presence of varying confining pressures,thereby contributing significantly to our understanding of loess landslide formation and providing a robust theoretical framework for preventive measures.
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.
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.
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.
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.
