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
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear ...Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.展开更多
In the past two decades,various super-resolution(SR)microscopy techniques have been developed to break the di®raction limit using subdi®raction excitation to spatially modulate the°uorescence emission.P...In the past two decades,various super-resolution(SR)microscopy techniques have been developed to break the di®raction limit using subdi®raction excitation to spatially modulate the°uorescence emission.Photomodulatable°uorescent proteins(FPs)can be activated by light of speci¯c wavelengths to produce either stochastic or patterned subdi®raction excitation,resulting in improved optical resolution.In this review,we focus on the recently developed photomodulatable FPs or commonly used SR microscopies and discuss the concepts and strategies for optimizing and selecting the biochemical and photophysical properties of PMFPs to improve the spatiotemporal resolution of SR techniques,especially time-lapse live-cell SR techniques.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
The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life s...The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.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
Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual...Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.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
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
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
Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic an...Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.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
Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provi...Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.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
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This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金support from CAS West Light Grant (xbzgzdsys-202206)National Key Research and Development Program of China (2021YFA1401003).
文摘Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.
基金This project was supported by the National Basic Research Program (2013CB910103)the National Natural Science Foundation of China (Grant Nos.31421002,31370851 and 31300612)+1 种基金the Project of the Chinese Academy of Sciences (XDB08030202)the Beijing Natural Science Foundation (7131011)。
文摘In the past two decades,various super-resolution(SR)microscopy techniques have been developed to break the di®raction limit using subdi®raction excitation to spatially modulate the°uorescence emission.Photomodulatable°uorescent proteins(FPs)can be activated by light of speci¯c wavelengths to produce either stochastic or patterned subdi®raction excitation,resulting in improved optical resolution.In this review,we focus on the recently developed photomodulatable FPs or commonly used SR microscopies and discuss the concepts and strategies for optimizing and selecting the biochemical and photophysical properties of PMFPs to improve the spatiotemporal resolution of SR techniques,especially time-lapse live-cell SR techniques.
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 following grants:National Natural Science Foundation of China(grant nos.92354305,32271428,and 32201132)National Key R&D Program of China(grant no.2022YFC3401100)+1 种基金Fund for Knowledge Innovation of Wuhan Science and Technology Bureau(grant no.2022020801010558)Director Fund of WNLO.
文摘The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.
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.
文摘Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
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 Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004].
文摘Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.
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 following grants:National Key R&D Program of China(Grant no.2022YFC3401100)National Natural Science Foundation of China(Grant nos.32271428,92054110,32201132 and 31600692).
文摘Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.
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.
