Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation...Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.展开更多
Traditional nanofiltration membranes face challenges such as membrane fouling and difficulties in achieving precise separation of small organic molecules.A promising solution to these issues is the preparation of thin...Traditional nanofiltration membranes face challenges such as membrane fouling and difficulties in achieving precise separation of small organic molecules.A promising solution to these issues is the preparation of thin-film nanocomposite membranes.In this study,Cu and Ag bimetals were incorporated into covalent organic frameworks to fabricate thin-film nanocomposite membranes.The hydrophilic monomer 1,3,5-tris(4-aminophenyl)benzene of covalent organic frameworks was introduced as a water phase monomer during interfacial polymerization to enhance the organic-inorganic compatibility.The incorporated covalent organic frameworks within the thin-film nanocomposite membrane loosened the selective layer,resulting in an enhanced permeability of 24.6 LMH bar^(-1).The membrane exhibited a rejection rate over 99.0%for Congo Red,Xylene Brilliant Cyanine G,and Reactive Blue,while exhibiting relatively low rejection rates of MgCl_(2) and NaCl.Moreover,the outstanding catalytic capability of the incorporated bimetals led to a 4-nitrophenol conversion rate of 84.38%,enabling simultaneous conversion and separation.The integration of covalent organic frameworks and bimetals also imparted robust antibacterial properties,significantly enhancing operational stability.In conclusion,the covalent organic framework-Cu/Ag-based thin-film nanocomposite membrane demonstrated superior catalytic and separation capabilities,presenting a promising alternative for advanced filtration applications.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225702)the Maria Skłodowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinable DevelopmEnt)project-HORIZON-MSCA-2022-SE-01(Grant No.101131146)。
文摘Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.
基金sponsored by the National Natural Science Foundation of China(Grant No.NSFC-22378160 and U23A20688).
文摘Traditional nanofiltration membranes face challenges such as membrane fouling and difficulties in achieving precise separation of small organic molecules.A promising solution to these issues is the preparation of thin-film nanocomposite membranes.In this study,Cu and Ag bimetals were incorporated into covalent organic frameworks to fabricate thin-film nanocomposite membranes.The hydrophilic monomer 1,3,5-tris(4-aminophenyl)benzene of covalent organic frameworks was introduced as a water phase monomer during interfacial polymerization to enhance the organic-inorganic compatibility.The incorporated covalent organic frameworks within the thin-film nanocomposite membrane loosened the selective layer,resulting in an enhanced permeability of 24.6 LMH bar^(-1).The membrane exhibited a rejection rate over 99.0%for Congo Red,Xylene Brilliant Cyanine G,and Reactive Blue,while exhibiting relatively low rejection rates of MgCl_(2) and NaCl.Moreover,the outstanding catalytic capability of the incorporated bimetals led to a 4-nitrophenol conversion rate of 84.38%,enabling simultaneous conversion and separation.The integration of covalent organic frameworks and bimetals also imparted robust antibacterial properties,significantly enhancing operational stability.In conclusion,the covalent organic framework-Cu/Ag-based thin-film nanocomposite membrane demonstrated superior catalytic and separation capabilities,presenting a promising alternative for advanced filtration applications.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
