A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results ...A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results illustrate that the heat-release unit in micro-system intercross greatly affects other sensing units on the temperature.We study the method of etching process,which formed cavity to reduce the heat exchange efficiency and decrease temperature intercross effect.展开更多
The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large...The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large multi-parameter environmental simulation cabin are diffcult to be controlled quickly and accurately with a classical PID algorithm.Considering the dynamic state characteristics of the environmental simulation test chamber,a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature,pressure,and rotation speed control systems.Both simulations and experimental results have shown that compared with classical PID control,this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness.It can also resolve the contradiction between rapidity and small overshoot,and is suitable for application in a large multi-parameter environmental simulation cabin control system.展开更多
In this study, artificial leaf resistance was used to simulate leaf wetness. Specific to the solar greenhouse environment in Tianjin, microclimate monitoring equipment was installed for the collection of temperature g...In this study, artificial leaf resistance was used to simulate leaf wetness. Specific to the solar greenhouse environment in Tianjin, microclimate monitoring equipment was installed for the collection of temperature group and humidity group data, as well as solar radiation and leaf wetness in the greenhouse. In order to reduce the complexity of multivariate factor prediction and ensure the richness of selected data types, correlation analysis was made to the 2 groups of data, screening 5 000 groups of data, including the humidity group data RH, RH_(20), RH_(40), temperature group data T, T_(20), T_(40), and solar radiation W. The data were then analyzed by principal component analysis, screening out 4 groups of principal components to show the leaf wetness index.展开更多
Human brain development is a complex process,and animal models often have significant limitations.To address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-li...Human brain development is a complex process,and animal models often have significant limitations.To address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-like organoids,to more accurately model early human brain development and disease.To enable more consistent and intuitive reproduction of early brain development,in this study,we incorporated forebrain organoid culture technology into the traditional unguided method of brain organoid culture.This involved embedding organoids in matrigel for only 7 days during the rapid expansion phase of the neural epithelium and then removing them from the matrigel for further cultivation,resulting in a new type of human brain organoid system.This cerebral organoid system replicated the temporospatial characteristics of early human brain development,including neuroepithelium derivation,neural progenitor cell production and maintenance,neuron differentiation and migration,and cortical layer patterning and formation,providing more consistent and reproducible organoids for developmental modeling and toxicology testing.As a proof of concept,we applied the heavy metal cadmium to this newly improved organoid system to test whether it could be used to evaluate the neurotoxicity of environmental toxins.Brain organoids exposed to cadmium for 7 or 14 days manifested severe damage and abnormalities in their neurodevelopmental patterns,including bursts of cortical cell death and premature differentiation.Cadmium exposure caused progressive depletion of neural progenitor cells and loss of organoid integrity,accompanied by compensatory cell proliferation at ectopic locations.The convenience,flexibility,and controllability of this newly developed organoid platform make it a powerful and affordable alternative to animal models for use in neurodevelopmental,neurological,and neurotoxicological studies.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
In the quest for effective solutions to address Environ.Pollut.and meet the escalating energy demands,heterojunction photocatalysts have emerged as a captivating and versatile technology.These photocatalysts have garn...In the quest for effective solutions to address Environ.Pollut.and meet the escalating energy demands,heterojunction photocatalysts have emerged as a captivating and versatile technology.These photocatalysts have garnered significant interest due to their wideranging applications,including wastewater treatment,air purification,CO_(2) capture,and hydrogen generation via water splitting.This technique harnesses the power of semiconductors,which are activated under light illumination,providing the necessary energy for catalytic reactions.With visible light constituting a substantial portion(46%)of the solar spectrum,the development of visible-light-driven semiconductors has become imperative.Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light.In this comprehensive review,we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media,as well as the remarkable progress made in renewable energy production.Moreover,we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems.Finally,we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain.By unraveling the potential of heterojunction photocatalysts,this reviewcontributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.展开更多
Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in China.As phytoplankton are sensitive indicators of trophic changes inwater bodies,characterizing...Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in China.As phytoplankton are sensitive indicators of trophic changes inwater bodies,characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control.Here,we used direct microscopic count and environmental DNA(eDNA)metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin(Chengdu,Sichuan Province,China).The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis.Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling.At the phylum level,the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta,Chlorophyta,and Cyanophyta,in contrast with Chlorophyta,Dinophyceae,and Bacillariophyta identified by eDNA metabarcoding.Inα-diversity analysis,eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method.Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios>16:1 in all water samples.Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth.The results could be useful for implementing comprehensive management of the river basin environment.It is recommended to control the discharge of point-and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients(e.g.,Jianyang-Ziyang).Algae monitoring techniques and removal strategies should be improved in 201 Hospital,Hongrihe Bridge and Colmar Town areas.展开更多
Persistent toxic substances(PTS)represent a paramount environmental issue in the 21st century.Understanding the concentrations and forms of PTS in the environment is crucial for accurately assessing their environmenta...Persistent toxic substances(PTS)represent a paramount environmental issue in the 21st century.Understanding the concentrations and forms of PTS in the environment is crucial for accurately assessing their environmental health impacts.This article presents a concise overview of the components of PTS,pertinent environmental regulations,and conventional detection methodologies.Additionally,we offer an in-depth review of the principles,development,and practical applications of surface-enhanced Raman scattering(SERS)in environmental monitoring,emphasizing the advancements in detecting trace amounts of PTS in complex environmental matrices.Recent progress in enhancing SERS sensitivity,improving selectivity,and practical implementations are detailed,showcasing innovative materials and methods.Integrating SERS with advanced algorithms are highlighted as pivotal areas for future research.展开更多
The Qinghai-Tibet Plateau,with its high altitude and cold climate,is one of the most fragile ecological environments in China and is distinguished by its naturally elevated arsenic(As)levels in the soil,largely due to...The Qinghai-Tibet Plateau,with its high altitude and cold climate,is one of the most fragile ecological environments in China and is distinguished by its naturally elevated arsenic(As)levels in the soil,largely due to its rich mineral and geothermal resources.This review provides a comprehensive analysis of As content,focusing on its distribution,environmental migration,and transformation behavior across the plateau.The review further evaluates the distribution of As in different functional areas,revealing that geothermal fields(107.2 mg/kg),mining areas(53.8 mg/kg),and croplands(39.3 mg/kg)have the highest As concentrations,followed by river and lake sediments and adjacent areas(33.1 mg/kg).These elevated levels are primarily attributed to the presence of As-rich minerals,such as arsenopyrite and pyrite.Additionally,human activities,including mining and geothermal energy production,exacerbate the release of As into the environment.The review also highlights the role of localmicroorganisms,particularly those fromthe phyla Proteobacteria and Actinobacteria,which possess As metabolic genes that facilitate As translocation.Given the unique climatic conditions of the plateau,conventionalmethods for As controlmay not be fully effective.However,the review identifies promising remediation strategies that are environmentally adaptable,such as the use of local microorganisms,specific adsorbents,and integrated technologies,which offer potential solutions for managing and utilizing Ascontaminated soils on the plateau.展开更多
Researchers have recently developed various surface engineering approaches to modify environmental catalysts and improve their catalytic activity.Defect engineering has proved to be one of the most promising modificat...Researchers have recently developed various surface engineering approaches to modify environmental catalysts and improve their catalytic activity.Defect engineering has proved to be one of the most promising modification methods.Constructing defects on the surface of catalytic materials can effectively modulate the coordination environment of the active sites,affecting and changing the electrons,geometry,and other important properties at the catalytic active sites,thus altering the catalytic activity of the catalysts.However,the conformational relationship between defects and catalytic activity remains to be clarified.This dissertation focuses on an overview of recent advances in defect engineering in environmental catalysis.Based on defining the classification of defects in catalytic materials,defect construction methods,and characterization techniques are summarized and discussed.Focusing on an overview of the characteristics of the role of defects in electrocatalytic,photocatalytic,and thermal catalytic reactions and the mechanism of catalytic reactions.An elaborate link is given between the reaction activity and the structure of catalyst defects.Finally,the existing challenges and possible future directions for the application of defect engineering in environmental catalysis are discussed,which are expected to guide the design and development of efficient environmental catalysts and mechanism studies.展开更多
Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security...Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security.This study investigates the interplay between these land use changes and their environmental implications at macro(district)and micro(village)levels,focusing on agricultural productivity and resource sustainability.The study employs a mixed-method approach,integrating secondary data from official datasets and primary data gathered through structured household surveys,focus group discussions,and visual analysis techniques.Data from 20 villages,selected based on predominant land use characteristics,were analysed using statistical and geospatial tools,including ArcGIS and STATA,to quantify food grain losses and evaluate environmental degradation.Findings of this study reveal a 19%reduction in agricultural land over two decades(2000-2024),correlating with increased residential and industrial areas.Groundwater resources face severe overexploitation,with pollution from industrial clusters further degrading water and soil quality.The study estimates a total food grain loss of 1.5 million kilograms across surveyed villages due to land acquisitions.A strong positive correlation(R^(2)=0.98)between land acquisition and food loss underscores the direct impact of urbanization on agricultural output.The research underscores the urgency of sustainable land management practices,including preserving agricultural lands,optimizing groundwater usage,and enhancing community involvement in planning.By addressing these challenges,the study advocates for balanced urban expansion and food security to ensure ecological and economic resilience in the region.展开更多
Abandoned mines,especially pyrite-rich ones,release acid mine drainage(AMD)with high acidity and excessive amounts of heavy metals,threatening regional ecosystems.Six samples of mine drainage,nine samples of surface w...Abandoned mines,especially pyrite-rich ones,release acid mine drainage(AMD)with high acidity and excessive amounts of heavy metals,threatening regional ecosystems.Six samples of mine drainage,nine samples of surface water,and twelve samples of sediment were analyzed in this case study of the Dashu pyrite mine in southwest China.A comprehensive analysis of the pollution levels,pollution sources,and potential hazards of eight metals(Ni,Cd,Cu,Zn,Fe,Al,Pb,and Mn)that exceeded regulatory standardswas conducted bymonitoring 24 conventional and characteristic indicators.Ultimately,this research evaluated the environmental hazards associated with abandonedmine water using the"pressure-response"model,thereby providing valuable insights for the effective protection of the environment in mining regions.The primary pollutants in mine water were determined to be SO_(4)^(2−),Fe,and Mn,with concentrations of 7700,1450,and 6.78mg/L,respectively.A clear"source-sink"dynamic was observed between themine water and the surrounding water system.surface water was primarily polluted by Ni and Mn,while water system sediments were primarily polluted by Cu and Hg.Ion ratio and Pearson correlation analyses indicated heavy metals in surface water and sediments originated from the same AMD source.The"pressureresponse"model was used to assess the environmental hazards of water from abandoned mines.Mines W1,W2,W5,and W6 were classified as high-risk,while W3 and W4 were medium-risk.This study offers a novel approach and valuable reference for identifying and classifying environmental risks in abandoned mines and targeting AMD treatment.展开更多
Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The s...Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.展开更多
A large-scale high altitude environment simulation test cabin was developed to accurately control temperatures and pressures encountered at high altitudes. The system was developed to provide slope-tracking dynamic co...A large-scale high altitude environment simulation test cabin was developed to accurately control temperatures and pressures encountered at high altitudes. The system was developed to provide slope-tracking dynamic control of the temperature–pressure two-parameter and overcome the control difficulties inherent to a large inertia lag link with a complex control system which is composed of turbine refrigeration device, vacuum device and liquid nitrogen cooling device. The system includes multi-parameter decoupling of the cabin itself to avoid equipment damage of air refrigeration turbine caused by improper operation. Based on analysis of the dynamic characteristics and modeling for variations in temperature, pressure and rotation speed, an intelligent controller was implemented that includes decoupling and fuzzy arithmetic combined with an expert PID controller to control test parameters by decoupling and slope tracking control strategy. The control system employed centralized management in an open industrial ethernet architecture with an industrial computer at the core. The simulation and field debugging and running results show that this method can solve the problems of a poor anti-interference performance typical for a conventional PID and overshooting that can readily damage equipment. The steady-state characteristics meet the system requirements.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above ...In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.展开更多
The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a tra...The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.展开更多
Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in...Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.展开更多
Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including at...Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.展开更多
文摘A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results illustrate that the heat-release unit in micro-system intercross greatly affects other sensing units on the temperature.We study the method of etching process,which formed cavity to reduce the heat exchange efficiency and decrease temperature intercross effect.
基金supported by the Aeronautical Science Foundation of China(No.2012ZD51043)‘‘Fanzhou’’ Youth Scientifc Funds(No.20100504)
文摘The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large multi-parameter environmental simulation cabin are diffcult to be controlled quickly and accurately with a classical PID algorithm.Considering the dynamic state characteristics of the environmental simulation test chamber,a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature,pressure,and rotation speed control systems.Both simulations and experimental results have shown that compared with classical PID control,this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness.It can also resolve the contradiction between rapidity and small overshoot,and is suitable for application in a large multi-parameter environmental simulation cabin control system.
基金Supported by the Innovation Research and Experiments for Young Scientists(2018009)the Project for the Transformation and Promotion of Agricultural Science and Technology Achievements of Tianjin(201801040)+1 种基金the Modern Agriculture Industry System for Vegetables of Tianjin(ITTVRS2017018)the Science and Technology Planning Project of Tianjin(17YFZCNC00280)
文摘In this study, artificial leaf resistance was used to simulate leaf wetness. Specific to the solar greenhouse environment in Tianjin, microclimate monitoring equipment was installed for the collection of temperature group and humidity group data, as well as solar radiation and leaf wetness in the greenhouse. In order to reduce the complexity of multivariate factor prediction and ensure the richness of selected data types, correlation analysis was made to the 2 groups of data, screening 5 000 groups of data, including the humidity group data RH, RH_(20), RH_(40), temperature group data T, T_(20), T_(40), and solar radiation W. The data were then analyzed by principal component analysis, screening out 4 groups of principal components to show the leaf wetness index.
基金supported by the National Key R&D Program of China,No.2019YFA0110300(to ZG)the National Natural Science Foundation of China,Nos.81773302(to YF),32070862(to ZG).
文摘Human brain development is a complex process,and animal models often have significant limitations.To address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-like organoids,to more accurately model early human brain development and disease.To enable more consistent and intuitive reproduction of early brain development,in this study,we incorporated forebrain organoid culture technology into the traditional unguided method of brain organoid culture.This involved embedding organoids in matrigel for only 7 days during the rapid expansion phase of the neural epithelium and then removing them from the matrigel for further cultivation,resulting in a new type of human brain organoid system.This cerebral organoid system replicated the temporospatial characteristics of early human brain development,including neuroepithelium derivation,neural progenitor cell production and maintenance,neuron differentiation and migration,and cortical layer patterning and formation,providing more consistent and reproducible organoids for developmental modeling and toxicology testing.As a proof of concept,we applied the heavy metal cadmium to this newly improved organoid system to test whether it could be used to evaluate the neurotoxicity of environmental toxins.Brain organoids exposed to cadmium for 7 or 14 days manifested severe damage and abnormalities in their neurodevelopmental patterns,including bursts of cortical cell death and premature differentiation.Cadmium exposure caused progressive depletion of neural progenitor cells and loss of organoid integrity,accompanied by compensatory cell proliferation at ectopic locations.The convenience,flexibility,and controllability of this newly developed organoid platform make it a powerful and affordable alternative to animal models for use in neurodevelopmental,neurological,and neurotoxicological studies.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金supported by the National Natural Science Foundation of China (Nos.52072152 and 51802126)Jiangsu University Jinshan Professor Fund,Jiangsu Specially-Appointed Professor Fund,the Open Fund from Guangxi Key Laboratory of Electrochemical Energy Materials,Zhenjiang“Jinshan Talents”Project 2021,China PostDoctoral Science Foundation (No.2022M721372)+1 种基金the“Doctor of Entrepreneurship and Innovation”in Jiangsu Province (No.JSSCBS20221197)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (No.KYCX22_3645).
文摘In the quest for effective solutions to address Environ.Pollut.and meet the escalating energy demands,heterojunction photocatalysts have emerged as a captivating and versatile technology.These photocatalysts have garnered significant interest due to their wideranging applications,including wastewater treatment,air purification,CO_(2) capture,and hydrogen generation via water splitting.This technique harnesses the power of semiconductors,which are activated under light illumination,providing the necessary energy for catalytic reactions.With visible light constituting a substantial portion(46%)of the solar spectrum,the development of visible-light-driven semiconductors has become imperative.Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light.In this comprehensive review,we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media,as well as the remarkable progress made in renewable energy production.Moreover,we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems.Finally,we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain.By unraveling the potential of heterojunction photocatalysts,this reviewcontributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.
基金supported by the National Natural Science Foundation of China (No.72091511)the Science Fund for Distinguished Young Scholars of Hebei Province (No.E2022402064).
文摘Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in China.As phytoplankton are sensitive indicators of trophic changes inwater bodies,characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control.Here,we used direct microscopic count and environmental DNA(eDNA)metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin(Chengdu,Sichuan Province,China).The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis.Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling.At the phylum level,the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta,Chlorophyta,and Cyanophyta,in contrast with Chlorophyta,Dinophyceae,and Bacillariophyta identified by eDNA metabarcoding.Inα-diversity analysis,eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method.Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios>16:1 in all water samples.Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth.The results could be useful for implementing comprehensive management of the river basin environment.It is recommended to control the discharge of point-and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients(e.g.,Jianyang-Ziyang).Algae monitoring techniques and removal strategies should be improved in 201 Hospital,Hongrihe Bridge and Colmar Town areas.
基金supported by the National Natural Science Foundation of China(Nos.42077299,and U21A20290)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750400)the Ordos Key Research and Development Program(No.YF20240037).
文摘Persistent toxic substances(PTS)represent a paramount environmental issue in the 21st century.Understanding the concentrations and forms of PTS in the environment is crucial for accurately assessing their environmental health impacts.This article presents a concise overview of the components of PTS,pertinent environmental regulations,and conventional detection methodologies.Additionally,we offer an in-depth review of the principles,development,and practical applications of surface-enhanced Raman scattering(SERS)in environmental monitoring,emphasizing the advancements in detecting trace amounts of PTS in complex environmental matrices.Recent progress in enhancing SERS sensitivity,improving selectivity,and practical implementations are detailed,showcasing innovative materials and methods.Integrating SERS with advanced algorithms are highlighted as pivotal areas for future research.
基金supported by the Central Public-interest Scientific Institution Basal Research Fund(No.Y2024QC29)the Central Public-interest Scientific Institution Basal Research Fund(Nos.2024-jbkyywf-lwj and 2024-jbkyywf-zyj).
文摘The Qinghai-Tibet Plateau,with its high altitude and cold climate,is one of the most fragile ecological environments in China and is distinguished by its naturally elevated arsenic(As)levels in the soil,largely due to its rich mineral and geothermal resources.This review provides a comprehensive analysis of As content,focusing on its distribution,environmental migration,and transformation behavior across the plateau.The review further evaluates the distribution of As in different functional areas,revealing that geothermal fields(107.2 mg/kg),mining areas(53.8 mg/kg),and croplands(39.3 mg/kg)have the highest As concentrations,followed by river and lake sediments and adjacent areas(33.1 mg/kg).These elevated levels are primarily attributed to the presence of As-rich minerals,such as arsenopyrite and pyrite.Additionally,human activities,including mining and geothermal energy production,exacerbate the release of As into the environment.The review also highlights the role of localmicroorganisms,particularly those fromthe phyla Proteobacteria and Actinobacteria,which possess As metabolic genes that facilitate As translocation.Given the unique climatic conditions of the plateau,conventionalmethods for As controlmay not be fully effective.However,the review identifies promising remediation strategies that are environmentally adaptable,such as the use of local microorganisms,specific adsorbents,and integrated technologies,which offer potential solutions for managing and utilizing Ascontaminated soils on the plateau.
基金supported by The National Key R&D Program of China(No.2021YFB3500700)National Natural Science Foundation of China(Nos.21677010 and 51808037)Special fund of Beijing Key Laboratory of Indoor Air Quality Evaluation and Control(No.BZ0344KF21-04)。
文摘Researchers have recently developed various surface engineering approaches to modify environmental catalysts and improve their catalytic activity.Defect engineering has proved to be one of the most promising modification methods.Constructing defects on the surface of catalytic materials can effectively modulate the coordination environment of the active sites,affecting and changing the electrons,geometry,and other important properties at the catalytic active sites,thus altering the catalytic activity of the catalysts.However,the conformational relationship between defects and catalytic activity remains to be clarified.This dissertation focuses on an overview of recent advances in defect engineering in environmental catalysis.Based on defining the classification of defects in catalytic materials,defect construction methods,and characterization techniques are summarized and discussed.Focusing on an overview of the characteristics of the role of defects in electrocatalytic,photocatalytic,and thermal catalytic reactions and the mechanism of catalytic reactions.An elaborate link is given between the reaction activity and the structure of catalyst defects.Finally,the existing challenges and possible future directions for the application of defect engineering in environmental catalysis are discussed,which are expected to guide the design and development of efficient environmental catalysts and mechanism studies.
文摘Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security.This study investigates the interplay between these land use changes and their environmental implications at macro(district)and micro(village)levels,focusing on agricultural productivity and resource sustainability.The study employs a mixed-method approach,integrating secondary data from official datasets and primary data gathered through structured household surveys,focus group discussions,and visual analysis techniques.Data from 20 villages,selected based on predominant land use characteristics,were analysed using statistical and geospatial tools,including ArcGIS and STATA,to quantify food grain losses and evaluate environmental degradation.Findings of this study reveal a 19%reduction in agricultural land over two decades(2000-2024),correlating with increased residential and industrial areas.Groundwater resources face severe overexploitation,with pollution from industrial clusters further degrading water and soil quality.The study estimates a total food grain loss of 1.5 million kilograms across surveyed villages due to land acquisitions.A strong positive correlation(R^(2)=0.98)between land acquisition and food loss underscores the direct impact of urbanization on agricultural output.The research underscores the urgency of sustainable land management practices,including preserving agricultural lands,optimizing groundwater usage,and enhancing community involvement in planning.By addressing these challenges,the study advocates for balanced urban expansion and food security to ensure ecological and economic resilience in the region.
基金supported by the National Key Research and Development Program of China(No.2023YFC3710000)the National Natural Science Foundation of China(Nos.42277078 and 42307118).
文摘Abandoned mines,especially pyrite-rich ones,release acid mine drainage(AMD)with high acidity and excessive amounts of heavy metals,threatening regional ecosystems.Six samples of mine drainage,nine samples of surface water,and twelve samples of sediment were analyzed in this case study of the Dashu pyrite mine in southwest China.A comprehensive analysis of the pollution levels,pollution sources,and potential hazards of eight metals(Ni,Cd,Cu,Zn,Fe,Al,Pb,and Mn)that exceeded regulatory standardswas conducted bymonitoring 24 conventional and characteristic indicators.Ultimately,this research evaluated the environmental hazards associated with abandonedmine water using the"pressure-response"model,thereby providing valuable insights for the effective protection of the environment in mining regions.The primary pollutants in mine water were determined to be SO_(4)^(2−),Fe,and Mn,with concentrations of 7700,1450,and 6.78mg/L,respectively.A clear"source-sink"dynamic was observed between themine water and the surrounding water system.surface water was primarily polluted by Ni and Mn,while water system sediments were primarily polluted by Cu and Hg.Ion ratio and Pearson correlation analyses indicated heavy metals in surface water and sediments originated from the same AMD source.The"pressureresponse"model was used to assess the environmental hazards of water from abandoned mines.Mines W1,W2,W5,and W6 were classified as high-risk,while W3 and W4 were medium-risk.This study offers a novel approach and valuable reference for identifying and classifying environmental risks in abandoned mines and targeting AMD treatment.
基金supported by the Key Research and Development Program of the Ministry of Science and Technology of China(grant number:2016YF0900605)the Key Research and Development Program of Hebei Province(grant number:192777129D)+1 种基金the Joint Fund for Iron and Steel of the Natural Science Foundation of Hebei Province(grant number:H2016209058)the National Natural Science Foundation for Regional Joint Fund of China(grant number:U22A20364)。
文摘Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.
基金supported by the Aeronautical Science Foun-dation of China(No.2012XX51043)‘‘Fanzhou’’Youth Scientific Funds of China(No.20100504)
文摘A large-scale high altitude environment simulation test cabin was developed to accurately control temperatures and pressures encountered at high altitudes. The system was developed to provide slope-tracking dynamic control of the temperature–pressure two-parameter and overcome the control difficulties inherent to a large inertia lag link with a complex control system which is composed of turbine refrigeration device, vacuum device and liquid nitrogen cooling device. The system includes multi-parameter decoupling of the cabin itself to avoid equipment damage of air refrigeration turbine caused by improper operation. Based on analysis of the dynamic characteristics and modeling for variations in temperature, pressure and rotation speed, an intelligent controller was implemented that includes decoupling and fuzzy arithmetic combined with an expert PID controller to control test parameters by decoupling and slope tracking control strategy. The control system employed centralized management in an open industrial ethernet architecture with an industrial computer at the core. The simulation and field debugging and running results show that this method can solve the problems of a poor anti-interference performance typical for a conventional PID and overshooting that can readily damage equipment. The steady-state characteristics meet the system requirements.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
基金Supported by The National Undergraduate Innovation Training Program(Grant No.202310290069Z).
文摘In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.
文摘The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.
基金supported by the National Natural Science Foundation of China(Grant No.12075323)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300702).
文摘Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.
基金supported by the Basic Science Center Project of the National Natural Science Foundation of China(42388102)the National Natural Science Foundation of China(42174030)+2 种基金the Special Fund of Hubei Luojia Laboratory(220100020)the Major Science and Technology Program for Hubei Province(2022AAA002)the Fundamental Research Funds for the Central Universities of China(2042022dx0001 and 2042023kfyq01)。
文摘Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.
