Temporal Characteristics Research Articles

Lower Atmospheric Ducts over the South China Sea Related to the Monsoon, Atmospheric, and Ocean Conditions Based on ECMWF Reanalysis Data

Abstract Atmospheric ducting is a highly refractive propagation condition that frequently occurs at sea and significantly impacts radar and communication equipment. This paper analyzes the spatiotemporal distribution of lower atmospheric ducts (LAD) in the South China Sea (SCS) and the variation of their occurrence rate with the monsoon by using reanalysis data from the ECMWF from 1980 to 2022. Additionally, the study discusses the relationship between ducting occurrences and atmospheric and oceanic conditions. The results indicate that wind dynamics in the SCS significantly impact ducting incidents. During the high-incidence period of LAD, humidity-gradient-constructed ducts are the primary mechanism. Before the onset of the monsoon, the mountains in the western part of Luzon Island obstruct the easterly wind, resulting in high temperatures and strong evaporation along the western coast of the mountains. Meanwhile, low temperatures and humidity prevail in the eastern part of the mountains, and they lead to a stratified atmosphere characterized by dry and cold upper layers and warm and humid lower layers in the western part of Luzon Island, which causes a distinct decrease in humidity with height. After the onset of the monsoon, the air from the Indochina Peninsula to the ocean is dry and cold, but the high-altitude area blocks it. This weakens the horizontal mobility of the low-level humid atmosphere over the sea, resulting in atmospheric stratification in the eastern coastal area of the Indochina Peninsula. This stratification leads to dry and cold upper layers and warm and humid lower layers. Significance Statement Atmospheric ducting is a superrefractive propagation condition that frequently occurs at sea and has a significant impact on radar and communication equipment and is related to large-scale or medium- and small-scale atmospheric stratification. The distribution of land and sea around the South China Sea (SCS) and the monsoon are important factors affecting the existence of atmospheric ducts in this region. Many scholars have studied the mechanism of atmospheric ducts in local areas based on observation data (or reanalysis data). The literature on the atmospheric ducts in the SCS mainly focuses on the spatial and temporal statistical distribution of seasons, months, and days, and gives the spatial and temporal distribution characteristics of the region within the statistical time, emphasizing the important influence of the monsoon on the duct, but there is no relevant research on the reasons for the existence of the specific relationship and its temporal and spatial distribution characteristics. The manuscript analyzes the temporal and spatial distribution of lower atmospheric ducts in the SCS and the variation of their occurrence rate with the monsoon, quantifies the contributions of temperature, humidity, and air pressure to the ducting occurrence, meanwhile discussing the ducting occurrence relation with atmospheric and oceanic conditions. In the end, we demonstrate that the development of high-incidence areas for SCS ducts prior to and following the onset of monsoon season is connected to factors such as wind patterns, seawater evaporation, and topography. Furthermore, unstable vertical transport of water vapor in both the atmosphere and oceanic conditions plays a crucial role in facilitating the creation of humidity-type ducts.

Temporal characteristics and health effects related to NO2, O3, and SO2 in an urban area of Iran

This study reports on temporal variations of NO2, O3, and SO2 pollutants and their related health effects in urban air of Khorramabad, Iran using AirQ 2.2.3 software. Based on data between 2015-2021, hourly NO2, O3, and SO2 concentrations increase starting at 6:00 AM local time until 9:00 PM, 3:00 PM, and 7:00 PM local time, respectively, before gradually decreasing. The highest monthly NO2, O3, and SO2 concentrations are observed in October, August, and September, respectively. Annual median NO2, O3, and SO2 concentrations range between 17 ppb - 38.8 ppb, 17.5 ppb - 36.6 ppb, and ∼14 ppb - 30.8 ppb, respectively. Two to 93 days and 17 to 156 days between 2015-2021 exhibit daily concentrations of NO2 and SO2 ≤ WHO AQGs, respectively, while 187 to 294 days have 8-h maximum O3 concentrations ≤ WHO AQGs. The mean excess mortality ascribed to respiratory mortality, cardiovascular mortality, hospital admissions for COPD, and acute myocardial infraction are 121, 603, 39, and 145 during 2015-2021, respectively. O3 is found to exert more significant health effects compared to SO2 and NO2, resulting in higher cardiovascular mortality. The gradual increase in NO2 and possibly O3 over the study period is suspected to be due to economic sanctions, while SO2 decreased due to regulatory activity. Sustainable control strategies such as improving fuel quality, promoting public transportation and vehicle retirement, applying subsidies for purchase of electric vehicles, and application of European emission standards on automobiles can help decrease target pollutant levels in ambient air of cities in developing countries.

The increases in extreme climatic events over the northeastern Tibetan Plateau and their association with atmospheric circulation changes

In the context of global warming, increasing frequency of climate extremes poses a great challenge to natural systems and humankind. However, the spatial and temporal characteristics, as well as the mechanisms of extreme climate change, remain insufficiently understood, particularly in arid and semi-arid mountainous regions with high climate sensitivity and ecological fragility. In this study, we analyzed the spatial and temporal characteristics of 27 extreme climate indices (including intensity, duration, and frequency) in the Qilian Mountains, northeastern Tibetan Plateau, from 1961 to 2016 using the CN05.1 meteorological grid dataset, explored the relationship between the extreme climate indices and internal variability of the climate system (e.g., the North Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Arctic Oscillation (AO)), and further probed into the mechanisms affecting climate extremes using ERA5 reanalysis dataset. The results showed that extreme temperatures in the Qilian Mountains were significantly enhanced in terms of intensity, frequency, and duration during 1961–2016. The decrease rates of frost days (FD0) and ice days (ID0) were − 4.4 d per decade and − 4.7 d per decade, respectively, and the increase rate of growing season length (GSL) was 2.9 d per decade. Extreme precipitation intensity increased significantly, with the intensity indices R95P and PRCPTOT showing the most significant change trends of 4 mm per decade and 14.9 mm per decade, respectively (p < 0.05). The number of consecutive dry days (CDD) displayed a significant downward trend, with a rate of −6 d per decade, the number of moderate precipitation days (R10) showed an increasing trend of 0.12 d per decade. Correlation analyses revealed significant associations between the increases in extreme climates and the AMO and PDO. Additionally, the enhancement of the AO contributed to the increased extreme precipitation. Enhanced anticyclonic circulation in Eurasia and geopotential height, as well as increased cloud cover in winter, were the main reasons for the increase in extreme temperatures in the Qilian Mountains, whereas the significant increase in water vapor transport entering the east boundary of the Qilian Mountains in summer mainly contributed to the increase in extreme precipitation. This study highlights internal oscillations in the climate system that regulate the extreme climate variability in the Qilian Mountains at various spatial-temporal scales.

It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution.

For most herbivorous animals, group-living appears to incur a high cost by intensifying feeding competition. These costs raise the question of how gregariousness (i.e., the tendency to aggregate) could have evolved to such an extent in taxa such as anthropoid primates and ungulates. When attempting to test the potential benefits and costs, previous foraging models demonstrated that group-living might be beneficial by lowering variance in intake, but that it reduces overall foraging success. However, these models did not fully account for the fact that gregariousness has multiple experiences and can vary in relation to ecological variables and foraging competition. Here, we present an agent-based model for testing how ecological variables impact the costs and benefits of gregariousness. In our simulations, primate-like agents forage on a variable resource landscape while maintaining spatial cohesion with conspecifics to varying degrees. The agents' energy intake rate, daily distance traveled, and variance in energy intake were recorded. Using Morris Elementary Effects sensitivity analysis, we tested the sensitivity of 10 model parameters, of which 2 controlled gregarious behavior and 8 controlled food resources, including multiple aspects of temporal and spatial heterogeneity. We found that, while gregariousness generally increased feeding competition, the costs of gregariousness were much lower when resources were less variable over time (i.e., when calorie extraction was slow and resource renewal was frequent). We also found that maintaining proximity to other agents resulted in lower variance in energy intake when resources were more variable over time. Thus, it appears that the costs and benefits of gregariousness are strongly influenced by the temporal characteristics of food resources, giving insight into the pressures that shaped the evolution of sociality and group living, including in our own lineage.

Improvements on Gaussian mixture model and its application in identifying aerosol types in two major cities in the Yangtze River Delta, China

Accurately identifying the authentic local aerosol types is one of the fundamental tasks in studying aerosol radiative effects and model assessment. In this paper, improvements were made to the traditional Gaussian Mixture Model, leading to the following results: 1) This study introduces several improvements to the traditional Gaussian Mixture Model (GMM), referred to as M-GMMs. These improvements include the incorporation of multivariate kurtosis coefficients, Mahalanobis distance instead of Euclidean distance, and weights of variables. The M-GMMs overcome the issues related to dimensional units and correlations among multiple parameters, thereby enhancing the estimation of the covariance matrix. 2) The proposed M-GMMs model was evaluated for its clustering performance using machine-generated data with known classifications and real iris flower data. The results demonstrated that the classification performance of M-GMMs was superior to other models. Furthermore, compared to the slightly less effective K-means algorithm (which requires manual definition of the number of aerosol types), the M-GMMs model was able to automatically iterate and produce consistent classification results based on similar characteristics. 3) There is still a significant disparity between the characteristics of real stations and typical aerosols. Directly evaluating local aerosols using the characteristics of typical aerosols results in substantial errors. However, the M-GMMs model can effectively reflect the authentic aerosol characteristics at the local level. 4) The M-GMMs model was utilized to perform cluster analysis on the Xuzhou and Nanjing stations of AERONET. This analysis yielded quantitative proportions, temporal distribution characteristics, and spectral distribution features of aerosol types in the two regions. The improved M-GMMs model presented in this paper enables more accurate and continuous characterization of aerosol type variations. Its findings hold significant theoretical and practical value in reassessing aerosol radiative effects.

Analysis of temporal trends and characteristics of gender affirming hysterectomies pre- and post-expansion of health care coverage at a large integrated california health care system

Analysis of temporal trends and characteristics of gender affirming hysterectomies pre- and post-expansion of health care coverage at a large integrated california health care system

Estimation of Corn Net Primary Productivity and Carbon Sequestration Based on the CASA Model: A Case Study of the Fen River Basin

The utilization of remote sensing technology to assess changes in crop net primary productivity (NPP), biomass, and carbon sequestration within the Fen River Basin, a crucial agricultural region in China, is important for achieving agricultural modernization, enhancing ecological environment quality, and obtaining carbon neutrality objectives. This study employed satellite remote sensing and the Carnegie–Ames–Stanford approach (CASA) model as research methods to investigate the temporal and spatial distribution characteristics of corn NPP in the Fen River Basin. Correlation analysis was conducted to examine the response of corn NPP to various environmental factors in the region, while aboveground biomass and carbon sequestration of corn were estimated using a biomass inversion model driven by NPP and principles of photosynthesis in green plants. The findings revealed that, from a temporal perspective, corn NPP in the Fen River Basin exhibited a unimodal variation pattern, with an average value of 368.65 gC/m2. Spatially, the corn NPP displayed a discernible differentiation pattern, with the highest values primarily observed in the middle reaches of the Fen River Basin. Throughout the spatial and temporal variations in corn NPP during 2011–2020, the carbon sequestration capacity of corn exhibited an upward trend, particularly since 2017. The corn NPP displayed a positive correlation with temperature and precipitation. The response to solar radiation was mildly negative and a mildly positive correlation. In 2020, the aboveground biomass and carbon sequestration of corn followed a normal distribution, with the highest values concentrated in the northwestern part of the lower Fen River.

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage(FVC), Wetness(WET), Evapotranspiration(ET), Land Surface Temperature(LST) and Normalized Difference Bare Soil Index(NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of "high in the east and low in the west, multi-point development". (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73% in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

Characteristics of groundwater drought and its correlation with meteorological and agricultural drought over the North China Plain based on GRACE

Groundwater drought is distinctive comparing with other droughts, featuring high concealment, long duration, and obvious hysteresis. It may have significant negative effects on agriculture, eco-environment, and social economy sectors. Therefore, investigating the temporal and spatial characteristics of groundwater drought and its driving factors is meaningful for monitoring and assessing the risks of groundwater shortage. In this study, a groundwater drought index based on groundwater storage anomalies (GWSAs) derived from GRACE satellites was developed to detect and analyze drought events. The spatiotemporal variations and trends of historical groundwater droughts from 2002 to 2021 in the North China Plain were evaluated. In addition, the correlations between groundwater drought and meteorological and agricultural drought were analyzed by the detrended cross-correlation analysis. The results indicated that (1) the standardized groundwater storage anomaly index (SGSAI) could better identify and characterize groundwater drought by eliminating the spatial heterogeneity of GWSAs; (2) from 2002 to 2021, the intensity, frequency, duration, and area of groundwater drought showed an increasing trend; (3) groundwater drought had an obvious hysteresis (>9 months) to meteorological drought and the correlation between groundwater drought and meteorological drought increased, while the relationship with agricultural drought decreased; (4) long-term overexploitation of groundwater resources might be the main driving factor for the exacerbating groundwater drought in this area.

Optical identification and follow-up observations of SRGA J213151.5+491400

Context. The paper is comprised of optical identification and multiwavelength studies of a new X-ray source discovered by the Spectrum Roentgen-Gamma (SRG) observatory during the ART-XC survey and its follow-up optical and X-ray observations. Aims. We aim to identify SRGA J213151.5+491400 in the optical wavelengths. We determine spectra and light curves in the optical high and low states to find periodicities in the light curves and resolve emission lines in the system using optical ground-based data. We intend to study the spectral and temporal X-ray characteristics of the new source using the SRG surveys in the high and low states and NICER data in the low state. Methods. We present optical data from telescopes in Türkiye (RTT-150 and T100 at the TÜBİTAK National Observatory) and in Russia (6-m and 1-m at SAO RAS), together with the X-ray data obtained with ART-XC and eROSITA telescopes aboard SRG and the NICER observatory. Using the optical data, we performed astrometry, photometry, spectroscopy, and power spectral analysis of the optical time series. We present optical Doppler tomography along with X-ray data analysis producing light curves and spectra. Results. We detected SRGA J213151.5+491400 in a high state in 2020 (17.9 mag) that decreased by about 3 mag into a low state (21 mag) in 2021. We find only one significant period using optical photometric time series analysis, which reveals the white dwarf spin (orbital) period to be 0.059710(1) days (85.982 min). The long slit spectroscopy in the high state yields a power-law continuum increasing towards the blue with a prominent He II line along with the Balmer line emissions with no cyclotron humps, which is consistent with a magnetic cataclysmic variable (MCV) nature. Doppler Tomography confirms the polar nature revealing ballistic stream accretion along with magnetic stream during the high state. These characteristics show that the new source is a polar-type MCV source. ART-XC detections yield an X-ray flux of (4.0−7.0) × 10−12 erg s−1 cm−2 in the high state. eROSITA detects a dominating hot plasma component (kTmax &gt; 21 keV in the high state) declining to (4.0−6.0) × 10−13 erg s−1 cm−2 in 2021 (low state). The NICER data obtained in the low state reveal a two-pole accretor showing a soft X-ray component at (6−7)σ significance with a blackbody temperature of 15−18 eV. A soft X-ray component has never been detected for a polar in the low state before.

Carbon reduction effects of digital financial inclusion: Evidence from the county-scale in China

All countries in the world are faced with varying degrees of environmental pollution problems and carbon reduction responsibilities. In the case of China, digital transformation emerges as a new growth catalyst for the future economy and a pivotal avenue for optimizing resource allocation, enhancing energy structure, and reducing energy dependence for development. Therefore, this study delves into the carbon reduction effect of digital financial inclusion and explores its pathways. It focuses initially on the counties of the Yangtze River Delta in China, utilizing nighttime light data to estimate carbon emissions and scrutinizing the spatial and temporal distribution characteristics of emissions. Empirical results reveal that digital financial inclusion positively influences carbon emission reduction, with notable spatial and temporal variations in the emission reduction effect. Furthermore, the study uncovers that digital financial inclusion contributes to reducing carbon emissions through the optimization of industrial structure and the promotion of technological innovation. These research findings offer a scientific foundation for guiding the government in developing digital finance to attain local carbon emission reduction targets. Concurrently, local governments should leverage the role of digital financial inclusion in optimizing industries and driving technological innovation to propel the low-carbon, sustainable development of China.

Analysis of characteristics and changes in three-dimensional spatial and temporal distribution of aerosol types in Central Asia

Atmospheric aerosol types and characteristics have regional and seasonal characteristics mainly due spatial and temporal differences in emission sources and diffuse transport conditions. We explored regional three-dimensional spatial and temporal distribution characteristics of aerosol types in Central Asia from daytime to nighttime by using long-term (2007–2021) CALIPSO lidar measurements. The three results are as follows: (1) Average aerosol optical depth (AOD) values during the 14 years were 0.44 and 0.47 during daytime and nighttime, respectively, with an overall decreasing trend, among which the AOD in spring in the southern border region and in winter in the northern border region showed high values, 0.66 and 0.31 during daytime and 0.69 and 0.33 during nighttime, respectively, and nighttime AOD values were higher than those of daytime, possibly due to the lower signal-to-noise ratio of the CALIPSO during the daytime than during the nighttime. (2) The primary representative aerosol type in the Taklamakan Desert region being pure sand and dust, and more apparent winter-polluted sand and dust exist along the northern slope of the Tianshan Mountains in Xinjiang in winter than in other areas. High-altitude soot mainly existed below 4 km and was primarily concentrated in northern Central Asia, with the highest values (0.016 and 0.003) in summer and winter, respectively, which may be due to different diffusion and transport conditions. (3) Dust aerosols in spring were mainly concentrated in the region of 2–6 km in the Taklamakan Desert area; pure dust particles in summer and fall lifted height diffusion and gradually moved to the northern border region; polluted dust was mainly in northern Xinjiang in fall and winter and spread to northern Central Asia; and the average top height of aerosols in the transmission process reached the top of the troposphere, and transmission height was higher than source area.

A novel deviation measurement for scheduled intelligent transportation system via comparative spatial-temporal path networks

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.85 and a relative distance of 3.47. Furthermore, case analyses are invested in regions of China's Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

Rural revitalization in China: Measurement indicators, regional differences and dynamic evolution

It is crucial to scientifically assess China's rural revitalization and grasp its evolution laws. This paper constructs an indicator system to measure the level of rural revitalization in China from 2011 to 2021 using the entropy weight method. Then, we explore the spatial and temporal divergence and dynamic evolutionary characteristics of rural revitalization using the Dagum Gini coefficient and Kernel density. We found that the level of rural revitalization in China is low but fluctuating and increasing. Regionally, eastern China scores higher than central, western and northeastern China. In terms of dimensions, ecological livability scores are higher than prosperous industry, effective governance, affluent living and civilized countryside in that order. The regional differences in the level of rural revitalization are mainly reflected between regions, especially between eastern and western China, but the gap between regions is narrowing year by year. And the results of the Kernel density show that the level of rural revitalization in China shows a slow and balanced growth, but the eastern China shows a polarization growth. These findings can provide a comprehensive and objective outline of the advantages and shortcomings of rural revitalization development in China, and provide a policy reference for the comprehensive and stable promotion of rural revitalization construction.

A novel algorithm for estimating phytoplankton algal density in inland eutrophic lakes based on Sentinel-3 OLCI images

As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken as the research area. Algorithms for estimating algal density of cyanobacteria-dominated and non-cyanobacteria-dominated water types were developed based on Mie theory. The results demonstrated that the developedalgorithm had favorable estimation performance for inland eutrophic lakes, with a determination coefficient (R2) of 0.88, a mean absolute percentage error (MAPE) of 51%, an unbiased mean absolute percentage error (UMAPE) of 39%, and a root mean square error (RMSE) of 23.99 × 106 cells/L. Furthermore, comparison with other algorithms for estimating algal density showed that the developed algorithm had the lowest MAPE of 60% and UMAPE of 43%, with the RMSE of 23.42 × 106cells/L. Extensive evaluation based on satellite-ground synchronous data demonstrated the applicability of the developed algorithm to the Sentinel-3 OLCI sensor, enabling the determination of spatial and temporal distribution characteristics of algal density in the three lakes from 2016 to 2022 using Sentinel-3 OLCI images. The results of algal density inversion revealed a continuous decreasing trend in algal density in Lake Dianchi from 2016 to 2022, while the algal density in Lake Taihu and Lake Chaohu both decreased after 2019.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Seismic data AVO analysis in time frequency domain using synchroextracting transform

Time-frequency analysis is a crucial tool for processing and interpreting seismic data in hydrocarbon exploration. Accurately determining the temporal and spectral characteristics is essential for applications such as reservoir characterization and fluid identification. High-resolution representation of seismic data time-frequency properties improves reliability in these applications. Recent advancements in post-processing techniques aim to enhance conventional methods. The synchroextracting transform (SET) is an innovative approach that combines a post-processing procedure with the short-time Fourier transform. It retains time-frequency information associated with spectral components while eliminating excessive energy dispersed in the time-frequency domain. This study evaluates the effectiveness of SET for seismic data time-frequency analysis, comparing it with contemporary post-processing techniques using synthetic signals and field seismic data. Results show that SET provides higher resolution for distinct signal components and effectively reduces energy dispersion. SET exhibits advantages such as robustness against random noise, reasonable computational cost, and offering more interpretable time-frequency information regarding attenuated amplitudes. Building on synthetic experiments, we demonstrate the capability of SET to generate high-resolution single frequency sections of seismic data, facilitating accurate analysis of amplitude variation with offset (AVO) in the time-frequency domain.

Experimental investigation of the characteristics of the plasma flow generated in quasi-stationary plasma accelerator using optical methods

The spatial and temporal dependencies of the characteristics of the hydrogen plasma flow generated in quasi-stationary plasma accelerator were investigated. The spatiotemporal structure of discharge radiation in the interelectrode gap was studied. The range of changes in the length of the plasma glowing region in the interelectrode gap during the discharge pulse was determined. The region with bright plasma radiation located in the output face of the accelerator electrode system was observed. The presence of impurities and increased electron concentration values were observed in this region. Fluctuations in the radiation intensity of the plasma flow were detected along the entire length of its propagation. The spatial and temporal characteristics of these fluctuations were determined. The electron concentration values near the output face of the electrode system were obtained by measuring the Stark broadening of the Hβ line. For the first time, the time dependence of the electron concentration of free plasma flow was obtained using two methods simultaneously. The measurements were conducted at a distance, which significantly exceeds the characteristic size of the electrode system and where the influence of interelectrode processes of plasma flow generation is reduced. The first is based on measuring the Stark broadening of Hβ. As a second method, heterodyne interferometry was used.

Effects of the hydrologic process and geochemistry on dissolved carbon in shallow groundwater surrounding Qinghai Lake

A high concentration of dissolved carbon in groundwater increases drinking water health risks and carbon transport. Understanding the comprehensive impact of hydrologic processes and geochemistry on dissolved carbon in shallow groundwater also is a fundamental prerequisite for estimating the carbon budget of lakes in the Tibetan Plateau. This study investigated the spatial–temporal characteristics of the hydrologic process, geochemistry and dissolved carbon in shallow groundwater by the stable isotope tracer method, Piper diagram and Boomerang envelope model. The driving factors of dissolved carbon in shallow groundwater were explored by correlation analysis and redundancy analysis. The results showed low dissolved inorganic carbon (DIC) concentrations and high dissolved organic carbon (DOC) concentrations during the thawing period and rainy season and high DIC concentrations and low DOC concentrations during the freezing period. The seepage velocity, soil carbon dioxide dissolution and gypsum dissolution were the main factors influencing DIC concentrations during the thawing period. The mineralization and decomposition of DOC and dissolution of carbonate rocks were the main factors influencing DIC concentrations during the freezing period. The concentrations of DOC were mainly controlled by the adsorption of Ca2+ and Mg2+, microbial activity and pollutants produced by human activities. Our results are useful for ecological sustainable development, human health, and research on the carbon transport in groundwater in the Tibetan Plateau.

Identification of ecological security patterns of alpine wetland grasslands based on landscape ecological risks: A study in Zoigê County

Climate change and human activities have increased ecological risks and degraded ecosystem functions in alpine wetland grassland regions, where ecological security remains largely unexplored. The construction of ecological security patterns (ESP) can help to synchronize regional ecological security and sustainable development and provide ideas to address these challenges. This article determines the current ESP of Zoigê County, China, by analyzing the spatial and temporal characteristics of landscape ecological risk (LER) and generating an ecological network by combining the InVEST model, the landscape connectivity index, and the circuit theory model. Management zoning and targeted conservation recommendations are proposed. The results indicate that the region has significant spatial heterogeneity in IER. Ecological risk exposure is increasing, with high values mainly concentrated in the central part of the region. Meanwhile, ecological protection areas were identified, which included 2578.44 km2 of ecological sources, 71 key ecological corridors, 25 potential ecological corridors, 4 river ecological corridors, 66 pinch points, and 58 barriers. This study provides a valuable reference for the ecological development of Zoigê County, as well as insights into the formation of ESP in other alpine wetland grassland regions.