Spatial-temporal Characteristics Research Articles

Spatial-temporal Variations and Pollution Assessment of Heavy Metals in Sediments of Qionghai Lake in Xichang City

Based on the analysis of the total concentrations of 10 metals in the sediment core and total concentrations and chemical fractions of seven metals in the surface sediments of Qionghai Lake in Xichang City, Sichuan Province, the spatial-temporal characteristics of metal accumulation and pollution over the past century and the potential ecological risk of metals in surface sediments were studied. Before the 1970s, metal concentrations in the sediment core were stable. The total concentrations of Al, Fe, K, and Cr in the sediment core exhibited visible peaks in the 1970s, which were related to the enhanced input of fine-grained topsoil caused by increasing precipitation, lake reclamation, and deforestation. Since the 1990s, the total concentrations of Al, Fe, K, and Cr decreased with the reduced topsoil erosion, whereas the total concentrations of As, Cd, Cu, Pb, and Zn gradually increased or remained stable. The enrichment factor results showed that Cd, Pb, and Zn were the main contaminants, with Cd as the typical contaminant in the sediment core. The Cd contamination started in the 1960s and has remained at a moderate level since the 1990s. In the surface sediments, the total concentrations of Cd were higher in the northwest lake area, and no visible spatial concentration trends of the other metals were displayed. The bioavailable fractions of Cd, Pb, and Zn accounted for 95%, 63%, and 48% of the total metal concentrations on average. Among the bioavailable fractions, Cd was mainly in the acid-soluble fraction, and Pb and Zn were mainly in the reducible and oxidized fractions. The bioavailable fractions of the other metals were less than 27%. The results of total concentrations and bioavailable fractions of metals revealed that Pb and Zn in the surface sediments were slightly or moderately contaminated, and Cd was moderately contaminated on average. Cd contamination was at a severe level in the northwest lake area. The concentrations of anthropogenic Cd, Pb, and Zn in the surface sediments estimated from the total and bioavailable concentrations were comparable (P>0.05), indicating that anthropogenic metals primarily existed in bioavailable fractions in the sediment. Integrating the assessment results from sediment quality guidelines, potential ecological risk index, and chemical forms of metals, Cd in surface sediments may pose a high ecological risk, whereas the other metals has a low ecological risk.

Temporal characteristics of hypersonic flows over a double wedge with Reynolds number

Laminar hypersonic flows at Mach 7.10 with unit Reynolds numbers of 5.2×104, 1.04×105, and 4.14×105 m−1 over a 30°/55° double-wedge configuration were studied to investigate the spatial–temporal characteristics of the flow in a time-accurate manner. Close comparisons were made between previous kinetic and current continuum approaches to test the validity of the continuum assumption, especially considering the presence of large gradients associated with shock–shock and shock–boundary layer interactions, as well as spanwise instabilities. Previous results from direct simulation Monte Carlo, which inherently predicts rarefied effects such as velocity slip and temperature jumps, were found to be in very close agreement with the current work, even for the lowest Reynolds number. The impact of velocity slip and temperature jumps on flow and surface parameters was investigated, and comparisons were made with a no-slip and constant temperature wall model. The temporal and spatial variation of two- and three-dimensional flows were thoroughly investigated using two-dimensional (2D), three-dimensional (3D) periodic sidewall boundary conditions, and a full 3D configuration consistent with existing experimental data. Close comparisons among the 2D and 3D cases were made. The characteristics of 2D periodic oscillations were reported for the moderate Reynolds number case for the first time. The presence of spanwise instabilities, even at a relatively low free stream pressure of about 100 Pa, establishes that the flow field depends on spanwise effects and is fully 3D. High-fidelity numerical schlieren videos captured strong spanwise oscillations for 3D configurations.

Spatial–Temporal Characteristics of Human Thermal Comfort in Xinjiang: Based on the Universal Thermal Climate Index from 1981 to 2019

Xinjiang is one of the world’s most sensitive and vulnerable regions to climate change. However, little is known about the current status and changes in thermal comfort conditions in this area. Using the Universal Thermal Climate Index (UTCI) derived from the newly available high-resolution dataset HiTiSEA, the spatial–temporal pattern and changes thereof over annual and seasonal scales across Xinjiang from 1981 to 2019 were evaluated. The results reveal that the distribution and change of thermal comfort in Xinjiang have apparent regional heterogeneity and seasonal characteristics. Across all scales, 7 of 10 UTCI thermal stress categories were observed, from slight cold stress to strong heat stress. Annually, the mean UTCI is 3 °C and has significantly increased at 0.37 °C decade−1. The mean number of comfortable days (CDs) is 114 days, with a range from 0 to 189 days. On the space scale, the Tarim Basin experiences the highest UTCI value, while the Ili River Valley, the north side of the Tianshan Mountains, and the peripheral areas of the Tarim Basin have a higher number of CDs. Seasonally, summer has the highest UTCI value, while winter is the lowest. The trend for all seasons is upward, and spring increases fastest. Results also indicate that air temperature has a positive correlation with climate comfort, and the influence of air temperature on climate comfort is most significant. Further research indicates that the range and intensity of population exposure to uncomfortable climates in Xinjiang have increased. The distribution and expansion of population exposure are similar to the population density. These findings contribute to a systematic understanding of the local climate environment and can be helpful for the assessment of the impact of climate change and optimize tourism development.

Evaluation of the Smart City and Analysis of Its Spatial–Temporal Characteristics in China: A Case Study of 26 Cities in the Yangtze River Delta Urban Agglomeration

The smart city is recognized as a potent instrument for creating efficient urban environments and improving the quality of life of urban residents. However, there is an absence of research establishing a comprehensive evaluation model for the smart cities (SCs) and focusing on their spatiotemporal analysis. Thus, a comprehensive evaluation framework was developed and applied to 26 cities in the Yangtze River Delta Urban Agglomeration (YRDUA) in China from 2017 to 2020 to assess the smart city level (SCL) in China and analyze these cities’ spatial–temporal characteristics. The results indicated the following: (1) The overall SCL in the YRDUA has exhibited sustainable improvement, and the gap between cities is gradually narrowing. (2) The SCL of the YRDUA exhibits a higher SCL in the east and a lower SCL in the west of the YRDUA. The global spatial correlation of the SCL was random. Nantong, Hefei, Jiaxing, Zhoushan, Chizhou, Tongling, and Wuhu showed significant local spatial correlation. (3) The comprehensive evaluation framework is applicable for analyzing the SCs in China, and this framework can also be extended to other countries. Pertinent recommendations are put forth to enhance the SCL through the formulation of policies and fostering intercity collaboration. This research not only enriches the theoretical research on smart city evaluation but also clarifies the spatial–temporal characteristics of the SCs in China, thereby providing valuable insights that can foster sustainable smart city development.

Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain

The growing concern surrounding climate change has gradually drawn attention to the influence of climate factors on drought occurrence. In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought in North China Plain (NCP) in this study. Furthermore, six climate factors: air temperature (AT), precipitation (P), evapotranspiration (ET), specific humidity (SH), soil moisture (SM), and soil temperature (ST) were selected. The pole symmetric mode decomposition (PSMD) and improved gridded trend test (IGT) were used to analyze the spatial–temporal characteristics of drought and climate factors in NCP from 1982 to 2020. By calculating the cumulative climatic factors of 0 months, 1 month, 2 months, and 3 months, the correlation between drought and the climatic factors with different cumulative scales was analyzed. The results showed that: (1) from 1982 to 2020, the drought in NCP showed a downward trend and the climate factors showed an upward trend; (2) with the increase in AT, P, ET, SH, SM, and ST, VHI showed an upward trend, and SM showed the strongest correlation with VHI; (3) the optimal cumulative lag time (CLT) for AT, P, ET, SH, SM, and ST were 1.67 months, 1.48 months, 1.95 months, 1.69 months, 0.89 months, and 1.81 months, respectively; and (4) AT was the main driving factor of drought in NCP. This study contributes to the early warning and prediction of drought events, providing a scientific basis for water management authorities in drought management and decision making, and mitigating the negative impacts of drought on socio-economic aspects.

Antibiotic resistant genes profile in the surface water of subtropical drinking water river-reservoir system

To comprehensively understand antibiotic resistant genes (ARGs) profile in the subtropical drinking water river-reservoir system, this study selected Dongzhen river-reservoir system in Mulan Creek as object to investigate the spatial-temporal characteristics of ARGs diversity, bacterial host and resistance mechanism, and to analyze the key environmental factors driving ARGs profile variation. The results indicated that a total of 440 ARGs were detected in the target system, and the ARGs distribution pattern in the reservoir was attributed to autologous evolution or the comprehensive influence of feeding river system. The predominant bacterial host at different sites showed similar variations to dominated ARGs, and Proteobacteria, Actinobacteria and Bacteroidetes harbored most ARGs at phylum level, which showed the highest proportions of 74%, 37% and 35%, respectively. Antibiotic efflux was the primary resistance mechanism in all samples from wet season (45%–60%), yet the samples from dry season exhibited multiple resistance mechanisms, including inactivation (37%–52%), efflux (44%), and target alteration (43%). The total relative abundances of ARGs in the target system ranged from 0.89 × 10−2 to 1.71 × 10−2, and seasonal variation had a more significant influence on ARGs abundance than spatial variation (R = 0.68, P < 0.01). Environmental factors analysis indicated that the concentrations of nitrite nitrogen and total organic carbon were significant factors explaining ARGs number and various resistance mechanism proportions (P < 0.01), accounting for 48.7% and 61.1% of the variation, respectively; ammonia nitrogen concentration, total organic carbon concentration, temperature and pH were the significant influence factors on the relative abundance of ARGs (P < 0.05), with standardized regression weights of 0.700, 1.414, 1.447, and 1.727, respectively. In summary, in the surface water of the target system, ARGs diversity was primarily driven by ARGs horizontal transfer and antibiotics biosynthesis. Nutrients mainly promoted ARGs abundance by providing abundant energy, rather than increasing bacterial reproductive capacity.

Evaluating the sustainability of land use integrating SDGs and its driving factors: A case study of the Yangtze River Delta urban agglomeration, China

Achieving the sustainability of land use (SLU) is of significance in the implementation of the Sustainable Development Goals (SDGs). However, limited research has been conducted on the integrating SDGs into land use assessment. This paper constructs a SLU evaluation framework oriented to SDGs to investigate the spatial-temporal characteristics of SLU during 2000–2020 and its driving factors, taking 192 counties in Yangtze River Delta urban agglomeration (YRD), China as a case. Results show that the SLU level in YRD demonstrates an overall decrease trend of fluctuations, along with obvious regional differences. The spatial pattern of the SLU level in YRD is high in the northern region and low in the southern region, but this gap is gradually narrowing. The spatial distribution of SLU has positive agglomeration characteristics, manifested as HH and L-L types. Furthermore, SLU depends more on the combination of natural and socioeconomic factors. Industrial structure, fiscal deficit, urbanization level, vegetation coverage and precipitation characteristics can promote the SLU, while the economic development level, government intervention and population density have a negative impact on SLU.

The frontooccipital interaction mechanism of high-frequency acoustoelectric signal.

Based on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions. However, the processing mechanism of acoustoelectric signal between different brain regions remains sparse. Here, with acoustoelectric signal generated in the left primary visual cortex, we investigated the spatial distribution characteristics and temporal propagation characteristics of acoustoelectric signal in the transmission. We observed a strongest transmission strength within the frontal lobe, and the global temporal statistics indicated that the frontal lobe features in acoustoelectric signal transmission. Then, cross-frequency phase-amplitude coupling was used to investigate the coordinated activity in the AE signal band range between frontal and occipital lobes. The results showed that intra-structural cross-frequency coupling and cross-structural coupling co-occurred between these two lobes, and, accordingly, high-frequency brain activity in the frontal lobe was effectively coordinated by distant occipital lobe. This study revealed the frontooccipital long-range interaction mechanism of acoustoelectric signal, which is the foundation of improving the performance of acoustoelectric brain imaging.

Study on the spatial-temporal evolution characteristics, patterns, and driving mechanisms of ecological environment of the Ecological Security Barriers on China's Land Borders

The national ecological security barrier holds a pivotal position within the framework of the national strategy for ecological conservation and restoration. Concurrently, the border areas, as a focal point of the national homeland security strategy, have garnered considerable attention. These two concepts collectively present a profound implication for upholding national security. To this end, monitoring the ecological changes of the ecological security barriers, and exploring the driving mechanisms of such changes constitutes the foremost imperative in comprehending and advancing the national ecological security barrier theory. However, current research has yet to extend the application of ecological security barrier theory to the national scale.This paper integrates the theory of ecological security barrier with China's land border cities belt, constructing the Ecological Security Barrier of China's Land Borders (ESBCLB). Then, this paper produces a Remote Sensing Ecological Index (RSEI) products for the early 21st century (2000–2021, 21 periods) by using Google Earth Engine (GEE). Based on pixel scale calculations and classifications, the spatial-temporal evolution characteristics and patterns of the ESBCLB are objectively obtained. Finally, this paper employs the Geographically and Temporally Weighted Regression (GTWR) model to explore driving mechanisms of six primary indicators, namely, economy, human activity, transportation, topography, hydrography and climate, which may have impacts on the ecological environment of the ESBCLB. Through analysis, this paper concludes that (1) On the whole, the ecological environment changes within the ESBCLB are not substantial, with regions where the ecological environment remains unchanged accounting for 73.78%. Among the regions where ecological changes have occurred, those north of the Aksu Prefecture and Kizilsu Kirgiz Autonomous Prefecture exhibit a predominantly improved trend, while those to the south show a degraded trend. (2) Among ten evolution patterns identified based on the evolution characteristics, the primary evolution patterns for the ESBCLB are pattern of no significant change and persistence as well as pattern of no significant change and counter persistence. Apart from these, pattern of very significant better and persistence and pattern of significant better and persistence dominate the majority of cases; (3) Overall, economy, human activity, transportation, topography, hydrography, and climate exhibit spatial heterogeneity in driving mechanisms of the RSEI. Among them, human activity, topography, hydrography, and climate show positive correlations with the RSEI of the ESBCLB. While economy and transportation show negative correlations. The significance of this paper lies in that it provides a scientific basis and theoretical support for the specific application of national ecological security barrier and the study of the evolution of regional ecological environment.

Spatial–Temporal Characteristics and Influencing Factors on Carbon Emissions from Land Use in Suzhou, the World’s Largest Industrial City in China

Exploring carbon emissions in Suzhou, a city with a significant heavy industry presence, and understanding the factors that influence these emissions are crucial in achieving China’s dual-carbon goals within the framework of global climate governance. This study utilized land use data and statistical data from 2008 to 2020 in Suzhou. The carbon emission coefficient method was employed to calculate carbon emissions, while GIS technology was used to analyze their temporal and spatial distribution, as well as carbon emission risk. Additionally, the LMDI model was applied to investigate the contribution of influencing factors and TAPIO was used to analyze the decoupling relationship between the main influencing factors and carbon emissions. The study yielded the following findings: (1) From 2008 to 2020, land use changes in all regions of Suzhou are obvious, and there are mutual transformations among different land types. (2) The overall carbon emission in Suzhou showed an upward trend, with a spatial distribution characterized by higher emissions in the northern regions and lower emissions in the southern regions. (3) The risk and pressure index of carbon emission in all regions of Suzhou are too large, and the amount of carbon emission and carbon absorption is seriously out of balance, resulting in an overall carbon imbalance. (4) Among the influencing factors on land use carbon emissions in Suzhou, energy intensity exerted the strongest negative effect, and economic growth showed the strongest positive effect. (5) Decoupling analysis showed that economic growth and carbon emissions are generally shifting towards a strong decoupling and, except for Zhangjiagang, other regions have a good development model. Based on the research findings, this paper proposes specific suggestions for reducing carbon emissions, aiming to provide actionable recommendations for Suzhou and other urban areas in achieving low-carbon and environmentally sustainable cities.

Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion

Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance.

Spatial-temporal clogging development in leachate collection systems of landfills: Insight into chemical and biological clogging characteristics

The clogging of leachate collection systems (LCSs) is a typical challenge for landfills operation. Although clogging occurs in different LCS components, its spatial–temporal distributions remain unclear. This study aimed to systematically investigate the dynamic clogging development in simulated LCSs by monitoring changes in clogging characteristics over time. Results revealed that clogging accumulated in all components of the simulated LCS during a 215-day period, including chemical clogging and bio-clogging. Distinct spatial variations in clogging components were observed along the leachate flow of the simulated LCS, with the geotextile being severely clogged due to bio-clogging (70.1 ± 3.0%-80.0 ± 0.5%). Additionally, chemical clogging mainly occurred at the top (85.4 ± 0.8%-95.0 ± 0.9%) and middle (91.2 ± 0.8%-94.9 ± 1.1%) gravel layers. Nevertheless, the percentage of chemical clogging decreased from 72.0 ± 2.1% (day 42) to 42.5 ± 2.7% (day 215) at the bottom gravel layer. Chemical clogging was the main type in the pipe, accounting for 69.6 ± 0.5% (day 215). In addition, the ratios of bio-clogging to chemical clogging changed over time in all LCS components. The spatial–temporal characteristics of clogging across LCS components can enhance the understanding of clogging mechanisms, facilitate the design optimization of LCSs, and promote the formulation of effective control strategies.

Effects of climate change on wind erosion in the three provinces of Northeast China.

The three provinces of Northeast China are crucial to national commodity grain production. Soils in those areas have begun to severely degrade after long-term high-intensity use, with wind erosion as one of the main reasons. Based on meteorological and soil data from 1981 to 2019, we evaluated the spatial-temporal characteristics of wind erosion on bare land in the three provinces of Northeast China by using the revised wind erosion equation (RWEQ), and analyzed the contributions of meteorological factors to wind erosion on bare land. The results showed that, the meteorological factors of wind erosion were overall high in southwestern part and low in northeastern part of the region. In general, wind erosion in the region was substantial, especially in Liaoning. During the 39 years, wind erosion significantly increased throughout the whole year and during the growing season, at a rate of 129 and 105 t·km-2 per decade, respectively. The obvious increase in wind erosion was observed in the northwest Liaoning, Liaohe Plain, and Changbai Mountain area. Wind speed and air temperature were the main factors affecting wind erosion during the year and non-growing season, which contributed less during the growing season when precipitation contributed the most. We concluded that climate change has aggravated soil wind erosion in the three provinces of Northeast China.

High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) &gt; Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) &gt; Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) &gt; Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

Spatial–Temporal Characteristics and Influencing Factors of Marine Fishery Eco-Efficiency in China: Evidence from Coastal Regions

Marine fishery is an important part of China’s maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries.

Spatial-temporal variation characteristics and coupling coordination of the “water resources – water environment – water ecology” carrying capacity in the Three Gorges Reservoir Area

The coordinated control and management of water resources, the water environment, and water ecology are essential for optimizing the management of water resources, improving the water environment, rehabilitating water ecosystems, and achieving sustainable development. Most studies conducted to investigate the water carrying capacity have focused primarily on water resources or water environment. However, it is challenging to conduct such research in areas where the interactions between water resources, the environment, and ecology are intricate. In this study, an evaluation indicator system for the water resources - water environment - water ecology carrying capacity (WCC) was constructed, and a coupling coordination model and GeoDetector were employed to systematically analyze the coupling coordination indices (CCI) among the water resource carrying capacity (WRECC), water environment carrying capacity (WENCC), and water ecology carrying capacity (WECCC), as well as the main influencing factors in the Three Gorges Reservoir Area (TGRA) from 2005 to 2018. The results showed that the numbers of counties and districts with a high CCI class gradually increased over this period. The percentage of Class V fluctuated from 0% to 7.7%, and the stability of the water system steadily increased. Spatially, the overall distribution pattern gradually increased from southwest to northeast, and both the single and interactive effects of anthropogenic factors on water systems gradually increased. The CCI was predominantly affected by factors such as the amount of surface water resources per capita, indices of water conservation, forest coverage, discharge of domestic sewage per capita, and water replenishment of the ecosystem. These results show that coordination between the WCC components should be strengthened to further improve water use and domestic water treatment efficiency. To achieve superior-class, effective, and environmentally friendly scientific governance, the productivity of water resources should be increased in areas where the disparity between water availability and the demand for socioeconomic development is severe, and sewage treatment processes should be closely regulated. It is also important to combine resource development and utilization with water ecological protection and management.

Mapping and Analyses of Land Subsidence in Hengshui, China, Based on InSAR Observations

In this paper, we use interferometric synthetic aperture radar (InSAR) annual and time-series analysis of RADARSAT-2 SAR data, spanning from September 2011 to October 2022, to study the temporal–spatial characteristics of land subsidence in Hengshui, North China Plain. The data reveal two large-scale subsidence areas in Hengshui, individually located to the north of Hengshui city around the Hutuo River and to the east or south along the Fuyang, Suolu and Qingliang Rivers. The fastest subsidence arises after 2017, with the maximum rate exceeding 11 cm/year. We correlate the observed subsidence with the central table variation of groundwater depression, groundwater table variation of three confined aquifers, hydraulic head declines of three confined aquifers and the time-dependent total hydraulic head variation. We find a spatial consistency between land subsidence and groundwater depression or hydraulic head declines of three confined aquifers, and subsidence displacement and total hydraulic heads both manifest clear seasonal variability. This suggests that the subsidence is primarily caused by groundwater extraction for agricultural use. We also observe that the subsidence rates in Hengshui did not decrease but rather increased when the groundwater table significantly rose after September 2019. It is very likely that as a result of the occurrence of thick and widespread continuity of clay layers with high compressibility in the Quaternary deposit of Hengshui, a new preconsolidation head could be generated due to groundwater table drop, leading to the effective hydraulic head still existing in the aquitards even if the groundwater table rises in the aquifer systems.

Research on Spatial–Temporal Characteristics and Driving Factors of Urban Development Intensity for Pearl River Delta Region Based on Geodetector

The Pearl River Delta (PRD) is one of the most dynamic economic regions in the Asia-Pacific region. At present, it still faces many problems, such as the over-exploitation of urban land and unbalanced development. Through the study of the spatial–temporal characteristics of the development intensity of the PRD region and its driving factors, the key points and difficulties of urban development can be intuitively found. In previous studies, geodetector was widely used to determine the impact of driving factors. This paper uses several different research methods, including the Moran index, the semi-variability index, hot and cold spots, etc., based on the land use data of the PRD region in 1990, 2000, 2010, and 2020 to analyze the spatial–temporal characteristics of the development intensity. Combined with the socio-economic data of the statistical yearbook, factor detection and interaction detection of the 10 driving factors of development intensity are carried out based on geodetector, and reasonable optimization suggestions are put forward for the current situation of the region. The main conclusions are as follows: (1) The overall development intensity of the PRD region shows an upward trend, showing a “core periphery” spatial pattern of high center and low periphery centered around the Pearl River estuary. (2) The spatial distribution of cold and hot spots shows agglomeration, mainly in the form of high aggregation and low aggregation. (3) The driving factors for the development intensity for the PRD region in the past 30 years mainly include population agglomeration level, industrial structure level, economic strength level, terrain slope, etc. Among them, any two factors have a stronger interaction than a single factor, and all are enhanced by two factors. The dominant factors of interaction in different periods are different.

Identifying human mobility patterns using smart card data

ABSTRACT Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.

Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology

Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R2 values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.