Spatial Distribution Characteristics Research Articles

Investigating long-term changes in surface water temperature of Dongting Lake using Landsat imagery, China.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals

The collection of multiple-channel electrophysiological signals enables a comprehensive understanding of the spatial distribution and temporal features of electrophysiological activities. This approach can help to distinguish the traits and patterns of different ailments to enhance diagnostic accuracy. Microneedle array electrodes, which can penetrate skin without pain, can lessen the impedance between the electrodes and skin; however, current microneedle methods are limited to single channels and cannot achieve multichannel collection in small areas. Here, a multichannel (32 channels) microneedle dry electrode patch device was developed via a dimensionality reduction fabrication and integration approach and supported by a self-developed circuit system to record weak electrophysiological signals, including electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG) signals. The microneedles reduced the electrode–skin contact impedance by penetrating the nonconducting stratum corneum in a painless way. The multichannel microneedle array (MMA) enabled painless transdermal recording of multichannel electrophysiological signals from the subcutaneous space, with high temporal and spatial resolution, reaching the level of a single microneedle in terms of signal precision. The MMA demonstrated the detection of the spatial distribution of ECG, EMG and EEG signals in live rabbit models, and the microneedle electrode (MNE) achieved better signal quality in the transcutaneous detection of EEG signals than did the conventional flat dry electrode array. This work offers a promising opportunity to develop advanced tools for neural interface technology and electrophysiological recording.

Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

AbstractA novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

Spatial and Temporal Distribution and Flow Characteristics of High-Quality Development of Modern Service Industry in Chinese Sub-provincial Cities

Based on the new development concept of the service industry, this paper constructs a comprehensive evaluation index system for the high-quality development of modern service industry from a total of 22 indicators in four dimensions, namely, development efficiency, structural optimization, innovation agglomeration and industrial scale. The composite index of 15 sub-provincial cities in China from 2013 to 2022 was measured by entropy weight method, describing the current development status and development differences of each city, and with the help of Kernel density estimation method and Dagum Gini coefficient method, the law of inter-city evolution was inscribed, which revealed the spatial differences of the four major geographic regions and their sources and contributions. The study shows that the overall high-quality development of the service industry in the 15 cities is at a relatively low level, with obvious differences, but all of them are on a slowly rising trend. According to the division of the four major geographic regions, the northeast region has the largest intra-regional differences, and the eastern region has the smallest intra-regional differences. Meanwhile, the spatial imbalance between regions is still aggravating, and there is a clear differentiation between the development level of the eastern and western regions.

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia.

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu-Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu-Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

Data-driven insights into age-friendly smart community development in China: A case study of Beijing

The aging society has prompted a re-evaluation of community development strategies to better accommodate the needs of older populations. In this context, the concept of age-friendly smart communities has gained prominence, aiming to leverage technologies to enhance the quality of life for older residents. This article summarizes a general concept of an age-friendly community within the framework of smart city development in China. It then explores residential compounds in Beijing to investigate their potential for implementing age-friendly smart communities. Through data description and classification, the spatial distribution and local characteristics of residential compounds were examined. The results highlight housing compounds with the potential to become age-friendly smart communities, based on factors such as building age, population demographics, and typological differences reflecting community conditions and surrounding resources. Overall, by examining the intersection of the aging population, urban regeneration, and technology integration, this article contributes to understanding how data-driven insights can shape the evolution of smart communities in an aging society.

Influencing Factors of the Orienteering Team of Coaches in Selected Universities in Guangdong Province, China

Orienteering has its origins in early field military activity in Sweden. Hong Kong, China is the first region in China to come into contact with orienteering. In 1983, the Chinese People's Liberation Army Sports Institute held the "Orienteering Experimental Competition" in the Baiyun Mountain Scenic Area of Guangzhou City, Guangdong Province, which opened the prelude to the development of orienteering in China. Orienteering is a representative high-level comprehensive sports event for college students in Guangdong Province, China, with the sports teams participating in the Guangdong University Games and the Guangdong University Orienteering Championship from 2018 to 2022. Among them, the China Guangdong University Games (held every three years in 2019 and 2022 respectively), the China Guangdong University Student Orienteering Championship (held once a year), combined with the statistics of the registration list of the events in the past five years, a total of 84 universities participated in the China Guangdong University Games and the China Guangdong University Student Orienteering Championship. This study has certain scope and limitations in formulating the research plan and direction. Firstly, this paper analyzes the spatial distribution characteristics of orienteering teams in colleges and universities in Guangdong Province. The main objective is to verify the influence of sports human resources, sports facilities resources, physical education teaching resources and sports training resources on the development of orienteering teams in Guangdong Province, China. Questionnaires were distributed and collected in this study. A total of 84 universities participated in the above 6 orientation activities for college students in Guangdong Province, and 5 representative universities were selected as research objects. Among them, four orienteering teams with the university as the research object have achieved good results in the competition and have been established for a long time, and the other university is a place where researchers work. One coach and four athletes per university. Respondents felt that different resources had a strong sense of identity with the impact of orienteering on Guangdong University in China. There was no significant difference between the self-efficacy level of the respondents and the impact of orienteering on Guangdong University, China, and there was no direct relationship between the two resources. According to the results of the study, a framework for strengthening orienteering in universities in Guangdong Province can be developed to improve the level of orienteering strength in universities in Guangdong Province, China.

Retrospective and Prospective Research on Cultivation of Innovative and Entrepreneurial Talents between Universities and Enterprises

This paper utilizes the CNKI journal database to retrieve literature related to "school-enterprise collaboration" and "innovation and entrepreneurship". It employs bibliometrics and utilizes CiteSpace for quantitative analysis and information mining, demonstrating the spatial distribution characteristics and evolutionary context of research on school-enterprise collaboration in innovation and entrepreneurship. The research results indicate that the academic community has a high level of interest in the topic of school-enterprise collaboration in innovation and entrepreneurship, covering aspects such as the current situation of talent cultivation, problem analysis, platform research for nurturing talents, and student innovation and entrepreneurship ability cultivation, and has achieved certain results. However, research on the talent cultivation system of school-enterprise collaboration in innovation and entrepreneurship is still in its early stages, and a systematic theoretical model has not yet been formed. Therefore, establishing a systematic ecological model for talent cultivation in school-enterprise collaboration in innovation and entrepreneurship, and strengthening research on ensuring the operation of the talent cultivation system for school-enterprise collaboration in innovation and entrepreneurship, will become an important trend in future research on talent cultivation in school-enterprise collaboration in innovation and entrepreneurship.

Spatial and Temporal Distribution Characteristics and Influential Mechanisms of China’s Industrial Landscape Based on Geodetector

The industrial landscape constitutes a crucial aspect of a region’s historical and cultural identity, serving as a valuable asset in the development of industrial tourism. Exploring the industrial landscape supports initiatives in industrial tourism, acts as a catalyst for community revitalization, and contributes to sustainable urban progress. The primary objective of this research was to investigate the spatial distribution characteristics and underlying determinants of China’s industrial landscape (CIL) to inform urban planning, cultural heritage preservation, and sustainable development initiatives. This study utilized analytical tools, such as the nearest neighbor index, geographic concentration index, and hot spot analysis, to comprehensively examine the spatial distribution of CIL. Additionally, Geodetector was employed to explore the correlating factors behind this distribution. The findings reveal the following: (1) CIL exhibited a pronounced agglomerative spatial pattern characterized by a high degree of concentration, significant disparities, and substantial spatial autocorrelation. (2) Over time, the agglomeration of CIL varied, intensifying initially and then diminishing, with the center of gravity of its distribution shifting eastward before subsequently moving westward in a directional trend resembling “northeast–southwest”. (3) There was a diverse array of industrial landscape types within China, with notable disparities in the prevalence of different categories. The manufacturing and transportation sectors boasted the highest number of heritage sites. (4) The distribution pattern of CIL was shaped by factors such as the level of economic development, socio-demographic conditions, transportation infrastructure, and cultural milieu. The interplay between these factors had a substantial impact on this distribution pattern.

Spatial Distribution Characteristics and Sustainable Inheritance Strategies of National Traditional Fine Arts Intangible Cultural Heritage in China

A total of 417 intangible cultural heritage objects (ICHOs) are intertwined with traditional Chinese art, showcasing China’s rich historical heritage and distinctive creative allure. However, ICHOs currently grapple with a significant succession quandary due to modernization endeavors and the impacts of globalization. Hence, this study scrutinizes the spatial distribution of ICHO projects and inheritors and examines methodologies for inheriting these 417 ICHOs, employing approaches such as the closest neighbor index, Moran’s I index, kernel density estimation, geographic concentration analysis, and imbalance index assessment. Research indicates that ICHOs exhibit a spatial aggregation pattern, yet there is no substantial spatial correlation observed in their distribution. The national distribution highlights two core density zones: Beijing and Shanghai. In China, ICHO programs display uneven distribution across various types, levels, regions, and cultural zones. Regarding the ethnic composition of projects and inheritors, ICHO projects predominantly focus on the Han ethnic group, with a scattering of minority representations. Minority ICHO projects and inheritors are concentrated in central and eastern areas, while Han ICHO projects and inheritors are concentrated in central and western regions. Among the 148 ICHO projects lacking inheritors, 203 have one inheritor, and only 66 have several inheritors. Shanghai serves as the core density zone for 269 ICHO projects with inheritors, while Beijing holds that status for 148 ICHO projects without inheritors. Out of the 148 ICHO projects lacking inheritors, 115 belong to the Han ethnic group, with 33 split among 16 different ethnic minorities, and 2 have no inheritors. These disparities underscore the uneven distribution of ICHO projects and the critical issue of inheritance. This study identified education, tourism, digital communication, incentivizing inheritors, and international cooperation as potential strategies for preserving ICHA. Moreover, a sustainable inheritance pathway integrating government, education, tourism, and media is imperative for the effective perpetuation of ICHOs’ legacy.

Spatial distribution characteristics and influencing factors of traditional villages based on geodetector: Jiarong Tibetan in Western Sichuan, China.

Jiarong Tibetan is a regional group with distinctive regional characteristics and possess precious traditional village resources. Studying the spatial distribution and influencing factors of traditional villages is of vital significance for the protection and renewal of villages and the revitalization of cultural heritage. Nevertheless, due to the fact that the Jiarong Tibetan inhabited area has not been clearly defined for a long time, there is a lack of holistic discussion on the distribution features and driving mechanisms of traditional villages in this region. In order to fill this research gap, the paper is the first to break away from the existing county administrative divisions to define the study area. Moreover, the analysis is carried out by using the nearest neighbor index, kernel density, GoeDa and Geodetector, etc. for traditional villages at national-level and provincial-level. The results show that the spatial distribution of the traditional villages of Jiarong Tibetan is characterized by typical aggregation, with the core intensive area in Danba County and the sub-core intensive area in the central and northern parts. The results of factor detection show that the spatial distribution pattern of the traditional villages is the consequence of the synergistic effect of multiple factors, and the interaction effect is significantly enhanced. The economic level and climatic conditions play a controlling role, and population, elevation, intangible cultural heritage and rivers also have notable effects. The findings of study can offer scientific guidance and suggestions for the inheritance and development of traditional villages in Jiarong Tibetan settlement area.

Spatial Distribution Characteristics and Source Analysis of Ions in Lancang River and Yangtze River Source Region During Wet Period

Spatial Distribution Characteristics and Source Analysis of Ions in Lancang River and Yangtze River Source Region During Wet Period

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

Spatial analysis of dengue fever incidence and serotype distribution in Vientiane Capital, Laos: A multi-year study

Laos is a hyperendemic country of all 4 dengue serotypes. Various factors contribute to the spread of the disease including viral itself, vectors, and environment. This study aims to analyze dengue data and its incidence in nine districts of Vientiane Capital, Laos spanning from 2019 to 2021 by data collected from Mittaphab Hospital. The Maximum Entropy algorithm (MaxEnt) was applied to assess spatial distribution and identify high-probability locations for dengue occurrence by analyzing crucial environmental and climatic conditions. Dengue cases were more prominent in female (54.88 %) and highest case number was found in worker group (29.02 %) followed by student (28.47 %) and officer (16.92 %). In this study, the age group 21–30 years old had the highest infection rate (42.23 %), followed by 10–20 years old (24.21 %). Most of dengue cases was primary infection (91.61 %). Dengue serotype 2 predominated in 2019 and 2020 and substitute by serotype 1 in 2021. Across the nine districts of Vientiane Capital, the highest incidence of dengue was found in Xaythany district population in 2019, shifting to Chanthabouly district in 2020 and 2021. The MaxEnt revealed potentially most suitable areas for dengue were widely distributed central south part of Vientiane, Laos. Additionally, the best predictive variable for dengue occurrence was normalized difference vegetation index. Understanding of case characteristics and spatial distribution features of dengue will be helpful in effective surveillance and disease control in the future.

Spatial distribution, influencing factors and innovative development countermeasures of farmer cooperatives in Hunan Province, China

China’s rural reform is reliant on farmers' cooperatives as a key organization vehicle. It plays an important role in promoting rural revitalization. Such as, realizing the organic connection between small farmers and agricultural modernization. This study used the nearest neighbor index and kernel density estimation to analyze the spatial distribution and structural characteristics of farmer cooperatives in Hunan Province. It revealed the spatial differentiation law of cooperatives. Also using geographical detectors to figure out the main factors that affect the spatial distribution. The results show that: ① Hunan Farmers Cooperatives show significant spatial agglomeration. The overall spatial distribution shows the spatial characteristics of "one core, one circle, and multiple points". And the spatial distribution pattern of "large agglomeration, finger-shaped radiation distribution". Among them, the distribution of provincial demonstration cooperatives is relatively balanced. ② Cooperatives in six types of industries, such as planting, forestry, and feeding, showed agglomeration distribution. Different industrial cooperatives spread out in different ways, such as in an anti-"L" shape, a dual-core shape, or a one-center area. ③ The 24 influence factors selected from the five dimensions of the natural environment, social economic basis, production basis, output capacity, and facility basis have high, general, and weak influence on cooperatives' spatial distribution. The development and distribution of cooperatives mainly depend on resource endowment, social and economic development level, and market dependence. The spatial distribution heterogeneity of different professional cooperatives is mainly affected by slope, per capita disposable income of rural residents, road density, and other factors. ④ The progression of farmer cooperatives in Hunan Province should prioritize high-quality development, emphasizing the need for innovative approaches and transformative strategies within rural industrial organizations. It is imperative to optimize the spatial distribution of cooperatives, strategically contributing to the establishment of a novel framework for modern agricultural development in Hunan Province. Additionally, there is a critical emphasis on spearheading collaborative initiatives among cooperatives of varied industrial types, aimed at fostering the integrated development of rural primary, secondary, and tertiary industries.

The Influence of Spatial Heterogeneity of Urban Green Space on Surface Temperature

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.

Critical Threshold-Based Heat Damage Evolution Monitoring to Tea Plants with Remotely Sensed LST over Mainland China

Tea plants (Camellia sinensis (L.) Kuntze) are a cash crop that thrive under warm and moist conditions. However, tea plants are becoming increasingly vulnerable to heat damage (HD) during summer growing seasons due to global climate warming. Because China ranks first in the world in both harvested tea area and total tea production, monitoring and tracking HD to tea plants in a timely manner has become a significant and urgent task for scientists and tea producers in China. In this study, the spatiotemporal characteristics of HD evolution were analyzed, and a tracking method using HD LST-weighted geographical centroids was constructed based on HD pixels identified by the critical LST threshold and daytime MYD11A1 products over the major tea planting regions of mainland China from two typical HD years (2013 and 2022). Results showed that the average number of HD days in 2022 was five more than in 2013. Daily HD extent increased at a rate of 0.66% per day in 2022, which was faster than that in 2013 with a rate of 0.21% per day. In two typical HD years, the tea regions with the greatest HD extent were concentrated south of the Yangtze River (SYR), with average HD pixel ratios of greater than 50%, then north of the Yangtze River (NYR) and southwest China (SWC), with average HD pixel ratios of around 40%. The regions with the least HD extent were in South China (SC), where the HD ratios were less than 40%. The HD LST-weighted geographical centroid trajectories showed that HD to tea plants in 2013 initially moved from southwest to northeast, and then moved west. In 2022, HD moved from northeast to west and south. Daily HD centroids were mainly concentrated at the conjunction of SYR, SWC, and SC in 2013, and in northern SWC in 2022, where they were near to the centroid of the tea planting gardens. The findings in this study confirmed that monitoring HD evolution of tea plants over a large spatial extent based on reconstructed remotely sensed LST values and critical threshold was an effective method benefiting from available MODIS LST products. Moreover, this method can identify and track the spatial distribution characteristics of HD to tea plants in a timely manner, and it will therefore be helpful for taking effective preventative measures to mitigate economic losses resulting from HD.

Examining the complex relationship between Urbanization and ecological environment in ecologically fragile areas: a case study in Southwest China.

The sustainable development of ecologically fragile areas and the implementation of regional coordinated development strategies cannot be separated from the coordinated development and common progress of urbanization and the ecological environment, and this is particularly the case in Southwest China. This study examines the interplay between urbanization and the ecological environment across 26 cities in Southwest China from 2009 to 2019, utilizing 30 statistical indicators to analyze their coupling coordination relationship and its spatiotemporal evolution. The Entropy TOPSIS method, the coupling coordination degree model, and the obstacle factors model were used to calculate the subsystem score, coupling coordination degree, and obstacle factors, respectively. Our findings reveal an upward trajectory in urbanization scores across the 26 cities, juxtaposed with a fluctuating downward trend in ecological environment scores. The coupling coordination degree of urbanization and ecological environment in most cities maintained a rapid upward trend and showed spatial distribution characteristics of "strong core, weak middle, and edge." Moreover, our analysis identified public transport facilities, aggregate purchasing power, and cultural supply service services as primary obstacle factors impeding the development of coupling coordination degrees. These research results offer valuable insights for informing future endeavors in achieving high-quality development and fostering ecological civilization.

Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.

Source Analysis and Bioavailability of Soil Cadmium in Poyang Lake Plain of China Based on Principal Component Analysis and Positive Definite Matrix Factor

In order to identify the source and bioavailability of soil Cd in the alluvial geological high background area, 3799 topsoil samples and 140 root soil samples were collected from the Jiulong area on the south bank of Poyang Lake, Jiangxi Province. The results confidently indicate that the range of topsoil Cd content is 0.02~8.12 ppm, with an exceedance rate of 14.6%. It is noteworthy that exceedance points were mostly distributed in quaternary sediments. The geostatistical analysis clearly shows that the spatial distribution characteristic of topsoil Cd is higher in the north and lower in the south. The area with extremely high values (&gt;90% quantile) is predominantly located in the tidal flat and low-lying areas of the mainstream of the Xinjiang River. The PCA analysis confirms that the main source of topsoil Cd is man-made activities related to industry and mining. Soil Cd is primarily derived from upstream industrial and mining discharge (58.5%), as revealed by the PMF model analysis. The active Cd content in root soil is 77 ppb, with higher levels observed in the north and lower levels in the south. These findings suggest a significant risk of Cd diffusion along the upstream stream and deposition in the low-lying parts of the north. It is worth noting that Cd’s bioavailability and active forms in paddy fields are the highest, which can have a detrimental effect on food security. The correlation analysis suggests that genetic sources primarily control the bioavailability of soil Cd, followed by soil physicochemical properties such as SOC, Fe-Al oxides, and soil texture. Preventing source pollution and cutting off water system transmission are effective methods for preventing and controlling soil Cd pollution. These methods ensure the safety of cultivated land in the lakeside area of Poyang Lake Plain.