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In the Bamen Bay area of the Qinglan Harbor Mangrove Provincial Nature Reserve in Wenchang, Hainan Province, China, mangrove aboveground biomass (AGB) was estimated using high-resolution UAV ortho-imagery and UAV LiDAR data. The spatial distribution characteristics of AGB were studied using global Moran's I index and hotspot analysis. Optimal geographic detectors and regression models were employed to analyze the relationship between AGB and key environmental factors. The results indicate that (1) the average AGB in the study area was 141.22 Mg/ha, with significant spatial variation. High AGB values were concentrated in the southwestern and northeastern regions, while low values were mainly found in the central and southeastern regions. (2) Plant species, water pH, soil total potassium, salinity, dissolved oxygen, elevation, soil organic matter, soil total phosphorus, and soil total nitrogen were identified as major factors influencing the spatial distribution of AGB. The interaction results indicate either bifactor enhancement or nonlinear enhancement, showing a significantly higher impact compared with single factors. (3) Comprehensive regression model results reveal that soil total nitrogen was the primary factor affecting AGB, followed by soil total potassium, with water pH having the least impact. Factors positively correlated with AGB promoted biomass growth, while elevation negatively affected AGB, inhibiting biomass accumulation. The findings provide critical insights that can guide targeted conservation efforts and management strategies aimed at enhancing mangrove ecosystem health and resilience, particularly by focusing on key areas identified for potential improvement and by addressing the complex interactions among environmental factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Altitude differentiation has a substantial effect on the synergistic control of PM2.5 and O3 pollution. This study targets the Fenwei Plain, which is affected by mountain range blockage, divided into different altitude scales, and employs the methods of correlation analysis and geographical detector to explore the spatiotemporal heterogeneity of PM2.5 and O3 between different altitude zones and to identify the key controlling factors of pollutants between different altitude areas. The results showed that PM2.5 showed a significant decreasing trend from 2014 to 2023, whereas O3 exhibited an opposite trend. The concentrations of both pollutants decreased with increasing altitude, particularly for PM2.5, which showed significant altitudinal differentiation under the influence of topography. PM2.5 was negatively correlated with gross domestic product (GDP) and precipitation, and positively correlated with SO2. In contrast, the correlation of O3 with these factors was opposite to that of PM2.5. For spatial differentiation, NO2 and SO2 were the main factors influencing the spatial differentiation of PM2.5 and O3 at different altitudes. The explanatory power of the spatial divergence of PM2.5 and O3 was greatly increased by the interactions between the two precursors and between the precursors and meteorological factors. Furthermore, the explanatory power of the PM2.5 dominant factor increased with elevation, while the explanatory power of the O3 dominant factor was relatively high across low, middle, and high altitudes. This study serves as a guide for reducing air pollution in the Fenwei Plain and offers a novel perspective for the study of PM2.5 and O3 influenced by terrain.

Non-surface coal mining is an important means to obtain coal resources. It is crucial to study the influencing factors and ecological risks of soil heavy metal contents of non-surface coal mines for the protection and management of mine area ecosystems. The current study on soil heavy metals in the Fengfeng coal mine, a non-surface mine located in southern Hebei Province, primarily focuses on assessing local soil pollution levels and analyzing the linear effects of influencing factors, which makes it difficult to provide a scientific basis for the effective treatment of soil heavy metals in the entire region. To comprehensively evaluate the pollution level of soil heavy metal of the Fengfeng coal mine and deeply analyze the main influencing factors and their nonlinear mechanisms, 49 surface soil samples were collected and the contents of seven heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were determined. The single pollution index method and potential ecological risk assessment method were employed to evaluate the soil heavy metal pollution level and potential ecological risks of the mine area, and the geographic detector was utilized to analyze the main influencing factors of the spatial distribution of soil heavy metal content. The results indicated that the soil heavy metal contents of seven types in the mine area exhibited certain spatial heterogeneity, with an overall distribution pattern of high in the north and low in the south. Only the average content of Cd in the samples exceeded the national natural background value. The comprehensive index of soil potential ecological risk was between 45.70 and 132.77, ranging from mild to moderate risk levels. The single heavy metal pollution indices were ranked as Cd (1.818)>Pb (0.744)>Cu (0.715)>Ni (0.714)>Cr (0.701)>Zn (0.684)>As (0.656). The spatial pattern of soil heavy metal content in the Fengfeng coal mine was influenced by human factors such as mining activities and gangue hills, as well as natural factors such as precipitation and soil moisture. Among them, enterprise distribution and transportation activities were the main influencing factors of the spatial differentiation of heavy metal Ni; mining activities were the main influencing factor of heavy metals Cu, Cd, Cr, Pb, and Zn; soil erosion and soil moisture were the main influencing factors of heavy metal As.

[Objective] To implement the new development concept and achieve the “dual carbon”, this study reveals the spatiotemporal evolution characteristics of the carbon rebound effect in the transportation industry and its impact mechanism, expands theoretical boundaries of energy rebound research, provides scientific evidence and strategic references for energy-saving and carbon-reduction practices in the transportation sector based on energy structure transformation. [Methods] This study employed spatial kernel density estimation to analyze the spatiotemporal evolution characteristics of the carbon rebound effect in China’s transportation sector from 2009 to 2021. Panel quantile regression was used to investigate the influence of different factors on the carbon rebound effect at various levels, thereby identifying corresponding driving or restraining mechanisms. A geographic detector model was introduced to further refine the influencing mechanisms. [Results] (1) Before the pandemic, the carbon rebound effect in China’s transportation industry primarily followed a pattern of sharp decline followed by gradual rebound, with spatial correlation observed. (2) The carbon rebound effect in the transportation sector were significantly influenced by spatiotemporal factors. Over time, a spatial distribution pattern gradually developed, with weak rebounds predominating and strong rebounds being secondary. (3) The influencing mechanism of the carbon rebound effect in the transportation sector can be divided into four driving or restraining modes (industry-regulation-facility-market driving mode; industry-regulation-energy driving mode; population-finance-government-R&D restraining mode, and population-finance-government restraining mode). Most provinces were affected by low quantile driving and restraining mechanisms, with only a few provinces continuing to be influenced by high quantile restraining and driving mechanisms, primarily located in the Northeast, Northwest, or Southwest regions. (4) Based on the mechanisms influencing carbon rebound effect in the transportation sector, the geographic detector model was introduced to investigate the impact of multi-factor coordination on carbon rebound effect, with the aim of enhancing the cohesion of regional collaborative governance. [Conclusion] The carbon rebound effect is largely homologous to the energy rebound phenomenon. To incorporate the carbon rebound in the transportation industry into policy considerations, and adopt different policy tools based on its temporal and spatial evolution characteristics are essential for accelerating the transformation of transportation energy and adapting to local conditions for emission reduction.

Background The "Fourteenth Five-Year Plan for Healthy Aging" proposes to promote the realization of healthy aging, which not only requires the elderly to be healthy, but also pays attention to the security. With the deepening of the aging society, there is a significant gap in the level of participation of the current pension insurance system for urban and rural residents in various provinces. Under this background, it is of great significance to study the endowment insurance system. Objective To describ the spatial distribution of the basic old-age insurance participation level of urban and rural residents in 31 provinces in China from 2013 to 2020, to observe the spatial correlation and difference of the participation level among provinces, to analyze the relevant influencing factors, and to provide suggestions for the government and medical and health administrative departments to formulate the old-age insurance policy. Methods The data for this study is sourced from the China Statistical Yearbook from 2014 to 2021. The level of participation in urban and rural residential insurance is calculated based on the ratio of the number of urban and rural residents participating in basic pension insurance by region in the public management, social security, and social organization modules to the end of year population in the population clump. The various indicators of influencing factors are all from the 2014-2021 China Statistical Yearbook. Using ArcGIS 10.8 statistical analysis software, the basic pension insurance participation levels of urban and rural residents in 31 provinces were matched with provincial administrative divisions in China. Descriptive research and analysis were conducted on the differences in pension insurance participation levels among 31 provinces. Using methods such as global spatial autocorrelation and Getis Ord Gi * local hotspots, the spatial correlation and hot and cold spots of participation levels were studied, Study the relevant influencing factors by combining geographic detectors. Results Overall, from 2013 to 2020, the number and level of urban and rural resident insurance participants in China had been continuously increasing year by year; from a vertical structure perspective, there were significant differences in the level of insurance participation among provinces; from a horizontal perspective, from 2013 to 2020, the growth rate of most provinces was positive. The participation level of basic pension insurance for urban and rural residents in 31 provinces of China from 2013 to 2020 had spatial correlation. The central provinces were hot areas, while Shanghai is a cold area. The influencing factors on the participation rate were economic development factors, social development factors, policy factors, and cultural and educational factors in descending order. Conclusion The overall level of participation in basic pension insurance for residents has been increasing year by year, but there are differences in the level of participation and growth rate among provinces. The level of economic development is an important factor affecting the level of participation in basic pension insurance for residents.

In the northwest desert region, soil moisture plays a significant role in the water cycle of the soil vegetation atmosphere system by acting as a carrier of heat and moisture between the land and the atmosphere. The local industrial structure and underlying surface types have been significantly impacted by the Kekeya Greening Project's execution. The study of the Kekeya project's effects on soil moisture can offer pertinent theoretical underpinnings for the project's implementation in the areas of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Using the soil moisture data from 2000 to 2020 as the data source, the author studied the temporal change and mutation year of soil moisture in Kekeya using trend analysis and mutation analysis first. The geographical dependency of soil moisture was then studied using the Moran index. Ultimately, factor detection, interaction detection, and risk area detection of geographical detectors provided information about the factors influencing soil moisture in Kekeya. According to the findings, there was a clear rising trend in the soil moisture in Kekeya, along with a seasonal variation in the usual distribution. When moderate rain occurs, the soil moisture reacts more strongly than when light rain occurs. The pace of urbanization has the least effect on soil moisture, while GDP (gross domestic product) has the largest. When two factors combine, the impact on soil moisture is greater than when one item acts alone. [ABSTRACT FROM AUTHOR]

Accurate estimation of the distribution of POC in the sea surface is an important issue in understanding the carbon cycle at the basin scale in the ocean. This study explores the best machine learning approach to determine the distribution of POC in the ocean surface layer based on data obtained using satellite remote sensing. In order to estimate and verify the accuracy of this method, it is necessary to obtain a large amount of POC data from field observations, so this study was conducted in the Mediterranean Sea, where such data have been obtained and published. The research initially utilizes the Geographic Detector (GD) method to identify spatial correlations between POC and 47 environmental factors in the region. Four machine learning models of a Bayesian optimized random forest (BRF), a backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Model validation yielded that the BRF exhibited superior performance in estimating sea-surface POC. To build a more accurate tuneRanger random forest (TRRF) model, we introduced the tuneRanger R package for further optimization, resulting in an R2 of 0.868, a mean squared error of 1.119 (mg/m3)2, and a mean absolute error of 1.041 mg/m3. It was employed to estimate the surface POC concentrations in the Mediterranean for May and June 2017. Spatial analysis revealed higher concentrations in the west and north and lower concentrations in the east and south, with higher levels near the coast and lower levels far from the coast. Additionally, we deliberated on the impact of human activities on the surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in this area. [ABSTRACT FROM AUTHOR]

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland. [ABSTRACT FROM AUTHOR]

Urban development of clusters situated along rivers significantly affects the health of the river ecosystems, the quality of urban environments, and the overall well-being of local communities. Ecosystem service supply value (ESSV) measures the delivery of ecosystem goods and services within a specific timeframe in a particular area. Using the lower Yellow River urban agglomeration (Shandong section) as a case, we comprehensively applied land use structure and intensity change analysis, quantitative calculation of ESS, and geographical probe methods to unveil ESS and its mechanism of response to the spatio-temporal evolution of the intensity of land use in urban agglomeration along the river. The key results were as follows: (1) Over the past two decades, farmland and construction land areas have continued to decrease and increase, respectively, with the intensity of land use change being highest from 2005 to 2010. (2) ESS has continued to rise over the past 20 years, with the income in 2020 being 11.142 billion yuan, an increase of 31.13%. The "low-value areas" are mainly concentrated in Liaocheng City, Dezhou City, and Tai'an City, which are characterized by predominantly flat terrains where farmland constitutes the principal land use type. Conversely, "high-value areas" are largely in the counties bordering the Yellow River, including the upper estuary in the north and the rugged, southeastern terrains. (3) Areas with concentrated ESSV were primarily localized in the northern estuary area and along the Yellow River in a scattered point-like pattern. The spatial distribution of hotspots has become increasingly concentrated, transitioning from points to planes. Conversely, cold spots initially increased in number before subsequently decreasing. Waterbody was the most sensitive ESSV-determining factor. (4) The spatial heterogeneity of ESSV emerges as a consequence of the interaction of multiple factors, and among these interactions, those involving NDVI and POP contain the greatest explanatory power. Our findings are expected to offer a scientific foundation for optimizing land spatial patterns and enhancing ecological management in the lower Yellow River region. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in absorbing carbon dioxide and accelerating the achievement of carbon neutrality. As the ecological barrier of North China, the Taihang Mountains are pivotal to the ecological construction project of China. Nevertheless, the dynamic development of the vegetation carbon sink in the region and the impact factors on the sink have not been systematically evaluated. This study employed a comprehensive approach, utilising remote sensing technology and meteorological and topographic data, in conjunction with the net ecosystem productivity (NEP) estimation model to reveal the characteristics of vegetation carbon sinks in the Taihang Mountain, and then revealed the dynamics evolution of the NEP and the inter-annual trend by using Theil–Sen Median slope estimation, the Mann–Kendall test, and the coefficient of dissociation and analysed the driving roles of the influencing factors by using the parameter optimal geographic detector. Our findings suggest that the NEP in the Taihang Mountain area has a clear growth trend in time, the average value of NEP in the Taihang Mountain area is 289 gC-m−2-a−1 from 2000 to 2022, and the spatial distribution shows the characteristics of high in the northeast and low in the middle and west, with a gradual increase from the northeast to the southwest; the areas with high fluctuation of NEP are mainly distributed in the areas around some cities that are susceptible to the interference of natural or anthropogenic factors. The vegetation carbon sinks in the Taihang Mountains are influenced by a variety of natural factors, among which the explanatory power of each natural factor is as follows: DEM (0.174) > temperature (0.148) > precipitation (0.026) > slope (0.017) > slope direction (0.003). The natural factor DEM had the strongest explanatory power for NEP changes, and the two-by-two effects of the natural factors on vegetation carbon sinks were all significantly stronger than the effects of a single factor, in which the interaction between DEM and precipitation had the strongest explanatory power; distinguishing from climate change factors, the contribution of anthropogenic activities to NEP changes in more than 90% of the area of the Taihang Mountainous Region was more than 60%, and the driving force of anthropogenic factors on NEP changes in the Taihang Mountainous Region was significantly stronger than that of natural climate change. The contribution of anthropogenic factors to NEP changes in the Taihang Mountains was significantly stronger than that of natural climate change. The results of this study can not only provide a reference for carbon reduction and sink increase and ecological restoration projects in the Taihang Mountains but also benefit the research paradigm of vegetation carbon sequestration in other regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rock & Mineral Analysis is the property of Editorial Board of Rock & Mineral Analysis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploring the coupling coordination degree (CCD) between digital village construction (DVC) and agricultural carbon emissions (ACE) is crucial for promoting village revitalization and sustainable agricultural development. Analyzing data from 30 provinces in China in 2011–2020, this paper employs the CCD model, the Dagum Gini coefficient, and the geographic detector for in-depth analysis. The results show that the overall level of CCD gradually increases over time, but the national CCD still remains in a state of "low coordination," and there are apparent spatial differences in the CCD among provinces. In addition, the overall difference in CCD shows a decreasing trend, and the contribution of inter-regional differences has gradually become the most critical source of CCD's regional difference. Finally, the spatial differences of CCD are the result of two-factor interaction, among which the innovation level is the most core driving factor. The above findings provide important implications for China and other developing countries to fully leverage the interaction between village digitalization and agricultural decarbonization to promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Arable land green and low-carbon utilization (ALGLU) is an important pathway to safeguard food safety and achieve the green transformation and progress of agriculture, playing a crucial role in promoting agricultural ecological protection and economic sustainability. This study takes the Yangtze River Delta region (YRD), where rapid urbanization is most typical, as the study area. On the basis of fully considering the carbon sink function of arable land, the study measures the green and low-carbon utilization level of arable land in the region using the Super-slack and based measure (Super-SBM) model, and analyzes its spatial and temporal evolution using the spatial autocorrelation model, the center of gravity, and the standard ellipsoid model, and then analyzes its impact with the help of the geographic detector and the geographically weighted regression model. We analyzed the multifactor interaction and spatial heterogeneity of the factors with the help of the geodetector and geographically weighted regression model. Results: (1) The ALGLU in the YRD has shown a fluctuating upward tendency, increasing from 0.7307 in 2012 to 0.8604 in 2022, with a growth rate of 17.75%. The phased changes correspond to national agricultural development policies and the stages of socio-economic development. (2) There are significant spatial differences in the level of ALGLU in the YRD, with high levels distributed in the southwest of Jiangsu, northern Zhejiang, and northwest Anhui, while low levels are distributed in the southwest of the YRD. Positive spatial autocorrelation exists in the level of ALGLU in the YRD. The spatial transfer trends of the gravity and standard deviation ellipses essentially align with changes in the spatial pattern. (3) The level of ALGLU in the YRD is affected by many factors, with the intensity of interaction effects far exceeding that of individual factors. When considering single-factor effects, precipitation, topography, and farmers' income levels are important factors influencing the level of ALGLU. In scenarios involving multiple-factor interactions, agricultural policies become the primary focus of interaction effects. Furthermore, the driving effects of influencing factors exhibit spatial heterogeneity, with significant differences in the direction and extent of driving effects of each factor in different cities. This study can provide valuable insights for future ALGLU in the YRD and regional sustainable development. [ABSTRACT FROM AUTHOR]

In order to explore the characteristics and driving mechanisms of land use and landscape pattern changes in Duyun City, two remote sensing images of Duyun City in 2010 and 2020 were used as data sources. After visual interpretation and accuracy verification, two land use data were formed for analysis. Finally, the driving factors were explored using geographic detectors. The results show that the land type in the study area is mainly forest land, followed by cultivated land, grassland, and construction land;during the research period, the area of cultivated land, grassland, and unused land decreased, while the area of forest land, gardens, construction land, and water increased. The absolute value of land use change dynamics is garden land>unused land>construction land>grassland>water area>cultivated land>forest land. Among them, the growth rate of garden and construction land is relatively large;due to the large base area, the dynamic changes of cultivated land and forest land are not significant. In terms of landscape pattern, the degree of landscape fragmentation has increased. The Shannon Diversity Index (SHDI) and Shannon Uniformity Index (SHEI) showed a slight decrease, indicating a decrease in the degree of diversity among different landscape types. In 2010, land use and landscape patterns were greatly influenced by elevation factors. With the development of the social economy, social and economic factors such as nighttime lighting and population dominated in 2020, and the impact of human activities increased, while the impact of natural factors relatively weakened. [ABSTRACT FROM AUTHOR]

Faced with substantial climatic problems, industrial parks are crucial to attaining sustainable development objectives and China's carbon emission pledges. This study develops an output-oriented undesirable output Super-SBM model under non-incremental settings to evaluate the carbon emission efficiency of 169 national high-tech zones from 2008 to 2021. It utilizes the Dagum Gini coefficient and kernel density estimation approaches to analyze spatial variances and dynamic changes, as well as geographic detectors to assess the variables influencing the spatial development of carbon emission efficiency. This study uncovers a spatial distribution pattern of carbon emission efficiency within the eastern region of the national high-tech zone that is much superior to that in the western region. This tendency is mostly driven by inter-regional disparities. Carbon emission efficiency differences between various high-tech zones are progressively widening, displaying left-tail and polarization phenomena. Economic development gaps emerge as the main intrinsic factor contributing to spatial variations in carbon emission efficiency, with their interaction with land resource utilization being a key driving force. External factors, particularly differences in government interventions, dominate the spatiotemporal evolution of carbon emission efficiency, and their combined effect increases the evolution's explanatory power. These research findings offer a solid foundation for crafting region-specific carbon reduction policies in national high-tech zones and provide valuable insights for enhancing carbon emission efficiency in a coordinated manner. [ABSTRACT FROM AUTHOR]

With economic development and urbanization in China, the rural settlements have experienced great change. To explore the evolution process of rural settlements in terms of land, population and industry can reveal the development law of rural spatial distribution, population structure and industrial economy in different stages and regions. Studying the development status and evolution characteristics of villages in the upper Tuojiang River basin in Southwest China in the past 20 years are of significant value. The upper Tuojiang River basin includes the main types of terrain found in the Southwest region: mountainous, plains, and hills, exhibiting a certain typicality of geographical characteristics. This study took towns and townships at the town-level scale as the basic unit of research, and constructed an evaluation system for village evolution based on' land, population, and industry'. It employed Criteria Importance Through Inter-Criteria Correlation (CRITIC) analysis to examine the characteristics of village evolution in the area from 2000 to 2020, and used geographic detector analysis to identify the leading factors affecting village evolution. The results show that: (1) From 2000 to 2010, villages in the upper Tuojiang River basin experienced significant changes, and the pace of these transformations slowed from 2010 to 2020. (2) From a comprehensive perspective, from 2000 to 2020, villages in hilly areas show a decline, while villages in plain areas near the city center show a positive urbanization development. (3) Road accessibility and distance from the city center are the main factors that explain the spatial differentiation of village evolution degree in the study area. This study elucidates the spatiotemporal evolution characteristics of villages in the upper Tuojiang River basin and identifies the primary factors contributing to their changes, which will provide a reference for investigating the development of rural areas in different terrains of Southwest China. [ABSTRACT FROM AUTHOR]

Existing research underscores the significance of block form in fostering urban vitality. However, there is a dearth of evidence on its influence on the catering industry's vitality. Additionally, current studies investigating the correlation between block form and urban vitality have frequently neglected disparities among various types of blocks with dominant functions. We employ a multi-scale geographically weighted regression and a geographic detector to elucidate the effects of block form and the heterogeneity of blocks with dominant functions on the catering industry's vitality. Our findings suggest that the suitable block form can mitigate the catering industry's reliance on factors such as the regional population and public transportation infrastructure, to a certain degree. High-rise buildings and irregular block plans positively influence the catering industry's vitality, while the effects of block area, building density, and functional density display considerable spatial heterogeneity. Commercial blocks are most influenced by block form, whereas public service blocks are least affected. The methodology of this study can be replicated globally to guide urban planners in judiciously allocating commercial facilities, based on the varying spatial form requirements of different blocks, thereby fostering a vibrant city. [ABSTRACT FROM AUTHOR]

To clarify the spatial distribution rules and driving factors of China ′ s rural tourism model villages and facilitate high-quality development of rural tourism, this study used the spatial analysis methods of kernel density, standard deviation ellipse, geographic detector, and spatially weighted regression to investigate the spatial distribution pattern and its influencing factors. The study produced several interesting results. At the national scale, the model villages show a northeast – southwest distribution trend, with a central axis generally along the line of Shandong–Henan–Sichuan. There is also an obvious agglomeration distribution trend having the spatial characteristics of a “three-pole, multi-core, and contiguous”distribution. The spatial distribution of different types of model villages varies greatly. At the provincial scale, the model villages in high-latitude and high-altitude areas of provinces have a sparse spatial distribution, most provinces show low-focus distribution characteristics, the development degree of model villages is best in Beijing, Shanghai, and Hainan, and a rural tourism agglomeration area has been initially formed. The spatial distribution of the model villages is affected by the economic development level (impact intensity 0.65), the level of policy support (0.56), scale of the tourism industry (0.48), and road traffic (0.42)factors. The intensity of the impact decreases in turn and presents a certain degree of spatial heterogeneity, and its natural distribution tends toward model villages being distributed on the third level of terraces below the elevation of 500 m, the third level of river within a buffer zone of 6 km, and the subtropical monsoon area. The research shows that the model villages are clustered and distributed in space under the effects of both human and natural factors. [ABSTRACT FROM AUTHOR]

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The poverty-stricken counties on the Qinghai-Tibet Plateau are characterized by ecological vulnerability and ecological degradation, which are vulnerable to economic development and human activities, thereby leading to frequent alterations of Land carbon sink changes. Therefore, it is of great significance to study the changes of land carbon sink capacity during the process of eradication of extreme poverty in poverty-stricken counties. Using remote sensing images and socio-economic data, this paper constructed land transfer matrix and land carbon sink model to analyze the changes of land carbon sink capacity in poverty-stricken counties and identified the relevant impact factors through geographic detectors. The results show significant changes in carbon sink-type land and a continuous decrease in the snow area, resulting in an obvious trend of grassland area decrease and barren area increase. Land carbon sink capacity of poverty-stricken counties during the eradication of extreme poverty is higher with an added value of 2.74 times than that before this period. Using the geographic detector to analyze the related factors, strong explanatory power was found in cash crops per capita, share of secondary industry, population density and per capita savings on the changes of carbon sink capacity in poverty-stricken counties. [ABSTRACT FROM AUTHOR]

IntroductionThis research examines the coupling coordinated development dynamics between marine ecological governance and marine economic development in China’s coastal provinces. The marine ecological governance and economic development data of China’s 11 coastal provinces were comprehensively analyzed from 2011 to 2021.MethodsWe established a multi-dimensional evaluation index system for marine ecological governance and economic development, employing the global entropy weight method for quantification. Furthermore, the research examines and analyzes the trends in coordinated development and divergence between these two systems by constructing a coupled coordination degree(CCD) model, utilizing the Theil index decomposition method, and employing the geodetector detector.ResultsThe Northern marine economic circle outperforms the Eastern and Southern ones regarding marine ecological governance, while the Eastern marine economic circle is the most advanced in marine economic development. The results of the CCD model indicate that Guangdong, Shandong, Hebei, and Jiangsu have the best coupling coordinated development, while Hainan, Tianjin, and Guangxi are at the bottom. The results of the Theil index decomposition method indicate that intra-regional disparities are the primary cause of coupling coordinated regional development variations. At the same time, the results of the geographic detector further substantiate that energy structure, degree of industrial agglomeration, and foreign overseas direct investment are the principal influencing factors.DiscussionThe study presents policy recommendations derived from the findings intended to foster the integrated advancement of marine ecology and economy in coastal provinces, reduce regional development disparities, and establish a scientific foundation for achieving a sustainable marine economy.

In urban areas, variations in the functional usage of buildings or their clusters directly influence the 2D/3D morphology of buildings which can lead to changes in the ventilation potential (VP) at different heights, but there is a lack of refined quantitative relationship analysis between 3D building form parameters (BFPs) and VP at different heights within a certain urban area. The objective of our study is to obtain simulated wind field data from different heights, considering distances from roads, and transverse and longitudinal sections. We employ partial correlation analysis, simple correlation analysis, and Geographic detectors to uncover the extent and mechanisms of the influence of BFPs on the ventilation effectiveness of neighborhoods in a major urban center in China, specifically Nanjing. The findings demonstrate that, at all heights, there is an inverse relationship between wind speed and building density (BD), with the location of higher wind speed generally aligning with the intersection points. The correlation coefficients between BD and simulated wind speed are 0.040 (1.5 ​m), −0.475 (10 ​m), and −0.314 (30 ​m), while the correlation coefficients between building height (BH) and simulated wind speed are 0.237. However, no significant correlation is observed between floor area ratio (FAR) and simulated wind speed. Geographic detector analysis reveals differences in the correlation between BFPs and simulated wind speed at different heights within various building functional areas. Our study provides some practical information for urban planners and designers to carry out wind sensitivity and adaptation planning and urban design.

Abstract Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000–2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

[Objective] This study was aimed to understand the spatiotemporal changes in extreme climate and its influence on vegetation. [Methods] Based on daily meteorological data (1960—2020) from 115 weather stations in the Qinling-Daba mountains (QBMs) and surrounding areas and satellite-derived normalized difference vegetation index (NDVI), the spatiotemporal variation characteristics of the NDVI in the QBMs and its response to 19 extreme climate indices were analyzed. Geographic detector was used to identify the contribution rate of extreme climate indices and their influence on the NDVI. [Results] (1) The NDVI significantly increased in the majority of the QBMs (80.34%) and decreased in only a few regions (1.09%) from 2000 to 2020. The interannual change rate in the study area was approximately 0.03/10 a. (2) Noticeable warming was observed in the QBMs from 1960 to 2020, with the temperature increasing more at night than during the day, and regions with a larger change in extreme temperature events were mostly found in the Western Qinling Mountains (WQMs). During the study period, extreme precipitation events showed weak changes, extreme precipitation intensity increased in the southwestern QBMs, while in the eastern QBMs, extreme precipitation intensity decreased but extreme precipitation frequency increased. (3) Extreme precipitation events were the main factor affecting NDVI in the west Qinling region, while extreme temperature events were the main factor affecting NDVI in the Qinling and Daba mountain regions; the impact of extreme climate events on NDVI was not independent, and the interaction between extreme climate indices enhances the impact of a single factor on NDVI in a double-factor or nonlinear way. [Conclusion] The spatiotemporal changes in extreme climate and its influence on vegetation were clarified, which could establish scientific evidence for the protection and restoration of vegetation in the QBMs in response to global climate change.

Abstract The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Alpine grasslands, a crucial component of the Qinghai–Tibet Plateau, play a vital role in maintaining ecological barriers and facilitating sustainable development, and the exact stability change is also the key to coping with climate change and implementing ecological protection projects. The purpose of this study was to identify the spatial and temporal distribution of multi-stage alpine grassland and explore its inter-annual distribution and growth stability. The Guoluo Tibetan Autonomous Prefecture, China (hereinafter referred to as Guoluo), where alpine grassland is widely distributed, was selected as the research area. Long-term stable grassland samples constructed using the Mann–Kendall–Sneyers mutation test method were analyzed alongside random forest classification to identify multi-stage grassland distribution trends from 1990 to 2020. Based on the Fractional Vegetation Cover (FVC) and coefficient of variation (Cv), spatial and temporal changes in grassland quality and their driving factors were discussed. The results show the following: (1) Remote sensing grassland extraction, based on the establishment of long-term stable grassland samples and random forest classification, demonstrated high accuracy and reliability, with OA and Kappa coefficients consistently above 0.89 and 0.77, and PA and UA maintained consistently at approximately 0.9. (2) The distribution of grassland in Guoluo corresponded to the spatial patterns determined by the natural geographical environment, showing a gradual trend from high-cover grassland in the southeast to low-cover grassland in the northwest. The proportion of medium and high-cover grasslands slightly increased, indicating an improvement in grassland quality. However, the encroachment and degradation caused by human activities and climate change resulted in a slight decrease in the proportion of grassland area compared with 1990. (3) Despite the overall grassland ecosystem still having relative stability, local grassland quality changes dramatically, mainly in the north of Maduo County. And significant fluctuations in the area of grassland quality were noted over the last two decades, suggesting potential degradation in ecosystem stability. Climate change and human activities were identified as primary drivers of these changes. Climate change is dominant in the alpine region. The low-warming region is dominated by human activities. These findings offer essential insights for the planning and implementation of alpine grassland ecosystem protection and restoration initiatives and also have important value for exploring the evolution law of alpine grassland ecosystems. [ABSTRACT FROM AUTHOR]

Identifying the spatial characteristics of ecosystem service (ES) supply and demand is crucial for effective ecosystem management and restoration. Past related studies have primarily focused on balancing ES supply and demand and supply clustering, with less attention focused on the drivers of demand clustering and their spatial evolution. This study explored the spatiotemporal supply–demand dynamics in four crucial ecosystem services (ESs) in the Sichuan-Yunnan ecological buffer area region between 2005 and 2019, namely water yield, net primary production, soil conservation, and habitat quality. Self-organizing maps and geographical detectors were used to classify supply–demand ES bundles as their main drivers, respectively. The main results of the study included: (1) A decline in habitat quality, whereas net primary productivity, water yield, and soil conservation increased. However, there were increasing demands for habitat quality, water yield, and net primary productivity, despite the decrease in demand for soil conservation. (2) Demand for habitat quality was met by supply, whereas there were deficits in soil conservation, water yield, and net primary productivity, which contributed to the demand in the east exceeding that elsewhere. (3) The proportion of ES bundle 2 increased, whereas those of the remaining ES bundles declined. Similarly, the areas of ES demand bundles (ESDBs) 1 and 4 decreased, whereas those of 2 and 3 increased. While the spatial extent of the ESBs remained relatively stable, those of the ESDBs in the northern regions increased. Key factors influencing the spatial distribution of ES supply include human activities, population density, and precipitation, whereas land use, population density, and the human activity index primarily affect demand distribution. The results of this study can act as a reference for comprehensive regional ecosystem management. [ABSTRACT FROM AUTHOR]

The natural ecological background of the Yellow River Basin is fragile. Long-term human development and construction activities have led to habitat fragmentation and biodiversity reduction. The Landsat remote sensing image data of 2000, 2010 and 2020 were selected to calculate four indicators of greenness, humidity, dryness and heat to build remote sensing ecological index (RSEI). The RSEI was used to evaluate spatial-temporal evolution characteristics and trends of ecological environment quality in 8 cities and counties (districts) along the main stream of the Yellow River in Henan Province, and geographic detector was used to analyze its driving factors. The results indicate that a) the average RSEI in the study area from 2000 to 2020 is 0.7311, and the overall ecological environment quality is above the middle level. Lingbao City and Zhengzhou City have poor ecological environment quality, while Xin' an County and Mengjin District have good ecological environment quality. b) Over the past 20 years, the ecological environment quality has first been deteriorated and then improved, and most of the areas show an overall improvement trend. The improvement of ecological environment quality in the lower reaches is better than that in the middle reaches. c) The single factor detection results indicate that the driving factors that characterize human activities, such as land use types and GDP, gradually enhance their impact on the quality of the ecological environment. The results of multi factor interaction detection indicate that there is a strong interaction between GDP, land use type and population density with other factors. [ABSTRACT FROM AUTHOR]

Based on the spatial data of co-construction parks from government websites, using ESDA, network hierarchical clustering and geographic detector to simulate and analyze the spatial distribution characteristics, urban preferences and influencing mechanisms of co-construction parks. The results show that: ① Since 2003, the number of co-construction parks in 41 cities in the Yangtze River Delta has been on the rise, and the regional center of co-construction parks has moved from north to south; ② Spatially, it is an irregular circle-like distribution with multi-point diffusion,and presents a regional distribution, with huge spatial spillover and equilibrium. ③ The co-construction parks cities have formed 5-level clusters, and the classification results have urban spatial correlation and volatility, and especially Yancheng, Nantong of the first and second level cities are the most preferred cities for co-construction parks selection. ④ Factor interaction has a greater determinant effect on the spatial and temporal distribution and regional preference of co-construction parks. The primary driving factor is policy and system, and the rest of the driving factors in order from large to small are training exchanges, exchange platforms, secondment of cadres, transformation of industrial scientific research bases, and degree of market openness. There are obvious urban spatial differences among the factors. [ABSTRACT FROM AUTHOR]

: Under the changing environment, global drought occurs frequently, which has a great impact on social economy and ecology. Current researches focus on the research and development of drought forecasting technology, but pay little attention to the theory of drought forecasting, such as the temporal and spatial distribution and driving mechanism of drought predictability. This work took the Loess Plateau as the research object, constructed a support vector machine model to forecast regional drought from 2008 to 2019, and quantified drought predictability with Kling, Gupta Efficiency (KGE). Then the spatial distribution characteristics of drought predictability at different time scales were investigated, and the key factors influencing changes in the distribution of drought predictability were identified. The results showed that the drought predictability of the Loess Plateau showed an upward trend with the increase of the time scale. The KGE average of SPEI12 was 73.8% higher than that of SPEI1. The predictability of seasonal drought is the highest in autumn, followed by summer, spring and winter. The drought predictability showed spatial heterogeneity, showing a high distribution pattern in the north and low distribution pattern in the south, and the drought predictability in the central Loess Plateau in spring and in the northwest desert region of the Loess Plateau in winter was extremely low. There were differences in the main factors affecting the predictability of inter, seasonal drought. The highest explanatory power in autumn was temperature, summer and spring was aridity index(AI), and winter was the coupling of air, sea PRE_AMO. Meanwhile, the influence of both variable factors on drought predictability of the Loess Plateau was significantly higher than that of single factor. This study provided a new insight into the predictability of drought and was helpful to further improve the ability of drought forecasting and early warning in the Loess Plateau. [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to explore the coordinated development level of water resources carrying capacity and grain production resilience in the lower Yellow River, and to provide a new policy perspective for promoting the coordinated development of water resources protection and utilization and grain production, [Methods]Based on the evaluation index system of water resources carrying capacity and grain production toughness, the index of water resources carrying capacity and grain production toughness in the lower reaches of the Yellow River during 2010-2021 was estimated by entropy weight method, and the spatiotemporal evolution characteristics of their coordinated development were analyzed by coupling coordination degree model and kernel density estimation, At the same time, the dominant factors and interaction factors affecting the coupling coordination degree of the two were explored by means of geographic detectors, [Results]In time dimension, the coupling coordination degree between water resources carrying capacity and grain production toughness increased from 0, 484 0 to 0, 747 6, and the coupling coordination type increased from near imbalance to intermediate coordination, In terms of spatial dimension, the highest value of the coupling coordination degree between the two was found in Jinan, Shandong Province, and the lowest value concentrated in Henan Province, Annual precipitation, residential water consumption, urban green area, cultivated land area and financial support to agriculture were the main driving factors on the coordinated development of the two, and the explanatory power of the interaction of the leading factors showed the characteristics of double factor enhancement or nonlinear enhancement, [Conclusion]The coupling coordination degree of water resources carrying capacity and grain production toughness in the lower reaches of the Yellow River increased year by year and gradually showed a spatial distribution of high level in the east and low level in the west, At the same time, the development of the two systems was increasingly close, and the interaction was gradually enhanced, [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to optimize the fine management of cultivated land in Shaanxi Province, to coordinate the contradiction between food demand growth and environmental constraints, to maintain regional food security, and to promote agricultural modernization.[Methods]The Guanzhong area was taken as the research area. Based on land use data and other natural economic data, Lorentz curve, coefficient of variation method, landscape index, spatial autocorrelation and other methods were used to construct an evaluation model of cultivated land fragmentation to explore the temporal and spatial changes of cultivated land fragmentation(CLF)in Guanzhong area, and geographic detectors were used to analyze the driving force of CLF in the area.[Results](1)The spatial distribution of cultivated land resources in Guanzhong area was relatively balanced, and the degree of cultivated land specialization remained at a high level.(2)The spatial and temporal differences in the degree of CLF in Guanzhong region were significant, and the overall degree of CLF showed a downward trend, but the degree of CLF in some areas showed a trend of increasing and decreasing, and the overall trend was low in the east and high in the west.(3)The spatiotemporal differentiation of CLF in Guanzhong region was affected by many factors, and the comprehensive influence of socio-economic factors was significantly stronger than that of natural factors, among which altitude, population density, per capita arable land area and regional GDP were the main influencing factors. The interaction effect between factors was stronger than that of single factor, and two-factor enhancement and nonlinear enhancement were the mainstay.[Conclusion]The problem of cultivated land fragmentation in Guanzhong area has been improved to a certain extent in the past 20 years, but there are large regional differences, and the comprehensive management of cultivated land fragmentation should be further strengthened in terms of social and economic factors, so as to promote the development of agricultural mechanization and modernization in Guanzhong area. [ABSTRACT FROM AUTHOR]

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA. [ABSTRACT FROM AUTHOR]

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Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of "land use carbon emissions-spatial and temporal patterns-influencing factors," and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 × 104tC in 2010-23597.68 × 104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of "northwest--southeast." The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions. [ABSTRACT FROM AUTHOR]

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil. [ABSTRACT FROM AUTHOR]

Internet search behavior is an intuitive expression of user needs and behavioral inertia in a virtual space, providing a new perspective for long-term sequenced and broad-scale spatial tourism demand analysis. Based on the Baidu search index of 9 keywords of rural tourism in China from 2011 to 2020, this paper tried to explore the spatial-temporal characteristics and influencing factors of rural tourism demand by means of spatial autocorrelation and geographic detector. The research found that: ① As for temporal characteristics of rural tourism demand, it shows an "inverted U-shaped" change pattern during 2011-2020, and exhibits non-linear phases characterized by periods of rapid development and fluctuating development. It shows "two peaks and two valleys" during the year, and the characteristics of off-season/peak season, and weekend effects are obvious. ② In terms of spatial distribution, it is basically in clustered distribution, and the clustered effect fluctuates slightly each year. The local spatial pattern shows the characteristics of declining gradients in which the east is high and the west is low, and it remains basically stable during 2011-2020. ③ There are five persistently significant influencing factors, and their explanatory power is as follows: GDP>number of star-rated hotels>total population> highway density>country-level rural tourist spots. Other factors have relatively low explanatory power or have great changes in ten years. This research reveals the spatio-temporal pattern of rural tourism demand and its influencing factors from the macro level with the help of network big data, which provides theoretical guidance and practical reference for optimizing industrial layout and promoting effective matching of supply and demand. [ABSTRACT FROM AUTHOR]

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution. [ABSTRACT FROM AUTHOR]

Exploring the dynamic variation in ecosystem services and clarifying the driving mechanism will help with the formulation of effective ecological environment protection policies. Accordingly, this study sought to reveal the complex variability in ecosystem services in the Yellow River Delta (YRD) at a higher temporal resolution and the transition between the main driving factors in different periods. To this end, we used the economic equivalent factor valuation method to quantify the ecosystem service value from 2000 to 2019 at 5-year intervals. Furthermore, the Geo-detector model was used to identify the main driving factors and interaction between the driving factors of ecosystem service value variations. Then, we analyzed the temporal and spatial dynamic variations in the ecosystem service value and the transitions between the main driving factors in different periods. The main results are as follows: (1) From 2000 to 2019, the ecosystem service value of the YRD showed an increasing trend followed by a decline, whereby water and construction land increased and the other classes of land decreased. Overall, the inland and coastal distribution patterns exhibited low and high values, respectively. (2) The main driving factors of ecosystem service value variations were the NDVI and topographical factors (aspect, slope, elevation), which had q values that were stable and greater than those of the other factors. Although human activity, tourist resource concentration and traffic convenience factors had a comparatively minor effect on ecosystem services, we noted a trend where their effects increased from 2000 to 2019. (3) The detection of interactions revealed complex mechanisms affecting the variation in the YRD. Interactions between variables had a stronger influence than individual effects. The interactions between the Normalized Difference Vegetation Index (NDVI) and other factors consistently had the most significant impact. These interactions primarily shaped the spatial and temporal distribution of ecosystem services. The NDVI and human activities exhibited nonlinear enhancement. These results contribute to improving our cognition of the factors and mechanisms influencing ecosystem services, offering theoretical support for the improvement of ecosystem services in the YRD. [ABSTRACT FROM AUTHOR]

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region. [ABSTRACT FROM AUTHOR]

Emergency shelter construction is one of the important measures to enhance the disaster prevention and mitigation capabilities of the entire society and effectively respond to emergencies. In order to explore the spatial distribution characteristics and influencing factors of emergency shelters in Hebei Province, data on the distribution of emergency shelters in Hebei Province were collected. Various methods such as coefficient of variation, imbalance index, spatial Gini coefficient, and global Moran's index were used to explore the spatial distribution characteristics of emergency shelters in Hebei Province. Methods such as geographic detectors were utilized to analyze the influencing factors of spatial distribution. The basic conclusion is that the spatial distribution of emergency shelters in Hebei Province shows a balanced distribution at the urban and geographical regional scales, presenting an overall clustering trend, and exhibiting characteristics of large dispersion and small clustering. The spatial distribution of emergency shelters is influenced by various factors such as topographical, demographic, economic, and urban development factors, among which topographical factors have the greatest impact. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to analyze the spatiotemporal differentiation and variation of soil moisture in the Mongolian Plateau, and to quantify the influence of key driving factors, so as to provide a theoretical basis for regional ecological restoration and sustainable ecosystem development. [Methods] Based on the ERA5 soil moisture data, the spatial differentiation and changes of soil moisture at the depth of 0—289 cm in the Mongolian Plateau from 2000 to 2020 were revealed, and the impacts of different environmental factors such as meteorology, normalized difference vegetation index (NDVI), terrestrial water storage anomaly (TWSA), soil texture, and topography were further quantified. [Results] (1) From 2000 to 2020, soil moisture in the Mongolian Plateau displayed a distribution characteristic of high level in the northeast and low level in the southwest. From shallow to deep layers, the soil moisture presented a change trend of no significant increase (0—28 cm), no significant decrease (28—100 cm) and significant decrease (100—289 cm) . (2) There were 58.5%, 76.7%, 91.3%, and 98.8% of the regions with Hurst index greater than 0.5. The change trend of soil moisture in most regions in the future will be the same as in the past. The drying of soil moisture in the northwestern Mongolian Plateau and central Inner Mongolia may further aggravate. (3) Temperature, TWSA, precipitation, and NDVI were the dominant environmental factors affecting the spatial distribution of soil moisture. Most of the factor interactions showed a bi-factor enhancement effect, and the spatial differentiation of soil moisture in the Mongolian Plateau was the result of the joint action of multiple factors. (4) The changes in surface and subsurface soil moisture were mainly positively affected by precipitation, and the dominant control areas accounted for 98.7% and 94.8% of the total vegetated area of the plateau, respectively. The main control areas of precipitation and TWSA on soil moisture at the layer of 28—100 cm concentrated in areas covered by forestland and grassland, with the dominant area accounting for 38.7% and 38.8%, respectively. The decrease in TWSA was the main driving force leading to the drying of soil moisture in the layer of 100—289 cm, with the main dominant area accounting for 58.6%. The increase in water consumption by vegetation and the increase in temperature dominantly controlled the changes in soil moisture in layer of 100—289 cm in 16.2% and 14.8% of the plateau, respectively. [Conclusion] The spatial and temporal differentiation and change patterns of soil moisture are obvious, and the main driving forces of soil moisture at each depth are different. This is of great significance to the ecological restoration of the Mongolian Plateau and the sustainable development of the ecosystem. In the future, the impact of human activities on it should be analyzed in depth. [ABSTRACT FROM AUTHOR]