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The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high-high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio-economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision-making basis for biodiversity conservation and efficient pest control.

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The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

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Identifying the influencing factors of soil heavy metal content changes is the basis for reducing or preventing soil heavy metal pollution. Taking an agricultural experimental field in Changping District of Beijing as an example, the heavy metal content changes in As, Cr, Cu, Ni, Pb, and Zn from 2012 to 2022 were firstly analyzed. Secondly, the influencing factors of the heavy metal content changes were detected based on the geographical detector at the single-target and multi-target levels, respectively. Finally, comparative experiments with the correlation analysis method and existing studies were set up to evaluate the effectiveness of the identification method of influencing factors developed in this study. The results showed that human activity factors have exacerbated the changes in soil heavy metal content in the study area as follows： ① At the single-target level, the land use type was the main influencing factor on the changes in Cr, Cu, and Zn contents, and the annual deposition flux influenced the changes in As. The results of the interaction detection showed that there was an enhancement effect among the factors, and the interaction of the human activity factors dominated for the factor identification. ② The results of the multi-target level detection covered the results of the single-target level detection, which could identify more influencing factors. The land use type affected the changes in Cu, Zn, Cr, Ni, and As, and the changes in As and Zn were influenced by the annual deposition fluxes. ③ The multi-target identification method coupled with geographical detector and principal component analysis could effectively identify the influencing factors of soil heavy metal content changes, which was much more effective than the single soil heavy metal correlation method. The developed multi-target identification method for influencing factors of heavy metal content changes can provide technical support for the regional pollution monitoring and macro-management of soil heavy metals.

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Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen's slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, 93.08 % of the land in the research region exhibited a declining LDI trend, 3.95 % showed no change, and only 2.96 % showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.

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Intensifying variability in precipitation under a changing climate is projected to amplify fluctuation in terrestrial hydrological cycle, leading to more severe water-related disasters. The connections between interannual variability of hydrological components and factors influencing these connections have not been clearly defined yet. Based on terrestrial water budget from Climate Data Record, we identify dominant factors influencing partitioning interannual variability of precipitation (P) into that of evapotranspiration (E), runoff (Q), and water storage deviation (ΔS) across the globe by employing geographical detector model (GDM). Sensitivities of the variability partitioning to dominant factors are quantified for different hydroclimate regions by linear regression model and law of total differential. Results show that dominant factors influencing precipitation variability partitioning (VP) are different across distinct hydroclimate conditions. Comparing the statistical index (q value) of the GDM, it can be seen that surface air temperature (Ta), snow water equivalent (SWE) and water storage capacity (Smax) are dominant factors of VP in humid, semi-arid and arid regions, respectively. Changes in P variability largely can transfer into Q variability in humid region. The P variability partitioned into Q variability is dramatically reduced in semi-arid region with SWE decreasing, while P variability partitioned into ΔS variability increases with Smax increasing in arid region. Joint effects of Ta and coefficient of variation of precipitation (Pcv) are found to be the most important interaction in determining VP across the globe. Furthermore, warmer temperatures in humid region cause >90 % of the change in precipitation variability to be transferred to Q variability change. In semi-arid region with snowfall, decreased SWE has strong effect on changes in ΔS (30-40 %) and Q (20-40 %) variability. Our findings imply a changing VP and more severe impacts of hydrological extremes under future climate, where intensive changes in Ta, SWE and land cover are projected.

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In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies.

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Agricultural irrigation and resettlement have significant impacts on carbon storage in arid inland river basins. With the background of "Comprehensive development measures for agricultural irrigation and resettlement in Shule River Basin (SRB)", this paper uses land use data to estimate regional carbon storage through InVEST model and revises the result by using net ecosystem productivity (NEP). The influence of land use change on carbon storage and the driving factors of carbon storage spatial differentiation were analyzed by using the optimal parameters geographical detector (OPGD). It can be inferred from the results that: (1) During 2000-2020, the increase of cropland and grassland area is the main type of land use change in the central oasis area of Yumen City and Guazhou County. Cumulative carbon storage increased by 1.75 × 107 t. (2) NEP in the central oasis area of Yumen City and Guazhou County showed a fluctuating upward trend, and it generally behaves as a carbon sink. The average annual NEP was 1.78 × 105 t, and the carbon sink increased by 0.95 × 105 t. (3) The main factors responsible for driving are vegetation, elevation, potential evapotranspiration, and precipitation. The explanatory power of each factor in carbon storage spatial differentiation was enhanced by the interaction between natural and anthropogenic factors. The interaction between vegetation and the human factor is more significant than that of the human single factor. (4) Agricultural irrigation and resettlement measures did not cause a decline in ecosystem carbon storage in Yumen City and Guazhou County in the central part of SRB. Conversely, the region's ecosystems have seen an increase in carbon storage as a result of the increase in cropland. (5) The introduction of the NEP modification method and the OPGD model improves the accuracy of carbon storage estimation and obtains better driving results in spatial differentiation. The study idea provides a new perspective for the estimation of carbon storage as a whole, and provides a reference basis for the formulation of ecological protection policies.

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The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures.

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BACKGROUND: The range of Glires is influenced by human activities and climate change. However, the extent to which human activities and environmental changes have contributed to this relationship remains unclear. We examined alterations in the distribution changes and driving factors of the Himalayan marmot, plateau pika, and plateau zokor on the Qinghai-Tibet Plateau (QTP) using the maximum entropy (MaxEnt) model and a geographical detector (Geodetector). RESULTS: The MaxEnt model showed that the contribution rates of the human footprint index (HFI) to the distribution patterns of the three types of Glires were 46.70%, 58.70%, and 59.50%, respectively. The Geodetector results showed that the distribution pattern of the Himalayan marmot on the QTP was influenced by altitude and the normalized difference vegetation index (NDVI). The distribution patterns for plateau pikas and plateau zokors were driven by HFI and NDVI. Climate has played a substantial role in shaping suitable habitats for these three Glires on the QTP. Their suitable area is expected to decrease over the next 30-50 years, along with their niche breadth and overlap. Future suitable habitats for the three Glires tended to shift toward higher latitudes on the QTP. CONCLUSION: These findings underscore the impacts of environmental and human factors on the distribution of the three Glires on the QTP. They have enhanced our understanding of the intricate relationships between Glires niches and environments. This can aid in identifying necessary interventions for developing effective early warning systems and prevention strategies to mitigate Glires infestations and plague epidemics on the QTP. © 2024 Society of Chemical Industry.

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In the context of sustainable development, it is important to thoroughly investigate the coupling mechanism between China's eco-environmental quality and human activities, as well as identify the influencing factors, in order to provide scientific references for achieving sustainable development goals in China. This study applied trend analysis, coupling coordination degree, LMDI, and optimal parameter geographic detector models to explore and evaluate the coupling mechanism between China's eco-environmental quality and human activities. The findings of the study were as follows:① During the research period, there was a growth trend in China's coupling coordination degree, human activities, and eco-environmental quality. Human activities and coupling coordination degree exhibited a spatial differentiation pattern with the Hu Line as the boundary, showing an "east high, west low" distribution. The eco-environmental quality demonstrated a "south high, north low" differentiation pattern. ② The overall trend of China's coupling coordination type transformation was shifting from lower-level to higher-level coordination types. ③ Based on the geographic detector and LMDI models, the dominant factors influencing the coupling coordination degree in most provinces east of the Hu Line were social and economic factors, as well as the comprehensive coordination index. In contrast, the dominant factors in most provinces west of the Hu Line were natural environmental factors and coupling degree. ④ The evaluation of the impact of changes in human activities on eco-environmental quality revealed that the regions east of the Hu Line were mainly characterized by favorable development and effective protection, whereas the regions west of the line were mainly characterized by destructive development and ineffective protection. It is suggested that the regions on both sides of the Hu Line should prioritize development based on local prerequisites influencing the coupling coordination degree and the relative relationship between human activities and eco-environmental quality. It is crucial to actively adjust development strategies and pursue a sustainable development path towards the high-level coordination between eco-environmental quality and human activities.

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The ecological environment of the middle Yellow River is highly vulnerable. Conducting a scientific assessment of landscape pattern vulnerability holds great significance, as it serves as the basis for the rational construction of the ecological environment in this area. Based on five periods of land use data from the middle Yellow River from 1990 to 2018, the landscape pattern vulnerability index was employed to analyze the spatio-temporal evolution of the landscape pattern vulnerability. Furthermore, the influencing factors for landscape pattern vulnerability in different natural geomorphological divisions were explored using an optimal parameters-based geographical detector model. The results showed that:① From 1990 to 2018, cultivated land (which accounted for 36.96 % to 39.97 % of the area) remained the predominant landscape in the middle Yellow River. Among all landscape types, cultivated land and construction land exhibited the most significant changes. The area of cultivated land decreased by 10 185.00 km2, whereas the area of construction land increased by 7 678.46 km2. ② From 1990 to 2018, the landscape pattern was dominated by low and medium vulnerability and accounted for 70 %-80 % of the total area. The high and higher vulnerability areas were concentrated in the loess hilly and gully region, whereas the lower vulnerability area was concentrated in the valley plain and the earth-rock mountain regions. During this period, landscape pattern vulnerability underwent an incipient decrease, followed by a subsequent increase. From 1990 to 2000 and from 2000 to 2005, the changes in the level of landscape pattern vulnerability were dominated by a "reduction in the degree of vulnerability". However, from 2005 to 2010 and from 2010 to 2018, it was mainly an "increase in the degree of vulnerability". ③ Annual precipitation and NDVI were the main factors influencing the vulnerability of landscape patterns, whereas the influencing factors varied across different natural geomorphological divisions:the loess hilly and gully region and the earth-rock mountain region were dominated by natural factors, with annual precipitation and DEM being the dominant factors, respectively; the loess plateau tableland-gully region, valley plain region, and sandy land and desert region were dominated by human factors, with population density, degree of land use, and distance from roads being the dominant factors, respectively. The interaction results of any two influencing factors were manifested as two-factor enhancement or nonlinear enhancement. Risk detection revealed that high vulnerability areas of landscape patterns in different natural geomorphological divisions were distributed over distinct ranges of their corresponding dominant factors. Therefore, in the practices of ecological management in the middle Yellow River, appropriate management strategies should be implemented based on the vulnerability characteristics of different natural landforms, to further improve the ecological management level of the watershed.

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Site contamination has caused serious harm to human health and the ecological environment, so understanding its spatial and temporal distribution patterns is the basis for contamination assessment and site remediation. For this reason, this study analyzed the spatial-temporal distribution patterns of organic pollutants and their driving factors in the Yangtze River Delta based on site sampling data using the optimal-scale geographical detector. The analysis results showed that:① There was a significant scale effect in the spatial distribution of organic pollutants in the Yangtze River Delta, and its optimal geographic detection scale grid was 8 000 meters. ② The main control factor of the spatial distribution of pollutants in the Yangtze River Delta originated mostly from the biological field, followed by the chemical field. ③ At the depth of 0-20 cm of soil, the explanatory power of sucrase content, urease content, microbial nitrogen amount, total nitrogen content, and cation exchange amount were stronger for the spatial distribution of organic pollutants. At the soil depth of 20-40 cm, the factors with stronger explanatory power on the spatial distribution of organic pollutants were soil moisture, population, and total nitrogen content. With the deepening of soil depth, the explanatory power of the factors of the hydrodynamic field increased. ④ Population, total nitrogen content, and polyphenol oxidase content had stronger explanatory power for the spatial distribution of organic pollutants in the spring. The spatial distribution of organic pollutants was more complex in autumn, and the factors showed stronger enhanced-nonlinear and enhanced-bi phenomena.

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For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

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The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China.

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Background: With the ever-increasing occurrence of extreme weather events as a result of global climate change, the impact of extreme temperatures on human health has become a critical area of concern. Specifically, it is imperative to investigate the impact of extreme weather conditions on the health of residents. Methods: In this study, we analyze the daily death data from 13 prefecture-level cities in Jiangsu Province from January 2014 to September 2022, using the distributed lag nonlinear model (DLNM) to comprehensively account for factors such as relative humidity, atmospheric pressure, air pollutants, and other factors to evaluate the lag and cumulative effects of extreme low temperature and high temperature on the death of residents across different age groups. Additionally, we utilize the Geographical Detector to analyze the effects of various meteorological and environmental factors on the distribution of resident death in Jiangsu Province. This provides valuable insights that can guide health authorities in decision-making and in the protection of residents. Results: The experimental results indicate that both extreme low and high temperatures increase the mortality of residents. We observe that the impact of extreme low temperatures has a delayed effect, peaking after 3-5 days and lasting up to 11-21 days. In contrast, the impact of extreme high temperature is greatest on the first day, and lasts only 2-4 days. Conclusion: Both extreme high and low temperatures increase the mortality of residents, with the former being more transient and stronger and the latter being more persistent and slower. Furthermore, residents over 75 years of age are more vulnerable to the effects of extreme temperatures. Finally, we note that the spatial distribution of resident deaths is most closely associated consistent with the spatial distribution of daily mean temperature, and there is significant spatial heterogeneity in deaths among residents in Jiangsu Province.

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Rural tourism is a powerful way to revitalize the countryside, and its spatial pattern is crucial for sustainable development. This paper analyzes the spatial distribution of rural tourism characteristic villages in Henan Province by taking 723 villages as the research object and using the nearest neighbor index, kernel density analysis, and spatial autocorrelation. It investigates the influencing factors utilizing the optimal parameters-based geographical detector (OPGD) model. The results show that, firstly, the overall spatial distribution of the rural tourism characteristic villages in Henan Province is characterized by aggregation and unbalanced distribution, and the overall spatial distribution density demonstrates the aggregation characteristics of "four cores and one belt". Secondly, the rural tourism characteristic villages can be divided into four primary categories, agricultural industry, rural culture, and featured villages and towns. The spatial distributions of the four main categories are all clustered. Thirdly, the primary factors affecting the differences in the spatial distribution of the rural tourism characteristic villages are the topographic features, economic development level, tourism market potential, traffic capacity, and relevant policies, among which the critical factor is the number of A-class scenic spots in the tourism market potential. To promote the optimisation of the spatial pattern of rural tourism, it is necessary to strengthen resource integration. Furthermore, it is important to conduct in-depth exploration of more factors in order to provide comprehensive guidance for the sustainable development of rural tourism.

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The purpose of this study was to reveal the spatial and temporal evolution patterns of habitat quality in karst counties of Guizhou plateau and its driving factors and to provide scientific basis for balanced ecological conservation and sustainable development of karst regions. Using DEM data, meteorological data, socio-economic data, and four periods of land use data in 1989, 2003, 2010, and 2020, the InVEST model was used to analyze the spatial and temporal evolution characteristics of habitat quality in Puding County from 1989 to 2020 and to quantitatively detect the driving forces of its spatial divergence. The results were as follows:① Arable land and forest land were the main land use types in Puding County, which constituted the surface cover landscape matrix. Land use changes from 2003-2010 were the most significant, among which forest land had the largest increase of 86.42%; arable land was the most severely lost land use type, with an area decrease of 157.57 km2, mainly flowing to forest land and construction land. ② From 1989 to 2020, the average value of habitat quality index in Puding County increased from 0.60 to 0.73. Spatially, the distribution pattern of "high-low-high" was generally from northeast to southwest, with the high value areas of habitat quality mainly distributed in the woodland and grassland areas in the northeast and the low value areas concentrated in the construction land in the central and south areas. ③ Land use type was the primary factor affecting the spatial and temporal distribution of habitat quality, with an explanation of 91.00%. In the interaction detection, the interaction of any two influencing factors was greater than that of individual factors alone, and the interaction between land use type and average annual precipitation was the strongest, reaching 96.00%; the interaction with lithological factors reached 94.00%, with natural and human factors jointly dominating the spatial and temporal changes in habitat quality. From the results of this study, we concluded that the habitat quality of Puding County was generally good from 1989 to 2020, and the relationship between land use type changes and habitat quality was close. Optimizing the land use structure and reducing the influence of human activities are important to improve the habitat quality of Puding County.

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Hutuo River Basin straddles Shanxi and Hebei provinces, and Hutuo River was once cut off due to economic development and urban expansion after 2000; however, with the national emphasis on ecological civilization and the implementation of the South-North Water Diversion Project, the ecological protection of Hutuo River Basin has been significantly improved. MODIS data, Landsat data, and night light remote sensing data were selected based on the google earth engine (GEE) platform, and a new evaluation index system was generated by combining the biological richness index, vegetation cover index, land stress index, and pollution load index in the ecological environment index (EI) and the humidity index in the remote sensing ecological index (RSEI), using the variation coefficient method and entropy weighting method to assign weights to these indices. An ecological environment evaluation model was constructed to evaluate and classify the ecological environment quality of Hutuo River Basin from 2000 to 2020, and the driving factors were interpreted by using geographic probes. The results showed that:① on a time scale, the ecological environment of Hutuo River Basin was in a decline period from 2000 to 2015 and a recovery period from 2015 to 2020. From a grid scale, the ecological environment quality in the central part of the basin showed a state of improvement year by year, and in the western and eastern parts of the basin, the ecological environment quality in the decline period decreased year by year, whereas the ecological environment quality in the recovery period improved. ② Hot spot analysis showed that the spatial distribution of the ecological environment quality in Hutuo River Basin was high in the middle and low on both sides. Cold spot regions were mainly located in major cities and towns in the eastern and southern parts and scattered in the river valley area on the west side. ③ Geodetection analysis showed that the single factor detection drivers were mainly population density, vegetation net primary productivity (NPP), fractional vegetation cover (FVC), and geomorphological type. The dominant factor of cross-detection was "geomorphological type + FVC." With the deepening of ecological civilization construction and the implementation of Hutuo River Protection Regulations, in combination with different factors such as the natural environment and social characteristics in this basin, the research on ecological environment evaluation in Hutuo River Basin can provide data support for proposing localized policies to improve the ecological environment.

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Understanding the factors driving propagation from meteorological to hydrological drought is crucial for drought mitigation. In this study, an integrated framework based on the Soil and Water Assessment Tool model, standardised drought indices and Geographical Detector were used to investigate how and to what extent watershed properties and human activities affect the spatial heterogeneity of drought propagation in the Wei River Basin, a typical arid and semi-arid region in China. Results indicated that (1) spatially, the propagation times increased from southwest to northeast. Seasonally, the propagation was shorter and stronger in summer and autumn. (2) The aridity index significantly affected the spatial distribution of drought propagation time for the entire basin, especially in summer, while human activities primarily drove spatial distribution in the sub-basins. The explanatory power of any two independent factors was non-linearly enhanced after the interaction. (3) Watershed properties potentially impacted the anthropogenic driving factor of drought propagation. Strong anthropogenic effects on drought propagation often occurred in watersheds with moderate drought levels, steep slopes, low elevations, and small areas, and the key factors varied seasonally. These findings help elucidate the multifaceted effects of watershed properties and human activities on drought propagation. The proposed framework and the results of this study provide valuable guidance for formulating precise drought control strategies in the Wei River Basin and worldwide.

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As the common wealth of all mankind, intangible cultural heritage carries the memory of history. The implementation of dragon and lion dance technical grade evaluation can be called pioneering work for the legacy inheritance. The purpose of this study is to analyze the distribution characteristics, trends, types, and driving factors of dragon and lion dance athletes. Athletes who were awarded a grade certificate by Chinese Dragon and Lion Dance Sports Association from 2018 to 2021 were taken as the analysis objects. In ArcGIS10.8, trend surface analysis and standard deviational ellipse are used to study the spatial distribution characteristics. Using the global and local Moran's I in GeoDa, this paper explores the concentration degree and types of dragon and lion dance athletes. The factors driving the distribution difference are analyzed with the help of geographical detectors. Continuous data such as total GDP, sports field areas, permanent populations, and altitude are taken as the software input after preprocessing. The results show that there are significant differences in the distribution of provinces and four geographical regions. For dragon dance athletes, there is a linear growth trend in the west-east direction and an inverted U-shape trend in the south-north direction. For lion dance athletes, there is a trend of log function in the west-east direction and an inverted U shape in the south-north direction. The global Moran's I is 0.2386, and there are obvious characteristics of H-H, L-L, and L-H aggregation types. Nomination of sports intangible cultural heritage, total GDP, permanent resident population, urbanization development level, the number of colleges and universities, and the proportion of tertiary industry are the leading factors, and the explanatory power of interactive factors is significantly enhanced. Therefore, it is suggested to strengthen the cultural heritage protection of dragon and lion dance, increase capital investment, enhance public participation, and raise government attention.