Geographical Detector Research Articles

Geospatial analysis of sports tourism resources in China's urban clusters: a case study of the Sichuan-Chongqing region utilizing GIS and the geographic detector

IntroductionThis study aims to elucidate the temporal and spatial distribution patterns of sports tourism resources within the Sichuan-Chongqing Urban Agglomeration, examining how these distributions reflect the developmental pathways of urban regions. The theoretical framework posits that the availability and allocation of sports tourism resources are influenced by various geographical and socio-economic factors, with topography, economic conditions, and population density playing pivotal roles in determining their distribution and development.MethodsUtilizing GeoDetector technology alongside indices such as the Geographic Concentration Index and the Disparities Index, we conducted a comprehensive analysis of the spatiotemporal dynamics of sports tourism resources, revealing that the distribution of these resources serves as a key indicator of urban development speed.ResultsOur findings indicate that topographical features significantly influence sports tourism resource development differently in Chengdu compared to Chongqing. Furthermore, we identified a strong positive correlation between population density and the distribution of sports tourism resources, highlighting the motivational role of populated areas and emphasizing potential inequalities if attention is focused solely on densely populated regions.DiscussionThis research provides novel insights into the complex relationships governing sports tourism resource distribution by employing advanced geo-analytical tools. It offers a strategic roadmap for equitable and sustainable urban development by addressing disparities in resource allocation, ultimately contributing to informed policy-making and enhanced regional resilience. This research aids in the strategic planning and sustainable development of sports tourism, providing a blueprint for urban development in the region that balances the needs of diverse populations and landscapes.

Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Heilongjiang Province in the Past 20 years

PM2.5 is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM2.5 remote sensing data from 2000 to 2021, the Theil-Sen Median trend analysis, Mann-Kendall significant inspection, and spatial auto correlation were used to analyze the characteristics of space-time changes in PM2.5 concentration, and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM2.5 spatial differences. The results showed that： ① The average PM2.5 value of Heilongjiang Province was between 22.01 and 41 μg·m-3 from 2000 to 2021. From 2008 to 2015, the average PM2.5 value was higher than the secondary concentration limit （35 μg·m-3） of the "Environmental Air Quality Standard." The turning point of the PM2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM2.5 pollution. The PM2.5 concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin, Daqing City, and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM2.5 spatial differences. The remaining key driver factors were in turn： high-end, population density, average annual wind speed, land use, night lights, annual years precipitation, slope, annual relative humidity, and NDVI. Interactive detection showed that the interpretation of PM2.5 points after interaction was higher than a single factor after interaction, indicating that affecting PM2.5 spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM2.5 had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM2.5 pollution and NDVI and land use played an inhibitory effect on PM2.5 pollution. PM2.5 was significantly different from the action role of various influencing factors and the average annual temperature, annual average wind speed, and NDVI impact scale were the smallest, with a variable bandwidth of 43； population density and land use impact scale were the largest, with a variable bandwidth of 140.

Carbon Sequestration Zoning and Management in Megacities from a Sustainable Perspective: A Case Study of Beijing

To address the contradiction between the development of megacities and ecological restoration, as well as the carbon sequestration capacity guarantee, this study was based on the sustainable development framework and adopted the research approach of "clarifying carbon sequestration characteristics, evaluating carbon sequestration suitability, and comprehensively dividing carbon sequestration areas" to propose carbon sequestration zoning and management measures for Beijing under the sustainable development framework. First, we used the improved Carnegie Ames Stanford Approach （CASA） model and soil microbial respiration equation to quantitatively estimate the net ecosystem productivity （NEP） of vegetation in Beijing from 2001 to 2020. The spatiotemporal distribution pattern and evolution characteristics were analyzed and trend, volatility, and continuity were used as evaluation indicators for carbon sequestration characteristics. Second, from a sustainable perspective, considering the natural, social, and economic conditions of Beijing, the driving factors of NEP in Beijing were analyzed from the perspectives of single-factor detection and interactive detection using geographic detector methods. Finally, the driving factors were used as suitability evaluation indicators for sustainable carbon sequestration. Risk detection was used to assign values to different intervals of the driving factors based on their corresponding NEP mean values. Combined with the NEP carbon sequestration characteristics in Beijing, the sustainable carbon sequestration zones in Beijing were determined. The results showed that： ① On the time scale, the overall trend of NEP change in Beijing from 2001 to 2020 was fluctuating upward, and on the spatial scale, NEP in the northwest was larger than that in the southeast. The overall trend showed a divergent expansion from the center of Beijing, and 79.6% of the regional NEP showed an increasing trend； however, the scope of the region with a sustainable growth or a reversal from a decrease was still relatively small. The region with the least volatility among the carbon sequestration regions included Huairou, Yanqing, and Fangshan Districts. ② The major drivers of spatial variability of NEP in Beijing were, in descending order of explanatory degree, vegetation cover, land use type, elevation, degree of human interference, slope, level of economic activity, population density, GDP, and average temperature. The interaction of the drivers was a two-way enhancement or non-linear enhancement and the characteristics of spatial variability of NEP were affected by the joint action of multiple drivers. ③ Combined with the evaluation of the suitability of sustainable carbon sequestration and the characteristics of the evolution of carbon sequestration, Beijing was divided into five categories： carbon sequestration core areas, carbon sequestration key areas, carbon sequestration priority areas, carbon sequestration potential areas, and carbon sequestration supplemental areas, which accounted for 47.5%, 17.7%, 15.4%, 11.4%, and 8.0% of the municipal area, respectively. The results of this study can provide a basis for the delineation of carbon sequestration areas and the formulation of management measures in Beijing. To realize sustainable development and carbon neutrality, megacities should define carbon sequestration zones by combining the effects of multiple drivers and the evolutionary characteristics of carbon sequestration, to adopt targeted carbon sequestration strategies.

The spatio-temporal evolution and influencing factors of the coupling coordination degree between tourism supply and demand in the Yangtze river economic belt

The imbalance between supply and demand is a pressing issue in the development of the tourism industry. Understanding the coupling coordination relationship and impact mechanism of supply-demand in the tourism system can help achieve high-quality tourism development. This study focuses on the Yangtze River Economic Belt (YREB) as the research area, quantifies the tourism supply index (TSI) and tourism demand index (TDI) from 2011 to 2020 using the tourism development index model, calculates the coupling coordination degree (CCD) of TSI and TDI based on the coupling coordination model, and explores the factors influencing the CCD using the geographic detector. The findings indicate that: (1) The TSI and TDI in various provinces show fluctuation but exhibit an overall upward trend. (2) There were apparent spatial disparities of the CCD, with a distribution characteristic of high in the east and low in other regions. The CCD gradually improved, with its gravity center slowly shifting toward the southwest. All provinces entered the intermediate stage of tourism development since 2014. (3) Basic service guarantee, consumption drive, and innovation drive are identified as the dominant factors influencing the CCD. The study can provide valuable insights for tourism coordination and sustainable development.

The dynamic patterns of critical ecological areas in the Yellow River Basin are driven primarily by climate factors but threatened by human activities

While the identification of critical ecological areas (CEAs) has been limited by the availability of resources, funds, and land space, these areas are central for maintaining the contributions from nature and ecosystem biodiversity. Thus, in this study, CEAs were systematically identified and classified via long-term ecosystem assessment with a multifunctionality–stability–integrity framework in the Yellow River Basin (YRB). The intensity and pathways of the driving factors of CEA dynamics were also determined by the geographical detector model (GDM) and the partial least squares structural equation model (PLS-SEM). The results revealed a gradual decrease in the proportion of CEAs in the upper and lower reaches from 2005 to 2020, in contrast with a significant increase in the middle reaches. The CEAs were distributed mainly in the Sanjiangyuan and Ziwuling regions and the Qingling, Qilian, Lvliang and Taihang Mountains. The areas with low, medium, and high levels of CEAs accounted for 12.48%, 17.73% and 12.54%, respectively, of the YRB. Moreover, climate factors, especially precipitation, were the foremost drivers influencing the dynamic patterns of CEAs. However, notably, the CEAs in the YRB experienced continuous degradation from 2005 to 2020 due to intensified human activities. Our results confirmed the important role of climate factors in determining the distribution of CEAs while underscoring the necessity of mitigating human disturbances to maintain ecosystem functions. These investigations of the dynamics and drivers of CEAs based on long-term multi-dimensional ecosystem assessments could provide useful insights for developing ecological conservation and sustainable development strategies.

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions?

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

High-risk driving factors of rain-induced flooding hazard events on the Loess Plateau and its ecological subregions

Rain-induced flooding hazards are prevalent on the Loess Plateau (LP). Descriptive statistics, kernel density estimation, and geographical detector methods were used to explore the spatial and temporal distribution, driving factors, and their high-risk intervals of rain-induced flooding hazard events (RFHEs) on the LP and whether they differ across the entire LP and its ecological subregions. The findings showed that 91 RFHEs occurred mainly in the south-central LP during 2004–2020. The daily rainfall, surface relief amplitude (SRA), elevation, normalized difference vegetation index (NDVI), soil texture, and gross domestic product (GDP) were identified as the driving factors of RFHEs on the LP. However, the driving factors of RFHEs in the Sandy and Agricultural Irrigation Regions (Subregion C), and Earth-rocky Mountainous Region and River Valley Plain Region (Subregion D) all had an added population factor compared to the entire LP, but they lacked NDVI and SRA factors, respectively. The driving factors for the Loess Plateau Gully Region (Subregion A) lacked SRA and soil texture factors. The Loess Hilly and Gully Region (Subregion B) lacked NDVI, soil texture, and population factors. There were also differences between high-risk intervals on the LP and its subregions. The high-risk daily rainfall for the entire LP was 64.5 mm, while it was 64.5, 82.3, 14.7, and 50.0 mm for subregions A, B, C, and D, respectively. Therefore, adopting uniform standards on the LP may over-estimate or under-estimate RFHE occurrence in ecological subregions. These findings contribute to guiding decision-makers involved in ecosystem management and hazard prevention.

Greening the marine map: a comprehensive study of China’s marine ecological and economic synergy

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.

Driving Factors for Vegetation NDVI Changes in a Cold Temperate Zone: Climate, Topography, and Land Use

Exploring the spatio-temporal evolution and driving mechanism of the NDVI (Normalized Difference Vegetation Index) is important in order to understand the operating forces of the ecosystem and the response process of environmental change. We analyzed spatio-temporal vegetation changes by using the trend analysis method during 2001–2020 based on the MODIS NDVI, the meteorological data, the DEM (Digital Elevation Model) and land use types data. We quantitatively revealed the influence degree and mechanism of each detection factor and their interaction on the spatial differentiation of vegetation by using the geographical detector model. Results showed that the vegetation NDVI showed an increasing trend with an increasing rate of 0.021/10 a during 2001–2020 and mainly distributed in the northwest and southwest of the Greater Khingan Mountains. The explanatory power values of each driving factor are as follows: land use (0.384) > elevation (0.193) > slope (0.159) > annual precipitation (0.104) > aspect (0.069) > average annual temperature (0.056). The explanatory power of interaction between driving factors were relatively high, as follows: Land use ∩ Aspect (0.490) > Land use ∩ Slope (0.471) > Land use ∩ Annual precipitation (0.460) > Land use ∩ elevation (0.443) > Land use ∩ Annual temperature (0.421) > Aspect ∩ elevation (0.408). Our research was of great significance for understanding the growth law of vegetation, protecting the ecological environment, and sustainable development in cold temperate zones.

The Influence of Short-Term Water Level Fluctuations on the Habitat Response and Ecological Fragility of Siberian Cranes in Poyang Lake, China

The landscape of the Poyang Lake wetland is significantly influenced by changes in water levels, impacting the distribution of habitats for migratory birds. While long-term effects of water level variations have been extensively studied, short-term impacts on Siberian crane habitats and their ecological vulnerability remain poorly understood. This study utilized 35 years (1987–2022) of Landsat remote sensing data and daily water level records from Poyang Lake to examine the effects of short-term water level fluctuations on the spatial distribution and ecological vulnerability of Siberian crane habitats. The geographic detector method was employed to quantify the explanatory power and interaction effects of factors, including short-term water level fluctuations, on ecological vulnerability. The findings reveal significant differences in the habitats of wintering Siberian cranes across various water level intervals and short-term fluctuation patterns. Short-term water level fluctuations can result in the largest suitable wintering habitat area for Siberian cranes, reaching 1856.41 km2 in this study. These habitats are highly sensitive to short-term water level changes, with rising and falling trends potentially leading to habitat loss. Oscillating water levels in the short term create broader and more concentrated habitats. Notably, fluctuations at low water levels support the sustainability and stability of crane habitats. Furthermore, short-term water level trends and nature reserves play a critical role in maintaining habitat ecological vulnerability; well-managed and protected nature reserves exhibit significant explanatory power, both in single-factor analysis and in their interaction with other environmental factors. Specifically, these protected areas show explanatory power exceeding the 20% threshold for both water level fluctuations and ranges, highlighting the crucial role of anthropogenic management in mitigating ecological vulnerability. This study emphasizes the necessity of scientifically informed regulation of short-term water level fluctuations to protect Siberian crane habitats and provides a strong scientific basis for decision-making support.

Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved.

Changing characteristics, driving factors and future predictions of land use in the Weigan-Kuqa River Delta Oasis, China

The oasis serves as the central component of the arid ecosystem and plays a crucial role in supporting human activities. However, the ecological environment in the oasis region is fragile, and even a minor alteration in land use (LU) can significantly impact the stability of the ecosystem. Therefore, it is imperative to undertake comprehensive research on the spatio–temporal patterns of LU change in the oasis, reveal its driving factors, and predict future development. This is crucial for devising scientifically and logically sound land management strategies, upholding the equilibrium between humans and land in arid areas, and attaining sustainable development of the regional ecology and economy. This study focuses on the Weigan–Kuqa River Delta Oasis in China as the research area, analyzes the changes in LU in the oasis from 2010 to 2022 using various methods such as transition matrix, dynamic degree, intensity analysis, and center of gravity shift. The study also investigates the factors influencing these changes using the optimal parameters–based geographical detector (OPGD). Additionally, it predicts the future trends in LU development under four different scenarios using the mixed–cell cellular automata (MCCA), and illustrates distribution characteristics by combining Moran’s I index and hotspot analysis. The results suggest that: (1) Between 2010 and 2022, the LU in the oasis changed rapidly, with consistent increase in the amount of construction land, arable land, and garden land, while the amount of forest-grassland and unused land decreased overall. (2) Population density played a leading role in the changes, but soil type also had a significant impact. Over the course of time, the influence of roads and transportation has progressively increased. (3) Compared with 2022, the acreage of arable land, garden land, and construction land increases under the four future scenarios: natural development scenario (NDS), economic development scenario (EDS), cropland development scenario (CDS), and ecological protection scenario (EPS). However, the acreage of forest–grassland and unused land decrease. From a spatial perspective, large towns, the downstream of alluvial fans, and the central oasis are key areas where the distribution of hot spots and sub–hot spots of each LU type varies significantly among the four scenarios. The EPS provides a certain level of protection for forest-grassland areas and water bodies, making it the most appropriate development model for oases. These findings have the potential to offer valuable academic guidance for oasis land resource management and are crucial for achieving coordinated development at regional level.

Characteristics and driving factors of spatiotemporal changes in soil erosion in the karst plateau mountainous region over 20 years.

Soil erosion is one of the main issues that endangers global ecosystems. This study explored the spatiotemporal distribution of soil erosion and its drivers in the karst plateau mountainous region. A detailed examination of topography, soil, vegetation, land use, and precipitation data from 2000 to 2020 was conducted in Bijie City using the revised universal soil loss equation model. We also explored the driving forces using a geographical detector. The findings show that between 2000 and 2020, soil erosion first decreased, followed by an increase. The southwest, south, and northern regions contained the highest intensity of soil erosion. Land use, slope, and precipitation are the primary factors influencing soil erosion, with slopes having the greatest impact. By improving our understanding of the dynamics of soil erosion and the primary variables that influence it in karst plateau mountainous environments, our findings can assist in the development of strategies and technical support for sustainable soil and water conservation.

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people’s health and well-being.

Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions.

Assessing the Interaction Impacts of Multi-Scenario Land Use and Landscape Pattern on Water Ecosystem Services in the Greater Bay Area by Multi-Model Coupling

Water ecosystem services (WESs) are intrinsically associated with the livelihood of urban residents and are frequently disrupted by human activities. Land use and landscape patterns are key driving factors of alterations in WESs. However, existing research primarily quantifies single-factor influences and often overlooks the interactions between these factors. This study addresses this gap by employing a multi-model coupling approach, integrating the Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and Geographical Detector (GD) models alongside various indicators to analyse the evolution of land use, landscape patterns and WESs in the Greater Bay Area from 2000 to 2020, and to simulate spatio-temporal change patterns in different scenarios from 2030 to 2050. Additionally, this study examines the multi-factorial interactions between land use, landscape patterns, and WESs. The results indicate that (1) urbanisation steadily increased, leading to intensified landscape fragmentation, and water yield (WY) and total phosphorus (TP) consistently increased, while total nitrogen (TN) in water gradually decreased; (2) urban areas exerted the most significant impact on WY in the Greater Bay Area while Patch density (PD) had a stronger influence on WY, and Shannon’s diversity index (SHDI) had the most pronounced effect on TN and TP; (3) the interaction between any two land-use types or landscape indices exerted a greater impact on WESs compared with the impact of individual factors alone. The interaction between urban areas and cropland substantially influenced WY (q¯ = 0.634) and most strongly affected TN and TP in water (q¯ = 0.74 and 0.73, respectively). SHDI and PD had the most significant impact on WY in the economic development scenario (q¯ = 0.19) and exhibited the greatest influence on the TN and TP levels in the ecological priority scenario (q¯ = 0.12 and 0.15, respectively). Our findings can provide theoretical and technical support for the integrated scientific planning of regional water ecosystems and the development of comprehensive land use policies in the future.

Seasonal and Diurnal Characteristics and Drivers of Urban Heat Island Based on Optimal Parameters-Based Geo-Detector Model in Xinjiang, China

In the context of sustainable urban development, elucidating urban heat island (UHI) dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations and potential driving factors, cities can implement effective strategies to reduce their impacts on the environment and public health. However, the driving factors of a UHI in arid regions remain unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were 1.37 ± 0.86 °C, with the SUHII being most pronounced in summer (2.44 °C), followed by winter (2.15 °C), spring (0.47 °C), and autumn (0.40 °C). Moreover, the annual mean SUHII was stronger at nighttime (1.90 °C) compared to during the daytime (0.84 °C), with variations observed across seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime (3.91 °C) compared to nighttime (0.39 °C), with daytime and nighttime SUHIIs decreasing from summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban form, and socio-economic factors (GDP and population density) emerged as key drivers, with the GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects, measures like urban environment enhancement by improving surface conditions, blue–green space development, landscape optimization, and economic strategy adjustments are recommended.

The Assessment of the Spatiotemporal Characteristics of Net Water Erosion and Its Driving Factors in the Yellow River Basin

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R2 = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB.

Spatial Analysis of Lung Cancer Patients and Associated Influencing Factors from the Perspective of Urban Sustainable Development: A Case Study of Jiangsu Province, China

With global environmental changes, lung cancer has become one of the most common types of cancer worldwide, posing a significant public health challenge. Jiangsu Province, located in the eastern part of China, is an economically and socially developed region. According to the latest cancer registration data in Jiangsu Province, lung cancer ranks first in both incidence and mortality of cancer in the province. Thus, studying the spatiotemporal distribution of lung cancer cases and analyzing the influence of various factors on this distribution are crucial for the effective prevention and control of the disease in Jiangsu Province. This study takes the statistical data of lung cancer patients in Jiangsu Province in 2020 as the research object, uses Geographic Information System (GIS) visualization and spatial analysis to study the spatial distribution characteristics of lung cancer patients in Jiangsu Province, and employs the geographical detector to numerically express the impact of various environmental factors on the distribution of lung cancer patients in Jiangsu Province. The results reveal a notable spatial clustering of lung cancer cases, with high-incidence areas concentrated in Suzhou, Nanjing, and Xuzhou cities. Among the seven environmental factors examined, PM2.5, SO2, and PM10 concentration exert the most significant influence. This study employs multifactorial spatial analysis to elucidate the intricate relationships between people’s health and air quality, medical resource distribution, and lung cancer incidence in the process of pursuing sustainable development in cities and provides an important reference for the improvement in lung cancer prevention and control strategies.

Spatio-temporal evolution and prediction of carbon storage at the source of the Fen River and Sanggan River based on a PLUS-InVEST model

Investigating the evolution of land use and its impact on carbon storage is of significant importance for mitigating regional climate change and promoting green low-carbon development. Ningwu County is located in the source region of the Fenhe and Sanggan River, and its ecological status significantly influences the carbon storage (CS) of the watershed ecosystem. In this study, the PLUS-InVEST model was employed to analyze the land use evolution from 1990 to 2020 in Ningwu County, Shanxi Province, as well as their impacts on CS. Additionally, the study simulated and predicted land use changes in Ningwu County by 2040 under four scenarios: natural development (NDS), ecological protection (EPS), cultivated land protection (CLPS), and urban development (UDS), while estimating the corresponding changes in ecosystem CS. Furthermore, the study utilized optimal parameters-based geographical detector to explore the mechanisms underlying the spatial differentiation of CS. The results indicated that the areas of forest land and construction land in the study area consistently increased from 1990 to 2020, whereas the area of cultivated land continuously declined, with grassland, water bodies, and unused land exhibiting a fluctuating increasing trend. The spatial distribution of CS was highest in the northwest, second highest in the southeast, and lowest in the middle region. Over these 3 decades, CS had shown a continuous increase. It is projected that by 2040, the areas of forest and grassland will experience the most significant increase under the EPS; cultivated land only increase under the CLPS; while construction land display the greatest increase under the UDS. Compared to 2020, these four scenarios for 2040 indicate an increase in regional CS, with the EPS showing the largest increment. The primary factors influencing the spatial differentiation of CS in Ningwu County are human activities, followed by topography and climate change; the interactions among these factors exhibit a reinforcing relationship, with the interaction between the distance from construction land and slope having the most substantial impact on the spatial differentiation of CS.