Geographical Detector Method Research Articles

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem’s carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest > farmland > grassland > barren, while the ranking of the carbon sequestration enhancement capacity is farmland > forest > grassland > barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia.

Identifying priority conservation areas for threatened amphibian habitats in the Yangtze river Delta region to maintain regional biodiversity.

The complex life cycle traits of amphibians make them especially sensitive to environmental change, and their ongoing conservation requires the maintenance of suitable habitat that accounts for such life cycle characteristics which may impacted by local environmental dynamics arising from climate change and human disturbance. Many existing studies on amphibian habitats disregard this important issue, leading to uncertainty in managing critical habitats. The application of appropriate conservation practices is therefore constrained by the fact that the major factors influencing amphibian habitats, and their spatio-temporal dynamics at different life stages, are poorly understood. In responding to this notable shortcoming, we applied a Bayesian Belief Network model and geographical detector method to identify spatial and seasonal changes in threatened amphibian habitats of the Yangtze River Delta region (YRDR). Our results reveal the relative importance of different environmental factors contributing to spatio-temporal change of threatened amphibian habitats in YRDR. The results highlight the spatially heterogenous nature of amphibian habitats and the impacts of urbanization, as evidenced by quantitative metrics of percentage area of built-up land and distance to built-up land. Habitat conservation priority analyses also indicate the importance of prioritizing habitat restoration efforts in severely degraded areas. Analysis of seasonality indicates that amphibians face particularly severe challenges during their autumn hibernation period (Slope=-0.002). In revealing seasonal variations in the level of threat to amphibians at different life stages across different habitats, this study fills an important knowledge gap and therefore supports regional amphibian habitat conservation and restoration efforts. The results of the study further imply that current conservation strategies need to comprehensively account for seasonal changes, life-cycle stages, and spatial heterogeneity.

Evolution and Attribution Analysis of Habitat Quality in China’s First Batch of National Parks

In October 2021, China established its first group of national parks, representing a milestone in enhancing the country’s nature reserve system and aligning with global trends in ecological conservation. This study aims to assess habitat quality changes and identify the driving factors in five national parks using multi-temporal land use data from 2000 to 2020. By integrating the land use transfer matrix with the InVEST model, we quantified habitat quality changes, while the geographical detector method was employed to analyze the key natural and socioeconomic drivers. Results showed that grassland and cultivated land were predominantly converted into forestland, leading to improvements in habitat quality in some parks. Specifically, Wuyishan National Park exhibited the highest and most stable habitat quality index, while Three-River-Source National Park experienced significant improvement (+34.10%). However, the Giant Panda, Northeast China Tiger and Leopard, and Hainan Tropical Rainforest National Parks experienced habitat degradation, with decreases of 15.15%, 14.50%, and 13.90%, respectively. Key drivers, such as NDVI, temperature, precipitation, elevation, and population density, were found to significantly influence habitat quality across the parks. This study highlights the ecological benefits of forestland restoration and the risks posed by the conversion of forest to cultivated or construction land, providing valuable insights for optimizing conservation strategies in China’s national parks.

Identifying geochemical anomalies associated with tungsten polymetallic mineralization using geographical detector and unsupervised machine learning methods: Application to the Nanling metallogenic belt, China

The identification of geochemical associations and anomalies related to mineralization is a critical aspect of mineral exploration. Conventional quantitative methods for determining these associations primarily involve correlating various geochemical elements with ore-forming elements and mineral deposits. However, these approaches often face challenges in elucidating the complex interrelationships among geochemical elements and are less effective in regions without known mineral deposits. The geographical detector has emerged as a powerful tool for detecting spatial heterogeneity and identifying key driving factors in mineralization processes. Since geochemical elements are regional variables characterized by spatial heterogeneity, the geographical detector is particularly suited for analyzing interactions among these elements. Additionally, unsupervised machine learning algorithms have proven adept at managing complex interrelations among geochemical elements and extracting deeper insights into mineralization patterns. Despite their advantages, these algorithms carry inherent systematic errors, which introduce uncertainties when relying on a single method, thereby increasing exploration risks. This study applies the geographical detector to the Nanling metallogenic belt, one of the most significant tungsten (W) polymetallic metallogenic belt in South China, to investigate the complex relationships between the ore-forming element W and other geochemical elements, as well as to identify associations pertinent to W polymetallic mineralization. Furthermore, 3 widely used unsupervised learning algorithms, including one-class support vector machines (OCSVM), isolation forest (IF), and deep auto-encoder neural networks (DAE), are employed to detect geochemical anomalies. A model averaging strategy is implemented to calculate the average and standard deviation of geochemical anomaly scores, providing robust anomaly predictions and the quantifying uncertainties associated with systematic errors across different algorithms. The enhanced prediction-area (P-A) plot is used to further assess these anomalies and their uncertainties. Key results include: (1) Geochemical elements such as Sn, Mo, Ag, and Bi have the largest impact on the enrichment of W, followed by Cu, Pb, and Be, with these elements often found in co-occurring or associated minerals linked to W polymetallic mineralization. Additional elements, including Nb, Th, Al2O3, U, As, F, Y, Cd, and Li, also contribute to W enrichment, reflecting the geochemical characteristics of plutons associated with W polymetallic deposits; (2) Element interactions are complex, with most showing nonlinear enhancement effects on W enrichment, which is critical for large-scale W mineralization in the region; (3) Among the machine learning algorithms, IF and OCSVM demonstrate superior and relatively stable performance, whereas DAE exhibits more scattered and disorganized anomaly detection, possibly due to limitation in the pixel-based anomaly recognition; (4) 6 prospective areas for mineral exploration have been identified based on geochemical anomaly scores and their associated uncertainties. This study not only introduces a novel methodology for quantitatively analyzing the complex relationships among geochemical elements, selecting relavant geochemical associations, and elucidating key geological processes related to mineralization. It also provides valuable insights for mineral exploration in the Nanling metallogenic belt.

Seasonal Variations of PM2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM2.5 concentrations in the CCUA; (2) The PM2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM2.5 pollution in the CCUA, especially during winter.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Spatially Heterogeneous Relationships between Ecosystem Service Trade-Offs and Their Driving Factors: A Case Study in Baiyangdian Basin, China

Clarifying the complex relationships among ecosystem services (ESs) and their driving mechanisms is essential for effective ecosystem management and enhancing human welfare. Nonetheless, the current research on these issues still remains limited; therefore, further theoretical exploration is required. This study aims to quantitatively illustrate the trade-off strength of ESs and investigate the spatiotemporal heterogeneity connections between these relationships and various anthropogenic and natural factors in Baiyangdian basin, China, integrating InVEST, RMSE, geographical detector and MGWR methods. From 2000 to 2020, the total water yield (WY) and nutrient export (NE) increased, while the total carbon storage (CS) and habitat quality (HQ) decreased slightly. The trade-offs of ESs showed spatiotemporal heterogeneity. The most serious trade-off occurred between regulating services (CS and NE) and supporting services (HQ) in 2000, which was mainly distributed in the densely forested and grassed western and northern regions of the basin. The trade-off intensities of half of the pairwise ESs in 2020 increased, with the strengthened areas mainly located in the southeast of the watershed where built-up lands are concentrated. Various factors dominated the trade-offs among ESs, with the interactive effects of multiple drivers being more significant than those of individual factors. Land use type, vegetation cover and precipitation have the most pronounced effect on the trade-offs among ESs. The findings of this study may suggest and advocate for spatial ecological strategies to enhance the integrated and holistic advancement of various ESs and also serve as a reference for regional ecosystem governance and the attainment of sustainable growth.

Changes in Spatiotemporal Pattern and Its Driving Factors of Suburban Forest Defoliating Pest Disasters

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations.

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China

The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Remote Sensing Monitoring and Multidimensional Impact Factor Analysis of Urban Heat Island Effect in Zhengzhou City

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.

Spatial–Temporal Changes and Driving Factor Analysis of Net Ecosystem Productivity in Heilongjiang Province from 2010 to 2020

Net ecosystem productivity (NEP) is an important indicator for the quantitative evaluation of carbon sources/sinks in terrestrial ecosystems. An improved CASA model and soil respiration model, combined with MODIS and meteorological data, are utilized to estimate vegetation NEP from 2010 to 2020. A Theil–Sen trend analysis, a Mann–Kendall test, the Hurst index, and geographical detector methods were employed to analyze the spatiotemporal variations in NEP in Heilongjiang Province and its driving factors. The results show the following: (1) The overall NEP in Heilongjiang Province exhibited a fluctuating upward trend from 2010 to 2020, with a growth rate of 4.74 g C·m−2·yr−1, and an average annual NEP of 404 g C·m−2·yr−1. Spatially, NEP exhibits a distribution pattern of “low from east to west to high from north to south in the central region”, with 99.27% of the area being a carbon sink. (2) Significant regional differences were observed in the spatial trend of NEP changes, with 78.39% of regions showing increasing trends and 17.53% showing decreasing trends. Future NEP changes are expected to continue, with regions showing a persistent increase (58.44%), potential decrease (19.95%), potential increase (5.65%), and persistent decrease (11.88%). (3) The geographical detector results indicate that altitude is the dominant factor affecting NEP, followed by slope, temperature, population density, etc. The interaction-detector results show that the interaction between each factor shows an increasing trend, and the interaction between any two factors is higher than that of a single factor. The research results can provide scientific references for reducing emissions, increasing sinks, and protecting ecosystems in Heilongjiang Province.

Spatial Interaction and Driving Factors between Urban Land Expansion and Population Change in China

The rational matching of urban land and population has become an important prerequisite for sustainable urban development. In this paper, the traditional urban land scale elasticity model was improved, and combined with the gravity model, the spatial interaction between land expansion and population change in 618 cities in China during the period 2006–2021 was investigated. The geographical detector method was used to reveal what drives them. The main results were as follows: (1) China’s urban land expansion rate was 1.83 times faster than the population growth rate during 2006–2021. After the implementation of the New-type Urbanisation Plan in 2014, the ratio of land expansion rate to population growth rate dropped from 2.46 to 1.12. (2) Among the six interaction types identified, land rapid expansion is the most significant, accounting for 41.59% of urban samples. (3) The geographical detector method found that the indicators of urban development rights such as the level of administrative hierarchy and the ratio of fiscal revenue to fiscal expenditure were the main factors affecting land expansion and that economic indicators such as gross domestic product and employment opportunities dominated population change. Fortunately, the intervention role of urban development rights has declined, and the constraints of market mechanisms, resources and environment have gradually become the dominant factors in urban land expansion and population change. These findings provide a theoretical basis for alleviating the human–land contradiction and achieving sustainable urban development.

Spatial and temporal distribution and environmental determinants of freeze-thaw erosion intensity in Qiangtang grasslands, China

Context Analysing freeze-thaw erosion is of great significance to ecological environment protection and land resource utilisation in high altitude areas. Aims We used seven indicators (temperature, precipitation, vegetation cover, elevation, slope, slope orientation, and sand content) to calculate the freeze-thaw erosion intensity index for different seasons from 2000 to 2019. Methods We used a graded weighted evaluation model and a geographical detector method to analyse spatiotemporal pattern and driving factors of freeze-thaw erosion intensity in Qiangtang grasslands. Key results (1) From 2000 to 2019, the total area of freeze-thaw erosion was higher in the non-growing season than in the growing season. The area of moderate and above-average freeze-thaw erosion increased over time in the non-growing season but decreased in the growing season. The spatial distribution of freeze-thaw erosion was mainly determined by the annual range of precipitation and temperature, which reflect the intensity and frequency of freezing and thawing cycles. (2) Vegetation cover was an indirect factor that influenced the soil moisture and stability. The slope was another important factor that affected the spatial distribution of freeze-thaw erosion in different regions. Conclusions The results show that in 2000–2019 the area of freeze-thaw erosion showed a downward trend. The erosion degree in the non-growing season is on the rise. Implications Our study provides new insights into the dynamics and mechanisms of freeze-thaw erosion in Qiangtang grasslands and contributes to the understanding and management of water and climate change impacts on this region.

Evaluation of the Spatial Pattern of Fitness and Leisure Venues at the City Scale

Reasonable layout of fitness and leisure places is an important part of the implementation of the national fitness strategy. The analysis of the spatial pattern and influence mechanism of fitness and leisure places from the perspective of city has certain guiding significance for optimizing urban planning and construction and rationally allocating sports resources. This paper uses kernel density, buffer zone, inverse distance interpolation, Kriging interpolation, geographical detector and other geographical spatial analysis methods to analyze the spatial distribution pattern and distribution causes of five types of fitness and leisure places in counties (cities, districts) under the jurisdiction of Jinhua City. It is found that the overall spatial distribution characteristics of fitness and leisure places in Jinhua City are ' core-periphery ', and different forms of fitness and leisure places show different distribution characteristics. The spatial pattern of fitness and leisure places in Jinhua City has the characteristics of relying on traffic arteries and high-density population areas, and has a positive correlation with the spatial layout of housing prices. Jinhua fitness and leisure places generally have good traffic accessibility.

Evaluating the Spatial Heterogeneity and Driving Factors of Sustainable Development Level in Chengdu with Point of Interest Data and Geographic Detector Model

Over the past few decades, China has undergone the largest and fastest urbanization process in world history. By 2023, Chengdu’s urbanization rate had reached 80.5%, significantly higher than the national average of 66.16%. Studying the urbanization experience of Chengdu is of great significance for optimizing urban planning policies in Chengdu and other cities in China. Although much literature has explored the urbanization process from macro and micro perspectives, studies using a top-down approach to examine urban fringe expansion are relatively scarce. This study first applies the entropy weight method to analyze the spatial-temporal evolution trends of urban development, identifying areas of imbalanced development and prominent issues. Secondly, the K-means machine learning algorithm and nightlight data are used to reconstruct and classify urban regions, and a comparative analysis is conducted with administrative divisions to further identify unreasonable areas in urban spatial distribution and structure. Finally, POI data and the geographical detector method are used to analyze the micro-driving forces in areas of imbalanced development, identifying major limiting factors and solutions. The study found that the gap between urban and rural development in Chengdu is narrowing during the urbanization process, but there is severe differentiation in the second circle of Chengdu, where economic development is accelerating but residents’ happiness is declining. Moreover, analysis based on urban nightlight data and land-use data reveals that the expansion areas on the urban-rural fringe are mainly concentrated in the second circle of Chengdu. Micro-level driving factor analysis found that the western region of the second circle has many but small urban settlements, with a dense road network but scattered functional areas. The eastern region has inefficient and extensive use of construction land. Additionally, the mismatch between student status and household registration has resulted in relatively lagging educational resource development, and high entry barriers have hindered the progress of urbanization, leading to low per capita welfare expenditure. These reasons are the main factors causing the decline in residents’ happiness, and this impact shows significant differences at different temporal and spatial scales. Encouraging innovation in research and development or education can serve as a long-term and effective driving force for promoting sustainable urbanization. This study provides valuable insights for scientifically planning sustainable urban development and promoting the urbanization process.

Quantitative effects of anthropogenic and natural factors on the spatial distribution of heavy metals and bacterial communities in sediments of the Pearl River Delta

Heavy metal (HM) contamination of aquatic ecosystems has increasingly posed threats to ecological balance and health. Analysis of sediments to determine water pollution sources is widely reported, while quantitative research about the effects of environmental factors on the distribution of HMs and relevant microbial communities is limited. Here, we investigated 6 kinds of HM pollution characteristics (As, Cd, Cr, Cu, Pb, and Zn) of the Pearl River Delta (PRD) by collecting surface sediment samples around the main stream and estuary and explored the influencing factors with a geographical detector method (GDM). The shifts in the corresponding microbial community was further analyzed, and the influence mechanism was clearly elucidated using partial least squares path modeling (PLS-PM). Results showed that (i) most HM contents of the sediments greatly exceeded the local background values, wherein, Cd had the highest pollution level; (ii) temperature was considered the dominant natural factor of HM distribution, except for Pb, with an influence degree of 0.68–0.97; (iii) industrial activity is an significant anthropogenic factor for HM distribution, and the electroplating industry was the main source for Cr, Cu and Zn; (iv) the network analysis revealed that As, Cu and Cr were enriched in some HM-tolerant bacteria (mainly Chloroflexi, Acidobacteriota and Proteobacteria), while heavy Cd pollution induced a reduction in sensitive bacterial taxa; (v) PLS-PM demonstrated that human activities largely affect bacterial communities by causing HM pollution. This study could provide guidance for better management of HM pollution control practices, such as specific industrial governance and layout optimization.

Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China

Abstract. The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.1<AOD<0.3) over the ECS, whereas, in contrast, CER increases with increasing AOD (positive S) in the polluted atmosphere (AOD>0.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.

Modeling gully initiation by two codeless nonlinear methods: A case study in a small watershed on the Tibetan Plateau

Land and soil resources are scarce in the Tibetan Plateau, and the region is facing ecological pressure from climate warming and increasing human activities. As a major ecological problem, gully erosion is destroying land and soil resources on the Tibetan Plateau, but related research is limited, and susceptibility areas and influencing factors are unclear. Machine learning methods are often applied to study gully initiation susceptibility, but they require a programming foundation. Therefore, the Redui watershed on the southern Tibetan Plateau with severe gully erosion was selected to evaluate the susceptibility and influencing factors of gully initiation through 12 influencing factors including topography, human activity, and underlying surface conditions, and all 2310 gully headcut sites. Two non-code nonlinear modeling methods, the categorical Regression (CATREG) and geographical detector (Geodetector) methods, were first used in the spatial modeling of gully initiation susceptibility. The results showed that the gully initiation susceptibility of the hillslope around the alluvial fan was highest. The very high susceptibility areas of the CATREG model and Geodetector model account for 18.2% and 16% of the total, respectively. The main influencing factors of gully initiation were elevation, relief, and soil type recognized by CATREG, and elevation, human footprint, and soil type recognized by Geodetector. Elevation is the primary factor controlling downstream susceptibility in both models. The primary factors in the upper and middle reaches are soil type and relief identified by CATREG. Human footprint, soil type, and distance to road are primary factors in the upper and middle reaches identified by Geodetector. The explanatory power of elevation, elevation-relief interaction, Geodetector model and CATREG model were 39%, 54%, 46.4% and 73.8%, respectively, at extremely significant levels (P < 0.001), which means that the influencing factors were well considered and that the methods have great application potential in the future.

The impact of tropical cyclones and water conservancy projects on island's flash floods.

The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted.