Evaluation and driving factors of ecological environment quality in the Tarim River basin based on remote sensing ecological index.

Changes in the ecological environment quality (EEQ) in the main inland Tarim River Basin in China substantially impact the regional development. Indeed, comprehensive ecological environment measures have been implemented in the Tarim River Basin since 2000. In this context, the main objective of the present study was to investigate the spatiotemporal evolution of the EEQ and monitor the effectiveness of ecological restoration measures in the Tarim River Basin over the 2000-2020 period using remote sensing data. First, a Remote Sensing Ecological Index (RSEI) was constructed based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Second, the spatial distributions and factors of the RSEI were analyzed by using Moran's Index and Geodetector. The results indicated that the overall RSEI values for the Tarim River Basin increased from 0.22 in 2000 to 0.25 in 2020. Moreover, the values for areas with poor EEQ decreased from 50.7% to 44.73%, while those with moderate EEQ increased from 11.45% to 16.91%. Therefore, the results demonstrated a slight overall improvement in the EEQ of the study area over the 2000-2020 period. On the other hand, the EEQ in the Tarim River Basin exhibited a significant spatial autocorrelation in the 2000-2020 period, with a relatively stable overall spatial distribution. Areas with high-high aggregation were distributed in the high-elevation mountainous areas in the western, northern, and southern parts of the study area. In contrast, areas with low-low aggregation were observed in the central and eastern low-elevation desert areas. The EEQ in the Tarim River Basin was driven by the interactions of several factors, including the normalized difference vegetation index, land surface moisture, land surface temperature, normalized differential build-up and bare soil index, and elevation. In particular, heat was the main driving factor that severely impacted the EEQ in the study area. Indeed, increase in the heat values could directly enhance meltwater runoff from glaciers in the basin, thereby resulting in short-term improvement in the basin EEQ. Furthermore, rapid urbanization from 2015 to 2020 resulted in a decrease in the average RSEI value of the Tarim River Basin by 0.1 over this period, consequently, the EEQ level decreased slightly. Briefly, the EEQ in the Tarim River Basin showed an overall increasing trend from 2000 to 2020, further demonstrating the effectiveness of a series of implemented ecological restoration measures in the Tarim River Basin over this period.

Spatial variations in health service utilization among migrant population: a perspective on health equity.

As health equity becomes a prioritized goal in global health policy, extensive research has revealed that socio-economic and geographical factors jointly exacerbate barriers to medical service access for both internal and international migrant populations, further accelerating existing health disparities. This study explores healthcare service utilization disparities among internal migrants in China, a population profoundly affected by the country's economic reforms and urbanization since the late 1970s. These transformations have led to significant migratory movements and subsequent healthcare challenges for these populations. Leveraging data from the 2017 China Migrant Dynamic Survey, comprising 169,989 samples across 28 provinces, we introduce a novel metric-the "No Treatment ratio" (NT-ratio). This ratio quantifies the proportion of migrants who, after falling ill, choose not to seek treatment relative to the total migrant population in a given province or region, serving as a critical measure of health risk. Building upon Anderson's Behavioral Model of Health Services Use, we adapted the model to better reflect the unique circumstances of migrant populations. The study employs spatial autocorrelation, hotspot analysis, and geodetector techniques to dissect the multifaceted factors influencing healthcare disparities. Our Findings reveal that the NT-ratio is significantly higher in eastern and northeastern China. Key factors influencing the NT-ratio include age, left-behind experiences, health education, and per capita medical resources. In response to these disparities, we recommend optimizing the distribution of medical resource, strengthening tiered diagnosis and treatment systems, and integrating health, education, and social security resources. These measures aim to improve healthcare utilization among migrant populations and reduce health inequities, aligning with global health objectives.

Spatio-temporal variation and influencing factors of ecol-environment quality in Jiuzhaigou County, Sichuan, China under seismic disturbance.

Accurately monitoring and evaluating changes in ecological environment quality under earthquake disturbances is of great significance for the restoration and protection of regional ecological environment. In view of the "8·8" earthquake in Jiuzhaigou County in 2017, we used high-precision remote sensing image to analyze the vege-tation damage caused by the earthquake, and calculated remote sensing ecological index (RSEI) for the pre-earthquake period, post-earthquake period and 3-year recovery period based on GEE platform to analyze the spatio-temporal variation of ecological environment in Jiuzhaigou County, Sichuan Province. Then, we used geodetector to reveal the influencing factors of spatio-temporal variations in ecological restoration. The results showed that the fractional vegetation cover of Jiuzhaigou County decreased from 0.71 before the earthquake to 0.69 after the earthquake. The area of higher coverage zone decreased by 310.78 km2, while the area of others increased. The mean RESI decreased from 0.50 in the pre-earthquake period to 0.42 in the post-earthquake period, and increased to 0.50 after the 3-year recovery period. The ecological environment quality in the three period was mainly at the good and ave-rage levels, and it was distributed in the central and southern mountains and the eastern river valley. Annual precipitation, elevation, wet and greenness were the main factors controlling ecological quality restoration in Jiuzhaigou County, and the increases in the interaction among these factors would affect the spatial variations of regional ecological environment quality restoration.

Spatiotemporal variation and driving factors of oases in the arid region of Northwest China.

Based on Landsat images and digital elevation model data during 2000-2020, we investigated the spatio-temporal variations and driving forces of oases in the arid region of Northwest China, using an object-oriented method for oasis classification, and employing trends analysis, centroid migration, and geographic detectors methods. The results showed that from 2000 to 2020, the oasis area in the arid region of Northwest China exhibited a linear increasing trend, with a rate of 1079.66 km2·a-1. The growth rate of oasis area, from highest to lowest, was Alxa, Southern Xinjiang, Hexi Corridor and Northern Xinjiang, respectively. Oases in the arid region of Northwest China were mainly distributed in bands or dots along the northern and southern foothills of Tianshan Mountain, Kunlun Mountain, the northern foothills of Qilian Mountain, and the Alxa Plateau. The oasis area in Northern Xinjiang increased while that in the south decreased. Oases in Southern Xinjiang mainly expanded along rivers, with some edges experiencing recession. Expansion and recession of oases in the Hexi Corridor occurred along the rivers in the northwest. Alxa oasis expanded in a scattered pattern with no significant recession areas. The centroids of oases in Northern and Southern Xinjiang generally shifted northeastward, while that in the Hexi Corridor moved northwestward. The centroid of Alxa oasis fluctuated in a north-south direction. The interpretations of agricultural production potential for spatial differentiation of oases in Northern Xinjiang and the Hexi Corridor were the most significant, at 43.6% and 45.3% respectively. Precipitation was the strongest environmental factor affecting Alxa oasis distribution, with an interpretation of 27.6%. Soil types were the strongest factor affecting the distribution of oases in Sou-thern Xinjiang, with an interpretation of 44.9%. The interaction among human activities in oases in the arid region of Northwest China was mainly enhanced by two factors, while the interaction among natural factors was enhanced by both two factors and nonlinear enhancement.

The spatiotemporal evolution characteristics and influencing factors of habitat quality in the typical region of the Lunan Economic Belt: a case study of Donggang District, Rizhao.

Habitat quality is a key indicator for evaluating the biodiversity of a region. This study aims to assess the current status and characteristic changes of habitat quality in Donggang District, Rizhao while analyzing the potential factors influencing the quality of the habitat. The study employed the Integrated Valuation of Ecosystem Services and Tradeoffs Habitat Quality (InVEST-HQ) mode to assess the spatiotemporal variations in habitat quality in the Donggang District from 2008 to 2022. GeoDetector was utilized to investigate the impact of various factors on the spatial differentiation characteristics of habitat quality. The potential correlation between economic development and habitat quality was further explored using the Spearman correlation coefficient and Grayscale Association Analysis. The results reveal that habitat quality in Donggang District is highest in the east and lowest in the west, with significant correlations to land use and vegetation coverage. The study highlights a decline in habitat quality over the period, linked to rapid economic growth and industrial expansion. These insights are crucial for balancing urban biodiversity with economic development.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector.

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%-3.54%) characterized by NO3-N, phytoplankton source (38.43%-50.05%) indicated by chl and PC, agricultural cultivation (16.1%-17.63%) marked by TP and CODMn, industrial wastewater (17.64%-25.1%) primarily associated with TN, and natural source (10.32%-13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

Evaluation and driving force analysis of ecological environment in low mountain and hilly regions based on optimized ecological index.

In low mountain and hilly regions, vegetation cover is higher and plant growth has an accumulative effect, sequestering carbon more strongly. The traditional remote sensing based ecological index (RSEI) lacks the consideration of vegetation productivity, and using it to evaluate ecological environment in low mountain and hilly regions will be biased. In this study, the vegetation productivity was introduced to construct a natural remote sensing based ecological index (NRSEI) that responds to the low mountain and hilly regions, as an example of Gaizhou City, China. Additionally, this study explored the spatiotemporal evolution of ecological environment quality from 2014 to 2020 and quantified the influences of factors. The results show that the first principal component (PC1) increased from 56 to 67% to 65-87% and considered the accumulation process in the ecosystem. NRSEI was more valid. From 2014 to 2020, the quality of the ecological environment generally declined and then increased. The area with "Excellent" increased from 23 to 38%. The quality of ecosystems in the west, northwest, and south deteriorated significantly, a distribution pattern of "high in the center, low in the north and south". Landuse and topographic conditions dominate the impacts on the ecosystem in the context of social, economic and policy influences. The interactions of the factors were two-factor enhancement that together affect the ecological environment. The results contribute to the development of urban conservation policies in low mountain and hilly regions.

Spatio-temporal pattern change of LULC and its response to climate in the Loess Plateau, China.

Exploring land use/cover (LULC) change is essential for the sustainable development of ecologically fragile areas. The main objective of this study was to clarify the characteristics and differences in the spatiotemporal changes of LULC on the Loess Plateau (LP) based on the transfer matrix and land use dynamics and to quantitatively describe the impact of natural factors on LULC using a geodetector. The results indicated that the overall LULC change in the LP was characterized by a decrease in the area of cropland, grassland, and bare land, and an increase in the area of woodland and built-up land. This trend shows a clear phase-change characteristic around 2000. LULC changes were primarily affected by human activities in the southeastern agricultural region. The project of returning farmland to forest and grassland had a great impact on LULC change in the central region. Vegetation was sensitive to temperature and precipitation, and the impact of LULC change was significantly higher than that in the humid region in the northwest arid region. NDVI, PRE, and TEM were determined to be the main contributors to LULC changes in the LP. These results provide a scientific basis for the sustainable development of LP.

Spatio-temporal differentiation characteristics of coal mine safety levels in China based on spatial spillover effects.

In order to solve the problem of "potpourri" of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China.

The stability of ecosystems in high mountain canyon areas is poor, and the interaction between humans and the land is complex, making these ecosystems more vulnerable to destruction. Quantitatively assessing the ecosystem service value (ESV) in high mountain canyon areas and revealing its spatiotemporal evolution patterns and driving factors play a crucial role in the construction of regional ecological barriers and the assurance of ecological security. This study focuses on the Upper Minjiang River as the research area, using the InVEST model and the Equivalent Factor Method to estimate ESV. This combination aims to address the inadequacy of the Equivalent Factor Method in reflecting the variability of ESV across different regions, and the sensitivity of the InVEST model to data changes that results in insufficient accuracy of ESV assessments. By harnessing spatial au-tocorrelation and the geodetector method, we unravel the spatiotemporal evolution characteristics and driving factors of ESV. The results show that: (1) From 2000 to 2020, the ESVs estimated by the two estimations increased by 31.28% and 22.47%, respectively, both indicating that the eco-environment quality of the upper Minjiang River has been continuously improved. (2) When Moran's I was greater than 0.5 (p < 0.05), the spatial clustering of "High-High" and "Low-Low" ESV was obvious. It is clear that the ESV varies geographically. High values are primarily found in the study area's center and southern regions, as well as on both banks of the Minjiang River, whereas low values are more common in the region's northern region. (3) Slope and human activity intensity (HAI) are the principal contributors to the spatial differentiation of the ESV, more than 60% of the interaction types between the two factors were classified as dual-factor enhancement. The synergistic reinforcing effects of HAI, slope, elevation, and temperature collectively shape the shifts in ESV spatial distribution. This study offers a novel evaluative lens on the ESV of the Upper Minjiang River area, supplying a sturdy data support for crafting specific ecological preservation and rejuvenation strategies in the coming years.

Coupling and coordination of food security and agricultural carbon emission efficiency: Changing trends, influencing factors, and different government priority scenarios.

Seeking a balance between food security and carbon mitigation is key to achieving sustainable agricultural development. This study evaluates the coupling coordination degree (CCD) between the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in China from 2010 to 2021 using the coupled coordination model. By adjusting the model coefficients, different government priority scenarios are simulated to explore their impact on CCD. The Geodetector method is employed to identify the influencing factors of CCD, investigate their interactions, and assess the differences in these factors across various government priority settings. The average CCD between FSI and ACEE exhibits a notable upward trend, rising from 0.4583 in 2010 to 0.6595 in 2021. Furthermore, regional disparities are widening, particularly in the major production areas. Catch-up effects exist within regions. Policy simulations showed staged interactions between food security and agricultural carbon efficiency, shifting from food security to balanced production and ecology, then to prioritizing low-carbon production for food security. Adjusting policy priorities can effectively improve coupling coordination in the short term, with increasing impact as priority shifts. CCD is influenced by policy, technology, economy, and society, varying with policy priorities. In the baseline scenario, key factors for CCD include the urban-rural income gap, technological advancement, urbanization, and farmers' education level. When the government prioritizes food security, the impact of narrowing income gaps and agricultural industry agglomeration becomes more pronounced. Conversely, emphasizing carbon emission efficiency enhances the influence of technological advancements and urbanization on CCD. Tailoring agricultural production strategies to local conditions and emphasizing interactive effects among factors is crucial for achieving environmentally friendly and high-quality agricultural development goals.

Spatiotemporal changes and driving factors of alpine land cover in Tianshan world natural heritage sites.

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between -2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making.

Exploring the temporal-spatial characteristics and determinants of high-quality development of city clusters in China.

The complexity, severity, and uncertainty of the international situation have prompted the development of city clusters to focus more on resilience and the building of infrastructures and safeguards. Chinese-style modernization proposes a new realization path for the high-quality development (HQD) of city clusters, based on which an evaluation system for HQD indicators of city clusters is constructed. We also measured the HQD levels of 19 city clusters from 2011 to 2021 and analyzed their spatial differentiation characteristics, agglomeration evolution characteristics, and influencing factors by using kernel density, standard deviation ellipse, Moran's index, geographic detector, and geographically weighted regression. The study revealed that (1) the overall level of HQD of China's city clusters shows a trend of continuous growth, and there is obvious polarization in the high quality of city clusters in different regions. (2) The spatial distribution of HQD in city clusters decreased in the "East, Center and West" direction, but the spatial patterns of "Southeast highlighting" and "Northwest rising" became more obvious. (3) The HQD of city clusters shows obvious spatial agglomeration characteristics and overall presents a spatial pattern of "hot in the east and cold in the west", with the scope of the cold spot area gradually shrinking, and the hot spot area tends to spread outward, with mature city clusters at the core. (4) The influencing factors of HQD in Chinese city clusters are diverse, with financial levels, digital economics, human capital and green innovations having decreasing influence on HQD in city clusters but showing an obvious two-factor enhancement trend, with financial levels being able to effectively stimulate the driving potential of other factors. Financial levels can effectively stimulate the driving potential of other factors. (5) The coefficients of the driving factors affecting the HQD of city clusters vary significantly spatially, with human capital, financial levels and green innovations showing a north‒south hierarchical banded distribution of "high in the south and low in the north", and digital economy shows an east-west hierarchical belt distribution of "high west and low east". Based on the above conclusions, the realization path of accelerating the HQD of China's city clusters is proposed by optimizing the functional division of labor of the city clusters, giving full play to the comparative advantages of the hinterland city clusters, and relying on the high level of the city clusters for opening up.

[Spatial and Temporal Changes in Habitat Quality and Driving Forces in Typical Loess Hill and Gully Areas：A Case Study of the Zuli River Basin].

Loess hills and gully areas are one of the important ecological barriers in China, and the study of the spatial and temporal changes of its habitat quality and its driving force is of great significance to guaranteeing the ecological security of China and safeguarding the national ecological rights and interests. Taking the Zuli River Basin as an example, the spatiotemporal distribution of the remote-sensing ecological index （RSEI） from 2000 to 2020 was systematically investigated using the Google Earth Engine platform and Landsat remote-sensing data. Combined with the coefficient of variation CV, the Theil-Sen Median slope estimation, the Mann-Kendall test of significance, and the Hurst index, the spatial and temporal changes of habitat quality in the study area were analyzed over a period of 20 years, and the effects of six major driving factors on the spatial distribution of RSEI were investigated using the geodetector method. The results of the study showed that： ① From 2000 to 2020, the value of the RSEI showed a downward and then upward trend, with an average annual increase of 0.084 5·ï¼ˆ10 a）-1. ② During the 20-year period, the habitat quality improvement area accounted for 92.06%, of which the significant improvement area accounted for 28.49%, and the improvement area was mainly in Huining County, whereas the habitat degradation area only accounted for 7.82%. The trend of future ecological conditions showed that 74.98% of the areas would show a trend of continuous improvement or future improvement, but there would still be a potential risk of ecological degradation in 23.48% of the areas in the future. ③ Climate factors such as precipitation were the key factors affecting the habitat quality in the Zu Li River Basin； the interaction between factors had a higher explanatory power than that of any single factor on the habitat quality, among which the interaction between the precipitation factor and the elevation factor had the strongest explanatory power. The interaction between the terracing factor and the environmental factor significantly increased the explanatory power of the spatial variance, which indicated that terracing played an important role in improving habitat quality. The results of this study can provide a scientific basis for the management and sustainable development of the ecological environment in the loess hills and gullies.

[Analysis of Temporal and Spatial Carbon Stock Changes and Driving Mechanism in Xinjiang Region by Coupled PLUS-InVEST-Geodector Model].

Based on the goal of "dual-carbon" strategy, it is important to explore the impacts of land use change on carbon stock and the drivers of spatial differentiation of carbon stock in Xinjiang. Here, we predicted the land use types in Xinjiang in 2035 under different scenarios and analyzed the impacts of land use on carbon stock, which is of great theoretical and practical importance for policy formulation, land use structure adjustment, and carbon neutrality target achievement in Xinjiang. The coupled PLUS-InVEST-Geodector model was used to explore the spatial and temporal patterns of carbon stock change under the scenarios of rapid development, natural change, arable land protection, and ecological protection in Xinjiang in 2035 and to quantitatively reveal the attribution of influences on the changes in carbon stock from the perspectives of land use change and the combination of nature-socioeconomic-accessibility. The results showed that： ① From 1990 to 2020, the area of arable land and construction land in Xinjiang increased, and in terms of the transfer direction, it was mainly shifted from unutilized land to grassland. ② On the time scale, the carbon stock in Xinjiang showed the fluctuation of "decrease-increase-decrease," with an overall increasing trend. The transfer of unutilized land to grassland was the main reason for the increase in carbon stock； on the spatial scale, the carbon stock in the Altai Mountains in the north, the Tianshan Mountains in the middle, and the Kunlun Mountains in the south was higher, whereas the carbon stock in the Tarim Basin and the Junggar Basin was lower. ③ In 2035, the carbon stock of the natural development and rapid development scenarios decreased by 27.24 Tg and 71.17 Tg compared with 2020, respectively, and the ecological protection and arable land protection scenarios increased by 492.55 Tg and 46.67 Tg. The ecological protection scenario could significantly increase the carbon stock of the Xinjiang Region compared with that in the other scenarios, and the distribution pattern of the carbon stock in the four scenarios was more or less the same as that in 2020. In addition to land transformation, soil erosion intensity was the main driver of spatial differentiation of carbon stocks in Xinjiang （q value of 0.3501）, followed by net primary productivity of vegetation. The results of multifactor interactions showed that the spatial differentiation of carbon stocks in Xinjiang was the result of the joint action of multiple factors. All the factors had a synergistic enhancement under the interactions. The interaction between soil erosion intensity and the net primary productivity of vegetation was the main driver of the spatial differentiation of carbon stocks in Xinjiang.

[Source and Influencing Factor Analysis of Soil Heavy Metals Based on PMF Model and GeoDetector].

In Lijiang City, as a typical example, 93 soil samples were collected from the study area, and soil pH； organic matter； and heavy metals arsenic （As）, mercury （Hg）, copper （Cu）, zinc （Zn）, lead （Pb）, cadmium （Cd）, and chromium （Cr） were determined. We explored the sources of heavy metals in the study area by means of Positive Definite Matrix Factorization （PMF） modeling and analyzed the impact of influencing factors by combining seven heavy metals with 13 influencing factors in a GeoDetector. The results showed that the mean values of soil heavy metals ω（As）, ω（Hg）, ω（Cu）, ω（Zn）, ω（Pb）, ω（Cd）, and ω（Cr） in the study area were 17.55, 0.19, 86.75, 164.84, 28.95, 0.39, and 167.87 mg·kg-1, respectively, which were greater than the background values of soils in Yunnan Province （except for As and Pb）. Regarding spatial distribution, the high values of Cu and Cr content were mainly concentrated in Yulong Naxi Autonomous County； the high value areas of As, Hg, Pb, and Cd were mainly concentrated in Ninglang Yi Autonomous County； and the high value of Zn content was mainly concentrated in Huaping County. Correlation analysis and PMF modeling revealed that the main sources of heavy metals As and Hg in the study area were industrial sources, Zn was from transportation pollution sources, Cr and Cu were from natural sources, and Cd and Pb were from agricultural sources. Further, the factor detector of the GeoDetector found that soil pH and organic matter （OC） had strong explanatory power for the content of seven heavy metals, and the interaction detector found that the results following the interaction of different influencing factors were nonlinear enhancement or two-factor enhancement, in which the interaction of OC and pH was the dominant factor for the spatial differentiation of heavy metals. This provides an important scientific basis for the protection of the soil environmental health and sustainable development in Lijiang City.

Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links.

The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.

A hierarchical residual correction-based hyperspectral inversion method for soil heavy metals considering spatial heterogeneity.

Promising hyperspectral remote sensing exhibits substantial potential in monitoring soil heavy metal (SHM) contamination. Nevertheless, the local spatial perturbation effects induced by environmental factors introduce considerable variability in SHM distribution. This engenders non-stationary relationship between SHM concentrations and spectral reflectance, posing challenges for accurate inversion of SHM globally. Addressing this gap, a novel Hierarchical Residual Correction-based Hyperspectral Inversion Method (HRCHIM) is proposed for SHM, considering their spatial heterogeneity. Initially, a global model is constructed using ground hyperspectral data to predict SHM concentration, capturing overarching contamination trends. Subsequently, four hierarchical levels, segmented by residual standard deviation (SD) intervals, identify critical environmental factors via Geodetector. These factors inform local residual correction models, refining global model predictions. HRCHIM aims to synergize global trends and local stochasticity to enhance prediction accuracy and interpretation of SHM spatial heterogeneity. Validated through a case study of a Cadmium(Cd)-contaminated mine area, six critical environmental factors were identified, exhibiting significant differences across hierarchical levels. By incorporating hierarchical correction models, HRCHIM demonstrated superior inversion performance compared to other conventional methods, achieving optimal prediction accuracies (Rv2 = 0.94, RMSEv = 0.21, and RPDv = 4.11). This innovative method can facilitate more precise and targeted strategies for preventing and controlling SHM contamination.

Potential ecological risk assessment for trace metal(loid)s in soil surrounding coal gangue heaps based on source-oriented.

Coal is the predominant energy source in China, resulting in coal gangue. We used the absolute principal component score multiple linear regression (APCS-MLR) model and the geo-detector method (GDM) for determining the potential ecological risk, apportioning sources, and identifying driving factors for trace metal(loid)s (TMs) in soil surrounding coal gangue heaps. The average contents for the concerned TMs (Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn) in the soil of interest were 0.48, 0.18, 11.0, 36.0, 129, 99.2, 68.3 and 141 mg/kg, respectively. Potential ecological risk indicated that the soil was primarily within the "Moderate risk" level, and Cd was the primary pollutant. "The number of coal gangue units" and "the distance between the sampling point and the coal gangue heap" were the key driving factors included in the geo-detector method. Combining APCS-MLR model and GDM, the source apportionment was enhanced in terms of accuracy and reliability. Natural, mining, and unrecognized sources contributed 41.1 %, 39.2 %, and 19.7 % of the TM distribution, respectively. Considering the relationship between TMs, their sources, and corresponding potential ecological risks, mining sources (mainly affected by gangue accumulation) presented a primary linkage with Cd, and its contribution to potential ecological risk was the highest, accounting for 58.2 %. Therefore, further research should focus on effectively managing and controlling the potential ecological risks originating from mining sources and Cd.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.