Unraveling the complex interconnections between food-energy-water nexus sustainability and the supply-demand of related ecosystem services.

Matching the supply and demand of related ecosystem services can be an effective way to realize long-term sustainable management of the food-energy-water nexus (FEW Nexus) in drylands. However, few studies have focused on the matching of supply and demand for ecosystem services associated with advancing the sustainability of FEW-Nexus, there is limited research in this domain, which lacks systematic and quantitative analysis of the relationship between them and FEW Nexus sustainability. Here, this research takes the West Liaohe River Basin in the arid region of China as a case study. Based on a localized FEW Nexus sustainability evaluation index system, the FEW Nexus sustainability and the supply-demand matching characteristics of the corresponding ecosystem services in the West Liaohe River Basin from 2005 to 2015 were assessed. The relationship between them was analyzed quantitatively through the methods of coupling coordination degree and geographical detector. The results showed a synergistic improvement in both FEW Nexus sustainability and the supply-demand situation of combined ecosystem services. The supply of food production and water yield were able to meet their demands adequately from 2005 to 2015, with a strengthening surplus, leading to an overall surplus and gradual improvement in the integrated ecosystem services. This surplus synergistically promoted the process of FEW Nexus sustainability. The results of the geographical detector indicate that the supply-demand ratio of carbon sequestration was the main factor influencing FEW Nexus sustainability. Areas with higher FEW Nexus sustainability tended to have larger deficits in carbon sequestration, which was more evident in areas with high levels of urbanization. Therefore, the key to enhancing FEW Nexus sustainability in the basin is to balance the supply of and demand for carbon sequestration services. Overall, the present study not only provides a basis for strengthening the management of the supply-demand of ecosystem services associated with FEW to achieve regional sustainable development, but also offers insights into how the growing demand for the FEW Nexus is exerting pressure on the balance between supply and demand of related ecosystem services.

Spatial epidemiological characteristics and driving factors of myopia among school-age children based on geographical detector: a national study.

The present study aimed to examine the spatial characteristics of myopia and identify the socioeconomic and environmental factors influencing its prevalence. Myopia prevalence among children of school age of Han ethnicity in China was 56.6% in 2019, with the highest and lowest prevalence's in Shandong (66.8%) and Guizhou (47.3%), respectively. There was a spatial aggregation of myopia prevalence in China. Environmental factors (atmospheric PM2.5 concentration and forest coverage) and socioeconomic factors (gross domestic product per capita, per capita disposable income, hospital beds per thousand people, and Engel coefficient) have significant influences on myopia prevalence. The interaction of each factor on myopia showed nonlinear enhancement. Myopia prevalence among children of school age was spatially clustered, and environmental and socioeconomic conditions are associated with myopia prevalence. Our findings provide novel perspectives for the comprehensive prevention and control of myopia.

Spatial-temporal evolution characteristics of PM2.5 and its driving mechanism: spatially explicit insights from Shanxi Province, China.

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

Regional disparities and influencing factors of high quality medical resources distribution in China.

BACKGROUND: With the gradual increase of residents' income and the continuous improvement of medical security system, people's demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China. METHODS: The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR. RESULTS: The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors. CONCLUSIONS: China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR.

Spatiotemporal evolution of soil water erosion in Ningxia grassland based on the RUSLE-TLSD model.

Accurate assessment of grassland soil erosion before and after grazing exclusion and revealing its driving mechanism are the basis of grassland risk management. In this study, the long-term soil erosion in Ningxia grassland was simulated by integrating and calibrating the transport limited sediment delivery (TLSD) function with the revised universal soil loss equation (RUSLE) model. The differential mechanisms of soil loss were explored using the GeoDetector method, and the relative effects of precipitation changes (PC) and human activities (HA) on grassland soil erosion were investigated using double mass curves. The measured sediment discharges from six hydrological stations verified that the RUSLE-TLSD model could reliably simulate water erosion in Ningxia. From 1988 to 2018, the water erosion rate of grassland in Ningxia ranged from 74.98 to 14.98 t⋅ha-1⋅a-1, showing an overall downward trend. July to September is the period with the highest of water erosion. The slope is the dominant factor influencing the spatial distribution of water erosion. After grazing exclusion, the net water erosion rate in Ningxia grassland and sub-regions decreased significantly. The double mass curves results show that human activities were the main driver of net erosion reduction. The focus of water erosion control in Ningxia is to control soil erosion in different terrains and protect grassland with slopes greater than 10°.

The response of ecosystem vulnerability to climate change and human activities in the Poyang lake city group, China.

Ecosystem vulnerability is an ecological response of the environment to external damage. Studying the influencing factors and spatiotemporal changes of ecosystem vulnerability is helpful to maintain ecological balance. At present, studies on ecosystem vulnerability are relatively homogeneous and rarely integrate climate change and human activities. Based on a habitat-function framework, this study analyzed the response of ecosystem vulnerability on climate change and human activities in the Poyang Lake City Group (PLCG) in 2010, 2015 and 2020. The spatial agglomeration of ecosystem vulnerability has been analyzed by using GeoDa model. The interaction of factors on ecosystem vulnerability have been analyzed by using geographical detector. It can be seen that the ecosystem vulnerability of the PLCG have increased from 2010 to 2020. The impacts of climate change to the ecosystem vulnerability have showed a positive correlation. Meanwhile, the key factors leading to the change of ecological vulnerability are still human activities. This methodology demonstrates a high level of robustness when applied to other research domains. This research is conducive to maintaining the integrity of the ecosystem, realizing the development of man and nature, and promoting the sound and rapid development of economic society.

Comprehensive evaluation of community human settlement resilience and spatial characteristics based on the supply-demand mismatch between health activities and environment: a case study of downtown Shanghai, China.

INTRODUCTION: Under globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment. METHODS: Using data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience. RESULTS: i) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement. CONCLUSION: This paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

Alike but also different: a spatiotemporal analysis of the older populations in Zhejiang and Jilin provinces, China.

According to the 7th National Population Census, China is experiencing rapid growth of its ageing population, with large spatial disparities in the distribution of older folks in different regions. And yet, scant comparative research has been conducted on the two regions of Zhejiang and Jilin in particular, which differ considerably in economic development but witness nearly the same ageing trend. In response, this article compares Zhejiang, an advanced economic province, with Jilin, with its relatively low level of economic development, to explore the ageing issue and analyse the spatial correlation between older populations and socioeconomic factors. Using the spatiotemporal data analysis and geographical detector approaches, we obtain three significant findings: 1. both provinces have maintained steady rates of increase in ageing; 2. the older populations in Zhejiang and Jilin are mostly concentrated in the provincial capitals and nearby cities with reasonably established economies; and 3. the factors, including local fiscal expenditures, beds in hospitals and nursing homes, and coverage of social security, show a highly similar spatial pattern between older populations in Zhejiang and Jilin. The q-values of all the selected socioeconomic factors in Jilin showed a growth trend, indicating that the spatial correlation between these factors and ageing is strengthening year on year, that is, the resources gained from the socioeconomic development of Jilin have shifted steadily toward old-age services. As a consequence, a vicious circle of the slowing down of the economic growth drives away working forces and quickens the pace of population ageing, is present. From a policy perspective, Jilin province is strongly dependent on state-owned enterprises characterised by institutional rigidity, an inflexible market economy and an under-developed private sector, all of which are profoundly influenced by ageing. The consequence is large population outflows of young people. In contrast, the economy of Zhejiang province is partially decoupled from the ageing trend, so the gap in level of development between its counties has been narrowing. The policy implication here is that Zhejiang represents an active private economy that has coped successfully with ageing by attracting young migrants and developing new forms of development, such as the digital economy.

A practical method for identifying key factors in the distribution and formation of heavy metal pollution at a smelting site.

Smelting activities are the main pathway for the anthropogenic release of heavy metals (HMs) into the soil-groundwater environment. It is vital to identify the factors affecting HMs pollution to better prevent and manage soil pollution. The present study conducted a comprehensive investigation of HMs in soil from a large abandoned Zn smelting site. An integrated approach was proposed to classify and quantify the factors affecting HMs pollution in the site. Besides, the quantitative relationship between hydrogeological characteristics, pollution transmission pathways, smelting activities and HMs pollution was established. Results showed that the soils were highly contaminated by HMs with a pollution index trend of As > Zn > Cd > Pb > Hg. In identifying the pollution hotspots, we conclude that the pollution hotspots of Pb, As, Cd, and Hg present a concentrated distribution pattern. Geo-detector method results showed that the dominant driving factors for HMs distribution and accumulation were the potential pollution source and soil permeability. Additionally, the main drivers are variable for different HMs, and the interaction among factors also enhanced soil HMs contamination. Our analysis illustrates how the confounding influences from complex environmental factors can be distilled to identify key factors in pollution formation to guide future remediation strategies.

Study on Spatial and Temporal Characteristics of Surface Albedo at the Northern Edge of the Badain Jaran Desert Based on C + STNLFFM Model.

Obtaining surface albedo data with high spatial and temporal resolution is essential for measuring the factors, effects, and change mechanisms of regional land-atmosphere interactions in deserts. In order to obtain surface albedo data with higher accuracy and better applicability in deserts, we used MODIS and OLI as data sources, and calculated the daily surface albedo data, with a spatial resolution of 30 m, of Guaizi Lake at the northern edge of the Badain Jaran Desert in 2016, using the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM) and topographical correction model (C model). We then compared the results of STNLFFM and C + STNLFFM for fusion accuracy, and for spatial and temporal distribution differences in surface albedo over different underlying surfaces. The results indicated that, compared with STNLFFM surface albedo and MODIS surface albedo, the relative error of C + STNLFFM surface albedo decreased by 2.34% and 3.57%, respectively. C + STNLFFM can improve poor applicability of MODIS in winter, and better responds to the changes in the measured value over a short time range. After the correction of the C model, the spatial difference in surface albedo over different underlying surfaces was enhanced, and the spatial differences in surface albedo between shifting dunes and semi-shifting dunes, fixed dunes and saline-alkali land, and the Gobi and saline-alkali land were significant. C + STNLFFM maintained the spatial and temporal distribution characteristics of STNLFFM surface albedo, but the increase in regional aerosol concentration and thickness caused by frequent dust storms weakened the spatial difference in surface albedo over different underlying surfaces in March, which led to the overcorrection of the C model.

Do socio-economic factors matter? A comprehensive evaluation of tourism eco-efficiency determinants in China based on the Geographical Detector Model.

Constructed on the total-factor analysis framework, this paper develops a comprehensive evaluation system and adopts the Super-SBM model to both analyze and enunciate the characteristics of tourism eco-efficiency in China during 2000-2017. This paper also identifies the determinants associated with spatial differentiation of tourism eco-efficiency by employing a novel geographical technique, namely the Geographical Detector Model. The results indicate that the tourism eco-efficiency exhibits great potential for growth. Besides, pure technical efficiency drives the optimized development of eco-efficiency. Also, there is significant spatial variations in eco-efficiency across different provinces and regions in China. Urbanization contributes to tourism eco-efficiency remarkably, followed by openness, technical level, economic scale, industrial structure, capital effect, environmental regulation, and tourism growth. The relational interrelations of tourism eco-efficiency determinants are the bi-enhancement and the nonlinear-enhancement interactions. The implications of research findings are discussed and may be applied to a multitude of corporate environmental-economic management scenarios.

Land-use transition and its driving forces in a minority mountainous area: a case study from Mao County, Sichuan Province, China.

Land-use change is an important research topic in global environmental change. Analyzing land-use change and its driving factors can aid in the evaluation of the current and the determination of future land-use policies. This study took Mao County, Southwest China, as the study area and used the land-use change and statistical data surveyed in 2009 and 2019. With the help of geographic information system technology, a land-use transfer matrix was used to comprehensively analyze the characteristics of spatiotemporal differentiation of land use, while the driving mechanism was analyzed by constructing the influencing factors using a geographical detector model. The results showed that the change in land use in Mao County was drastic. The increasing land types included orchards, grasslands, built-up lands, and water bodies, whereas the decreasing land types included croplands, forestlands, and unused lands. The main driving factors of land-use transition depended on the type of land-use change. Elevation, distance from the county government, and population were the main driving factors of land-use change. Road density, distance from the river, distance from the town/township government, and gross domestic product also affected land-use change to a certain extent, whereas relief and slope had less impact.

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China.

Hongjiannao groundwater exchange was the largest desert freshwater lake in China (57.25 km2 in 1986). However, it shrank sharply over the past 34a (1986-2019), with the smallest lake area 31.41 km2 in 2015. The objective of this study was to use the Landsat images, ASTER GDEM V2 data, and meteorology and statistics data, in combination with the water balance model to calculate the dynamics of water balance elements, quantify and characterize the interannual variations in lake-groundwater exchanges, and analyze its influencing factors by using the geographical detector. The results showed that in the stable stage (1986-1997), the average rate of the lake area, water level, and lake volume change was -0.26 km2/a, -0.0483 m/a, and -0.0009 km3/a, respectively. Precipitation, river inflow, and groundwater were 0.0203 km3, 0.0485 km3, and 0.0098 km3, which accounts for the whole input were 25.83%, 61.70%, and 12.47%, respectively; evaporation was 0.0786 km3. In the reduction stage (1998-2015), the average rate of the lake area, water level, and lake volume change was -1.21 km2/a, -0.2422 m/a, and -0.0101 km3/a, respectively. Before 2006, precipitation, river inflow, and groundwater were 0.0154 km3, 0.0475 km3, and -0.0025 km3, respectively; from 2006 to 2009, precipitation, river inflow, and groundwater were 0.0143 km3, 0.0334 km3, and 0.0058 km3, respectively; after 2009, precipitation, river inflow, and groundwater were 0.0139 km3, 0.0199 km3, and 0.0085 km3, respectively. Evaporation decreased from 0.0714 to 0.0480 km3 from 1998 to 2015. In the growth stage (2016-2019), the average rate of the lake area, water level, and lake volume change were 1.38 km2/a, 0.27 m/a, and 0.0088 km3/a, respectively. Precipitation, river inflow, and groundwater were 0.0209 km3, 0.0005 km3, and 0.0373 km3, which accounts for the whole input were 46.63%, 52.12%, and 1.25%, respectively; evaporation was 0.0187 km3. Compared with the stable stage, groundwater in the growth stage reduced from 12.47% (0.0098 km3) to only 1.25% (0.0005 km3). From 1998 to 2004, Hongjiannao Lake experienced continuous losing conditions (discharge from the lake to groundwater), with a variable exchange volume of up to -0.01582 km3 in 1999. Through geographical detector analysis, it was found that temperature was the dominant factor from 1988 to 1997, while human factors were the dominant factors from 1998 to 2015.

Exploring the effect of economic and environment factors on PM2.5 concentration: A case study of the Beijing-Tianjin-Hebei region.

Air pollution, especially haze pollution is a serious environment problem that directly affects the sustainable development in China. Identifying the key factors affecting PM2.5 concentration and the interaction mechanism between them through quantitative analysis can greatly help a city devise PM2.5 pollution control strategy. Using the geographical detector model, we quantitative measured 13 cities in the Beijing-Tianjin-Hebei region's social factors and their interaction impacts on PM2.5 concentration in 2016. In the analysis process, factor analysis method is used to separate the factors preliminary. According to the results, the factors mainly divided into two categories, i.e. economic factor and environment factor. R&D ranks top in the studied cities in terms of factor detection results, presenting closely relationship between PM2.5 concentration and R&D. We also find the interaction between any two factors all enhance impact on PM2.5 concentration than any one alone. This study provided a scientific basic and guidance for measure the driving degree of social factors and their interaction effects.

Determining the contributions of climate change and human activities to the vegetation NPP dynamics in the Qinghai-Tibet Plateau, China, from 2000 to 2015.

Reflecting on the change in the global biodiversity pattern, the Tibetan Plateau, considered to be a "natural laboratory" for analyzing environmental change in China and around the world, has suffered profound changes in the vegetation ecosystem. This study introduces the gravity center model and geographical detectors to examine and discuss the spatial-temporal change pattern and the driving mechanism behind vegetation net primary production (NPP) in the Qinghai-Tibet Plateau from the year 2000 to 2015 while also quantitatively classifying the relative roles incorporated in the NPP change process. The study found that (1) from 2000 to 2015, the annual average NPP of the Tibetan Plateau demonstrated a declining trend from southeast to northwest. (2) The gravity center of vegetation NPP on the Qinghai-Tibet Plateau seems to have shifted eastward in the past 16 years, indicating that the level of vegetation NPP in the east depicts a greater increment and growth rate than the west. (3) In the arid regions, temperature and rainfall appear as the dominant factors for vegetation NPP, while slope and aspect parameters have constantly assumed dominancy for the same in the tropical rainforest-monsoon ecological zone in southeastern Tibet. (4) The structure of vegetation NPP exhibits an interaction between human and natural factors, which enhances the influence of single factors. (5) Considering the global ecological change and related human activities, certain differences are observed in the dominant and interaction factors for different study periods and ecological subregions in the Qinghai-Tibet Plateau. The research results could prove conclusive for vegetation ecological protection in the Qinghai-Tibet plateau.

Prevalence and Risk Factors of Comorbidities among Hypertensive Patients in China.

Hypertension is a severe threat to human being's health due to its association with many comorbidities. Many research works have explored hypertension's prevalence and treatment. However, few considered impact of patient's socioeconomic status and geographical disparities. We intended to fulfill that research gap by analyzing the association of the prevalence of hypertension and three important comorbidities with various socioeconomic and geographical factors. We also investigated the prevalence of those comorbidities if the patient has been diagnosed with hypertension. We obtained a large collection of medical records from 29 hospitals across China. We utilized Bayes' Theorem, Pearson's chi-squared test, univariate and multivariate regression methods and geographical detector methods to analyze the association between disease prevalence and risk factors. We first attempted to quantified and analyzed the spatial stratified heterogeneity of the prevalence of hypertension comorbidities by q-statistic using geographical detector methods. We found that the demographic and socioeconomic factors, and hospital class and geographical factors would have an enhanced interactive influence on the prevalence of hypertension comorbidities. Our findings can be leveraged by public health policy makers to allocate medical resources more effectively. Healthcare practitioners can also be benefited by our analysis to offer customized disease prevention for populations with different socioeconomic status.

Historical variability of cotton yield and response to climate and agronomic management in Xinjiang, China.

Cotton is the primary fibre crop in the world with high economic value, and its yield can be affected by climate and agronomic management. Xinjiang, the largest cotton-producing province in China, contributes to approximately 90 % of the national and over 20 % of global production. Earlier studies focused on cotton yield variability and/or drivers on the site scale, for only one to several counties or cities within Xinjiang, in several years before 2000, or provincial production for a short period. It remains poorly understood how and why cotton yields change in Xinjiang. This study analyzes the spatiotemporal variability of cotton yields at the provincial and county (73 counties) levels from 1989 to 2017 using yield statistics, and identifies the dominant climate and agronomic management factors as well as their optimal ranges, historical states, and interaction effects using the geographical detector method (Geodetector). The results show that the Xinjiang cotton yield has increased markedly over the past decades, with the long-term trend outweighing the interannual variability. High yields are mostly found in southern and northwestern Xinjiang. Yield has increased significantly in over 95.6 % of cotton-planting areas, primarily in the west. Nitrogen fertilization is the leading driver of cotton yield changes, mainly impacting long-term trends. The combination of nitrogen fertilization and agricultural mechanization enhances the explanatory power in a bivariate way and is the strongest in the interaction effect between factors. Temperature variability has the greatest influence on detrended yield variability, and the explanatory power is enhanced and the highest when combined with precipitation. In addition, historical states of these factors are generally lower than their optimal ranges, indicating potential for cotton yield enhancement through improved agronomic management practices and in the context of global warming. This study could enhance understanding of cotton Xinjiang yield variability and drivers, and provides scientific guidance for local cotton cultivation.

[Spatial-temporal Evolution and Quantitative Attribution of Habitat Quality in Typical Karst Counties of Guizhou Plateau].

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

[Driving Factors Analysis for Spatial-Temporal Distribution of Organic Pollutants in the Yangtze River Delta Based on the Optimal-scale Geographical Detector].

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

IRSEI-based monitoring of ecological quality and analysis of drivers in the Daling River Basin.

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