Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland.

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63-71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Advanced Machine Learning Models for Estimating the Distribution of Sea-Surface Particulate Organic Carbon (POC) Concentrations Using Satellite Remote Sensing Data: The Mediterranean as an Example.

Accurate estimation of the distribution of POC in the sea surface is an important issue in understanding the carbon cycle at the basin scale in the ocean. This study explores the best machine learning approach to determine the distribution of POC in the ocean surface layer based on data obtained using satellite remote sensing. In order to estimate and verify the accuracy of this method, it is necessary to obtain a large amount of POC data from field observations, so this study was conducted in the Mediterranean Sea, where such data have been obtained and published. The research initially utilizes the Geographic Detector (GD) method to identify spatial correlations between POC and 47 environmental factors in the region. Four machine learning models of a Bayesian optimized random forest (BRF), a backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Model validation yielded that the BRF exhibited superior performance in estimating sea-surface POC. To build a more accurate tuneRanger random forest (TRRF) model, we introduced the tuneRanger R package for further optimization, resulting in an R2 of 0.868, a mean squared error of 1.119 (mg/m3)2, and a mean absolute error of 1.041 mg/m3. It was employed to estimate the surface POC concentrations in the Mediterranean for May and June 2017. Spatial analysis revealed higher concentrations in the west and north and lower concentrations in the east and south, with higher levels near the coast and lower levels far from the coast. Additionally, we deliberated on the impact of human activities on the surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in this area.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020.

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

Ecological, environmental risks and sources of arsenic and other elements in soils of Tuotuo River region, Qinghai-Tibet Plateau.

Against the backdrop of global warming, the pollutants that were once "temporarily stored" in the permafrost are gradually being released, posing significant impacts on the environment. This has become an internationally focused hot topic. In this study, the contents of 11 elements such as As, Ti, Cd, Cr, Co, Mn, Cu, Pb, Ni, Zn and V in soil samples from 128 sampling points in the freeze-thaw area of the Tuotuo River in the source region of the Yangtze River on the Qinghai-Tibet Plateau were determined to evaluate the possible sources, contamination status and ecological, environmental and health risks of these elements. The mean values of As, Cd, Pb and Zn were higher than the corresponding Tibet soil background values. Among fourteen PTEs, As, Cd and Pb had the highest average values of enrichment factor and pollution index, indicating that freeze-thaw area soils showed moderate enrichment and pollution with As, Cd and Pb. Mean ecological risk factor (ER) of Cd was 109 and other PTEs mean ER values < 40, whereas ecological risk index (RI) values of all PTEs ranged from 59.5 to 880 and mean RI values was 152, indicating moderate ecological risk in study area. Explanatory power q value of total S (TS) content was 0.217 by GeogDetector, indicating TS was the most significant contributing factor to RI. Correlation analysis and PCA analysis showed that Cr, Cu, Ni, Co, Mn, Ti, V were mainly originated from natural sources, Cd, Pb and Zn from traffic activity, As from long-distance migration-freeze-thaw.

Spatial heterogeneity of socio-economic determinants of typhoid/paratyphoid fever in one province in central China from 2015 to 2019.

BACKGROUND: Typhoid fever and paratyphoid fever are one of the most criticial public health issues worldwide, especially in developing countries. The incidence of this disease may be closely related to socio-economic factors, but there is a lack of research on the spatial level of relevant determinants of typhoid fever and paratyphoid fever. METHODS: In this study, we took Hunan Province in central China as an example and collected the data on typhoid and paratyphoid incidence and socio-economic factors in 2015-2019. Firstly spatial mapping was made on the disease prevalence, and again using geographical probe model to explore the critical influencing factors of typhoid and paratyphoid, finally employing MGWR model to analysis the spatial heterogeneity of these factors. RESULTS: The results showed that the incidence of typhoid and paratyphoid fever was seasonal and periodic and frequently occurred in summer. In the case of total typhoid and paratyphoid fever, Yongzhou was the most popular, followed by Xiangxi Tujia and Miao Autonomous Prefecture, Huaihua and Chenzhou generally focused on the south and west. And Yueyang, Changde and Loudi had a slight increase trend year by year from 2015 to 2019. Moreover, the significant effects on the incidence of typhoid and paratyphoid fever from strong to weak were as follows: gender ratio(q = 0.4589), students in ordinary institutions of higher learning(q = 0.2040), per capita disposable income of all residents(q = 0.1777), number of foreign tourists received(q = 0.1697), per capita GDP(q = 0.1589), and the P values for these factors were less than 0.001. According to the MGWR model, gender ratio, per capita disposable income of all residents and Number of foreign tourists received had a positive effect on the incidence of typhoid and paratyphoid fever. In contrast, students in ordinary institutions of higher learning had a negative impact, and per capita GDP shows a bipolar change. CONCLUSIONS: The incidence of typhoid and paratyphoid fever in Hunan Province from 2015 to 2019 was a marked seasonality, concentrated in the south and west of Hunan Province. Attention should be paid to the prevention and control of critical periods and concentrated areas. Different socio-economic factors may show other directions and degrees of action in other prefecture-level cities. To summarize, health education, entry-exit epidemic prevention and control can be strengthened. This study may be beneficial to carry out targeted, hierarchical and focused prevention and control of typhoid fever and paratyphoid fever, and provide scientific reference for related theoretical research.

Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE.

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

Dynamic of land use, landscape, and their impact on ecological quality in the northern sand-prevention belt of China.

Changes in land use and landscapes have a direct impact on the regional eco-environment. It is of great importance to understand the change pattern of land use, landscapes, and their mechanism on the ecological quality, especially ecologically fragile areas. The northern sand-prevention belt (NSPB) is an important ecologically fragile area in China, which has a large influence on the ecological security of the entire country. Based on the land use data of the NSPB in 2000, 2010, and 2018, we studied the spatio-temporal characteristics of land-use change and change in landscape patterns. The ecological quality represented by the remote sensing-based desertification index (RSDI) was calculated using satellite images. The effects of land use and landscape patterns on RSDI were analyzed by geographic detector and geographically weighted regression. Important results include the following: (1) Land-use change in the study area was high during 2000-2010 but slower in 2010-2018. Grassland was the largest land-use type in the NSPB, and varied greatly in terms of total change and spatial location. The major change was the conversion between dense and moderate grass, with 64,860 km2 of dense grass turning into moderate grass, and 48,505 km2 changing the other way. (2) Among the four landscape metrics, patch density, area-weighted mean fractal dimension, and edge density increased, whereas the aggregation index decreased, which indicated that the landscape was developing towards heterogeneity, fragmentation, complexity, and aggregation. Spatially, the landscape metrics presented a strip distribution in the east of the NSPB. (3) The effects of various land-use types on ecological quality, from high to low, were unused land, woodland, dense grass, cropland, moderate grass, built-up land, sparse grass, and waterbody. The areas where the ecological quality was greatly affected by the landscape patterns were concentrated in the agro-pastoral ecotone and the forest-steppe ecotone. The results of this study reveal the trends of land use and landscape patterns in the NSPB over 18 years and can help to understand their mechanism on ecological quality, which is of significance for the management of this area.

Global pattern and drivers of water scarcity research: a combined bibliometric and geographic detector study.

Water scarcity, which refers to a deficit of freshwater resources availability in meeting anthropogenic and environmental water needs, is nowadays a growing concern in many countries around the world. Because water scarcity is often poor management induced, research is critical to advance knowledge and provide technical and policy support for water scarcity adaptation and solutions. Here, we address global water scarcity research pattern and underlying drivers, using the bibliometric analysis combined with geographic detector. The results indicate that water scarcity research exhibits great temporal and spatial variations. Predominant factors that control the numbers of water scarcity publications are gross domestic products (GDP) and population, which altogether explain 30-52% of the variance of the number of publications in different countries. Water scarcity research is biased in a few populated and affluent countries. Other factors, including physical water scarcity, research and development expenditure, and governance indicators can also be linked to water scarcity research. Keywords mining reveals that hotspots of research domains on causes, approaches, types, and effects of water scarcity show continental difference. The results have policy implications for guiding future water scarcity research. Research in developing countries suffering from physical and economic water scarcity should be enhanced to improve adaptive capacity and reduce vulnerability to water scarcity.

[Analysis of Spatial Distribution of Ecosystem Services and Driving Factors in Northeast China].

Northeast China is an important ecological barrier in China, and an in-depth understanding of the spatial distribution in ecosystem services （ESs）, and the driving factors is crucial for realizing the subsequent management and protection of ESs. In the study, we quantitatively assessed the characteristics of spatial distribution in ESs in Northeastern China using the InVEST, RWEQ, and RUSLE models and identified the driving factors of ESs spatial distribution in conjunction with the geodetector based on meteorological data, remote sensing data, and socio-economic data. The results showed that the spatial distribution of ESs in Northeast China had obvious spatial heterogeneity. The high values of habitat quality （HQ）, carbon sequestration （CS） services, and soil conservation （SC） services were mainly distributed in the northern part of the four eastern leagues of the Inner Mongolia Autonomous Region, the northern part of Heilongjiang Province, and the eastern part of Northeast China, which were high in fraction vegetation cover, and low values were mainly found in southwestern and eastern Heilongjiang Province, western Jilin Province, and western Liaoning Province. The high values of the water yield （WY） service and wind prevention and sand fixation （WPSF） service were distributed in the east of the Inner Mongolia Autonomous Region and the east of Liaoning Province. The high values of WY services and WPSF services were distributed in the eastern part of Northeast China and the four eastern provinces of the Inner Mongolia Autonomous Region. According to the geodetector results, slope had the strongest explanatory power for the spatial distribution of SC services with a q-value of 0.31, land use/cover change had the strongest explanatory power for the spatial distribution of HQ and CS services with q-values of 0.64 and 0.52, respectively, and fraction vegetation coverage and annual precipitation had the strongest explanatory power for the spatial distribution of WPSF and WY services with q-values of 0.24 and 0.64, respectively, and there were interactions among all the driving factors. The spatial distribution of ESs in Northeast China was mainly influenced by natural factors. The results will provide a scientific basis for subsequent management and enhancement of ESs in Northeast China.

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors.

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000-2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

[Spatial-Temporal Evolution and Prediction of Carbon Storage in Jiuquan City Ecosystem Based on PLUS-InVEST Model].

Based on the background of carbon peaking and carbon neutrality goal strategies, it is important to explore the impact of land use change on carbon storage and the drivers of spatial variation in carbon storage in the Northwest Arid Zone, which is vital to improve the carbon sink increment of the regional ecosystem and promote the regional carbon breakeven. The arid region of northwest China is an extremely fragile natural ecology, and with the rapid advancement of new urbanization, the rate of land use change has accelerated significantly, which has a certain impact on the carbon storage and fixation capacity of ecosystems. The PLUS-InVEST model was used to simulate the spatial and temporal evolution characteristics of carbon storage under natural development, intensive development, water resource constraint, and ecological protection scenarios in Jiuquan City in 2035, and the parameter optimal geographic detector model was used to analyze the spatial divergence drivers of carbon storage. The results showed that:① the area of cultivated land, watershed, and construction land in Jiuquan City showed a significant increasing trend from 1990 to 2020, whereas the area of the remaining land use types showed a decreasing trend. ② The carbon storage in Jiuquan City increased from 7 722 808.1 t to 7 784 371 t from 1990 to 2020, and the conversion of grassland into unused land was the main cause of the loss of regional carbon storage, accounting for 85% of the total loss. ③ All four development scenarios in 2035 showed an increasing trend of carbon storage, among which the ecological protection scenario had the most significant increase, with an increment of 76 989.29 t. ④ The degree of land use, population density, GDP density, and NDVI were the main driving factors of the spatial variation in carbon storage in Jiuquan City, among which the degree of land use had the strongest explanatory power (q value of 0.849), and the interaction of natural and anthropogenic factors enhanced the explanatory power of each factor on the spatial variation in carbon storage. The results of the study can provide a scientific basis and decision basis for the integrated ecosystem management and territorial space optimization in Jiuquan City.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM.

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

[Spatiotemporal Interaction Characteristics and Transition Mechanism of Carbon Intensity in China's Transportation Industry].

This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest.

The partial pressure of ocean surface CO2 (pCO2) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO3), potential of hydrogen (pH), phosphate (PO4), at the global ocean scale. Based on measured data from the Global Surface pCO2 (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO2, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27µatm, root mean square error of 15.34µatm, R2 of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO2 distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

[NDVI Changes and Influencing Factors of Different Soil and Water Conservation Zones in Shandong Province].

Exploring the characteristics of vegetation change and its influencing factors is essential to construct an ecological environment. Based on the NDVI dataset from 2000 to 2020, this study analyzed the spatial temporal attributes of NDVI changes in Shandong Province using the Sen trend analysis and the gravity center migration model. Furthermore, the spatial heterogeneity of NDVI and its influencing factors within the whole study area and different soil and water conservation zones were investigated using a Geo-detector model, considering population, hydrological, topographic, soil types, and vegetation types. The results were as follows： ① The NDVI in Shandong Province from 2000 to 2020 showed a fluctuating upward trend with significant seasonal characteristics that varied from different zones. The annual NDVI change showed a trend of single-peak in the Ⅲ-4-2t, Ⅲ-4-1xt, and Ⅲ-5-2w but showed a trend of double-peak in the Ⅲ-5-3fn. ② Regarding the spatial distribution, the NDVI was higher in the west-north and west-south areas and lower in the north and coastal areas. During the 21 years, the primary type of NDVI change was "medium-high coverage → high coverage," especially in the northeastern part of the soil conservation area of the Ⅲ-4-2t, the western part of the Ⅲ-4-1xt, and the ecological maintenance area of the Ⅲ-5-2w. Overall, 61.47% of the area had a positive trend of NDVI change with the gravity center of high coverage mitigating to the northeast, and the ecological environment was improved. ③ Soil types and population density were the dominant factors affecting NDVI in Shandong Province, with q values of 0.174 and 0.130, respectively. The chief factor in the Ⅲ-5-3fn, Ⅲ-4-2t, and Ⅲ-4-1xt was population density, with q values higher than 0.22, and the dominant factors in the Ⅲ-5-2w were soil types and vegetation types, with q values of 0.326 and 0.227, respectively. The interaction of the two factors enhanced the influence of the single factor, and the relationship between the influencing factors showed two-factor enhancement and nonlinear enhancement. The q-value of population density ∩ relative humidity was the highest, with a value of 0.257 in the Ⅲ-5-3fn. The q-value of population density ∩ soil types was the highest in the Ⅲ-4-2t and Ⅲ-4-1xt, reaching 0.297 and 0.378, respectively. The q-value of soil types ∩ vegetation types was the highest, with a value of 0.444 in the Ⅲ-5-2w. The results are expected to provide valuable references for improving the ecological environment of Shandong Province and lay a scientific foundation to make different conservation strategies for the individual soil and water conservation zones.

Coupling coordination between digital village construction and agricultural carbon emissions in China.

Exploring the coupling coordination degree (CCD) between digital village construction (DVC) and agricultural carbon emissions (ACE) is crucial for promoting village revitalization and sustainable agricultural development. Analyzing data from 30 provinces in China in 2011-2020, this paper employs the CCD model, the Dagum Gini coefficient, and the geographic detector for in-depth analysis. The results show that the overall level of CCD gradually increases over time, but the national CCD still remains in a state of "low coordination," and there are apparent spatial differences in the CCD among provinces. In addition, the overall difference in CCD shows a decreasing trend, and the contribution of inter-regional differences has gradually become the most critical source of CCD's regional difference. Finally, the spatial differences of CCD are the result of two-factor interaction, among which the innovation level is the most core driving factor. The above findings provide important implications for China and other developing countries to fully leverage the interaction between village digitalization and agricultural decarbonization to promote sustainable agricultural development.

[Spatio-temporal Evolution and Driving Factors of Ecological Environment Quality in the Huaihe River Basin Based on RSEI].

Clarifying the spatio-temporal evolution of the ecological environment quality of a watershed and its response to the natural environment and human factors are crucial for policy implementation in the ecological environment of the watershed. Using the Google earth engine（GEE） to establish a remote sensing ecological index （RSEI）, the spatio-temporal changes in the ecological environment quality of the Huaihe River Basin from 2002 to 2022 were evaluated combined with trend analysis, variation coefficient, and Hurst index. The main driving factors of spatial differentiation of RSEI were explored using the geographic detector. The results showed that： ① In the past 21 years, RSEI of the Huaihe River Basin had generally improved, but it showed a gradual upward-downward trend. Overall, the area of poor and less poor grades decreased, the area of medium grades increased, and the area of good and excellent grades increased. The improved area accounted for 55.93%, and the degraded area accounted for 22.01%. ② In terms of spatial distribution, RSEI gradually deteriorated from east to west （except in the northwest and southwest marginal mountainous areas）. The stability was better in the east and worse in the western and central areas. In the future, the ecological quality change in the basin was prone to be anti-sustainable and mainly improved. ③ Factor detection results showed that the spatial differentiation of RSEI in the basin was mainly driven by vegetation factors, followed by altitude. The interaction between two factors enhanced the driving force for RSEI spatial differentiation, in which the interaction between vegetation factor and elevation had the strongest driving force for RSEI spatial differentiation, reaching 86.3%.

Spatial analysis of the prevalence of abdominal obesity in middle-aged and older adult people in China: exploring the relationship with meteorological factors based on gender differences.

Background: In recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies. Methods: This study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran's I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective. Results: The spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence. Conclusion: Meteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.

Study on the ecological environment quality and its driving factors of the spatial transformation of production-living-ecological space in Baishan City.

In order to strengthen the overall planning and coordination of urban construction and ecological space in Baishan City, we should formulate a scientific land type planning of Production-Living-Ecological Space (PLES). In this paper, land use dynamic attitude model, land use transfer matrix, land use function center of gravity transfer model, eco-environmental quality index(EEQI) and geographic detector model are used to discuss the spatio-temporal evolution of PLES in Baishan City from 2000 to 2020. Spatio-temporal evolution of EEQI and its influencing factors. The results show that: (1) During the study period, Baishan City showed the characteristics that the production space first increased and then decreased, the ecological space decreased, and the living space continued to increase. Among them, the ecological space is the dominant space of Baishan City, covering an area of more than 80%. From the perspective of the transformation of PLES, from 2000 to 2020, Baishan City is mainly characterized by the transformation of ecological space into production space and living space. In the second type of space, green ecological space, forestry ecological space and other ecological space have decreased, while other types of space have increased in varying degrees. (2) During the study period, the overall EEQI of Baishan City remained in good condition, and the ecological environment quality(EEQ) of the three periods were 0.6571, 0.6412 and 0.6562 respectively. The higher EEQI is distributed in Changbai County and Linjiang City, while the areas with lower EEQI are concentrated in the north-central part of Hunjiang City, the middle part of Jiangyuan District and the northwest of Fusong County. (3) According to the analysis of the influencing factors of EEQ, the influence of the factors of spatial differentiation of EEQI in Baishan City changed significantly from 2000 to 2020, and the average annual rainfall was the core factor affecting the spatial differentiation of EEQ in Baishan City, the second is the urbanization rate and the distance from the county government, and the interaction between the average annual rainfall and the distance from the county government has a strong influence on the spatial differentiation of EEQ in Baishan City. This study reveals the evolution of spatial types and EEQ of PLES in Baishan City, and provides a scientific reference for the effective management and utilization of land resources in Baishan City.

[Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI].

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.