Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation

In order to determine the distribution area and amount of Artemisia annua Linn. (A. annua) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicated that the amount of A. annua in China was about 700 billion in 2019. A. annua was mainly distributed in the circular coastal belt of Shandong Peninsula, central Hebei, Tianjin, western Liaoning, and along the Yangtze River and in the middle and lower reaches of Jiangsu, Anhui, and the northern Chongqing provinces. The main factors affecting the amount are the precipitation in the wettest and the warmest seasons, the average annual precipitation, and the average temperature in the coldest and the driest seasons. The results show that the amount of A. annua is strongly influenced by precipitation and temperature.

[Ecological Environment Assessment and Driving Mechanism Analysis of Nagqu and Amdo Sections of Qinghai-Xizang Highway Based on Improved Remote Sensing Ecological Index]

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

[Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020]

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)^-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

[Identifying Driving Factors and Their Interacting Effects on Sources of Heavy Metal in Farmland Soils with Geodetector and Multi-source Data]

The identification of heavy metal sources in farmland soils is essential for the rational health condition management and sustainable development of soil. Using source resolution results(source component spectrum and source contribution)of a positive matrix factorization(PMF)model, historical survey data, and time-series remote sensing data, integrating a geodetector(GD), an optimal parameters-based geographical detector(OPGD), a spatial association detector(SPADE), and an interactive detector for spatial associations(IDSA)model, this study explored the modifiable areal unit problem(MAUP) of spatial heterogeneity of soil heavy metal sources and identified the driving factors and their interacting effects on the spatial heterogeneity of soil heavy metal sources in categorical and continuous variables, respectively. The results showed that the spatial heterogeneity of soil heavy metal sources at small and medium scales was affected by the spatial scale, and the optional spatial unit was 0.08 km² for detecting spatial heterogeneity of soil heavy metal sources in the study region. Considering spatial correlation and discretization level, the combination of the quantile method and discretization parameters with an interruption number of 10 could be implied to reduce the partitioning effects on continuous variables in the detection of spatial heterogeneity of soil heavy metal sources. Within categorical variables, strata(PD 0.12-0.48) controlled the spatial heterogeneity of soil heavy metal sources, the interaction between strata and watersheds explained 27.28%-60.61% of each source, and the high-risk areas of each source were distributed in the lower sinian system, upper cretaceous in strata, mining land in land use, and haplic acrisols in soil types. Within continuous variables, population (PSD 0.40-0.82) controlled the spatial variation in soil heavy metal sources, and the explanatory power of spatial combinations of continuous variables for each source ranged from 61.77% to 78.46%. The high-risk areas of each source were distributed in evapotranspiration (41.2-43 kg·m^-2), distance from the river (315-398 m), enhanced vegetation index (0.796-0.995), and distance from the river (499-605 m). The results of this study provide a reference for the research of the drivers of heavy metal sources and their interactions in arable soils and provide an important scientific basis for the management of arable soil and its sustainable development in karst areas.

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.

Evaluation of the Protection Effectiveness of Natural Protected Areas on the Qinghai-Tibet Plateau Based on Ecosystem Services

Evaluating the protection effectiveness of natural protected areas is an important step in successful management. Adopting 330 natural protected areas on the Qinghai-Tibet Plateau as research subjects, the regional dominant ecosystem service function was selected, and various temporal and spatial analysis methods were employed to analyze the evolution characteristics and influencing factors of ecosystem service patterns from 2000 to 2020. Our results indicated that (1) the water conservation function stabilized after fluctuation and decline, the soil conservation function fluctuated upward, and the windbreak and sand fixation function exhibited an increase after a decreasing fluctuation. (2) The protection effectiveness of25 protected areas significantly improved, that of 151 protected areas improved, that of 84 protected areas stabilized, that of 56 protected areas worsened, and that of 14 protected areas significantly worsened. (3) The top three influencing factors in descending order were precipitation change > altitude > mining area density. (4) Remarkable protection results were achieved in national protected areas, established management institutions, earlier established areas (before 2000), and areas exhibiting alow built-up area density (<0.75%) and low mining density (<1%). Our study provides technical support for the construction and management of protected areas and improvement in ecosystem service functions on the Qinghai-Tibet Plateau.

Spatiotemporal Dynamics of Ecological Vulnerability and Its Influencing Factors in Shenyang City of China: Based on SRP Model

For Shenyang, the central city of Northeast China, its municipal-level Territorial Spatial Planning is of great significance to the whole of Northeast China. Territorial Spatial Planning is an essential carrier of China's ecological civilization construction. The demarcation of "three districts and three lines" defines the scope of ecological protection areas, which is of guiding significance to the future development of ecological civilization construction. The regional ecological vulnerability assessment can provide reference for ecological pattern planning and the demarcation of ecological red lines in "three districts and three lines". In order to explore the spatial distribution pattern of ecological vulnerability in Shenyang, predict the development trend of ecological vulnerability in the future and guide the construction of ecological civilization in Shenyang and provide certain basis for Shenyang's Territorial Spatial Planning and the delineation of "three districts and three lines". This paper based on the "sensitivity-resilience-pressure" model selected 13 indexes, to evaluate the ecological vulnerability of Shenyang from 2010 to 2020. Furthermore, the spatial distribution characteristics and influencing factors of ecological vulnerability in Shenyang are summarized using spatial autocorrelation analysis and geographic detector model, and the future development trend of ecological vulnerability in Shenyang in 2025 is predicted by using CA-Markov model. The results show that: (1) In 2010, 2015 and 2020, the total area of slightly vulnerable areas in Shenyang was large, and the ecological vulnerability showed a gradually vulnerable spatial change trend from south to north and from west to east. (2) The results of geographical detectors show that normalized difference vegetation index, economic density and nighttime light intensity are the main driving factors of ecological vulnerability in Shenyang. (3) The forecast result of CA-Markov model is reliable. In 2025, the ecological vulnerability of Shenyang will be mainly light and extreme vulnerability areas, and the areas of light and extreme vulnerability areas will increase in 2025. The research results can provide some reference for the delineation of "three districts and three lines" and ecological protection in Shenyang's Territorial Spatial Planning, and have certain significance for promoting regional sustainable development and balancing ecological protection and economic development.

[Soil Heavy Metal Content, Pollution, and Influencing Factors in Typical Farming Area of Sichuan Basin]

In order to identify heavy metal contents, the pollution characteristics and influencing factors of soil in typical farming areas in the Sichuan basin were analyzed, with Jiangjin District of Chongqing City chosen as the study area. Two hundred and forty-seven topsoil samples were collected and analyzed using the Nemerow index (NPI), geographical information system (GIS), and geodetector method. The results demonstrated that: 1 the arithmetic means of Cd, Cu, and Zn in the topsoil were 1.22, 1.10, and 1.98 times that of the soil background values in western Chongqing, respectively. 2 The high-value areas of the six heavy metals were mainly distributed in the northern, western, and central Jiangjin district, whereas the low-value areas were distributed in the eastern and southern Jiangjin district. 3 The NPI showed that the polluted sample points accounted for 22.1% of the total sample points, indicating that the overall soil pollution was mainly safety and vigilance in general. The high value of NPI was distributed in Dingshan street in the northern Jiangjin district. 4 The explanatory power of stratum on the distribution of heavy metal contents in the topsoil was the strongest, followed by that of the topographic factor. The interaction effect of the stratum and topographic factors on the distribution of heavy metal content in soil was the strongest. The results showed that the stratum and topographic factors were the key factors affecting the distribution of soil heavy metal contents in the study area.

Dynamic Evolutionary Analysis of Land Use/Cover and Ecosystem Service Values on Hainan Island

This study of Hainan Island, based on three periods of land use/cover data from 2008, 2013, and 2017, uses the intensity analysis model and landscape pattern index to portray the dynamic changes of land use on the island and a quantitative analysis of the spatial and temporal evolutionary characteristics of ecosystem service values (ESV) based on the equivalent factor method. At the same time, the response of ESV to landscape pattern changes is explored. The results indicate: (1) From 2008 to 2017, the cultivated land in the coastal areas around Hainan Island continued to expand, which squeezed out forest land and reduced its area. The growth of built-up areas in Haikou City and Sanya City was more dramatic. (2) A weakening trend in the intensity of land use on Hainan Island during the study period. There were significant changes in cultivated land, grassland, and bare land, with forest land, grassland, and water bodies transformed into cultivated land. Built-up areas increased mainly through the occupation of cultivated land, grassland, and water bodies. (3) The fragmentation of landscape patches and the diversity of landscapes on Hainan Island increased, with the distribution of landscape types tending to be balanced. (4) From 2008 to 2017, the overall ESV of the island showed an initial decrease before increasing; the main spatial distribution characteristic of the ESV was "high in the central and low in the surroundings". (5) The mean patch area, the Shannon diversity index, and the largest patch index showed clear positive correlations to ESV.

Spatial Variation and Its Local Influencing Factors of Intangible Cultural Heritage Development along the Grand Canal in China

Understanding the spatial variation of intangible cultural heritage (ICH) is essential for protecting and utilizing heritage resources but has rarely been investigated along the Grand Canal in China. Initially, we analyzed the spatial variation of ICH with different categories using GIS spatial analysis and other technologies. Subsequently, we used the geodetector statistical method to explore local factors influencing ICH concentrations in various cities along the Grand Canal. The results show that the distribution of ICH resources in different categories was unbalanced among focal cities, mainly concentrated in the northern and southern ends of the Grand Canal. Although socioeconomic factors have important impacts on the spatial distribution of ICH, the local geographic environments remain important in forming and developing ICH resources. This study provides an important reference for ICH resource systematic regeneration and utilization plans along the Grand Canal.

[Spatial-temporal differentiation and driving factors of vegetation coverage in Ningxia Helan Mountain based on geodetector]

Monitoring the regional changes in vegetation coverage and analyzing its driving factors are beneficial to realizing the sustainable development of ecological environment. Based on Landsat 5/8 remote sensing images from 1989 to 2021, vegetation coverage of Helan Mountain in Ningxia was estimated by pixel dichotomy model. In addition, the influence of 10 factors, including environmental factors and human factors, on the spatial-temporal variations of vegetation coverage was quantified by geodetector. The results showed that average vegetation coverage was 35.8% in the study area from 1989 to 2021. On the temporal scale, it showed an increasing trend, with an average increasing rate of 0.043·(10 a)^-1. On the spatial scale, vegetation coverage presented a distribution characteristic of decreasing from southwest to northeast. 58.1% of vegetation coverage in the study area would continue to improve in the future, but 30.7% of vegetation would have the potential risk of degradation. Precipitation was the dominant environmental factor driving the distribution of vegetation. Compared with single factor, the interaction between environmental factors and human factors had a stronger impact on vegetation coverage, while the interaction between precipitation and other factors played a leading role.

Spatial Differentiation of Digital Rural Development and Influencing Factors in the Yellow River Basin, China

The new development mode represented by the digital economy has provided new ideas for sustainable rural development. To comprehensively understand the status of digital rural development and propose scientific measures of rural revitalization in the Yellow River Basin (YRB), this study used counties as the research unit and data from 2020 to analyze the spatial differentiation characteristics and influencing factors by employing the Theil index, spatial autocorrelation analysis, and a geodetector model. The results showed that the digital rural development index in the YRB is slightly higher than it is in China overall, but the sub-index for the digital economy is lagging. The levels of digital rural development in the different reaches were lower reaches > middle reaches > upper reaches. Additionally, municipal districts and county-level cities have higher statuses than t general counties. Moreover, the decomposition of the Theil index shows that the intra-group differences in the upper reaches and general counties are the most important cause of the total differences. Moreover, the levels of digital rural development demonstrate spatial differences, with high and low levels in the east and west, respectively. An obvious reliable spatial correlation exists, and the spatial agglomeration featured with a similar level is significant. Finally, the influencing factors of spatial heterogeneity of digital rural development in the YRB and different reaches were different, with government expenditure being the main leading factor in the YRB and its upper reaches, while educational attainment and industrial structure are the leading factors in the middle reaches and lower reaches, respectively. The explanatory power of the interactions between the factors far exceeds that of a single factor, as shown through double-factor and nonlinear enhancement. This study provides a scientific reference for facilitating more targeted policy measures to achieving the goal of digital China and rural revitalization.

[Detection of Influencing Factors of Spatial Variability of Soil Respiration in Pangquangou Nature Reserve]

Soil respiration is an important process in maintaining global carbon balance. Taking the Pangquangou Nature Reserve as the research area, based on the field measurement of soil respiration (R_s) data combined with altitude (ELE), soil temperature (T), soil moisture (SWC), normalized vegetation index (NDVI), slope (slope), soil total carbon (C), total nitrogen (N), and soil bulk density (BD), we analyzed the main driving forces and interactions of R_s spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of R_s and its influencing factors in the study area was moderate. The R_s was significantly positively correlated with NDVI, T, and N (P<0.01) and negatively with ELE, slope, and SWC (P<0.01). The R_s was significantly correlated with BD(P<0.05) but not with C(P>0.05). ② The multivariate linear model composed of NDVI and T explained 64.3% of R_s spatial variation. ③ ELE, T, and NDVI were the dominant driving forces of R_s spatial differentiation in the study area, which could explain 64%, 59%, and 48% of the spatial variability. ④ The interaction of the two factors enhanced the explanatory power of R_s spatial differentiation, and the maximum interaction factors were ELE∩BD (q=0.73), and T∩slope (q=0.74), respectively. Therefore, in the process of R_s estimation, combined with topographical and environmental conditions, the interaction between multiple factors should be considered.

[Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Henan Province from 2015 to 2019]

PM_2.5 is the main component of haze, and Henan Province has become one of the key areas of PM_2.5 pollution control. Based on the PM_2.5 concentration data of Henan Province from 2015 to 2019, spatial autocorrelation, spatial hot spot detection, and other methods were used to analyze its temporal and spatial characteristics, and the geodetector method was introduced to analyze the interpretation strength of meteorological factors, air quality factors, and social factors on PM_2.5 concentration. The results showed that:from 2015 to 2019, the concentration of PM_2.5 in Henan Province showed an overall downward trend, the days of high pollution decreased, the days of low pollution increased, and the high pollution gradually transformed into medium pollution. The concentration of PM_2.5 had obvious characteristics of spatial aggregation. The five-year global spatial autocorrelation index first dropped and then rose, and the spatial hot spots were concentrated in northern Henan (Anyang, Hebi, Xinxiang, and Jiaozuo); the spatial cold spots were concentrated in western Henan (Sanmenxia, Luoyang, and Nanyang). The shift in space center of gravity showed a trend of going north. Single-factor detection showed that among the nine influencing factors, land use type (0.511), precipitation (0.312), and NO₂(0.277) were the most obvious factors affecting PM_2.5 concentration, and the other factors were PM₁₀(0.255), temperature (0.209), wind speed (0.183), O₃(0.121), GDP(0.073), and population (0.046). Interaction detection showed that the combined effect of multiple factors was more significant than that of single factors. These results can provide theoretical support for the control of air pollution in Henan Province.

[Geographical Detection of Spatial Heterogeneity and Drivers of PM2.5 in the Yangtze River Economic Belt]

Based on ground monitoring data, we explored the spatiotemporal characteristics and drivers of PM_2.5 in the Yangtze River Economic Belt (YREB) in 2018 using spatial autocorrelation analysis and geodetector modeling methods. The results showed that:① the PM_2.5 concentration in the YREB posed the obvious characteristics of low values in summer and high values in winter, seasonal variation in spring and autumn, monthly U-shaped variation, and daily pulse variation. The low value area was mainly concentrated in the south bank of the upper reaches, whereas the high value area was located in the north of the middle-lower reaches of the YREB. ② PM_2.5 pollution in the YREB had a stable positive spatial correlation, and the local association pattern showed a significant HH and LL spatial convergence. ③ The spatial correlation of PM_2.5 in the YREB decreased with the increase in geographical distance, and its spatial autocorrelation threshold was approximately 870 km, within which the spatial agglomeration of PM_2.5 pollution was strong. ④ The influences of natural and anthropogenic factors on PM_2.5 had significant spatial differences. Altitude, relief, and population density were the high impact factors of PM_2.5 pollution in the YREB. The interaction of factors had a far greater explanatory power on PM_2.5 pollution than that of single factors. The dominant interaction factor was industrial structure ∩ altitude, which reflected the complexity of the drivers of air pollution in the YREB.

The impact of meteorological factors on COVID‐19 of California and its lag effect

As of March 30, 2021, COVID‐19 has been circulating globally for more than 1 year, posing a huge threat to the safety of human life and property. Understanding the relationship between meteorological factors and the COVID‐19 can provide positive help for the prevention and control of the global epidemic. We take California as the research object, use Geodetector to screen out the meteorological factors with the strongest explanatory power for the epidemic, then use partial correlation analysis to study the correlation between the two, and finally construct a distributed lag non‐linear model (DLNM) to further explore the relationship between the dominant factor and COVID‐19 and its lag effect. It turns out that temperature has a greater impact on COVID‐19 and the two have a significant negative correlation. When the temperature is lower than 50°F, it has a significant promotion effect on the epidemic, and the relative risk (RR) increases approximately exponentially as the temperature decreases. The delayed effect of the cold effect on the epidemic can be as long as 15 days. This study has shown that more attention should be paid to epidemic prevention and control when the temperature is low, and the delay effect of temperature on the spread of the epidemic cannot be ignored.

[Quantitative analysis of total content differentiation and driving factors of phenolic acids in Angelicae Sinensis Radix of Gansu Dao-di herbs from perspective of geographical space]

Dao-di herbs, produced in a specific region and screened through long-term clinical application, is characterized by high stable quality, good efficacy, and high popularity. With favorable climate conditions, Gansu gives birth to the Dao-di herbs Angelicae Sinensis Radix which is widely used in clinical practice, and multiple regions in Gansu, with similar ecological environment produce Angelicae Sinensis Radix. In this study, the spatial correlation and difference of phenolic acid content in Angelicae Sinensis Radix from Dao-di producing areas, emerging producing areas, and emerging planting areas in Gansu were analyzed based on ArcGIS to explore the &quot;quality(chemical type)&quot; characteristics of genuine Angelicae Sinensis Radix. Moreover, spatial distribution law and main driving factors of the total phenolic acid content in Angelicae Sinensis Radix in Gansu were analyzed based on geodetecctor. This study is expected to lay a basis for Dao-di research and production regionalization of Angelicae Sinensis Radix.

Study on Spatial-Temporal Change of Urban Green Space in Yangtze River Economic Belt and Its Driving Mechanism

Urban green space plays an important role in beautifying the environment, improving the quality of life of residents, and promoting sustainable urban development. Rapid urbanization has led to great changes in the spatial structure and layout of urban green space. It is urgent to put forward the sustainable development strategy of green space through the research on the change of urban green space. Based on the geographical spatial differences of urban green space and integrating the factors of economy, society, industry, land use, and the environment, we constructed a research framework of "space-supply-demand" integration of urban green space by GI and geodetector methods, and we conducted an empirical study on the spatial-temporal changes of urban green space and its driving mechanism in prefecture-level cities along the Yangtze River Economic Belt in China. First, the urban green space along the Yangtze River Economic Belt is concentrated in spatial distribution, while uneven development appears in urban greening among the zones. Second, the influence of different factors on urban green space change varies greatly and can be divided into three types: key factors, important factors, and auxiliary factors. The driving mechanism of the spatial distribution of urban green space supply and demand is quite different, but urban population and commercial service facilities land are their key influence factors, having a comprehensive influence on the spatial-temporal changes of urban green space. Third, the factors are classified into three categories of high, medium, and low levels according to the mean of interacting forces; in particular, the factors of per capita GDP, utility land, industrial smoke (dust) emissions, and other factors have a very strong interactive effect with other factors. Fourth, according to the spatial distribution characteristics of urban green space and its driving mechanism, this paper puts forward planning and policy suggestions, providing reference for other areas to deal with the green space change.

Spatial Differentiation and Influencing Factors of Poverty Alleviation Performance Under the Background of Sustainable Development: A Case Study of Contiguous Destitute Areas in Hunan Province, China

Poverty eradication is a realistic requirement for the addressing of the urban-rural development imbalance. It consolidates the achievements of the poverty alleviation, and accelerates the realization of the United Nations Sustainable Development Goals. In research that deals with poverty, qualitative analysis is often used to study the connection between a single influencing factor and poverty reduction, and to solve regional poverty through government measures. However, these studies usually ignore the multidimensional nature of poverty, and the fact that poverty alleviation also needs to be approached from multiple perspectives. By constructing a theoretical framework of poverty alleviation performance from the perspective of sustainable development, this study selects contiguous poverty-stricken areas in the Hunan Province, China as the empirical study area, constructs an evaluation index system from the three dimensions of economic development, infrastructure and people's livelihood security, and selects influencing factors from three aspects of 'population', 'land' and 'industry'. The spatial differentiation characteristics and influencing factors of poverty alleviation performance in poverty-stricken areas were studied by using the methods of entropy weight method and geodetector. The results show: firstly, in the concentrated and contiguous poverty-stricken areas of the Hunan Province, the performance of poverty alleviation in the economic development makes little difference, showing a 'high-medium-low' cross-distribution pattern. The poverty alleviation performance of the infrastructure presents a distribution pattern of 'low in the middle and high on both sides. The poverty alleviation performance of people's livelihood security has significant spatial differentiation characteristics, which all present a reunion distribution. The overall poverty alleviation performance varies greatly, showing a funnel-shaped distribution in space. Secondly, the spatial differentiation of poverty alleviation performance in the concentrated and contiguous poverty-stricken areas of the Hunan Province is the result of the combined effects of multiple factors. 'Population' is the dominant factor affecting the performance of poverty alleviation, 'land' is the basic factor that causes the spatial differentiation of poverty alleviation performance, and 'industry' is the key factor for the improvement of the poverty alleviation ability.

The Link between Landscape Characteristics and Soil Losses Rates over a Range of Spatiotemporal Scales: Hubei Province, China

Controlling soil erosion is beneficial to the conservation of soil resources and ecological restoration. Understanding the spatial distribution characteristics of soil erosion helps find the key areas for soil control projects and optimal scale for investing in a soil and water conservation project at the lowest cost. This study aims to answer the question of how the spatial distribution of soil erosion in Hubei Province changed between 2000 and 2020. Moreover, how do the effects of natural factors and human activities on soil erosion vary over the years? What are the differences in landscape pattern characteristics and the spatial cluster of soil erosion at multiple administrative scales? We simulated the spatial distribution of soil erosion in Hubei province from 2000 to 2020 by the Chinese Soil Loss Equation model at three administrative scales. We investigated the relationship between soil erosion and driving factors by Geodector. We explored the landscape pattern and hotspots of land at different levels of soil erosion by Fragstat and hotspot analysis. The results show that: (1) The average soil erosion rate decreased from 2000 to 2020. Soil erosion is severe in the mountainous areas of western Hubei province, while it is less severe in the central plains. (2) Land-cover type, precipitation, and normalized difference vegetation index are the most influencing factors of soil erosion in 2000–2010, 2015, and 2020, respectively. (3) The aggregation index values at the town scale are higher than those at the city and county scales, while the fractal dimension index values at the town scale are lower, which indicates that soil erosion projects are most efficient when the project unit is ‘town’. (4) At the town scale, if the hotspot area (6.84% of the total area) is treated as the protection target, it can reduce 50.42% of the total soil erosion of Hubei province. Hotspots of soil erosion overlap with high erosion zones, mainly in the northwestern, northeastern, and southwestern parts of Hubei province in 2000, while the hotspots in northwestern Hubei disappear in 2020. In conclusion, land managers in Hubei should optimize the land-use structure, soil and water conservation in slope land, and eco-engineering controls at the town scale.

[Spatial Differentiation of Soil Organic Carbon Density and Influencing Factors in Typical Croplands of China]

Cropland soil organic carbon density (SOCD) is an important indicator for measuring soil fertility and soil quality. To understand the spatial differentiation characteristics of cropland SOCD and its influencing factors across China, a dataset on the cropland SOCD of 19 typical stations during 2005-2015 was collected from the China Ecosystem Research Network. The geodetector method was used to analyze the influencing factors affecting the spatial patterns of cropland SOCD. The results indicated that the mean cropland SOCD ranged from 0.83 kg·m^-2 to 4.97 kg·m^-2 in different stations across China, and was higher in humid monsoon regions than in arid and semi-arid regions. Under different land use patterns, the SOCD of paddy fields was higher than that of other croplands and showed a tendency of significant increase from 2005 to 2015, reaching 0.13 kg·(m²·a)^-1. The soil physical and chemical properties and precipitation were important influencing factors that affected the spatial patterns of cropland SOCD. In particular, the soil alkaline nitrogen content had the greatest impact on the cropland SOCD patterns. Furthermore, the interaction forces between the soil alkaline nitrogen content and latitude, soil type, precipitation, and soil pH were clearly strengthened. The findings can provide an important scientific basis for reducing cropland greenhouse gas emissions and increasing soil carbon sequestration across China.