Spatial Distribution Patterns Research Articles

Spatial distribution and environmental correlations of Culex pipiens pallens (Diptera: Culicidae)in Haidian district, Beijing.

Culex pipiens pallens Coquillett, 1898(Diptera: Culicidae)was the dominant health threat to mosquito species in Beijing, and it is important to unravel the spatial distribution and environmental correlations of Cx. pipiens pallens in Beijing. 3S technology methods and spatial statistics were used to clarify the distribution pattern. Subsequently, linear and spatial regression were performed to detect the environmental factors linked with the density of Cx. pipiens pallens. The same "middle peak" spatial distribution pattern was observed for Cx. pipiens pallens density at the community, subdistrict, and loop area levels in our study area. In addition, there were various correlated environmental factors at the community and subdistrict scales. At the community scale, the summary values of the Modified Normalized Difference Water Index (MNDWI) in 2 km buffer zone (MNDWI_2K) were negatively correlated, and the summary values of Normalized Difference Built-up Index (NDBI) in 800 m buffer zone (NDBI_800) was positively correlated to the Cx. pipiens pallens density. However, the summary values of Normalized Difference Vegetation Index and Nighttime Light Index significantly affected Cx. pipiens pallens density at the subdistrict scale. Our findings provide insight into the spatial distribution pattern of Cx. pipiens pallens density and its associated environmental risk factors at different spatial scales in the Haidian district of Beijing for the first time. The results could be used to predict the Cx. pipiens pallens density as well as the risk of lymphatic filariasis (LF) infection, which would help implement prevention and control measures to prevent future risks of biting and LF transmission in Beijing.

A Gaussian Function Model of Mesoscale Eddy Temperature Anomalies and Research of Spatial Distribution Characteristics

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

An improved model for discrete element method simulation of spatial gradient distributions of freeze-thaw-induced damage to sandstone

The investigation into the freeze-thaw (F-T) damage holds paramount theoretical significance in comprehending the mechanisms underlying F-T disasters, predicting catastrophes, and devising engineering protection systems. F-T damage in rock evolves progressively from the surface towards the interior, however, current models exhibit conspicuous deficiencies in simulating such progressive damage. Drawing upon experimental findings concerning the evolution of F-T damage in water-saturated rock, an improved discrete element model (DEM) for rock F-T damage is developed. In this model, thermal conduction model and volume expansion theory are integrated to describe the volumetric expansion characteristics during the freezing of internal pore water, thus effectively simulating the initiation and progression characteristics of frost heave cracks. We validated this model through comparisons with theoretical analyses and experiments, exploring the effects of model parameters. Employing moment tensor, the spatial gradient distribution patterns of F-T damage in water-saturated rock from the vantage point of fracture energy was analyzed. The results reveal that at −20 °C, 20 F-T cycles are requisite for the propagation of damage from the outer layer to the center of the Sichuan sandstone samples. The extent of damage in the outer layer exceeds that of the center, with the maximum fracture energy density of the outer layer being approximately 38.7 times that of the center. This model aptly delineates the progressive development of F-T damage characteristics and holds promise for further applications in identifying and delineating F-T damage zones in cold region rock engineering.

Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis

Storage batteries with elevated energy density, superior safety and economic costs continues to escalate. Batteries can pose safety hazards due to internal short circuits, open circuits and other malfunctions during usage, hence real-time surveillance and error diagnosis of the battery’s operational state is imperative. In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern of the physical field such as magnetic field and temperature when the battery is operational is acquired. Furthermore, this manuscript also investigates the diagnostic methodology for defective batteries with internal short circuits and fissures, that is, the operational state of the battery is evaluated and diagnosed by the distribution of the magnetic field surrounding the battery. To substantiate the method’s practical viability, the present study extends its examination to the 18650-battery pack. We obtained the magnetic field images of the normal operation of the battery pack and the failure state of some batteries and analyzed the relationship between the magnetic field distribution characteristics and the performance of the battery pack, providing a new method for the health monitoring and fault diagnosis of the battery pack. This non-contact method incurs no damage to the battery, concurrently exhibiting elevated sensitivity and extremely rapid response time. Meanwhile, it provides an effective means for non-destructive research on the batteries and can be applied to areas such as battery safety screening and non-destructive testing. This research not only helps to facilitate our understanding of the battery’s operating mechanism, but also provides robust support for safe operation and optimal battery design.

A Comparative Analysis of the Effect of Orbital Geometry and Signal Frequency on the Ionospheric Scintillations over a Low Latitude Indian Station: First Results from the 25th Solar Cycle

The equatorial post-sunset ionospheric irregularities induce rapid fluctuations in the phase and amplitude of global navigation satellite system (GNSS) signals which may lead to the loss of lock and can potentially degrade the position accuracy. This study presents a new analysis of L-band scintillation from a low latitude station at Guntur (Geographic 16.44°N, 80.62°E, dip 22.18°), India, for the period of 18 months from August 2021 to January 2023. The observations are categorized either in the medium Earth-orbiting (MEO) or geosynchronous orbiting (GSO) satellites (GSO is considered as a set of the geostationary and inclined geosynchronous satellites) for L1, L2, and L5 signals. The results show a higher occurrence of moderate (0.5 < S4 ≤ 0.8) and strong (S4 > 0.8) scintillations on different signals from the MEO compared to the GSO satellites. Statistically, the average of peak S4 values provides a higher confidence in the severity of scintillations on a given night, which is found to be in-line with the scintillation occurrences. The percentage occurrence of scintillation-affected satellites is found to be higher on L1 compared to other signals, wherein a contrasting higher percentage of affected satellites over GSO than MEO is observed. While a clear demarcation between the L2/L5 signals and L1 is found over the MEO, in the case of GSO, the CCDF over L5 is found to match mostly with the L1 signal. This could possibly originate from the space diversity gain effect known to impact the closely spaced geostationary satellite links. Another major difference of higher slopes and less scatter of S4 values corresponding to L1 versus L2/L5 from the GSO satellite is found compared to mostly non-linear highly scattered relations from the MEO. The distribution of the percentage of scintillation-affected satellites on L1 shows a close match between MEO and GSO in a total number of minutes up to ~60%. However, such a number of minutes corresponding to higher than 60% is found to be larger for GSO. Thus, the results indicate the possibility of homogeneous spatial patterns in a scintillation distribution over a low latitude site, which could originate from the closely spaced GSO links and highlight the role of the number of available satellites with the geometry of the links, being the deciding factors. This helps the ionospheric community to develop inter-GNSS (MEO and GSO) operability models for achieving highly accurate positioning solutions during adverse ionospheric weather conditions.

Mapping urban green space of the 36 major cities in China using ZY-3 satellite images based on a feature robust to low-brightness pixels

Abstract. Urban green spaces (UGS), integral to modern urban life, play a pivotal role in shaping ecological, low-carbon, resilient, and livable cities. Understanding the microstructure and macro patterns of UGS is necessary but limited. In this context, we investigated the spatial patterns in the 36 major cities in China using Ziyuan-3 remote sensing images. We used a method for extracting UGS suitable for complex urban environments, and then selected 5 landscape pattern indices such as percent of landscape and patch density to analyse the spatial pattern of UGS in the 36 cities. The results show that: (1) The overall accuracy, recall, F1 value, and intersection over union of the extraction results of UGS were 94.62%, 94.11%, 94.36%, and 89.33% respectively; (2) Overall, the green space rate of the 36 cities exhibited a spatial distribution pattern of “high in the east and low in the west, low in the north and high in the south”; (3) Variations among cities were weak in terms of patch shape, but significant in patch density. The findings of this study cater to the escalating demands of urban planning and management.

Determination of the temporal–spatial distribution patterns of ancient heritage sites in China and their influencing factors via GIS

In this paper, we analysed the temporal and spatial distributions of ancient archaeological sites in China as well as their influencing factors. Our aim was to reveal the developmental trajectory of Chinese civilisation and to explore the natural and cultural factors affecting human distribution, with the goal of providing insights for the conservation and development of ancient relics. We employed spatial analysis methods using ArcGIS 10.8 software, such as kernel density analysis and trend surface analysis, to analyse 1194 historical ancient sites listed in the National Cultural Relics Protection Units of China. The research findings are as follows: (1) the distribution of ancient sites demonstrates an agglomerative spatial pattern. The nearest neighbour index (R < 1) for sites from various historical periods indicates an agglomerative spatial distribution of ancient sites across historical periods, with the clustering degree being relatively poor for sites from the Wei-Jin, Northern and Southern Dynasties and in the Ming and Qing periods. The regions with a concentration of ancient sites are the middle and lower reaches of the Yangtze River and Yellow River Basins. Furthermore, there is a higher abundance of ancient sites in the southeastern region than in the northwestern region. (2) The distribution of ancient sites in different historical periods also exhibits an imbalance, with an overall decreasing trend in the number of ancient sites in China. Notably, more ancient sites were found for the prehistoric and pre-Qin periods. The temporal trend of ancient sites during historical periods follows a trajectory from northeast to northwest, northeast to southwest, etc. (3) The temporal and spatial distributions of ancient sites are influenced by multiple factors, including social development, the natural environment, geographical elements, and socioeconomic and political factors. Finally, based on an understanding of the spatiotemporal distribution pattern of ancient sites and the factors that influence this pattern, recommendations for conservation and development can be proposed and supported by modern technological methods, with the aim of offering insights for the protection and sustainable development of heritage sites.

Combining CENTURY model and vulnerability assessment index to estimate aboveground biomass and ecological vulnerability of grassland: An analysis in Northwest Sichuan Plateau

Evaluating the impact of climate change on grassland productivity using grassland ecological models is of great significance for the sustainable use of grasslands. Based on meteorological, soil, and grassland data, this study employed the Grassland Ecosystem Model (CENTURY) to scrutinize the spatiotemporal distribution characteristics of above-ground biomass (AGB) of grasslands in the northwest Sichuan Plateau (NWSP) and established a spatial distribution pattern of grassland vulnerability levels in the NWSP. Utilizing five on-site verification points, the CENTURY model worked well in simulating the AGB of grasslands in the NWSP and was suitable for investigating the impact of climate change on grassland productivity in the NWSP. Simulation outcomes revealed a zonal distribution pattern of the annual average AGB in grasslands across the NWSP, with the central region exhibiting higher levels surpassing 800 g m−². From 1961 to 2020, the AGB of grasslands showed a significant increase trend, with growth rates ranging from 14.2 to 25.9 g m−2·(10a)−1. Overall, the northern and central regions of the NWSP experienced substantial climate impacts, coinciding with a marked increase in biomass. From 1961 to 2020, the overall vulnerability of grasslands in the NWSP was relatively low, with areas exhibiting moderate or lower vulnerability levels accounting for 83.8 %. The overall spatial distribution pattern showed low vulnerability in the north and central regions, and high vulnerability in the eastern and southwestern edges.

Spatial distribution of Japanese encephalitis cases and correlated geo-environmental factors in southern and northern counties of China

Japanese encephalitis (JE) is a mosquito-borne disease with a spatial distribution that is linked to geo-environmental factors. The spatial distribution of JE cases and correlated geo-environmental factors were investigated in two critical counties in southern and northern China. Based on maps, enhanced thematic mapper (ETM) remote sensing datasets from Landsat and spatial datasets of JE cases, spatial distribution and spatial cluster analyses of JE cases at the village scale were performed by using the standard deviational ellipse and Ripleys K-function. Global and regional spatial cluster analyses of JE cases were also performed by using Moran's index. Regression analysis was used to analyze the relationships between geo-environmental characteristics and the risk of JE cases. At the study sites, the JE cases were not spatially clustered at the village or district (global) level, whereas there was a spatial cluster at the district (local) level. Diversity-related features for JE patients at the district and village levels were detected at two sites. In the southern counties, the distance of a village from a road was related to the village-level JE risk (OR: 0.530, 95 CI: 0.297–0.947, P = 0.032), and the number of township-level JE cases was linked to the distance of the district center from the road (R =-0.467, P = 0.025) and road length (R = 0.516, P = 0.012) in the administrative area. In northern China, the modified normalized difference water index (MNDWI) in the 5 km buffer around the village was related to village-level JE risk (OR: 0.702, 95% CI: 0.524–0.940, P = 0.018), and the number of township-level JE cases was related to the MNDWI in the administrative region (R =-0.522, P = 0.038). This study elucidates the spatial distribution patterns of JE cases and risk, as well as correlated geo-environmental features, at various spatial scales. This study will significantly assist the JE control efforts of the local Centers for Disease Control and Prevention (CDC), which is the base-level CDC, particularly concerning the allocation of medicine and medical staff, the development of immunological plans, and the allocation of pesticides and other control measures for the mosquito vectors of JE.

Single‐Pulse‐Driven Frame Printing of Chromatic Pixels in Lithium Niobate Crystal

AbstractHighly efficient and programmable writing of multidimensional optical data is of great value for next‐generation high‐throughput information technologies but has been rarely achieved. Here, a one‐step frame printing of chromatic pixels in lithium niobate crystal by using a single ultrafast laser pulse is reported. In this strategy, a phase superposition‐based spatial light modulation strategy is applied to split a single ultrafast laser pulse into multiple son pulses with designated optical properties and spatial distribution patterns. It is demonstrated that these son pulses allow for massively creating micro‐amorphous phase transition zones with on‐demand structural features that can modulate the intrinsic birefringence of the crystal matrix and generate wavelength‐selective interference in the visible band to form pixel‐level chromatic patterns, namely, single‐pulse‐driven frame color printing. The created chromatic pixels can be encoded into computer‐recognizable data arrays to play a role in high‐efficiency multidimensional information recording. The presented approach enables fast and programmable information batch writing in 3D space and can serve as a versatile tool boosting next‐generation information optics.

Moss as a passive biomonitoring tool for the atmospheric deposition and spatial distribution pattern of toxic metals in an industrial city.

Anthropogenic pollution impacts human and environmental health, climate change, and air quality. Karabük, an industrial area from the Black Sea Region in northern Türkiye, is vulnerable to environmental pollution, particularly soil and air. In this research on methodological aspects, we analyzed the concentrations of six potential toxic metals in the atmospheric deposition of the city using the passive method of moss biomonitoring. The ground-growing terrestrial moss, Hypnum cupressiforme Hedw., was collected during the dry season of August 2023 at 20 urban points. The concentrations of Cr, Cu, Cd, Ni, Pb, and Co were determined in mosses by the ICP-MS method. Descriptive statistical analysis was employed to evaluate the status and variance in the spatial distribution of the studied metals, and multivariate analysis, Pearson correlation, and cluster analysis were used to investigate the associations of elements and discuss the most probable sources of these elements in the study area. Cd and Co showed positive and significant inter-element correlations (r > 0.938), representing an anthropogenic association mostly present in the air particles emitted from several metal plants. The results showed substantial impacts from local industry, manufactured activity, and soil dust emissions. Steel and iron smelter plants and cement factories are the biggest emitters of trace metals in the Karabük area and the primary sources of Cr, Cd, Ni, and Co deposition.

Spatial pattern analysis reveals intra-specific competition and fractal characteristics of Pinus sylvestris var. mongolica populations in Xiaotaojia Gulley Watershed

The imprudent cultivation of Pinus sylvestris var. mongolica has led to degradation in the watersheds surrounding Chongli County. This study investigates the artificial forests of P. sylvestris var. mongolica in the Xiaotaojiagou watershed, Chongli County. Twenty-five subplots measuring 20 m × 20 m were established at upper, upper-middle, middle, and lower positions within the watershed. Spatial distribution patterns and fractal characteristics of P. sylvestris var. mongolica were analyzed using univariate pair correlation function g(r), marked correlation functions (marked correlation function Kmm(r), marked variation function γm(r), and Schlather’s Im(r) function), and fractal dimensions (box-counting dimension, information dimension). The findings are as follows: (1) P. sylvestris var. mongolica in the middle section of the watershed displayed the largest average diameters at breast height and tree height, while those in the lower section exhibited the largest average crown width. (2) P. sylvestris var. mongolica populations in the upstream, middle, and downstream sections of the watershed exhibited three alternating states: aggregated distribution, uniform distribution, and random distribution. However, with increasing spatial scale, the distribution gradually tended towards randomness. P. sylvestris var. mongolica in the upper-middle section of the watershed showed aggregated distribution at scales of 1.5–32.5 m, transitioning to random distribution and then to uniform distribution with increasing spatial scale. (3) Individual attributes of P. sylvestris var. mongolica (diameter at breast height, tree height, and crown width) exhibited high correlation and facilitation in the upstream and upper-middle sections at small-to-medium scales, whereas negative correlations and inhibitions were observed in the middle and lower sections. These attributes tended to be uncorrelated with increasing spatial scale. (4) Fractal dimensions indicated that the degree of aggregation of P. sylvestris var. mongolica population was lowest in the downstream section of the watershed. In conclusion, spatial distribution patterns and intraspecies relationships of P. sylvestris var. mongolica populations are influenced by spatial scale and environmental conditions, particularly in more resource-competitive downstream areas. Effective management and protection of P. sylvestris var. mongolica populations require targeted measures such as adjusting afforestation density, improving soil conditions, and implementing differentiated ecological restoration strategies to alleviate intraspecies competitive pressures and promote population health and sustainable development.

How to identify public risk perception in public health emergencies and explore its driving mechanism? An empirical analysis in the Xi'an epidemic

Public risk perception is the psychological perception and cognitive behavior towards public health emergencies, the high level of which can seriously threaten mental health and social stability. However, scholarly focus often neglects the specific risks associated with public health emergencies and the individual differences among the public. Addressing these gaps, this study takes the Xi'an epidemic as an example, proposing a Word2vec-BiLSTM-based risk perception identification model. Additionally, it investigates the spatiotemporal evolution characteristics of risk perception by introducing life cycle factors. Furthermore, adopting a triple definition of risk, the study innovatively employs the GTWR model to explore the driving influences of existing risk, foreseeable risk, and communication risk on risk perception. The results indicate temporal fluctuations in risk perception, with an initial increase followed by a decline, and a spatial distribution pattern characterized by "high in the middle and low on both sides". The influence of risk grade on risk perception shifts from positive to negative over time, being a dominant force. Population density exhibits a minor negative effect, while number of participants shows a gradually increasing positive correlation. These findings offer a novel perspective for policymakers to optimize legal regulations and enhance guidance mechanisms to reduce public risk perception.

Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models

Vegetation productivity is crucial for human production and livelihoods. Understanding net primary productivity (NPP) in historical contexts and predicting its future fluctuations is imperative for assessing the environmental sustainability of a region. However, relatively few researches have been conducted on predicting NPP, requiring further development and refinement of NPP prediction methods and models. This study introduces a novel approach that discretely couples the PLUS and CASA models for NPP prediction, and it validates the applicability of this approach in the research area. The objective of our study is to analyze the spatiotemporal patterns of NPP change in the Beijing-Tianjin-Hebei (BTH) region from 2001 to 2020, predict NPP under three different climate scenarios (SSP 1-2.6, SSP 2-4.5, SSP 5-8.5) in 2030, and identify an appropriate path for the future development of this region. The results indicate:(1) From 2001 to 2020, NPP in the research area has shown a gradual improvement trend and maintained a certain spatial distribution pattern in general. (2) The study discovered a correlation coefficient of 0.83 and an RMSE of 102.86 between predicted and actual NPP for 2020. This suggests that the method introduced in our study is suitable for predicting NPP in the research area. (3) NPP in the study area is predicted to decline in 2030 compared with 2020 under all three scenarios. Moreover, the SSP 1-2.6 scenario, representing a low-emission scenario, is suitable for the BTH region compared with other climate scenarios. This research sheds light on NPP variations in the BTH region over the past 20 years and the next 10 years, offering a scientific basis for relevant departments to formulate future policies.

The spatial pattern and association network of green finance development: Empirical evidence from China

AbstractGreen finance is a market‐driven approach to achieve the “double carbon” goal. However, the existing research predominantly focuses on the connotations and empowerment aspects of green finance. Notable regional disparities exist in China's financial markets and resource endowments, resulting in uneven levels of green finance development among various areas, significantly impeding the overall green transformation of the economy and society. In light of this, our paper explores the spatial patterns and association network of green finance development in China. The research findings indicate that: (1) The development of green finance in China exhibits an “east high, west low” spatial distribution pattern, with significant spatial clustering observed among neighboring provinces. The eastern region displays a “H‐H” clustering, while the western region shows an “L‐L” clustering. (2) The spatial correlation distribution of provincial green finance in China demonstrates an eastward concentration and westward dispersion trend, with strong correlation areas gradually expanding. Notably, Jiangsu, Zhejiang, Shanghai, and the Beijing‐Tianjin‐Hebei urban agglomerations lead nationally in attractiveness. (3) The external radiation capability of provincial green finance is progressively improving, with provinces such as Beijing, Hebei, Hubei, and Sichuan likely to occupy strategic structural positions, possessing greater development advantages. This study not only expands the theoretical scope of existing green finance research but also provides a practical reference for coordinating and achieving the high‐quality development of regional green finance from a practical perspective.

Spatial distribution of benthic taxonomic and functional diversity in the Yellow River Basin: From ecological processes to associated determinant factors

One of the fundamental objectives in ecology is to investigate the ecological processes and associated factors governing the abundance and spatial distribution patterns of biodiversity. However, the reaction of biological communities to environmental degradation remains relatively unknown, even for ecologically crucial communities like macroinvertebrates in aquatic ecosystems. Here, we sampled 117 locations to quantify relative contributions of geographical and environmental factors, including water quality, land use, climate, and hydrological factors, to determine the absolute and relative compositions of macroinvertebrate communities and their spatial distribution in the Yellow River Basin (YRB), the sixth-longest river system on Earth. We assessed relative roles of species sorting and dispersal in determining macroinvertebrate community structure along YRB. Our results demonstrated that alpha and beta diversity indices showed an increase from the up- to low-reaches of YRB. The middle and low-reaches exhibited elevated species diversity and both regions exhibited relatively stable community compositions. The biodiversity of macroinvertebrates was influenced by a combination of geographical factors and environmental variables, with environmental factors predominantly serving as the principal determinants. Results of multiple linear regression and variance decomposition showed that the effect of environmental factors was approximately three times greater than that of spatial factors. These findings provide support for the hypothesis that species sorting, driven by environmental gradients, plays a significant role in shaping the community structure of macroinvertebrates in running water ecosystems at the basin scales. Moreover, the factors contributing to substantial shifts in biodiversity across different segments of YRB indicate that distinct river sections have been influenced by varying stressors, with downstream areas being more susceptible to the impacts of water pollution and urbanization resulting from human activities.

Climate risk assessment and adaption ability in China's coastal urban agglomerations - A case study of Guangdong-Hong Kong-Macao greater bay area

As climate change intensifies, extreme weather events, especially the coastal flooding is becoming increasingly severe. Carbon emissions exacerbate climate change, leading to increased frequency and intensity of extreme precipitation, which may result in flooding. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a coastal city cluster in China, and adopts the scenario analysis approach to evaluate flood risks in the different carbon mitigation scenario from the perspectives of dangerousness, and adaption ability. The result shows that the Huizhou in the east of the GBA and the Zhaoqing in the northwest have the highest level of flood disaster risk. However, the central region of the GBA (Guangzhou, Dongguan, Zhongshan, and Foshan) is very vulnerable to disaster-inducing environments and hazard-affected bodies, which means that the flooding risk and the impact of such risk in the GBA display regional differentiation characteristics and the spatial patterns of them are different. According to the carbon neutrality scenario, the flood risk in the GBA peaks in 2030, and decreases during 2030–2090. In contrast, under the uneven development and high carbon pathways, the risk peaks in 2060, and the overall risk by 2090 is roughly the same as that in 2060. In addition, the regions with the highest flood disaster risk are Guangzhou, Foshan, Dongguan and Zhongshan along the Pearl River estuary. Overall, it shows that areas with higher levels of urbanization and more developed economies will face higher flooding risks. The “dangerousness-adaption ability” structure can reflect the spatial distribution pattern of flood disaster risks, identify the priority areas for flood prevention and control, and is conducive to risk management. At the same time, the research results show that ecological protection have a positive effect on reducing flood risk. In addition, corresponding policy recommendations for pre-disaster, mid-disaster and post-disaster have been proposed, providing references for flood risk management in the GBA.

Identification of spatial clusters of potentially toxic elements in different soil types using unsupervised machine learning and compositional data analysis

Soil health is an important concept and the generation of soil chemical composition data, including potentially toxic elements (PTEs) is one of its conceptual components. This study aims to reveal the spatial distribution patterns of soil PTEs contents, identify their potential sources, and unveil their geochemical associations in Aragatsotn region (Armenia). For that purpose, the contents of Cr, V, Ti, As, Zn, Cu, Co, Fe, Mn, Ba, and Pb were determined using the X-ray fluorescence spectrometer (Innov X-5000). The results showed that mean contents of Cr and As exceeded their upper continental crust by 1.50 and 3.12 times and their maximum acceptable values by1.53, 1.50 times, respectively. The detailed analysis of the spatial distribution pattern of PTEs demonstrated the presence of sites where all these elements displayed comparatively higher values. The combined application of compositional data analysis and geospatial mapping allowed to reveal multivariate outliers which were located in structural-metallogenic zones with an active exploitation of ore deposits. The application of unsupervised machine learning algorithm unveiled three groups within the main dataset and the clr-biplot identified the source-specific elements. Particularly, Group I included Cu and displayed the highest mean value among the identified groups. The soil samples included in Group I were found in areas where Calcisols were developed and comparatively high Cu contents were attributed to agricultural activities (vineyards) and vehicle emissions. The Group II is represented by the geochemical association of Fe, Co, Cr, Mn, Zn, and As. The formation of this group is conditioned by volcanic rocks of the local geological origin. However, no spatial pattern was identified in Group II distribution aligned with the soil types. The Group III included Ti, V, Pb, and Ba. This group may have a mixed origin as it is spatially distributed in areas where regional highways pass through and where Group II elements also exhibit their higher values.

Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

Distribution Pattern of Species Richness of Endemic Genera in Mountainous Areas of Southwest China and Its Influencing Factors

China’s southwest mountainous region is one of the hotspots of biodiversity in the world. However, the study on the pattern of species richness distribution of endemic plants in this region and its influencing factors is still in the exploration stage. A clear regional spatial distribution pattern of plants and its influencing factors are very important for the protection and management of plant diversity in southwest China. In this study, 511 county-level regions in the southwest mountainous region were taken as examples to establish a plant species resource bank in the southwest mountainous region, and the distribution pattern of the proportion and abundance of endemic plant families and genera was analyzed based on 19 climate and environmental factors and 8 human disturbance factors in the southwest mountainous region. The geographical weighted regression model (GWR) was used to reveal the main factors affecting the spatial distribution of species. The results showed that: (1) A total of 4688 species investigated in the southwest mountain belong to 19 families and 32 genera. (2) The endemic species richness in the southwest mountainous region showed a significant uneven spatial pattern, mainly concentrated in the Heng Duan Mountain area, southwest Sichuan and northern Yunnan area, and the Ruoergai-Hongyuan area of West Sichuan Plateau. (3) The results of the GWR model showed that the diurnal mean temperature range, precipitation seasonality and distance from the main road had significant effects on species richness. The study on the distribution of endemic genera and their influencing factors in the southwest mountainous region has important implications for the exploration of the evolutionary history and species conservation of the mountain flora.