Analysis Of Spatial Characteristics Research Articles

Comparative analysis of spatial transport characteristics of spring cold-front-type sandstorms in the Hexi Corridor

AbstractIn this study, we investigate the spatial transport characteristics of cold-front-type sandstorms with different intensities in the Hexi Corridor based on the hourly observation data from 13 ground-based meteorological stations in the Hexi Corridor, the data in the upper and lower layers from the Meteorological Information Comprehensive Analysis And Process System, and the three-dimensional aerosol observation data of the Vertical Feature Mask product obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization of the United States. The results show that the influence range and horizontal transport distance of cold-front-type sandstorms in the Hexi Corridor are determined by the intensity, thickness and width of the upper-level troughs and the intensity of cold fronts. Generally, cold fronts pass through the Hexi Corridor from noon to evening and stay in this region for a long time or move slowly, which is favorable to the horizontal long-distance transport of sandstorms. The intensity of the 200 hPa upper-level jets is directly proportional to both the influence range and transport distance of sandstorms which primarily occur on the left side of the exit area of an upper-level jet. The strengthening of upper-level jets induces the formation of the vertical circulation cell at middle and low levels. The ascending branch of the circulation cell lifts sand-dust particles from the surface to the upper layers, and the descending branch transports upper-level momentum to the near-surface. This circulation situation increases the near-surface wind speed and forms sandstorms. The occurrence time and descending branch of the secondary circulation cell caused by upper-level jets are important indicators for predicting the occurrence time and falling area of sandstorms. Dust aerosols are mainly concentrated at a height of 2–6 km,.,and they can reach the height of 12 km. When the dust is concentrated at the height of 2–8 km, and the wind speed of 500 hPa and 300 hPa reaches 28 and 32 m s− 1 or above respectively, the cold front sandstorm can realize the long distance transport of thousands of kilometers.

Spatial and Temporal Characteristic Analysis and Risk Assessment of Global Highly Pathogenic Avian Influenza H5N8 Subtype

H5N8 HPAI is a highly infectious avian disease that now poses a serious threat and potential risk to poultry farming, wild birds, and public health. In this study, to investigate the seasonality and transmission directionality of global H5N8 HPAI, the spatial and temporal analysis of H5N8 HPAI was conducted using time series decomposition and directional distribution analysis. An ecological niche model was developed for H5N8 HPAI in poultry to identify areas at high risk of H5N8 HPAI in poultry and associated risk factors. The results indicated that three global pandemics of H5N8 HPAI emerged from 2014 to 2022, all showing a southeast–northwest distribution direction. H5N8 HPAI occurred more frequently in winter and less frequently in summer. The southwestern border region and the southeastern region of North America, the southern region of South America, most of Europe, the southern border region and the northern border region of Africa, and the southwestern region and the southeastern region of Asia provide the suitable environment for the occurrence of H5N8 HPAI in poultry. Chicken density, duck density, population density, bio1 (annual mean temperature), and land cover were considered important variables for the occurrence of H5N8 HPAI in poultry. This study can help optimize the use of resources and provide new information for policymakers to carry out prevention and control efforts.

Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America

Hydroclimatic swings between dry and wet spells are increasing globally, raising concerns due to their severe impacts on society, agriculture, infrastructure systems, and water resource management. These extremes and their projected changes are traditionally assessed in isolation, which can underestimate associated risks and future resilience and adaptation plans. This study investigates climate change projections of such lagged compound dry and wet spells across North America using a single model initial-condition large ensemble (SMILE). Three dry-wet spell indices are merged into an integrated indicator to provide a comprehensive perspective on changing risks. We apply an ensemble pooling approach using the Canadian Regional Climate Model version 4 Large Ensemble (CanRCM4-LE) to enhance sample size for index estimation. Results suggest that hydroclimatic swings across North America are expected to become more frequent and intensified in a warmer climate. Trends of spatial fraction and aggregation during transitions between dry and wet spells indicate future water resource management challenges. Hot spots for intensified transitions with higher frequency, such as Northern Central America, and the southern part of Eastern and Western North America, including Mexico and the state of California, overlap with larger spatial extent and higher aggregation during transitions. The seasonal analysis of spatial characteristics indicates winter may become wetter and summer drier at higher warming levels, potentially intensifying transitions between dry and wet extremes. Clustered hydroclimatic swings may lead to severe environmental, hydrological, and socio-economic consequences, necessitating appropriate mitigation and adaptation measures.

Analysis of spatial characteristics and influencing factors of the flow network of highly educated talents from national and local perspective

Based on dynamic monitoring data on China’s population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a “core-periphery” progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents.

Analysis of spatial distribution characteristics of facial skin temperature on stress coping

Analysis of spatial distribution characteristics of facial skin temperature on stress coping

Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China

It is particularly important to analyze the influencing factors of urban green competitiveness and the spatial distribution characteristics under the constraint of carbon emissions. The research ideas of this paper: firstly, this paper selects the carbon emission intensity and urban green competitiveness data in 2010, 2013, 2015, 2018 and 2020 for panel data regression; secondly, this paper applies a variety of methods to carry out the robustness test, and the results show that the regression model is better, and analyzes the development of urban green competitiveness for the heterogeneity of large cities and small cities; subsequently, the use of inverse geographic matrix to analyze the spatial correlation between the global Moran index and local Moran index for urban green competitiveness, and to analyze the spatial and temporal pattern evolution of urban green competitiveness. The conclusions of the study show that, from the viewpoint of influencing factors, carbon emission intensity presents a significant negative effect on the development of urban green competitiveness, and has a greater impact on the green competitiveness of large cities than that of small cities. From the perspective of spatial correlation, urban green competitiveness presents positive spatial correlation and shows a growing trend over time. Finally, this paper puts forward relevant policy recommendations based on the findings of the study.

Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing

Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension’s pattern and evolution within the trajectory data, thereby inadequately identifying the behavioral inertia of the target group. A few detection methods that incorporate spatiotemporal features have also failed to adequately analyze the spatiotemporal sequence evolution information; consequently, detection methods that ignore temporal and spatial correlations are too one-sided. Recurrent neural networks (RNNs), especially gate recurrent unit (GRU) that design reset and update gate control units, process nonlinear sequence processing capabilities, enabling effective extraction and analysis of both temporal and spatial characteristics. However, the basic GRU network model has limited expressive power and may not be able to adequately capture complex sequence patterns and semantic information. To address the above issues, an abnormal trajectory detection method based on the improved GRU model is proposed in cloud computing in this paper. To enhance the anomaly detection ability and training efficiency of relevant models, strictly control the input of irrelevant features and improve the model fitting effect, an improved model combining the random forest algorithm and fully connected layer network is designed. The method deconstructs spatiotemporal semantics through reset and update gated units, while effectively capturing feature evolution information and target behavioral inertia by leveraging the integration of features and nonlinear mapping capabilities of the fully connected layer network. The experimental results based on the GeoLife GPS trajectory dataset indicate that the proposed approach improves both generalization ability by 1% and reduces training cost by 31.68%. This success do provides a practical solution for the task of anomaly trajectory detection.

SpATr: MoCap 3D human action recognition based on spiral auto-encoder and transformer network

Recent technological advancements have significantly expanded the potential of human action recognition through harnessing the power of 3D data. This data provides a richer understanding of actions, including depth information that enables more accurate analysis of spatial and temporal characteristics. In this context, We study the challenge of 3D human action recognition. Unlike prior methods, that rely on sampling 2D depth images, skeleton points, or point clouds, often leading to substantial memory requirements and the ability to handle only short sequences, we introduce a novel approach for 3D human action recognition, denoted as SpATr (Spiral Auto-encoder and Transformer Network), specifically designed for fixed-topology mesh sequences. The SpATr model disentangles space and time in the mesh sequences. A lightweight auto-encoder, based on spiral convolutions, is employed to extract spatial geometrical features from each 3D mesh. These convolutions are lightweight and specifically designed for fix-topology mesh data. Subsequently, a temporal transformer, based on self-attention, captures the temporal context within the feature sequence. The self-attention mechanism enables long-range dependencies capturing and parallel processing, enabling scalability for long sequences. The proposed method is evaluated on three prominent 3D human action datasets: Babel, MoVi, and BMLrub, from the Archive of Motion Capture As Surface Shapes (AMASS). Our results analysis demonstrates the competitive performance of our SpATr model in 3D human action recognition while maintaining efficient memory usage. The code and the training results are publicly available at https://github.com/h-bouzid/spatr.

Analysis of Spatial and Biochemical Characteristics of In Vitro Cariogenic Biofilms.

Background Dental caries is the most common bacterial disease of calcified tissues of teeth. Cariogenic biofilms formed on the tooth surface secrete organic acids and thus result in demineralization. Delving into the depth of biofilms is crucial to understand the pathogenic mechanisms and design improved therapeutic approaches. The aim of the study is to analyze the spatial and biochemical characteristics of cariogenic biofilms. Materials and methods Pulp tissue samples sourced from freshly extracted third molars were incubated with oral cariogenic bacteria namely Streptococcus mutans, Staphylococcus aureus, Escherichia coli, Entamoeba faecalis, and Candida albicans to form the biofilm. Spatial assessment of biofilms was done under FESEM (field emission scanning electron microscope, JSM-IT800, JEOL, Tokyo, Japan). FTIR (Fourier transform infrared spectroscopy, Alpha II, Bruker, Germany) spectra were assessed for chemical molecular interactions in 24- and 48-hour time periods. Results Morphological assessment with FESEM revealed rapid growth and aggregation within a short time period. FTIR spectra to analyze chemical constituents of biofilm presented with varied peaks of water, amide A, amide I, water, lipids, and phospholipids. Conclusion Further validation with more advanced imaging for an extended time period is vital to derive better conclusive evidence.

Analysis of traffic noise spatial distribution characteristics and influencing factors in high-density cities

Urban spatial structure factors can have considerable effects on traffic noise distribution. This study aimed to describe the spatial distribution characteristics of traffic noise and influencing factors in high-density cities. Fifteen indexes were selected from the three aspects of building layout, road organization and land use to analyse the influence of urban spatial structure factors on the noise spatial distribution. This paper selected the central area of Tianjin as the study area. The noise level was generated by the noise simulation software CadnaA, and the numerical statistics were determined by ArcGIS. The correlation between various urban indexes and the spatial distribution of traffic noise were determined with correlation analysis, and multiscale geographically weighted regression (MGWR) analysis was used to determine the degree and scope of influence of various urban indexes on the spatial distribution of traffic noise. The results indicate that there are significant differences in the spatial agglomeration forms of high, medium, and low noise areas among research units, and the spatial agglomeration of low noise areas in each unit is stronger. The building layout indexes, except for the average building height (ABH), are significantly negatively correlated with the noise levels in low noise areas (L70–L90). The road length index (RLI), road area density (RAD), road landscape shape index (RLSI), trunk road length index (TRLI), proportion of traffic land (PT), and degree of land mixing (DLM) are significantly positively correlated with the noise levels of all areas within the unit (L10–L90). Among the three aspects of building layout, road organization, and land use, the architectural landscape shape index (ALSI), RAD, and DLM have the greatest influence on the average spatial sound pressure level (Lavg), with mean regression coefficients of −0.254, 0.520, and 0.174, respectively. The ground space index (GSI), ALSI, proportion of residential land (PR) and PT are the global factors influencing Lavg.

Landscape ecological risk assessment study of the Yunnan section of the Tropic of Cancer

Natural landscapes constitute a vital component of ecosystems, and their stability and diversity are essential in maintaining regional ecosystem functions. The scientific and rational assessment of ecological risks in regional landscapes is crucial for activities such as ecosystem security evaluation, optimization of ecosystem function zoning, and establishment of landscape security patterns. We focused on the Yunnan section of the Tropic of Cancer, employing Landsat 8 remote sensing data to categorize landscape types and construct ecological risk indices. Using software platforms such as ArcGIS, GEE, and Fragstats4.2, an evaluation and spatial characteristics analysis of the ecological risk in the Yunnan section of the Tropic of Cancer was conducted, yielding the following findings: ① The overall level of ecological risk within the Yunnan section of the Tropic of Cancer is relatively low. The lower ecological risk zone covers an area of 19,100.35 km2, accounting for 34.09 % of the total study area, while the higher ecological risk zone spans 2,032.91 km2, constituting only 3.63 % of the total study area. ② Landscape types in areas of lower and low ecological risk are predominantly characterized by woodland landscapes, comprising 90.93 % and 62.19 %, respectively. Conversely, areas with high ecological risk and higher ecological risk are dominated by cultivated land, representing 67.75 % and 95.61 %, respectively. This underscores the importance of strengthening forestry development and controlling the expansion of cultivated land to mitigate regional landscape ecological risks. ③Regions with higher ecological risks in the landscape are primarily in the northwestern part of Yanshan County, the southern part of Gengma County, and the northern part of Wenshan city. These areas are susceptible to the impacts of intense farming, extreme weather, and the karst geological environment. In contrast, regions with high ecological risks are in the central and eastern parts of Mojiang County, the western part of Yanshan County, and the northern part of Wenshan city. These areas are mostly influenced by urban economic development, extensive construction projects leading to deforestation and grassland degradation, and the substantial demand for arable land due to a significant population increase. ④ The spatial pattern of ecological risk in the Yunnan section of the Tropic of Cancer, as indicated by Moran's I index, is positive and exceeds 0.6. The Z score significantly surpasses the reference value of 2.58, with p < 0.01. This indicates a strong degree of spatial autocorrelation and a high positive correlation in the spatial distribution characteristics.

Spatial and Temporal Characteristics Analysis and Demand Forecasting based on ARIMA Model - An Example of Yellow Taxi in New York

With the development of technology and urbanization, cars have greatly facilitated people's travel, but they also bring problems such as road congestion, pollution and accidents, which restrict the healthy development of cities. How to predict traffic flow efficiently and accurately has become a hot topic. Taxi data is highly recommended in urban transportation research due to the characteristics of broad spatial coverage, long driving time and relatively complete data. In this paper, the data of yellow taxis in New York during March 2023 are used to present the demand changes by means of line chart thermal map, and divide the database of March into two parts, one is training set and the other one is test set, then simulation and analysis using ARIMA model. It reveals the changing relationship between taxi demand and time. By predicting the traffic flow, this study can alleviate the congestion of urban main roads and improve the quality of citizens’ life. At the same time, forecasting commuting demand can balance supply and demand, provide more efficient services for operating companies, taxi drivers and passengers, and provide decision-making basis for government planning.

Analysis of Spatial Characteristics Contributing to Urban Cold Air Flow

To mitigate the urban heat island phenomenon at night, cool, fresh air can be introduced into the city to circulate and dissipate the heat absorbed during the day, thereby reducing high urban air temperatures. In other words, cold air flow (CAF) generated by mountainous and green areas should be introduced to as wide an area as possible within the city. To this end, it is necessary to first understand the characteristics of urban spatial factors that impact CAF, and to conduct concrete and quantitative analyses of how these urban spatial characteristics are contributing to air temperature reduction. In this study, the following are conducted: (1) an analysis of the relationship between cold air volume flux (CAVF) and the amount of air temperature reduction; (2) urban spatial categorization; (3) an analysis of the relationship between CAVF and the amount of air temperature reduction by urban spatial type; (4) a regression analysis between the amount of air temperature reduction and urban spatial characteristic factors that affect CAF; and finally, (5) the use of CAF to reduce urban air temperatures in urban planning and a design is proposed. Urban space was categorized into nine types using the results of the tertile analysis of CAVF and urban temperature reduction. It was determined that building height (BH) has a positive (+) influence on all urban spatial types, while building area ratio (BA) has a negative (−) effect. However, in the case of wall area index (WAI), the direction of influence varied depending on the development density; relatively low BA areas should focus on development that increases height to increase WAI, while relatively high BA areas should focus on development that reduces BA to reduce WAI by targeting development types closer to the tower type. And even in areas with similar development density, influence varies depending on the terrain elevation. Moreover, it is necessary to prepare improvement measures to increase the factors with CAF that positively influence air temperature reduction and decrease those with negative influence according to the characteristics of urban spatial types. Such results quantitatively and specifically confirmed the effects of spatial factors that affect CAF by urban spatial type on air temperature reduction. The results of this study can be used as useful information for the efficient use of CAF, a major element of urban ecosystem services.

Spatial characteristic analysis of inertia response based on the relative gain array

This paper proposes a novel method to evaluate the spatial distribution characteristics of inertia response in power systems. Under the effect of grid topology and the inertia distribution of generators, the influence of power disturbance on frequency variations during inertia response has distinctive spatial characteristics. Compared with early studies, this paper focuses on spatial characteristics and presents a quantitative method to evaluate inertia response. This paper first builds an inertia response model that retains spatial distribution characteristics by adopting DC power flow. Then, it introduces the relative gain array (RGA) theory for calculating the relative gain array between the rate of change of frequency (RoCoF) and the active power disturbance during inertia response. Based on the magnitude and distribution of the relative gain array elements, the paper conducts a quantitative analysis of inertia response, thus presenting a description of the spatial characteristics of inertia response. Simulation results of the IEEE 39-bus system validate the effectiveness of the proposed method in evaluating the spatial characteristics of inertia response.

Analysis of Spatial and Temporal Characteristics of Runoff Erosion Power in Fujiang River Basin Based on the SWAT Model

As an erosion dynamic index considering the three elements of flood, runoff erosion power (REP) can better reflect the influence of precipitation, underlying surface, and other factors on the erosion and sediment transport (ST) of flood events. Therefore, it is of great significance to study the variation characteristics of the REP and its relationship with ST in the basin for soil erosion control. In this paper, the Fujiang River Basin (FRB) was selected to analyze the characteristics of runoff and ST at four hydrological stations in the basin from 2009 to 2018, including Santai, Jiangyou, Shehong, and Xiaoheba. Combined with the concept of the REP, six kinds of water–sediment relationship were compared and analyzed. Furthermore, by constructing the SWAT model, the spatial distribution characteristics of runoff, ST, and REP in the FRB were analyzed in depth, and the spatial scale effect of the REP in the basin was explored. The conclusions are as follows: (1) The power function relationship between REP and sediment transport modulus (STM) is better than the other five kinds of water–sediment relationship. (2) Based on the SWAT model, the evaluation indexes of the monthly runoff and ST of the four hydrological stations are credible, good, and excellent in the rating period (RP) and the validation period (VP). (3) The annual REP in the main stream from upstream to downstream is mostly a single change trend, while in each primary tributary, the overall value is larger than that of the main stream and the interannual difference is obvious. The average annual REP generally shows the distribution characteristics of ‘large at the junction of the upper and middle reaches and small in the rest of the area’. With the increase in the control area, the multi-year average REP has a decreasing trend, especially when the catchment area above the sub-watershed is &gt;7318 km2; the change of the multi-year average REP is single and obviously slows down, with an average value of 23.8 mm·m3·s−1·km−2.

Analysis of spatial correlation characteristics and key factors of regional environmental governance efficiency in China

Analysis of spatial correlation characteristics and key factors of regional environmental governance efficiency in China

Analysis of temporal and spatial evolution characteristics and influencing factors of land use transformation in Hebei Province from the perspective of supply and demand

This study focuses on the counties and districts of Hebei Province as the research unit. It adopts a supply-demand perspective to analyze the spatial-temporal evolution characteristics of farmland utilization transformation in the province, investigate the coordination of this transformation, and explore the influencing factors. The weight of indicators is calculated using the entropy weighting method and Analytic Hierarchy Process The comprehensive evaluation model is then applied to calculate the supply-demand transformation index of farmland utilization in Hebei Province for the years 2005, 2010, 2015, and 2020. Furthermore, the spatial-temporal evolution characteristics are analyzed using the kernel density estimation method. The coupling coordination degree model is selected to explain the relationship between the supply-demand transformation of farmland utilization. Finally, the influencing factors are analyzed using the geographical detector model. The research findings are as follows: 1) The supply-demand transformation index of farmland utilization in Hebei Province has shown an increasing trend during the study period. The standard deviation of the supply transformation index has increased over time, while the demand transformation index has increased at a faster rate. High-density supply transformation is concentrated in the southeast, particularly in the eastern part of Shijiazhuang. Conversely, the northwest exhibits a low-density supply transformation. High-density demand transformation is observed in urban areas across the province, with a significant expansion from 2010 to 2015. 2) There is a strong correlation between the supply and demand transformation of farmland utilization. The coupling coordination degree has gradually improved from 2005 to 2015, transitioning from rapid to stable growth. The level of coupling coordination has shifted from imbalance to coordination. The mountainous areas in the northwest of Hebei Province exhibit relatively lower coupling coordination degrees, while the plains in the southeast demonstrate higher levels. 3) The supply transformation of farmland utilization is closely correlated with the natural environment, particularly elevation and topography. On the other hand, the demand transformation is closely associated with socio-economic development, with a scarcity of supply driving an increase in the demand transformation index. Industrial developed areas show a higher intensity of demand for farmland utilization. 4) To ensure the sustainable utilization of farmland while meeting food production needs, it is crucial to enhance contiguous farmland and mechanization levels, promote the integration of agriculture and tourism, establish an ecological barrier around Beijing-Tianjin, and optimize the ecological compensation mechanism for farmland. The above findings provide valuable insights into farmland utilization transformation and suggest important strategies for its sustainable development.

Correlation Analysis of Retail Space and Shopping Behavior in a Commercial Street Based on Space Syntax: A Case of Shijiazhuang, China

The visibility and spatial characteristics of commercial space are the key factors that affect the space vitality. However, the coupling visibility graphical analysis of commercial spaces and spatial characteristics to quantitatively analyse consumer behaviour in commercial street spaces and assess the vitality of retail spaces has not been adequately studied. In this paper, the aim is to conduct a visibility graph analysis of Zhuangli Street in Letai Center, Shijiazhuang, using space syntax theory, assessing the spatial vitality of the retail space by investigating the shop visits. First, a methodology for obtaining data on spatial characteristics and consumer behaviour of shopping streets was developed. Secondly, this article constructs a process for a visibility graph analysis of Zhuangli Street based on space syntax theory. Third, two combination variables of the space coefficient and depth coefficient of shop windows in retail spaces of a commercial street are proposed. Finally, the effect of combination variables and business types on spatial vitality was analyzed using correlation and multiple regression methods, and a space vitality prediction model was proposed. The results showed that the shop with the highest shop visits of retail spaces in the shopping street is 13.55 times higher than the smallest shop. The space coefficient of the shop window, depth coefficient of the shop window, and space connectivity of retail spaces in commercial streets have positive effects on space vitality. The workflow proposed in this paper can provide technical support for retail space design in commercial streets as well as evaluating and optimizing commercial street space design solutions.

Assessing nitrogen sources in Lake Erhai: A spatially explicit modelling approach

Due to the vulnerable ecosystem environments, plateau lakes in the southwest of China face high eutrophication risks. Excessive nitrogen input is an important driver of eutrophication in plateau lakes. However, it is lacked of accurate analysis of spatial and temporal distribution characteristics of nitrogen sources for plateau lakes, which restricts local water environment management. Taking a typical plateau lake, Erhai Lake, as an example, this study applied the MARINA (Model to Assess River Inputs of Nutrients to seAs)-Lakes model to 26 sub-basins of Erhai Lake to analyze the temporal (1998–2016) characteristics of nitrogen inputs and identify the main pollution sources in different sub-basins. The results showed that the most obvious decreasing trends in river export of nitrogen occurred in 2016. It decreased from 2.9 in 2010–2.3 Mg in 2016, which is associated with the series of water quality protection measures implemented. By distinguish nitrogen forms, we found that dissolved inorganic nitrogen was mainly from diffuse source (synthetic fertilizer) whereas point source (directly discharge of animal manure) was the dominant source for dissolved organic nitrogen. The highest river export of nitrogen was from Zhonghe sub-basin in the western areas with high population density and intensive agricultural system, and the lowest was from Mijuhe sub-basin in the northern areas. Tourism was the dominant source of river export of nitrogen in several sub-basins, including Zhonghe, Meitao, Haidongqin, Boluo, and Jinli. Our study developed an explicit spatial analysis approach to quantify river export of nitrogen by sources at sub-basin scale. Based on the primary sources of nitrogen within each sub-basin, we could divide Erhai Lake basin into different management zones. These results can not only help decision-makers formulate accurate and effective lake protection policies for Erhai Lake, but also provide new insights for other plateau lakes.

Examining coordination and equilibrium: an analysis of supply index and spatial evolution characteristics for older adult services in Zhejiang Province.

This study aims to analyze the spatial distribution and dynamic evolution of older adult service supply in Zhejiang Province from 2010 to 2019. Additionally, this research seeks to propose an optimized resource allocation strategy for older adult care services, promoting regional fairness and coordinated development. To evaluate the older adult service supply capacity, this research first constructed an evaluation index system based on the Chinese modernization development pattern. Then, an empirical analysis was carried out using a combination of the entropy-TOPSIS method, kernel density estimation, Markov chain analysis, Dagum Gini coefficient, and panel regression model. The results show an overall upward trend in the supply and service capacity of older adult care in the whole province. However, the spatial distribution of older adult service supply capacity in Zhejiang Province still exhibits a gradient effect, even in the most recent year of 2019. Furthermore, the supply capacity of older adult services shifted to a higher level in the whole province, and regions with high supply capacity had a positive spillover effect on adjacent regions. The overall difference in the older adult service supply capacity of the province showed a decreasing trend. The level of economic development, urbanization rate, transportation capacity, the level of opening up, and the proportion of employees in the tertiary industry had a significant impact on the supply capacity and spatial difference of older adult services. From the findings, this study puts forth countermeasures and suggestions to optimize the spatial distribution of older adult care services. This includes giving full play to the regional spatial linkage effect, promoting new-type urbanization construction, upgrading the transportation network, and expanding the opening up of the industrial structure. By implementing these measures, a more equitable and coordinated older adult services system can be developed in Zhejiang Province.