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Addressing the need to harmonize environment conservation and sustainable economic development within the Yellow River Basin (YRB) requires a profound comprehension of the spatiotemporal dynamics of urban ecosystem resilience. This study developed an index system utilizing the resistance-adaptability-recovery framework to measure these dynamics. By applying the advanced multi-attribute boundary area comparison method and a spatial autocorrelation model, we investigated the spatiotemporal variations and spatial correlation patterns of urban ecological resilience across the YRB. The results of this study indicated that: (1) from 2011 to 2020, the value of urban ecological resilience index (UERI) in the YRB consistently ranged between 0.43 and 0.83, and the resilience degree of the urban ecosystem in the YRB progressively improved, with notably higher resilience in the southeast compared to the northwest; (2) the resilience degree of the urban ecosystem in the YRB was non-equilibrium in space. Spatial analysis indicates significant disparities in resilience levels across different areas within the YRB, marked by considerable fluctuations in the global Moran's I index and significant changes in local autocorrelation clustering patterns; and (3) key factors such as wastewater discharge volume, sewage treatment rate, and the rate of non-hazardous treatment of domestic waste were identified as critical determinants of the overall ecological resilience. This research not only deepens our understanding of the factors driving urban ecological resilience but also aids in the formulation of strategic regional policy for sustainable development across the YRB. [ABSTRACT FROM AUTHOR]

Exploring the ecological footprint and ecological carrying capacity is an effective method to evaluate the sustainable development and utilization of natural resources. Gansu Province, one of the typical arid regions in northwest China that is primarily focused on agriculture, was selected to analyze the evolution of the water ecological footprint and carrying capacity in this paper. In addition, the breadth and depth of the water footprint were combined to further evaluate the current situation of water resource utilization and management across different regions. This study can complement the research on the ecological footprint in arid areas dominated by agriculture. The results showed that (1) the agricultural water footprint was the main footprint and the key to water conservation. The overall water ecological footprint indicated a slow decline trend from 2009 to 2022 in Gansu Province. There was a gradual reduction in the agricultural water footprint and a notable increase in the ecological environment water footprint, indicating water use structures were generally becoming reasonable. (2) During the period, the eco-capacity and per capita eco-capacity exhibited similar trends in fluctuation and change, and spatial distribution was relatively dispersed due to the precipitation, total water resources, and industrial structure in the regions. (3) For prefecture-level cities, the depth of the water ecological footprint showed obvious spatial agglomeration. According to the results, the water footprint breadth of Zhangye and Longnan was larger, and the water resource flow can meet the local water demand. The water footprint depth of Lanzhou, Jinchang, Baiyin, and Jiayuguan was high, indicating the stock of water resources needs to be consumed to satisfy social production and living. The results can provide a scientific basis for the effective management and rational conservation of water resources. [ABSTRACT FROM AUTHOR]

Accurate regional carbon sequestration estimates are essential for China's emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran's spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region. [ABSTRACT FROM AUTHOR]

Introduction: Investigation of the evolutionary trend of habitat quality in karst and rocky desertification zones is crucial for enhancing ecological security and conservation. Methods: Analysis of land use statistics from the years 2000, 2010, and 2020, changes in habitat quality (HQ) and land use (LULC) between 2000 and 2020 were analyzed using Huize County in Yunnan Province as an example. The InVEST and FLUS models were applied to simulate LULC under different scenarios in 2030 and 2040 and assess changes in spatial gradients of habitat quality at each timepoint and factors influencing them. Results: The findings indicated that (1) The predominant land use types are grassland and woodland, experiencing the most significant growth in urbanized areas, the main sources of which are paddy fields and high-cover grassland. (2) The habitat quality between 2000 and 2020 was average and displayed a consistent decline. The spatial distribution pattern indicates low HQ in urban areas, high HQ in the outskirts, low HQ in the south-west, and high HQ in the north-east. In all four scenarios, habitat quality predominantly decreases in urban areas and regions with a dense concentration of built-up land. (3) Habitat quality spatial distribution is primarily affected by the type of land use, with NDVI being the secondary determinant. Discussion: The ecological environment of Huize County must be restored and safeguarded with a focus on ecological priorities and harmonious development scenarios. This study provides methodological lessons for ecorestoration and policymakers in areas of karstic rocky desertification. [ABSTRACT FROM AUTHOR]

In recent years, the international community has increasingly focused on the "dual carbon" issue, as human-induced land use changes significantly impact ecosystem structure and carbon cycling. This study analyzes land use changes in the economic zone of the northern Gulf of Guangxi from 1980 to 2020, utilizing the InVEST model to simulate spatiotemporal changes in carbon storage and conducting zoning studies through spatial analysis. The findings reveal that ① forest land and arable land dominate the northern Gulf of Guangxi's land use, with notable changes observed in forest land, unused land, and construction land areas. Forest land and construction land have increased by 1761.5 km2 and 1001.19 km2, respectively, while unused land has decreased by 1881.18 km2 from 2000 to 2020. ② The total carbon storage values in the northern Gulf of Guangxi in 1980, 2000, and 2020 were, respectively, 504.91 × 106/t, 487.29 × 106/t, and 500.31 × 106/t, with the expansion of construction land and conversion of forest land being the main reasons for the decrease in carbon storage. ③ In the northern Gulf of Guangxi, there is a slight upward trend in total carbon storage values over time. Spatially, higher carbon storage values are observed in mountainous and hilly areas at high altitudes, while the central and southern coastal areas exhibit lower carbon storage values. ④ The local spatial autocorrelation results reveal that Pu Bei County exhibits high–high clustering of carbon storage, while He Pu County undergoes a transition from high–low to low–low clustering, and several other administrative areas in Beihai demonstrates low–low clustering. Due to the imperative of economic development, the expansion of urban construction land encroaches upon ecological land, resulting in a decline in carbon storage. Therefore, in the Northern Gulf of Guangxi, it is essential to implement measures such as reforestation and establish ecological protection areas such as forests, grasslands, and wetlands to develop effective carbon sequestration methods and compensate for the carbon loss caused by the expansion of construction land. [ABSTRACT FROM AUTHOR]

Abstract Addressing the need to harmonize environment conservation and sustainable economic development within the Yellow River Basin (YRB) requires a profound comprehension of the spatiotemporal dynamics of urban ecosystem resilience. This study developed an index system utilizing the resistance-adaptability-recovery framework to measure these dynamics. By applying the advanced multi-attribute boundary area comparison method and a spatial autocorrelation model, we investigated the spatiotemporal variations and spatial correlation patterns of urban ecological resilience across the YRB. The results of this study indicated that: (1) from 2011 to 2020, the value of urban ecological resilience index (UERI) in the YRB consistently ranged between 0.43 and 0.83, and the resilience degree of the urban ecosystem in the YRB progressively improved, with notably higher resilience in the southeast compared to the northwest; (2) the resilience degree of the urban ecosystem in the YRB was non-equilibrium in space. Spatial analysis indicates significant disparities in resilience levels across different areas within the YRB, marked by considerable fluctuations in the global Moran’s I index and significant changes in local autocorrelation clustering patterns; and (3) key factors such as wastewater discharge volume, sewage treatment rate, and the rate of non-hazardous treatment of domestic waste were identified as critical determinants of the overall ecological resilience. This research not only deepens our understanding of the factors driving urban ecological resilience but also aids in the formulation of strategic regional policy for sustainable development across the YRB.

Understanding the spatial interaction among residents, cooling service, and heat risk area in complex urban areas is conducive to developing targeted management. However, traditional urban thermal environment assessments typically relied on simple linear integration of associated indicators, often neglecting the spatial interaction effect. To explore the spatial interaction among the three elements, this study proposes an accessibility-based urban thermal environment assessment framework. Using Zhengzhou, a rapidly urbanizing city, as an example, remotely sensed images from three periods (2010, 2015 and 2020) were applied to extract urban green space (UGS) and hot island area (HIA). An improved two-step floating catchment area (2SFCA) method and bivariate local Moran’s I were employed to explore whether residents’ clustering locations are more likely to access cooling service or to be exposed to heat risk. The results demonstrate that the UGS in the city has been expanding, whereas the HIA shrank within the inner city in 2015 and then increased in 2020. Even though the urban thermal environment may have improved in the last decade, the spatial interaction among the residents, cooling service and heat risk area could be exacerbated. Spatial autocorrelation shows an increase in locations that are disadvantageous for resident congregation. Even when sufficient cooling services were provided, residents in these areas could still be exposed to high heat risk. The developed urban thermal environment framework provides a novel insight into the residents’ heat risk exposure and cooling service accessibility, and the findings could assist urban planners in targeting the improvement of extra heat exposure risk locations.

In order to explore the spatial distribution, spatial pattern evolution and spatial aggregation of the coupling and coordination of water resources utilization, low-carbon emission and green development of the Upper and Middle Yellow River, coupling coordination degree model, standard deviation ellipse and spatial autocorrelation analysis method were used to analyze the spatial heterogeneity of the coupling and coordination of water resources utilization, low-carbon emission and green development in 54 cities of the Upper and Middle Yellow River from 2010 to 2020. The results show that a) the comprehensive evaluation index of the three subsystems has been improved to varying degrees and the degree of coupling and coordination of each city is different. The proportion of primary coordination cities has increased from 55 % in 2010 to 74 % in 2020, and there are fewer intermediate coordination cities. b) The main migration direction of the center of gravity of the coupling coordination degree of the three subsystems is southwest, and the direction of the long axis of the standard deviation ellipse is north- west-southeast. The standard deviation of the long axis and the short axis is reduced by 2.21 km and 0.59 km, respectively, and there is a gathering trend of migration to the southwest. c) The coupling coordination degree of the three subsystems has a significant positive correlation in space. The high-high aggregation is mainly distributed in the south and west of the middle reaches of the Yellow River, the high-low aggregation is mainly distributed in Lanzhou City, and the low-low aggregation is mainly distributed in the west of the upper reaches of the Yellow River. Although the coupling coordination degree of the three subsystems of the Upper and Middle Yellow River has been increased year by year, the spatial heterogeneity is obvious. [ABSTRACT FROM AUTHOR]

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas. [ABSTRACT FROM AUTHOR]

Landscape ecological risk assessment (LERA) is the basis of regional landscape pattern optimization, and a tool that can help achieve a win-win situation between regional development and ecological protection. The landscape ecological risk (LER) of the southern end of the Qinling Mountains, China exhibited an increasing trend after the year 2000, but the degree of increase and the spatial and temporal dynamics were not clear, limiting the formulation and implementation of landscape optimization measures in the area. Here, we constructed a landscape pattern risk index ERI by combining data on landscape disturbance and landscape vulnerability from land use information for Shangluo City for years 2000, 2005, 2010, 2015, and 2020; then, we calculated a LER level and its spatial and temporal dynamics for Shangluo City for years 2000 to 2020. Moran's I and LISA indices were used to characterize the spatial correlation of ERI in Shangluo City. We found that Shangluo had a large proportion of medium-risk areas, and its LER shifted from medium-high, high in year 2000 to medium risk, medium-low and low risk in year 2020, and LER of Shangluo was clustered in space but the degree of clustering decreased in the past 20 years. We conclude that the development strategy of Shangluo should depend on providing a sustainably-developed environment. [ABSTRACT FROM AUTHOR]

During rapid urbanization, megacities see disparities in the spatial distribution of basic public service facilities and the urban built environment. Current research on basic public service facilities focuses on equal spatial distribution, leading to the overlook of their match with the surrounding built environment. Based on a case study on education, healthcare, and elderly care facilities in Chengdu central urban area, this paper analyzes the spatial distribution of these facilities and the built environment, and examines their spatial matching degree using a bivariate spatial autocorrelation model. The findings reveal the following insights: firstly, education and healthcare facilities present a "one core and multi-center" spatial distribution pattern, with the city center serving as the core and suburban subdistricts acting as the multiple centers, while elderly care facilities present a core-edge distribution pattern in contrast; secondly, the built environment presents a core-edge radial distribution pattern, with the city center acting as the nucleus that connects suburban subdistricts; thirdly, the spatial matching degree between the facilities and the urban built environment from the city center outwards is characterized by high, low, medium, and high levels. Notably, a significant spatial mismatch is observed in urban-rural transitional zones. In light of these findings, the paper comes to a conclusion that during urbanization, the efforts to equalize basic public service facilities in the urban-rural transitional zones may lead to spatial mismatches, to which urban planners and managers should pay close attention. [ABSTRACT FROM AUTHOR]

The power sector has been the industry that generates the most carbon emissions in China, with its share of emissions reaching 50% at its peak, causing serious environmental problems. Therefore, the power sector is the core and key part to achieve the overall national goal of reducing emissions. In this study, the Undesirable-SBM (Slack-Based Measure) model was used to assess the carbon emission efficiency of the thermal power sector in China by province in the period from 2010 to 2019. Further, the index decomposition is carried out based on the construction of the global carbon emission efficiency index in conjunction with the Malmquist index, and the causes and sensitivities of carbon emission efficiency changes are explored by technical efficiency changes and technical progress changes. Finally, the spatial correlation of carbon emission efficiency in thermal power industry is diagnosed by combining Moran index. The results show that the overall average carbon emission efficiency of China's provincial thermal power industry is low, with some regional differences, showing a decreasing order of east-west and central regions, and the carbon emission efficiency of thermal power industry has significant spatial correlation between regions, with positive spillover effects on neighboring provinces, and strengthening over time. The carbon emission efficiency itself is lower in the central and western regions, and it is easier to achieve a larger improvement, and the positive impact of technological changes on efficiency changes is stronger. Therefore, inter-regional technical cooperation can help improve carbon efficiency. [ABSTRACT FROM AUTHOR]

Protecting the ecological environment of the Yellow River Basin (YRB) is the key to promoting high-quality development of the whole YRB. Based on the information on environmental governance and the ecological environment in the YRB from 2016 to 2020, we constructed the regional eco-environment index (ECI) of the YRB, measured the government's environmental governance capacity, and used the entropy weight method and relevant spatial statistical tools to reveal the spatial distribution characteristics of the ECI in the YRB. In addition, we used the method of configuration qualitative comparative analysis to explore the adaptation path of environmental governance for improving the ECI. The study showed that the regional ECI of the YRB was obviously heterogeneous, showing a spatial pattern of "good in the upstream, middle in the downstream, and depressed in the midstream." There are three appropriate paths for environmental governance to improve the ecological environment: the government efficiency-oriented, rule of law-oriented, and pressure-oriented paths, among which the government efficiency-oriented path is the most common path. The findings propose the governance path of the ecological environment in the YRB from the perspective of configuration, reveal the configuration relationship among the factors affecting the government's environmental governance, broaden ideas for solving ecological environment improvement problems in the YRB, and enrich the research methods of the government's environmental governance. [ABSTRACT FROM AUTHOR]

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran's I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran's I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems. [ABSTRACT FROM AUTHOR]

Despite the cross-border availability of almost all goods and services online due to global Internet access, the domestic origin of sellers remains significant. This study examines the preferences for domestic versus cross-border goods and services in online purchases in the EU online market from 2020 to 2023. We use quantitative methods including ordinary least squares (OLS), decision trees, and spatial autocorrelation analysis. We find significant effects of currency, language(s), and Internet use on domestic online purchases, while cross-border online purchases are further influenced by prices and urbanization. Our analysis reveals patterns based on the origin of the seller: domestic, intra-EU, or non-EU seller. There is a strong preference for electronic goods and services, regardless of the seller's origin, while physical goods show a decreasing preference from domestic to intra-EU and non-EU sellers. Limited geographical effects and spatial patterns in online retailing were found, with a trend towards domestic localization. These differences in e-commerce by seller origin are primarily driven by country-specific characteristics (language(s), currencies) rather than geographic distance. The variation in the purchase of goods and services also depends on their physical and electronic form, that is, digital ordering and/or digital delivery. The expansion of e-commerce and the importance of country-specific characteristics require the development of standards to measure these influences. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the epidemiological characteristics and spatiotemporal clustering of hepatitis A in mainland China from 2004 to 2019 and to evaluate the practical impact of integrating hepatitis A vaccines into the Expanded Program on Immunization (EPI). Spatial and temporal autocorrelation and spatiotemporal scanning statistics were used to perform spatial and temporal characterization to quantify the spatial similarity or degree of aggregation of geographic data, and Geographical and Temporal Weighted Regression (GTWR) models were used to reveal spatial and temporal heterogeneity in the relationships between variables to test for spatial and temporal outbreaks of disease and other factors, such as socio-economic factors. Spatially, the incidence rates exhibited a west-high and east-low spatial differentiation, with the High-High (HH) clusters predominantly located in the western regions, maintaining stability butgradually diminishing. Hepatitis A prevalence peaked during the initial study period (2004-2008) showing significant spatial clustering. However, since the inclusion of hepatitis A vaccine in the immunization program in 2008, the incidence rates of hepatitis A in mainland China significantly decreased demonstrating the positive impact of immunization strategies. In addition to the effects of vaccination, socio-economic factors such as education level, water resources and age groups showed significant associations with hepatitis A incidence rates. Increased vaccine coverage and improved social conditions are crucial for controlling hepatitis A in China.

Abstract Background Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district. Methods Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence. Results From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016–2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014–2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations. Conclusions From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Background: Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district. Methods: Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence. Results: From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016–2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014–2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations. Conclusions: From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope. [ABSTRACT FROM AUTHOR]

The traits of rural domestic sewage emission are unclear, negatively affecting rural domestic sewage treatment and sewage management. This study used data from the Second National Pollution Source Census Bulletin to establish a data set. The spatial distribution characteristics and main factors influencing rural sewage discharge in the Northern Region were studied using spatial autocorrelation analysis and structural equations. The findings demonstrated that (1) a significant Spearman correlation between drainage water volume (DWV), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) and that the correlation coefficients between DWV and COD, NH3–N, TN and TP were 0.87**, 1.0**, 0.99**, 0.99**, respectively; (2) rural sewage discharge showed spatial autocorrelation, and rural domestic sewage discharge in the districts and counties with an administration was significantly higher than in the surrounding areas; and (3) social development was the main driver rural domestic sewage changes (path coefficient was 0.407**), and the main factors influencing rural domestic sewage discharge were the urbanization rate, years of education, and population age structure. This study obtained the spatial variation law and clarified the main influencing factors of rural domestic sewage to provide data support and a theoretical basis for subsequent rural sewage collection and treatment. Use of the Inner Mongolia Autonomous Region in northern China as a typical case, provides a theoretical foundation for scientific decision-making on rural domestic sewage treatment at the national and regional levels and offers new perspectives for managing pollutants. [ABSTRACT FROM AUTHOR]

Carbon storage plays a vital role in the provision of ecosystem services, and the alteration of land use has a profound influence on the carbon storage capacity of ecosystems. Therefore, in this study, two scenarios of natural evolution scenario (NES) and ecological conservation scenario (ECS) were set up based on the development of Mohe City,China. Meanwhile, a coupled model of LUCC and ecosystem carbon storage was established using Logistic-CA-Markov and InVEST models, as well as optimal parameters_based geographical Detector and GeoDa, to predict the distribution and change of ecosystem carbon storage based on LUCC in the future. The effects of different influencing factors on the spatial differentiation of carbon storage were also explored. The results show that: (1) From 1980 to 2020, the land use type in Mohe City was dominated by the forest and its area decreased; the area of wetland increased. From 2020 to 2040, ecological benefits will be shown under the ECS, with an increase in forest and a slower expansion of built-up. (2) Carbon loss in Mohe City from 1980 to 2020 was 4.04Tg. Under the ECS carbon storage increased slightly by 0.2Tg. Soil carbon storage was the main carbon pool in Mohe City, and forest was the largest contributor. The carbon storage of Mohe city in 2030 and 2040 has a strong positive spatial correlation. Hot spots in more than a cold spots area, the high value area is concentrated in the east, low concentrated in urban areas. (3) Apart from LUCC, mean annual precipitation was the most significant factors affecting the spatial differentiation variability of carbon storage. The interactions of mean annual precipitation and population density with other factors exhibit a non-linear enhancement,which had a coefficient of 21.91%. This study contributes to a deeper understanding of the relationship between LUCC and carbon storage. [ABSTRACT FROM AUTHOR]

IntroductionInvestigation of the evolutionary trend of habitat quality in karst and rocky desertification zones is crucial for enhancing ecological security and conservation.MethodsAnalysis of land use statistics from the years 2000, 2010, and 2020, changes in habitat quality (HQ) and land use (LULC) between 2000 and 2020 were analyzed using Huize County in Yunnan Province as an example. The InVEST and FLUS models were applied to simulate LULC under different scenarios in 2030 and 2040 and assess changes in spatial gradients of habitat quality at each timepoint and factors influencing them.ResultsThe findings indicated that (1) The predominant land use types are grassland and woodland, experiencing the most significant growth in urbanized areas, the main sources of which are paddy fields and high-cover grassland. (2) The habitat quality between 2000 and 2020 was average and displayed a consistent decline. The spatial distribution pattern indicates low HQ in urban areas, high HQ in the outskirts, low HQ in the south-west, and high HQ in the north-east. In all four scenarios, habitat quality predominantly decreases in urban areas and regions with a dense concentration of built-up land. (3) Habitat quality spatial distribution is primarily affected by the type of land use, with NDVI being the secondary determinant.DiscussionThe ecological environment of Huize County must be restored and safeguarded with a focus on ecological priorities and harmonious development scenarios. This study provides methodological lessons for ecorestoration and policymakers in areas of karstic rocky desertification.

Promoting the high-quality development of urbanization is a crucial way to achieve sustainable development goals. Water resources are a rigid constraint for such development. However, There is still room for improvement in the research on the spatio-temporal pattern and key driving factors of high-quality urbanization development under the constraint of water resources. Aiming at the problem that the high-quality development of urbanization in the Yellow River Basin (YRB) is facing water resources constraints, this paper discussed the spatial–temporal pattern and key drivers of the high-quality development of urbanization in the YRB under water resource constraints by using the Analytic Hierarchy Process model reformed by entropy technology, multi-objective fuzzy membership function, spatial autocorrelation analysis and obstacle model. The results revealed that, first, we found the High-Quality Urbanization Development Index (UQDI) exhibited steady growth, while both the Water Resources Index (WRI) and the Water Resources Constraint Intensity (WRCI) initially increased and then decreased during the study period. Second, the UQDI displayed a dual-core pattern, with the Shandong Peninsula urban agglomeration and the Central Plains urban agglomeration serving as the cores. WRI exhibited relatively stable spatial distribution, with the southeast region outperforming the northwest. WRCI also demonstrated a relatively stable spatial distribution, resulting in two weak-constraint areas, represented by Shandong and Henan, and a strong-constraint area centered around Ningxia. Third, the UQDI, WRI, and WRCI exhibited spatial interdependence. The spatial interdependence of UQDI and WRI increased, while that of WRCI decreased during the study period. The three indexes also displayed a clear pattern of local clustering in space. Two low-value aggregation areas, namely Shandong and Henan, and a high-value aggregation area located in Ningxia, emerged during the study period. Last, the WRCI was primarily influenced by the fundamental conditions of water resources. The main influencing factors included per capita water resources, mean annual precipitation, total water resources, water resources modulus, and the total amount of foreign investment utilization. In light of the research findings, the paper proposes policy recommendations for promoting high-quality urbanization development in the YRB. This study provides decision-making guidance for countries and regions in arid areas to solve the water resources constraints on the high-quality development of urbanization.

PurposeTo explore the inter-regional health index at the city level to contribute to the reduction of health inequalities.MethodsEmployed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran’s index and local Moran’s index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen.ResultsThe level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran’s I = 0.237; 2019, Moran’s I = 0.226; 2021, Moran’s I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas.ConclusionOur study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

BackgroundPulmonary tuberculosis (PTB) is a major infectious disease that threatens human health. China is a high tuberculosis-burden country and the Hunan Province has a high tuberculosis notification rate. However, no comprehensive analysis has been conducted on the spatiotemporal distribution of PTB in the Hunan Province. Therefore, this study investigated the spatiotemporal distribution of PTB in the Hunan Province to enable targeted control policies for tuberculosis.MethodsWe obtained data about cases of PTB in the Hunan Province notified from January 2014 to December 2022 from the China Information System for Disease Control and Prevention. Time-series analysis was conducted to analyze the trends in PTB case notifications. Spatial autocorrelation analysis was conducted to detect the spatial distribution characteristics of PTB at a county level in Hunan Province. Space-time scan analysis was conducted to confirm specific times and locations of PTB clustering.ResultsA total of 472,826 new cases of PTB were notified in the Hunan Province during the 9-year study period. The mean PTB notification rate showed a gradual, fluctuating downward trend over time. The number of PTB notifications per month showed significant seasonal variation, with an annual peak in notifications in January or March, followed by a fluctuating decline after March, reaching a trough in November or December. Moran’s I index of spatial autocorrelation revealed that the notification rate of PTB by county ranged from 0.117 to 0.317 during the study period, indicating spatial clustering. The hotspot areas of PTB were mainly concentrated in the Xiangxi Autonomous Prefecture, Zhangjiajie City, and Hengyang City. The most likely clustering region was identified in the central-southern part of the province, and a secondary clustering region was identified in the northwest part of the province.ConclusionThis study identified the temporal trend and spatial distribution pattern of tuberculosis in the Hunan Province. PTB clustered mainly in the central-southern and northwestern regions of the province. Disease control programs should focus on strengthening tuberculosis control in these regions.

In recent years, the international community has increasingly focused on the “dual carbon” issue, as human-induced land use changes significantly impact ecosystem structure and carbon cycling. This study analyzes land use changes in the economic zone of the northern Gulf of Guangxi from 1980 to 2020, utilizing the InVEST model to simulate spatiotemporal changes in carbon storage and conducting zoning studies through spatial analysis. The findings reveal that ① forest land and arable land dominate the northern Gulf of Guangxi’s land use, with notable changes observed in forest land, unused land, and construction land areas. Forest land and construction land have increased by 1761.5 km2 and 1001.19 km2, respectively, while unused land has decreased by 1881.18 km2 from 2000 to 2020. ② The total carbon storage values in the northern Gulf of Guangxi in 1980, 2000, and 2020 were, respectively, 504.91 × 106/t, 487.29 × 106/t, and 500.31 × 106/t, with the expansion of construction land and conversion of forest land being the main reasons for the decrease in carbon storage. ③ In the northern Gulf of Guangxi, there is a slight upward trend in total carbon storage values over time. Spatially, higher carbon storage values are observed in mountainous and hilly areas at high altitudes, while the central and southern coastal areas exhibit lower carbon storage values. ④ The local spatial autocorrelation results reveal that Pu Bei County exhibits high–high clustering of carbon storage, while He Pu County undergoes a transition from high–low to low–low clustering, and several other administrative areas in Beihai demonstrates low–low clustering. Due to the imperative of economic development, the expansion of urban construction land encroaches upon ecological land, resulting in a decline in carbon storage. Therefore, in the Northern Gulf of Guangxi, it is essential to implement measures such as reforestation and establish ecological protection areas such as forests, grasslands, and wetlands to develop effective carbon sequestration methods and compensate for the carbon loss caused by the expansion of construction land.

The scientific evaluation and optimization of neighborhood walkability play a pivotal role in enhancing urban vitality and fostering green development. A model was initially constructed for evaluating urban neighborhoods walkability, which encompassed five dimensions and eighteen indicators based on Maslow's demand theory and Kano model. Subsequently, a combination of multi-source data and the improved CRITIC-entropy method was employed to assess the neighborhood walkability of Hefei City. Then the hotspot analysis was conducted to identify the spatial clusters of high and low values in each dimension, and the reasons for the creation of these clusters were analyzed. Finally, the concept of neighborhood vitality was introduced to explore the spatial matching relationship between the walkability and vitality using the bivariate Moran's index, and the methods for optimizing walkability were proposed. The results indicate that walkability can be evaluated based on Maslow's demand theory and Kano model from five dimensions: accessibility, convenience, safety, richness and comfortableness. What is more, significant spatial heterogeneity and autocorrelation of walkability are observed within the study area, along with disparities between walkability and neighborhood vitality. Building upon these findings, an optimization strategy of neighborhood walkability is proposed, which can provide a reference for the future design and renovation of walking space in Hefei City. [ABSTRACT FROM AUTHOR]

Given that PM2.5 concentration is not only related to local pollutants, but also affected by long-distance transmission of PM2.5 in adjacent areas, the key to improving the prediction accuracy of PM2.5 concentration is to comprehensively consider the effect of local influencing factors and the transmission effect of PM2.5 in adjacent areas. For this purpose, a novel seasonal discrete multivariable grey prediction model, encompassing spatial influencing factors, has been established. Firstly, we analyze the mechanism of spatial influencing factors and the reasonableness of using PM2.5 concentration in adjacent areas as the spatial influencing factors. Secondly, based on the spatial agglomeration characteristics of PM2.5 concentration, the K - means clustering algorithm is used to cluster adjacent cities with similar PM2.5 concentration, then the comprehensive value of PM2.5 concentration in each city cluster is calculated by weighted average combination. On this basis, a driving term of spatial influencing factors and a cosine trigonometric function term are introduced into the novel model to characterize the effect of spatial influencing factors on PM2.5 concentration and the seasonal fluctuation of itself, respectively. More importantly, the Genetic Algorithm Toolbox is employed to optimally determine the emerging parameters of this model, and the time response function of the novel model is calculated by mathematical induction method. Lastly, the new model is deemed valid through testing its PM2.5 concentration predictions for the cities of Beijing, Tianjin, and Baoding in the Beijing-TianjinHebei region. Based on the original observations from 2018Q1 to 2023Q2, the novel model is built for PM2.5 concentration prediction in 2023Q3 to 2024Q2 for the three cities. The findings imply that the newly developed model outperforms its competitors significantly and has the potential to serve as a robust tool for predicting PM2.5 concentration. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect refers to the phenomenon where central urban areas exhibit higher temperatures than surrounding suburban areas due to concentrated human activities and building structures. This effect significantly impacts urban environments, the quality of life of residents, and energy demands. With the continuous advancement of urbanization, the UHI effect is becoming increasingly severe, making it a focal point in the fields of urban planning and architectural design. However, existing studies often rely on macro-climatic data and lack a thorough analysis of the thermodynamic properties at the urban micro-scale, failing to fully reveal the mechanism behind the impact of architectural layout on the UHI effect. This study begins with a thermodynamic perspective, exploring the changes in thermal stability of the boundary layer in different urban areas under the UHI effect through the heat flux equation. It analyzes the impact of different architectural layouts on the urban thermal environment. The research demonstrates that urban heating disturbances and the average state induced by the UHI effect have distinct regional characteristics in terms of boundary layer thermal stability. Furthermore, by utilizing spatial autocorrelation analysis and the principle of hot and cold spot identification, this paper deeply investigates the spatial autocorrelation and its evolution in urban architectural layouts, assessing the potential of architectural layouts in mitigating the UHI effect. These findings provide new perspectives and tools for urban planning, contributing to the optimization of the urban thermal environment, enhanced energy efficiency, and improved resident comfort. [ABSTRACT FROM AUTHOR]

This study examined the geographical distribution of home- and community-based services (HCBS) resources in Taiwan's Long-Term Care 2.0 policy and explored its association with the number of deaths among older adults. The main outcome of the study was determination of the number of deaths among older adults in townships (N = 346) in 2021. The results showed that home-based HCBS had a significant positive association with mortality among older adults; moreover, community-based and complementary services, which are highly clustered within a township and among its neighbors, exert a significant protective effect on mortality among older adults. Stratified analyses showed a significantly lower mortality among older adults using adult foster care and transportation services, but a significantly higher mortality among older adults using home-based professional care and respite care services, after considering the sociodemographic characteristics of older adults, urbanization, and the number of long-term care resources in the spatial analysis. [ABSTRACT FROM AUTHOR]

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north–south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network. [ABSTRACT FROM AUTHOR]

Carbon storage plays a vital role in the provision of ecosystem services, and the alteration of land use has a profound influence on the carbon storage capacity of ecosystems. Therefore, in this study, two scenarios of natural evolution scenario (NES) and ecological conservation scenario (ECS) were set up based on the development of Mohe City,China. Meanwhile, a coupled model of LUCC and ecosystem carbon storage was established using Logistic-CA-Markov and InVEST models, as well as optimal parameters_based geographical Detector and GeoDa, to predict the distribution and change of ecosystem carbon storage based on LUCC in the future. The effects of different influencing factors on the spatial differentiation of carbon storage were also explored. The results show that: (1) From 1980 to 2020, the land use type in Mohe City was dominated by the forest and its area decreased; the area of wetland increased. From 2020 to 2040, ecological benefits will be shown under the ECS, with an increase in forest and a slower expansion of built-up. (2) Carbon loss in Mohe City from 1980 to 2020 was 4.04Tg. Under the ECS carbon storage increased slightly by 0.2Tg. Soil carbon storage was the main carbon pool in Mohe City, and forest was the largest contributor. The carbon storage of Mohe city in 2030 and 2040 has a strong positive spatial correlation. Hot spots in more than a cold spots area, the high value area is concentrated in the east, low concentrated in urban areas. (3) Apart from LUCC, mean annual precipitation was the most significant factors affecting the spatial differentiation variability of carbon storage. The interactions of mean annual precipitation and population density with other factors exhibit a non-linear enhancement,which had a coefficient of 21.91%. This study contributes to a deeper understanding of the relationship between LUCC and carbon storage.

Abstract Background The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study’s objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively. Method Age-standardized incidence rates (ASIR) and death rates (ASDR) were collected from the GBD 2019. Trend analysis was conducted by Joinpoint regression of ASRs from 1990 to 2019. Spatial autocorrelation analysis was performed to obtain spatial patterns. The association between SDI and burden of CKD due to hypertension was estimated using a Pearson correlation analysis. Results The global ASIR and ASDR due to hypertension-related CKD were 19.45 (95% CI, 17.85 to 21.09) and 5.88 (95% CI, 4.95 to 6.82) per 100 K population in 2019, representing increases of 17.89% and 13.29% compared to 1990, respectively. The elderly population and males were found the highest ASIR and ASDR. The high SDI region had the highest ASIRs, while low SDI regions experienced the highest ASDRs. Joinpoint regression found both global ASIR and ASDR showed increasing trends, with the highest increases observed in middle- and high-SDI regions, respectively. The SDI exhibited a positive association with ASIRs but displayed an inverse V-shaped correlation with the average annual percentage change (AAPC) of ASIRs. Spatial autocorrelation analysis revel significant positive spatial autocorrelation for the AAPC of ASDRs and ASIRs, from 1990 to 2019. Conclusions Results met the objectives, and demonstrated a rising global burden of hypertension-related CKD. Factors such as aging, gender, and regional variations should be considered when designing control measures and developing healthcare systems to effectively address the burden of this complex condition.

Airborne fine particulate matter poses a serious threat to human health, and investigating the impact of green space landscape patterns on PM2.5 concentrations is conducive to reducing the risk of respiratory diseases by adjusting the green space pattern to decrease PM2.5 concentrations. Utilizing land use remote sensing monitoring data and PM2.5 concentration data in Chongqing city from 1980 to 2020 as the foundational dataset, the landscape pattern index method and spatial autocorrelation analysis were employed to study the characteristics of green space landscape patterns and PM2.5 concentration changes. Furthermore, a geographical and temporal weighted regression(GTWR)model was applied to explore the influence of changes in green space landscape pattern indices on PM2.5 concentrations and their spatiotemporal heterogeneity. The results show that PM2.5 concentrations in Chongqing city gradually increase from 1980 to 2010, followed by a gradual decrease from 2010 to the present; simultaneously, the spatial distribution exhibits significant agglomeration characteristics, primarily manifesting as low-low agglomeration in the east and high-high agglomeration in the west. Area index(TA), patch density index(PD), and patch cohesion index(COHESION)of forest, grassland and agricultural land show significant correlations with PM2.5 concentrations; specifically, area index of forest is negatively correlated, while area indexes of arable land and grassland are positively correlated; patch density indexes of forest and grassland are positively correlated, while patch density index of arable land is negatively correlated; patch cohesion indexes of forest, grassland and arable land are all negatively correlated. In the metropolitan area, area index of grassland and patch density index of arable land exhibit a stronger negative impact on PM2.5 concentrations; in the urban clusters of Three Gorges reservoir area in the northeast and Wuling mountain area in the southeast of Chongqing city, aggregation index(AI), patch density index, patch cohesion index of forest, and area index of arable land exert a stronger influence on PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Background: The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study's objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively. Method: Age-standardized incidence rates (ASIR) and death rates (ASDR) were collected from the GBD 2019. Trend analysis was conducted by Joinpoint regression of ASRs from 1990 to 2019. Spatial autocorrelation analysis was performed to obtain spatial patterns. The association between SDI and burden of CKD due to hypertension was estimated using a Pearson correlation analysis. Results: The global ASIR and ASDR due to hypertension-related CKD were 19.45 (95% CI, 17.85 to 21.09) and 5.88 (95% CI, 4.95 to 6.82) per 100 K population in 2019, representing increases of 17.89% and 13.29% compared to 1990, respectively. The elderly population and males were found the highest ASIR and ASDR. The high SDI region had the highest ASIRs, while low SDI regions experienced the highest ASDRs. Joinpoint regression found both global ASIR and ASDR showed increasing trends, with the highest increases observed in middle- and high-SDI regions, respectively. The SDI exhibited a positive association with ASIRs but displayed an inverse V-shaped correlation with the average annual percentage change (AAPC) of ASIRs. Spatial autocorrelation analysis revel significant positive spatial autocorrelation for the AAPC of ASDRs and ASIRs, from 1990 to 2019. Conclusions: Results met the objectives, and demonstrated a rising global burden of hypertension-related CKD. Factors such as aging, gender, and regional variations should be considered when designing control measures and developing healthcare systems to effectively address the burden of this complex condition. [ABSTRACT FROM AUTHOR]

Zhuchao Wu,1,* Gengfeng Fu,2,* Qin Wen,1,* Zheyue Wang,1 Lin-en Shi,2 Beibei Qiu,1 Jianming Wang1,3,4 1Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China; 2Department of STI and HIV Control and Prevention, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, 210009, People’s Republic of China; 3Department of Epidemiology, Gusu School, Nanjing Medical University, Nanjing, 211166, People’s Republic of China; 4Changzhou Medical Center, Nanjing Medical University, Nanjing, 211166, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianming Wang, Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China, Tel +86-25-86868414, Email jmwang@njmu.edu.cnPurpose: Pulmonary tuberculosis (PTB) is a severe chronic communicable disease that causes a heavy disease burden in China. Human Immunodeficiency Virus (HIV) and PTB coinfection dramatically increases the risk of death. This study analyzes the spatiotemporal dynamics of HIV, PTB and HIV-PTB coinfection in Jiangsu Province, China, and explores the impact of socioeconomic determinants.Patients and Methods: The data on all notified HIV, PTB and HIV-PTB coinfection cases were extracted from Jiangsu Provincial Center for Disease Control and Prevention. We applied the seasonal index to identify high-risk periods of the disease. Time trend, spatial autocorrelation and SaTScan were used to analyze temporal trends, hotspots and spatiotemporal clusters of diseases. The Bayesian space-time model was conducted to examine the socioeconomic determinants.Results: The case notification rate (CNR) of PTB decreased from 2011 to 2019 in Jiangsu Province, but the CNR of HIV and HIV-PTB coinfection had an upward trend. The seasonal index of PTB was the highest in March, and its hotspots were mainly distributed in the central and northern parts, such as Xuzhou, Suqian, Lianyungang and Taizhou. HIV had the highest seasonal index in July and HIV-PTB coinfection had the highest seasonal index in June, with their hotspots mainly distributed in southern Jiangsu, involving Nanjing, Suzhou, Wuxi and Changzhou. The Bayesian space-time interaction model showed that socioeconomic factor and population density were negatively correlated with the CNR of PTB, and positively associated with the CNR of HIV and HIV-PTB coinfection.Conclusion: The spatial heterogeneity and spatiotemporal clusters of PTB, HIV and HIV-PTB coinfection are exhibited obviously in Jiangsu. More comprehensive interventions should be applied to target TB in the northern part. While in southern Jiangsu, where the economic level is well-developed and the population density is high, we should strengthen the prevention and control of HIV and HIV-PTB coinfection.Keywords: pulmonary tuberculosis, HIV, spatial autocorrelation analysis, socioeconomic determinants, Bayesian space-time interaction model

Reducing carbon emissions and improving vegetation carbon sequestration (VCS) have long been recognized as the key ways to address the aim of carbon neutrality, requiring global effort and typical regional experiences. This paper develops an assessment framework for measuring the net carbon sink efficiency (NCSE) based on the concept of net-zero carbon sink to depict both carbon emission and VCS simultaneously. This paper primarily runs a spatial autocorrelation analysis to explore spatial characteristics of urbanization and NCSE of Yangtze River Delta urban agglomeration (YRD) in China from 2000 to 2020, followed by employs a spatial econometric model to investigate the spatial spillover effect of urbanization on NCSE. The results show that NCSE in YRD exhibits clear spatial disparities, descending from the southern part dominated by mountainous hills to the flatter northern region. The spatial evolutionary trend of urbanization and NCSE has a significant positive correlation, manifested as H-H and L-L agglomeration, covering most counties in southeast coastal areas and northern plains, respectively. The NCSE can improve even during the continued urbanization in YRD. Specifically, for every 1% increase in urbanization, NCSE will increase by 0.164%, of which 0.118% in direct and 0.046% in indirect growth, respectively. In addition, GDP per capita, industrial structure, technological progress, road area and direct foreign investment are benefit for NCSE. In contrast, population density, environmental regulation, and built-up area tend to decline NCSE. These findings may help to formulate the urban sustainable development planning and differentiated carbon neutrality policies.

The rapid development of urbanization has led to increasing uncertainties related to urban safety risks, which has brought certain challenges to the sustainable development of cities. The concept of urban resilience has found a new way to improve the ability of a city to absorb and resolve risks. However, the existing literature on the evaluation of urban resilience is mostly developed from a static perspective, lacking a systematic and dynamic understanding of the level of urban resilience. Therefore, this paper takes Hunan Province as the research object, determines the resilience evaluation indicators, collects the data of each indicator by using the observation method and the literature method, then chooses the comprehensive index method and other methods to measure the urban resilience level of Hunan Province in the years of 2010–2021, and observes the dynamic changes in the resilience level. And, we use the GeoDetector model to detect the dominant factors affecting the urban resilience level and the interaction between these factors. The results of this study show that: (1) The level of urban resilience in Hunan Province shows a steady upward trend from 2010 to 2021, but cities with low resilience levels hold a dominant position. Among all subsystems, the level of urban economic resilience is the highest. (2) From 2010 to 2021, the level of urban resilience in Hunan Province indicates a stepwise spatial structure in the spatial pattern, gradually decreasing from east to west. (3) The urban resilience of Hunan Province from 2010 to 2021 has a significant spatial agglomeration effect, mainly manifested as "L-H type" agglomeration and "L-L type" agglomeration. (4) The spatio-temporal differentiation of urban resilience is mainly caused by economic and social factors, while ecological, institutional, and infrastructure factors have a relatively small influence on the level of urban resilience. The interaction of impact factors will have a more significant influence on urban resilience. The research results of this article are of great significance for urban resilience construction in Hunan Province and even the whole country. [ABSTRACT FROM AUTHOR]

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting systems and the protection of cultivated land resources. This study takes Guangxi, China, as an example and constructs a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties in 2020. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) In 2020, the total value of cultivated land resources in Guangxi was USD 2859.3 billion, with the economic, social, and ecological values were USD 1674.2 billion, USD 417.7 billion and USD 767.3 billion USD, respectively. (2) The economic and social value of cultivated land per hectare at the county scale in Guangxi demonstrates similar spatial distribution patterns. High-value counties exhibit a strip-like distribution along the eastern edge, following a north–south orientation, while low-value counties form clusters in the central and western regions. (3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) The level of economic development in counties has a negative impact on the ecological value of cultivated land resources, while it has a positive impact on their social value. (5) Natural conditions, agricultural infrastructure conditions and agricultural development approaches are the key factors affecting the value of cultivated land resources. [ABSTRACT FROM AUTHOR]

Investigating the distribution characteristics of landscape ecological risk (LER) on terrain gradients is of great significance for optimizing the landscape pattern of ecologically vulnerable areas in mountainous regions and maintaining the sustainable development of the ecological environment. The Yuanmou Basin is a typical ecologically vulnerable area in the southwestern mountainous region of China, where issues such as soil erosion are pronounced, becoming one of the main factors restricting regional economic development. This study selected the Yuanmou Basin as the study area, and, using land use data from 2000, 2010, and 2020, constructed an LER assessment model based on disturbance and vulnerability. By integrating elevation and topographic position index data, we examined the spatiotemporal evolution characteristics of LER under different terrain gradients. The LER assessment results are summarized as follows: (1) From 2000 to 2020, the land use types of the Yuanmou Basin were mainly grassland, forest land and cropland. The land use showed a sharp increase in the cropland area and a simultaneous decrease in the grassland area, indicating a main land use evolution direction from grassland to cropland. (2) Over the span of 20 years, the average landscape ecological risk in the Yuanmou Basin slightly increased, specifically manifesting as a significant reduction in low ecological risk areas, while areas of medium and slightly lower ecological risks saw an increase. (3) The spatial distribution of LER in the Yuanmou Basin presents a pattern of being low on the periphery and high in the center, with significant positive spatial correlation, obvious spatial aggregation, as well as "high-high" and "low-low" clustering. (4) Low- and lower-risk areas in the Yuanmou Basin are distributed in the non-arid thermal zone and the medium–high terrain zone, while areas of medium, higher and high risk levels are mainly distributed in the arid thermal zone and the low terrain zone. The research results provide a scientific basis for optimizing and developing the land resources of the Yuanmou Basin. [ABSTRACT FROM AUTHOR]

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BackgroundAcute drug poisonings are increasing year by year and have become the leading cause of acute poisoning in Shanghai in recent years, and the related prevention and control work is faced with a tough situation. ObjectiveTo understand the composition of drugs leading to acute poisonings and describe the epidemiological tendency of reported acute drug poisonings in Shanghai. MethodsWe collected registered acute drug poisoning case information from the Shanghai Health Information System under Shanghai Health Statistics Center, including demographic characteristics and clinical diagnosis. There were totally 86476 cases reported from 2019 to 2021. The distributions of drugs and victims were described by year. Incidence tendency of acute drug poisonings was analyzed by chi-square test and the analysis focused on analgesic, hypnotics, and antidepressant drug-associated poisonings. Spatial autocorrelation analysis was performed by GeoDa1.20 through calculating global and local Moran's I. ResultsThere was an ascendant tendency in both case count (22132 cases in 2019, 29071 cases in 2020, and 35273 cases in 2021) and crude morbidity (0.89‰ in 2019, 1.21‰ in 2020, and 1.46‰ in 2021) of patients who received outpatient service or emergency treatment for acute drug poisonings from 2019 to 2021 in Shanghai. The top 3 kinds of acute poisoning drugs were analgesics, hypnotics, and antidepressants. The morbidity rates of acute drug poisonings associated with antidepressants (χ2=2700.15, P 60 years age group for the 3 years (male morbidity rate was 0.40‰-1.03‰, and female morbidity rate was 0.66‰-0.81‰). Regarding the causes of poisonings, accidental poisoning and exposure was the main cause in the ≤ 17 years groups (male constituent ratio was 57.64%-67.12%, and female constituent ratio was 55.27%-68.27%); suicide (male constituent ratio was 20.28%-43.51%, and female constituent ratio was 25.18%-52.02%) had a higher percentage than accidental poisoning and exposure (male constituent ratio was 16.97%-23.62%, and female constituent ratio was 12.76%-17.63%) in the 18-59 years age groups; accidental poisoning and exposure (male constituent ratio was 24.38%-45.18%, and female constituent ratio was 32.69%-38.11%) had a higher percentage than suicide (male constituent ratio was 12.35%-14.02%, and female constituent ratio was 11.92%-12.31%) in the > 60 years age group. The spatial autocorrelation analysis showed that the distribution of acute poisoning cases caused by analgesics, hypnotics, or antidepressants was not random. It was mostly positive spatial clustering. The high-morbidity area was always in the outer-ring circle, especially in Xuhui, Changning, and Jing'an districts, which suggested a high-high cluster pattern. ConclusionIn view of the increasing morbidity rate of acute drug poisoning cases in Shanghai in this study, it is urgent to take prevention and control actions. We should plan further studies and different strategies toward different victims with distinct drug poisoning characteristics and areas with high morbidity rates.

Increased spatial heterogeneity in recent years in the processes of attracting banking capital to the Russian economy negatively affects the pace of socio-economic development of regions. The purpose of the research is to assess the dynamics of changes in spatial heterogeneity in the processes of attracting banking capital in the Russian economy and to model the system of interregional relationships in these processes. Scientific novelty of research is the development of a methodological approach involving the systematic use of methods such as: spatial autocorrelation analysis according to the methodology of P. Moran and L. Anselin, regression analysis using panel data, testing cause-and- effect relationships using the Granger method, formation of a matrix of functional dependencies between regional systems. The developed methodological approach allowed to confirm the trend of increasing spatial heterogeneity in the processes of attraction of banking capital in the Russian economy, to identify regional centers with a high level of concentration and formation a matrix of interregional relationships. It is shown that almost all Russian banking capital is now concentrated in Moscow, St. Petersburg and the Kostroma region. The inflow of bank capital into the economy of these regions leads to its outflow from regions with inverse relationships (negative index of spatial autocorrelation). The results of the research can be used by the executive authorities of the federal and regional levels to find mechanisms to attract banking capital in the economy of regions. One such mechanism could be a reduction in the key rate of the Bank of Russia and State support for regional banks that use low interest rates for lending to households and enterprises in the real sector of the economy.

Plant translocations represent a solution for rescuing nonviable populations and recreating population networks. An essential criterion for translocation success is genetic diversity enhancement or maintenance in post-translocation generations. Genetic mixing, i.e. individual relatedness and spatial structure, also needs to be considered. We investigated the processes involved in the spatial patterns of genetic diversity in the first post-translocation generation (recruits and seed progeny) of translocated populations of the insect-pollinated, self-incompatible herb Campanula glomerata. We combined direct and indirect estimates of contemporary mating and gene dispersal using 15 microsatellite loci to estimate clonal extent, and genetic diversity and structure, to reconstruct sibship and parentage and to estimate pollen and seed dispersal distances. Genetic diversity in the first post-translocation generation is representative of the natural seed-source populations. Combining sources enhanced the effective number of breeders (Ne = 102–175). Clonal propagation was only marginal. Pollen and seed dispersal occurred at short distances within populations (up to 18 m), favouring fine-scale spatial genetic structure (SGS). This means that the enhanced genetic diversity, random mating and high admixture levels are ascribed to the mixed spatial arrangement of transplants. Stronger SGS might lead to biparental inbreeding and reduced seed set due to a lack of compatible neighbours, and impact the long-term population genetic sustainability. Increasing seed-dispersal distances within and among sites might be mediated by grazing livestock used for grassland management. Some long-distance pollen flow events (0.68–2.35 km) between populations were also detected, suggesting the potential of creating large translocated populations to restore connectivity by gene flow.

Changing landscape patterns would alter ecosystem components and functions, affecting the supply of ecosystem services. Understanding the spatial and temporal variations of ecosystem services is an important basis for ecosystem management and planning decisions and is of great significance for the realization of regional sustainable development. Based on Landsat TM/OLI remote sensing images from 1990, 2000, 2010, and 2020 in the Li River Basin, we explored the spatial and temporal variabilities of ecosystem services in the Li River Basin over the past 30 years, from both horizontal and vertical dimensions, using modified equivalence factor method and spatial autocorrelation analysis. The research findings are as follows: (1) Forestland has consistently been the dominant landscape type in the Li River Basin, with its area continuously increasing, while farmland, water bodies, and grassland have decreased, and construction land and bare land have increased. (2) The value of ecosystem services in the Li River Basin exhibited an initial increase followed by a decrease trend, with a net increase of 9.20 × 108 yuan. Forestland contributed the most to the value of ecosystem services. (3) Hydrological regulation and climate regulation are the dominant functions of the Li River Basin's ecosystems, accounting for over 50% of the total contribution. (4) The value of ecosystem services per unit area increases with increasing slope and elevation. The segments with slopes ranging from 15 to 25 degrees and elevation zones between 200 and 500 m have the highest total value of ecosystem services. (5) The overall level of ecosystem services in the Li River Basin is relatively high and continues to rise, but areas with a low ecosystem service value are gradually concentrated. (6) The Moran's I values for ecosystem services in all four periods are greater than 0, indicating a significant positive spatial autocorrelation. The overall pattern of ecosystem services is relatively stable, but there are significant spatial variations, which are characterized by lower values in the central area and higher values in the surrounding areas. The research findings provide a scientific basis for watershed ecological environment construction, optimal allocation of land resources, and sustainable landscape management. [ABSTRACT FROM AUTHOR]

This study proposes a strategy for selecting materials for Abies nephrolepis, a key subalpine species, to restore damaged forests in Mt. Gariwangsan while considering genetic diversity. The genetic diversity distribution of A. nephrolepis in Korea was evaluated using nuclear simple-sequence repeat (nSSR) markers and the genetic differences between populations were analyzed. A suitable genetic restoration population was selected and a strategy for selecting restoration materials was presented. The genetic diversity indices of 326 individuals across 10 populations, and the genetic relationships between populations, were analyzed using AMOVA, principal coordinate analysis, UPGMA, and STRUCTURE analysis. The genetic diversity of A. nephrolepis populations averaged 0.791, indicating relatively high genetic diversity. The results showed that the genetic differentiation pattern of A. nephrolepis was divided into two groups: Mt. Jungwangsan and other populations. Therefore, genetically diverse restoration materials can be secured from the Mt. Gariwangsan population to restore damaged areas. We found that individuals distributed within approximately 10 m were autocorrelated due to their high genetic similarity, whereas they were randomly distributed in the range of 15 m or more. The optimal sampling strategy involved collecting seeds from a minimum of 25 individuals within 10 m. This study suggests that genetic diversity in restoration material selection is necessary to enhance adaptability to environmental changes. [ABSTRACT FROM AUTHOR]

Carbon storage is a critical ecosystem service provided by terrestrial environmental systems that can effectively reduce regional carbon emissions and is critical for achieving carbon neutrality and carbon peak. We conducted a study in Kunming and analyzed the land utilization data for 2000, 2010, and 2020. We assessed the features of land utilization conversion and forecasted land utilization under three development patterns in 2030 on the basis of the Patch-generating Land Use Simulation (PLUS) model. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to estimate changes in carbon storage trends under three development scenarios in 2000, 2010, 2020, and 2030 and the impact of socioeconomic and natural factors on carbon storage. The results of the study indicated that (1) carbon storage is intimately associated with land utilization practices. Carbon storage in Kunming in 2000, 2010, and 2020 was 1.146 x 108 t, 1.139 x 108 t, and 1.120 x 108 t, respectively. During the 20 years, forest land decreased by 142.28 km², and the decrease in forest land area caused a loss of carbon storage. (2) Carbon storage in 2030 was predicted to be 1.102 x 108 t, 1.136 x 108 t, and 1.105 x 108 t, respectively, under the trend continuation scenario, eco-friendly scenario, and comprehensive development scenario, indicating that implementing ecological protection and cultivated land protection measures can facilitate regional ecosystem carbon storage restoration. (3) Impervious surfaces and vegetation have the greatest influence on carbon storage for the study area. A spatial global and local negative correlation was found between impervious surface coverage and ecosystem carbon storage. A spatial global and local positive correlation was found between NDVI and ecosystem carbon storage. Therefore, ecological and farmland protection policies need to be strengthened, the expansion of impervious surfaces should be strictly controlled, and vegetation coverage should be improved. [ABSTRACT FROM AUTHOR]

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Due to vast territory and disparate regional energy varieties and efficiency in China, the spatial and temporal distribution of CO2 emissions in regions is quite different. But the formulation of previous carbon reduction policies was mainly based on national or provincial emissions data, lacking of refined scale data. This paper first collected Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) night light datasets from 1992 to 2013 and constructed a CO2 emissions inversion estimation model. Then, the spatiotemporal dynamics were analyzed by global and local spatial autocorrelation methods. Findings are as follows: (1) Total CO2 emissions in the Yangtze River Delta showed an overall growth trend from 396 million tons in 1992 to 1.825 billion tons in 2013, with an average annual growth rate of 17.18%. (2) The relatively slow growth accounted for the highest proportion in five growth types of CO2 emissions and were mainly concentrated in the underdeveloped southwestern regions of the Yangtze River Delta. The rapid-growth were agglomerated in the eastern coast areas. (3) Hot spots and sub-hot spots were concentrated in Shanghai, Suzhou, and Ningbo. Cold spots and sub-cold spots included southwest part of Anhui and Zhejiang. The findings provided a decision-making basis for mitigating CO2 emissions more reasonably. [ABSTRACT FROM AUTHOR]

China's energy-intensive industry (EII) is characterized by high pollution, high energy consumption, and high emissions. It is essential to boost this sector's green total factor productivity (GTFP) in order to support the sustainable development of the China's economy and help to achieve the objective of carbon neutrality. This work measures the evolution of GTFP in EII and its subsectors at provincial and regional level from 2001 to 2019, identifies the causes of these changes, and finally analyzes the particular spatial aggregation effect of GTFP in EII. It is discovered that the GTFP of China's EII has significantly improved throughout the sample period and exhibits a spatial structure of "high in the coastal areas and low in the west and center." The main driver of GTFP growth for China's EII and its subsectors was technological advance. Smelting and pressing of ferrous metals (SPFM) and smelting and pressing of non-ferrous metals (SPNM) were the industries with the most significant technological progress. Remarkable spatial correlations existed among the GTFP of EII at provincial level. The GTFP values of EII in coastal regions were relatively high and tend to benefit the adjacent provinces but there was a polarization effect in the Middle Reaches of Yellow River (YR). Finally, policy implications are provided for the sustainable development of China's EII. [ABSTRACT FROM AUTHOR]