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Because significant global changes are currently underway in the Arctic, creating a large‐scale standardized database for Arctic marine biodiversity is particularly pressing. This study evaluates the potential of aquatic environmental DNA (eDNA) metabarcoding to detect Arctic coastal biodiversity changes and characterizes the local spatio‐temporal distribution of eDNA in two locations. We extracted and amplified eDNA using two COI primer pairs from ~80 water samples that were collected across two Canadian Arctic ports, Churchill and Iqaluit, based on optimized sampling and preservation methods for remote regions surveys. Results demonstrate that aquatic eDNA surveys have the potential to document large‐scale Arctic biodiversity change by providing a rapid overview of coastal metazoan biodiversity, detecting nonindigenous species, and allowing sampling in both open water and under the ice cover by local northern‐based communities. We show that DNA sequences of ~50% of known Canadian Arctic species and potential invaders are currently present in public databases. A similar proportion of operational taxonomic units was identified at the species level with eDNA metabarcoding, for a total of 181 species identified at both sites. Despite the cold and well‐mixed coastal environment, species composition was vertically heterogeneous, in part due to river inflow in the estuarine ecosystem, and differed between the water column and tide pools. Thus, COI‐based eDNA metabarcoding may quickly improve large‐scale Arctic biomonitoring using eDNA, but we caution that aquatic eDNA sampling needs to be standardized over space and time to accurately evaluate community structure changes. Despite the cold and well‐mixed coastal environment, eDNA composition was vertically heterogeneous, in part due to eDNA river inflow in the estuarine ecosystem, and differed between water column and tide pools. eDNA metabarcoding may quickly improve large‐scale Arctic biomonitoring, but we caution that water eDNA biomonitoring needs to be standardized over space and time to accurately evaluate community structure changes.

Cimatti, M., Ranc, N., Benítez-López, A., Maiorano, L., Boitani, L., Cagnacci, F., Čengić, M., Ciucci, P., Huijbregts, M.A.J., Krofel, M., López-Bao, J.V., Selva, N., Andren, H., Bautista, C., Ćirović, D., Hemmingmoore, H., Reinhardt, I., Marenče, M., Mertzanis, Y., Pedrotti, L., Trbojević, I., Zetterberg, A., Zwijacz-Kozica, T., Santini, L.

Background Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis. Methods Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence. Results In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM 10 , PM 2.5 , O 3 , NO 2 and SO 2 have impacts on TB incidence, and in different regions, the environmental factors show regional differences. Conclusions Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.

Summary Identification of spatio‐temporal distribution of vehicle loads is very important for understanding the exact loading conditions and behaviors of long‐span bridges. Using computer vision technology combining the monitoring information of the weigh‐in‐motion system (WIM) at one cross‐section and the camera along the bridge, a method to identify the spatio‐temporal distribution of vehicle loads for long‐span bridges is proposed. For moving vehicles, template images were sampled from the camera video at the location of the WIM, and the weight information of the captured vehicle was extracted from the output data sheet of the WIM based on the relationship of the pass time. Template matching and particle filter techniques were used to track the moving vehicle loads on the bridge. The images were processed using the computer vision technology. The video images obtained from cameras and the weight information measured by the WIM on the Hangzhou Bay Bridge were employed in this study. The effectiveness and accuracy of the proposed algorithm were validated through the in situ field test results on the Hangzhou Bay Bridge. Copyright © 2015 John Wiley & Sons, Ltd.

Objective Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). Methods Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011–2020). We employed spatial autocorrelation to examine the spatial–temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. Results The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R 2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). Conclusion This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

The Qihe River Basin, renowned as one of the cradles of Chinese civilization and a river that flows in the Psalms, boasts a rich cultural heritage. River basins have historically been the cradle of civilization, nurturing the prosperity and enduring cultures that have supported sustained human civilization. This research focuses on investigating the temporal and spatial transformations of traditional villages in the Qihe River Basin. By conducting a thorough literature review, consulting local chronicles, and undertaking field surveys, historical information on the ancient Qihe River and its significant tributaries was collected and analysed via ArcGIS 10.8. Spatial analysis techniques, such as the nearest neighbor index, kernel density estimation, standard deviation ellipse, geographic concentration index, and buffer analysis, were applied to analyse the spatiotemporal dynamics of the spatial distribution of traditional villages. Through a detailed exploration of various factors influencing village evolution, especially the role of rivers in driving changes, several key findings emerged: (1) Traditional villages in the Qihe River Basin exhibit a clustered distribution pattern; (2) the shifting centroid of traditional villages over six historical periods demonstrates a directional trend from southwest to northeast, with contemporary villages facing the risk of disappearance; and (3) factors such as proximity to water, elevation, slope, and socio-cultural elements are intricately linked to traditional villages, with water proximity emerging as a primary influencer. This study enhances our comprehension of the interplay between traditional village development and river basins, providing valuable theoretical insights for promoting the sustainable development of traditional villages in China.

Objective: Vaccine hesitancy is a major barrier to high coronavirus disease 2019 (COVID-19) vaccine coverage. To synthesize global research on COVID-19 vaccine hesitancy, a meta-analysis was conducted to provide scientific evidence for understanding its spatial and temporal variations and influencing factors. Methods: We searched the PubMed, Web of Science, and Embase databases for studies published in English between January 2020 and December 2023 and included cross-sectional and cohort studies with study populations that included the general adult population aged ≥18 years and provided quantitative data on COVID-19 vaccine acceptance or hesitancy. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis elements and guidance for abstracting and assessing data quality and validity. Two groups of investigators independently extracted the study characteristics, including the outcome variable (the vaccine hesitancy rate). Our meta-analysis used a random-effects model. The outcome of interest was COVID-19 vaccine hesitancy. The included studies were divided into two categories based on their definitions of COVID-19 vaccine hesitancy. Definition 1 combined vaccination behavior and willingness, and Definition 2 was based solely on willingness to vaccinate. Results: 855 studies were included in the final analytical dataset; 121 met Definition 1, and 734 met Definition 2. There were 277,285,178 participants in the included studies. In studies meeting Definition 1, hesitancy rates increased annually: 18.8 % in 2020, 29.1 % in 2021, and 30.8 % in 2022. However, in studies that met Definition 2, the hesitancy rates remained at 35 %. African studies reported the highest hesitancy rates globally (42.0 %), whereas European studies reported the lowest (16.5 %). Furthermore, there was a temporal association between mortality trends and COVID-19 hesitancy because the monthly cumulative death peaks coincided with lower hesitancy peaks. Conclusion: COVID-19 vaccine hesitancy increased across the continent during 2020–2022 and might be influenced by misinformation, policy changes, and public fatigue. Demographic factors like age, gender, and education also play a key role in vaccine hesitancy. The link between vaccine hesitancy and pandemic severity highlights the need for timely and effective public health responses. •Addressed heterogeneity in vaccine hesitancy definitions by identifying two primary categories.•855 studies with 277 M participants from 102 countries were included, providing a global perspective on vaccine hesitancy.•Global vaccine hesitancy rate was 28.2 % using Definition 1 and 34.8 % using Definition 2.•Hesitancy rates increased annually from 2020 to 2022, with Africa having the highest and Europe the lowest rates.•Found a pattern linking COVID-19 mortality and hesitancy, where higher disease burden leaded to lower hesitancy rates.

The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (I geo ) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment.

Background In the context of public health emergencies, the presence of medical and health talents (MHT) is critically important for support in any country or region. This study aims to analyze the spatial and temporal distributions and evolution of MHT in China and propose strategies and recommendations for promoting a balanced distribution. Methods This research used data from 31 provinces in China to construct a multidimensional index system for measuring the agglomeration level of MHT. The indices include talent agglomeration density (TAD), talent agglomeration scale (TAS), talent agglomeration intensity (TAI), and talent agglomeration equilibrium (TAE). Using provincial data from the years 1982, 1990, 2000, 2010, and 2020, a spatiotemporal analysis of the MHT agglomeration levels was conducted. Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran’s I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient. Results From the temporal level, the TAD and TAI of MHT showed an increasing trend over the studied period, whereas TAS decreased and TAE first increased and then decreased from 1982 to 2020. At the spatial level, the TAD, TAS, TAI, and TAE of MHT exhibited varied patterns among the eastern, central, and western regions of China, showing significant geographical disparities, generally demarcated by the Hu Huanyong Line. The regional dynamic distribution level of MHT in the country and the three regions were expanding. Spatial autocorrelation analysis using global and local Moran’s I for TAD, TAS, TAI, and TAE demonstrated significant regional differences. The Dagum Gini coefficient of TAD, TAS, TAI, and TAE revealed divergent trends in regional disparities, with overall declines in disparities for TAD and TAI, a slight increase for TAS, and fluctuating patterns for TAE. Conclusions From a temporal perspective, the overall number of MHT in China has been increasing annually at the national and provincial levels. From the spatial perspective, TAD, TAS, TAI, and TAE exhibit significant differences among the three regions. Kernel analysis reveals that the distribution differences are gradually expanding in national level and varying in regional level. Moreover, the global and local Moran’s I indices reveal varying spatial autocorrelation for TAD, TAS, TAI, and TAE. The Dagum Gini coefficients of TAD, TAS, TAI, and TAE show different patterns of decomposition.

To analyse inner- and inter-annual changes, disaster events of 55 countries in Africa from 1990 to 2014 recorded in the International Disaster Database (EM-DAT) were recounted by year and month and were reorganised in five different regions. Thematic maps of flood disasters in Africa between 1990 and 2014 were drawn using ArcGIS 9.3 to research the spatial distribution patterns of average annual flood frequency, total deaths, total affected, and damage. There were eight natural and socio-economic indicators chosen to explore the main factors influencing the spatio-temporal distribution of flood disasters in Africa, including precipitation, ENSO, runoff, forest coverage rate, reservoir capacity, per capita GDP, population, and urbanisation rate. Studies show that seasonal changes of flood disasters in various regions of Africa, except North Africa, are closely related to precipitation. Annual flood frequencies, from 1990 to 2014, showed a fluctuating upward trend and were in good agreement with ENSO years. In terms of spatial distributions, Ethiopia, Kenya, Somalia, Tanzania in eastern Africa, Nigeria in western Africa, and Libya, and Sudan in northern Arica are flood-prone countries, and main factors influencing spatial disparities include runoff, per capita GDP, population, and urbanisation rate.