Spatio-temporal Distribution Patterns Research Articles

Challenges and Potentials: Environmental Assessment of Particulate Matter in Spaces Under Highway Viaducts

Under-viaduct space (UVS) is becoming an important solution to urban mobility problems, and the construction and use of high-density city center highways and elevated bridges are increasing, which has a negative impact on the UVS. Air pollution is a problem in these spaces, but research on air pollution in UVSs is lacking. To further study air pollution in UVS, this study selected a case area of a UVS in central Shanghai and investigated the spatiotemporal distribution patterns of air pollution and the influencing factors. We found that air pollution in the UVS is significantly higher than the background levels, and the higher the background levels, the greater the difference between the pollution of the UVS and the background. In terms of the impact factor, air pollution is highly correlated with the built environment and traffic flow. The research provides evidence of the exposure to air pollution in under viaducts spaces in the microenvironment.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Potential Global Distribution and Habitat Shift of Prunus subg. Amygdalus Under Current and Future Climate Change

The genus of Prunus subg. Amygdalus are endangered Tertiary-relict plants that are an essential source of woody plant oil. In order to provide a theoretical basis for better protection and utilization of species in the Prunus subg. Amygdalus. This study collected global distribution information for six species within the Prunus subg. Amygdalus, along with data on 29 environmental and climatic factors. The Maximum Entropy (MaxEnt) model was used to simulate the globally suitable distribution areas for these species within the subgenus. The suitable results showed that the area under the test curve (AUC) values of the simulation results were more than 0.8, indicating that the simulation results have high accuracy. Temperature, precipitation, UV-B, and altitude were critical environmental factors affecting the distribution of each species in Prunus subg. Amygdalus. Currently, the distribution area of six species in this genus, from largest to smallest, is Prunus triloba (Lindl.) Ricker, Prunus tenella Batsch, Prunus amygdalus Batsch, Prunus pedunculata Maxim, Prunus mongolica Maxim and Prunus tangutica (Batal.) Korsh. The simulation results of distribution areas showed that under the ssp2.45 and ssp5.85 scenarios, the potential distribution areas of P. amygdalus, P. tangutica, and P. pedunculata all show a decreasing trend, while the distribution areas of P. mongolica and P. tenella, and P. triloba exhibit an increasing trend. The general distribution of P. amygdalus, P. mongolica, and P. tenella will trend to transfer in a northwest direction. P. tangutica and P. pedunculata were affected by other environmental factors (such as slope, altitude, and soil pH), and the distribution area has a tendency to move northeastward. The P. triloba moved to the southwest. The spatiotemporal distribution patterns of Prunus subg. Amygdalus can be used as a reference for forest management and to formulate species conservation strategies.

Climate Change Drove the Decline in Yangtze Estuary Net Primary Production Over the Past Two Decades.

Net primary productivity (NPP) is highly sensitive to multiple stressors under progressive and intensifying climate change and anthropogenic impacts. The importance of understanding spatiotemporal distribution patterns and the associated driving factors that govern estuary NPP is paramount for regional carbon (C) budget assessments. Using a combined remote sensing and machine learning (ML) approach, the average NPP of the Yangtze Estuarine-offshore continuum (YEOC) was measured at 273.19 ± 21.26 mgC m-2 day-1 over the past two decades. Temporally, NPP exhibited a significant downward trend between 2002 and 2022. Climate factors (climate fluctuations, sea level rise, and discharge) drove phytoplankton biomass (Chl-a) while light conditions (PAR and Kd490) affected photosynthesis rates. Together, they can explain 65% of the NPP variation. Anthropogenic disturbances (i.e., damming and nutrient emissions) were not significant. Additionally, changes in NPP decreased phytoplankton C sequestration rates from 11.9 to 10.4 Tg C year-1, reducing the estuary's C sink capacity, which relies on biological C fixation. This study highlights the climate's influence on the spatiotemporal transformation of YEOC NPP while enhancing our understanding of the response of EOC C budgets to climate change and anthropogenic activities.

Spatio-temporal Situation, Regional Differences, and Dynamic Evolution of the Distribution of Ecological Compensation in the Yellow River Basin

In the context of regional coordinated development and modernization of ecological and environmental governance capacity, the spatial collaborative governance of transboundary river basins has received extensive attention, but the ecological compensation of river basins is faced with the intertwined relationship of rights and responsibilities, and the horizontal ecological compensation mechanism has not yet been perfected. Based on the emergy ecological footprint model, the ecological compensation amount of 90 cities in the Yellow River Basin in 2007-2021 was measured, and the spatiotemporal pattern, regional differences, and dynamic evolution characteristics of distribution were explored by combining exploratory spatial data analysis, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain analysis. The results showed that： ① The spatiotemporal pattern of ecological compensation in the Yellow River Basin was different, the ecological compensation amount decreased slightly in the fluctuation and increased from the northwest to southeast gradient in general, and the high-value areas were concentrated in the areas with a superior ecological background or developed economy, and some upstream cities had given up many development opportunities to maintain the ecological security of the Yellow River Basin, however, failed to obtain reasonable ecological compensation. ② A significant positive spatial agglomeration phenomenon was observed in ecological compensation and the overall spatial distribution trend was "cold in the north and hot in the south." The number of low-payment areas and low-compensation areas gradually decreased, showing a gradually shrinking agglomeration layout from the periphery to the center, whereas the number of high-payment areas and high-compensation areas continued to increase and formed a spatial evolution characteristic of the coexistence of scattered distribution and group distribution. ③ The overall regional differences in ecological compensation expanded. The over-variation density was the main source of the overall differences, and the focus on alleviating the intra-regional and inter-regional differences was located in the downstream areas. ④ A slight multi-level differentiation phenomenon was present in ecological compensation. The influence of different neighbors on the horizontal transfer of ecological compensation was quite different. This spatial spillover effect easily formed a "space club convergence" phenomenon within a certain geographical spatial range, but with the expansion of time, the probability of maintaining the original level of ecological compensation in each city decreased, and mobility gradually increased. Therefore, increasing the investment in ecological compensation in different fields, strengthening the coordinated development between regions, and giving full play to the spatial spillover effect are important ways to solve the current spatial imbalance of ecological compensation in the Yellow River Basin.

Exploring environmental and climate features associated with yellow fever across space and time in the Brazilian Atlantic Forest biome.

The Atlantic Forest Biome (AFB) creates an ideal environment for the proliferation of vector mosquitoes, such as Haemagogus and Sabethes species, which transmit the Yellow Fever virus (YFV) to both human and non-human primates (NHP) (particularly Alouatta sp. and Callithrix sp.). From 2016 to 2020, 748 fatal cases of YF in humans and 1,763 in NHPs were reported in this biome, following several years free from the disease. This underscores the imminent risk posed by the YFV. In this study, we examined the spatiotemporal distribution patterns of YF cases in both NHPs and humans across the entire AFB during the outbreak period, using a generalized linear mixed regression model (GLMM) at the municipal level. Our analysis examined factors associated with the spread of YFV, including environmental characteristics, climate conditions, human vaccination coverage, and the presence of two additional YFV-affected NHP species. The occurrence of epizootics has been directly associated with natural forest formations and the presence of species within the Callithrix genus. Additionally, epizootics have been shown to be directly associated with human prevalence. Furthermore, human prevalence showed an inverse correlation with urban areas, temporary croplands, and savannah and grassland areas. Further analyses using Moran's Index to incorporate the neighborhoods of municipalities with cases in each studied host revealed additional variables, such as altitude, which showed a positive correlation. Additionally, the occurrence of the disease in both hosts exhibited a spatio-temporal distribution pattern. To effectively mitigate the spread of the virus, it is necessary to proactively expand vaccination coverage, refine NHP surveillance strategies, and enhance entomological surveillance in both natural and modified environments.

Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China

The circulation of tropical cyclones (TCs) exerts a multifaceted influence on the spatial and temporal distribution of surface pollutants. This study investigates the response of surface ozone (O3) concentration to the TCs in Fujian Province from June to December 2022 by analyzing the contributions of atmospheric pollutants, meteorological conditions, and dynamical transports. Empirical orthogonal function (EOF) decomposition methods are used to analyze the spatio-temporal distribution patterns of affected O3, and a Gradient Boosting Regression Trees (GBRT) machine learning model is employed to estimate surface O3 concentration, quantifying the influence of each factor. The results indicate an anomaly increase in O3 concentration during this period, with photochemistry-related meteorological conditions being the primary influencer, accounting for 66.9% of O3 variations, elucidating the interpretability of the GBRT model for attributing changes in O3 concentration. Low relative humidity and high temperature conditions have been identified as pivotal factors influencing the rise in O3 concentrations. The presence of TC undermines this predominant influence, amplifying the role of transport factors and other atmospheric pollutants. In the case studies of TC (Muifa and Nanmadol, 2022), the slow or stagnant TCs triggered persistent downdrafts in its periphery and brought favorable meteorological conditions such as clear sky and warm temperature for photochemistry. TCs also enhances the impact of horizontal and vertical dynamic transport on O3 concentrations. This work provides vital insights into the complex interplay between TCs and surface O3 concentrations, highlighting the need for targeted environmental and air quality management strategies in regions frequently impacted by TCs.

In-tunnel pollutant concentration measurement and ventilation control indexes for highway tunnels in mountainous area: A case study of no.1 Qinling tunnel, China

The pollutant concentration and hygiene standards in mountain tunnels are key factors determining the ventilation effectiveness of tunnel operation. Previous studies have mainly focused on the spatiotemporal distribution patterns of in-tunnel pollutants, while research on ventilation control indexes for pollutants in long mountain tunnels are relatively limited. Therefore, this paper aims to discussing the hygiene control indexes for operational ventilation of mountain tunnels. Field tests were conducted in the No.1 Qinling Tunnel of Xi'an to Han zhong Highway in China, and the distribution laws of traffic flow, wind speed, and concentrations of particulate matter (PM), carbon monoxide (CO), and nitrogen oxide (NOX) were on-site measured. The spatiotemporal distribution characteristics of pollutants in the long mountain tunnel were revealed, and the ventilation hygiene control indexes for harmful gases were calculated and compared to those of urban highway tunnels. The results show that the traffic volume of operational highway tunnels in mountainous areas of China is increasing rapidly, and the particulate matter emitted from diesel vehicle exhaust will pose a threat to the normal environmental quality in the tunnel. The average value of CO base emission factor in 2000 for the upline of No.1 Qinling tunnel was 0.0019 m3/(veh·km) by back-calculation, and the annual decline rate of CO in the ventilation design for mountain highway tunnels should be 6 %–8 % and revised every 8 years. It is recommended to consider the geographical location and traffic conditions of mountain tunnels when calculating the air demand required for diluting NO2. NOx has basically replaced CO as the primary pollutant with the most significant impact on human health in mountain tunnels. It is recommended that NOX be considered as the primary hygiene control indicator in the ventilation design of mountain tunnels in China. The pollutant distribution and ventilation control indexes of mountain and urban highway tunnels are quite different, which should be treated differently in engineering applications. The research results can provide a significant reference for ventilation design and environmental evaluation of highway tunnels in mountainous areas.

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province

BackgroundRifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease.MethodsWe gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period.ResultsIn our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23–28 and 44–54, with a rising proportion in the 51–80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05).ConclusionsEnhanced surveillance is crucial for detecting tuberculosis patients aged 23–28 and 44–54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Study on the spatiotemporal distribution patterns and influencing factors of cultural heritage: a case study of Fujian Province

The spatiotemporal distribution characteristics of cultural heritage reveal the trajectory of human activity changes, and a deep analysis of its natural and cultural factors holds significant reference value for the overall conservation and management of cultural heritages. This study focuses on the cultural heritage at the provincial level and above in Fujian, utilizing GIS spatial analysis to explore the spatiotemporal evolution of cultural heritages and their natural and human influencing factors. The research findings are as follows: (1) The distribution of cultural heritage in Fujian exhibits a clustering pattern, with dense areas transitioning from the upstream regions of the prehistoric and pre-Qin periods to the eastern coastal areas gradually. (2) The Ming and Qing dynasties have the highest number of cultural heritages, with the type of heritage transitioning from ancient sites in the early periods to ancient architecture, and in modern times, mainly important historical sites and representative architectural heritages. (3) The overall centroid coordinates of cultural heritage reveal a shift from the northern part of Fujian to the eastern and southern parts. (4) Natural factors significantly influence the distribution of cultural heritage, with a higher concentration in plain and hilly areas, on slight slopes with gradients between 0.5° and 2.0°, and on the southern and southeastern slopes, especially within a 1-kilometer radius of rivers. (5) The creation of cultural heritage during historical periods is closely linked to the regional history, culture, political, and economic environments. The positive development of these socio-cultural factors has a promotional effect on the quantity of cultural heritage. This study demonstrates the utility and applicability of GIS spatial analysis techniques in cultural heritage research, providing a methodological framework that can be adapted and applied internationally. The findings offer insightful data that can inform targeted conservation and development strategies for cultural heritage, ensuring their effective preservation and sustainable management across different regions.

Temporal and spatial dynamics of carbon and water use efficiency on the Qinghai-Tibet plateau and their ecosystem responses

Research on the carbon and water use efficiency (CUE, WUE) in the Qinghai-Tibet Plateau (QTP) holds significant importance due to its implications in understanding and addressing global environmental challenges, particularly climate change. This study comprehensively explored the spatiotemporal distribution patterns and trends of WUE and CUE on the plateau from 2001 to 2018 using linear regression analysis and geostatistical methods. The results revealed a gradual decrease in WUE from southeast to northwest, showing a non-linear variation trend during the study period. CUE exhibited relatively minor spatial variations but demonstrated a significant decrease trend in the eastern regions. The results from the Random Forest algorithm and path analysis indicated that ecosystem respiration (Re) was the predominant controlling factor for WUE and CUE in the cold alpine (PA) region. In the temperate, warm temperate (Te&WT) region, temperature emerged as the primary determinant for WUE, while Leaf Area Index (LAI) served as the absolute dominant factor for CUE. Additionally, LAI also played a crucial role in determining WUE and CUE in the tropical and subtropical (Tr&S) region. Partial correlation analysis highlighted the heterogeneity in the responses of WUE and CUE to environmental changes across different regional climates and vegetation types. The findings emphasized the significant impact of biological factors on terrestrial carbon and water cycling processes (WUE and CUE) within the context of global climate change. Furthermore, the study identified a potential linkage between WUE and CUE, with their relationship to vegetation dynamics showing significant variations across various ecosystems. WUE and CUE showed a positive correlation in the western regions, particularly in high-altitude areas, while a negative correlation was observed in eastern regions with high SOC. In the Te&WT and the Tr&S region, the increase of NDVI (Normalized difference vegetation index) suppressed the rise of both WUE and CUE, establishing a negative correlation between them. Conversely, in the PA region, a significant positive correlation trend between WUE and CUE was observed, indicating a positive response to the decrease in NDVI. These findings are of significant importance for advancing the understanding of and effectively addressing climate change, maintaining ecosystem stability, and promoting sustainable socio-economic development.

Effect of Meteorological Elements and Air Pollutants on Ozone in Yinchuan City Park

To examine the underlying determinants of ozone （O3） in Yinchuan's urban park during varying seasons and to ascertain the role played by meteorological events and air contaminants in influencing O3 concentrations at high altitudes, data on O3, meteorological factors, and air pollutants were collected through prolonged positional observations carried out at the Ningxia Yinchuan National Urban Ecosystem Research Station. Pearson correlation analysis and a structural equation model were utilized to investigate the spatio-temporal distribution patterns, trends, and the primary factors influencing O3. The findings demonstrated a notable seasonal variability in O3 levels in Yinchuan's urban park, displaying an "unimodal type" with the O3 concentration peaking in summer （131.18 μg·m-3） and bottoming out in winter （71.45 μg·m-3）. Among the meteorological factors, the highest impact on O3 was attributed to temperature and wind speed （temperature mainly through direct effects and wind speed mainly through indirect effects）. Conversely, air pollutants such as NOx and SO2 greatly affected O3 primarily through direct effects. Wind speed was identified as the primary influencing factor on O3 during spring and summer, potentially contributing 29% and 24.7%, respectively. Conversely, NO2 was implicated as the primary factor during autumn and winter, with an estimated contribution of 26.6% and 29.7%, respectively. Thus, a structural equation model can efficiently reveal the primary determinants behind O3 variations throughout various seasons, which could furnish a scientifically rigorous foundation and technical aid for mitigating and managing O3 levels in high-altitude regions.

Spatio-Temporal Diversification of per Capita Carbon Emissions in China: 2000–2020

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal.

Spatiotemporal Distribution and Dispersal Pattern of Early Life Stages of the Small Yellow Croaker (Larimichthys Polyactis) in the Southern Yellow Sea

Nursery habitats play a significant role in completing fish life cycles, and they are now recognized as essential habitats. Monthly variations in nursery ground distributions of Larimichthys polyactis were investigated in the southern Yellow Sea in 2019. Bayesian hierarchical models with integrated nested Laplace approximation were utilized to model the preferential nursery habitats of L. polyactis larvae. The study analyzed the spatial and temporal distributions of the larvae and juveniles based on three environmental variables: sea surface temperature, sea surface salinity, and depth. Additionally, this study examined the utilization of habitats by different fish life stages and ontogenetic shifts. A total of 3240 individuals were collected from April to June, with the peak occurring in May (0.05 ind./m3), and the distribution areas varied between different months. The prediction of the model reveals the ecological adaptability of L. polyactis to temperature variations. The optimal temperature for L. polyactis density ranges from 12.5 °C to 16.5 °C in April and 16.5 °C to 17.5 °C in May, demonstrating a broad temperature tolerance for L. polyactis survival. In addition, there are variations in distribution patterns among different developmental stages. Larimichthys polyactis spawn in the inshore and nearshore waters, and after hatching, larvae in the pre-flexion stage tend to remain aggregated near the spawning beds. However, larvae in the advanced development stage (post-flexion) and juveniles move towards the sandy ridge habitats along the coast and start to migrate offshore in June. This study provides valuable insights for the effective management of fishery resources in the area and can be utilized to identify marine areas with specific habitat features that require conservation.

Predicting the Potential Global Distribution of the Plum Fruit Moth Grapholita funebrana Treitscheke Using Ensemble Models.

The plum fruit moth, Grapholita funebrana Treitschke, is one of the most significant borer pests, often causing huge economic losses in fruit production. However, the potential distribution range of this economically important pest is still poorly understood. For this study, we simulated an ensemble species distribution model to predict the spatiotemporal distribution pattern of G. funebrana at a global scale. The results show that the suitable habitats for this moth, under current environmental conditions, are mainly distributed in Europe; East Asia, including China and Japan; Central Asia; and some parts of America. In future projections, the suitable habitats are predicted to generally expand northward, while the suitable area will remain unchanged overall. However, the area of highly suitable habitat will decrease to only 17.49% of that found under current conditions. None of the nine factors used were revealed to be predominant predictors in terms of contributing to the model, suggesting that the integrated effects of these variables shape G. funebrana's distribution. In this study, the distribution range that has been predicted, especially for the regions with a highly suitable habitat, poses a high risk of G. funebrana outbreaks, highlighting the urgency of pest management. Moreover, in the United States of America (USA) and Japan (for which G. funebrana distributions were not previously recorded), especially in areas highly suitable for this moth, monitoring and quarantine measures should be strengthened to prevent the colonization and further dispersal of this pest, as seen with its close relative G. molesta, which has become a cosmopolitan pest species, migrating from its native region (East Asia) to other continents, including the Americas.

Assessing urbanization dynamics using a pixel-based nighttime light indicator

Nighttime light (NTL) is a reliable indicator for measuring urban development, population density, and economic activities. This study sets out to explain the spatio-temporal pattern of urbanization and population distribution using pixel-based NTL data. Changes in NTL intensity in Puglia (Italy) were evaluated from 2014 to 2023, and how NTL is affected by various spatial metrics was investigated. Summer–winter differences at the pixel level were monitored utilizing the Normalized Nighttime Light Index (NNLI), and the relationship between this indicator and tourism was assessed. A consistent increase in NTL across the region in both summer and winter was measured, with variations among provinces. Urban areas with high population density showed greater NTL values, while inland areas showed low NTL intensity. The study also revealed that proximity to urban areas was the most influential factor in predicting NTL intensity. The analysis revealed that differences between summer and winter correlated with tourist activity, especially in coastal municipalities. Leveraging NTL images can support decision-making across various sectors and aid in developing evidence-based urban strategies tailored to local needs and challenges.

Modulation of soil nitrous oxide emissions and nitrogen leaching by hillslope hydrological processes

Soil nitrous oxide (N2O) emissions and nitrogen (N) leaching are key pathways for soil N loss in hillslope ecosystem, with potential implications for global warming and water body eutrophication. While soil N loss in hillslope ecosystem has been extensively studied, there is limited understanding of the spatiotemporal distribution patterns and factors driving soil N2O emissions and N leaching from a hillslope hydrology perspective. This study investigated N concentrations in leachate and soil N2O fluxes and their responses to soil hydrological factors on a tea plantation (TP) hillslope and a bamboo forest (BF) hillslope. Four distinct precipitation patterns—spring rainfall (SR), plum rain (PR), summer flood rain (SF), and drought period (DR)—were identified based on precipitation intensity, duration, and cumulative precipitation. Results showed that, soil N2O flux and leachate N concentrations were 8.2 times and 18.0 times higher On TP hillslope compared to the BF hillslope. The greatest soil N2O fluxes occurred during the PR period, while the lowest were observed during the DR period. Precipitation increased soil water content (SWC) and water-filled pore space, stimulating soil N cycling for N2O production. Fertilization activities and precipitation led to peak N concentration in leachate during the SR period. Additionally, soil wetness index (SWI) shaped spatial patterns of SWC, resulting in distinct spatial patterns of N2O emissions and nitrate leaching. Locations with higher SWI exhibited greater soil N2O flux and higher nitrate concentrations in leachate. This study emphasizes the significant effect of soil hydrological processes on soil N2O emissions and N leaching in hillslope ecosystems, providing valuable insights for N management in these environments.

Predicting habitat suitability for the soybean pod borer Leguminivora glycinivorella (Matsumura) using optimized MaxEnt models with multiple variables.

The soybean pod borer Leguminivora glycinivorella (Matsumura) is one of the most important soybean pests and often causes serious damage to Glycine max (L.) Merr., a leading source of dietary protein and oil in animal feed. However, the potential distribution patterns of this economically important pest and its driving factors require further investigation. Here, we used the optimized MaxEnt model to predict the potential distribution of this pest with multiple variables associated with climate, land use, and host plant, at its recorded range and a globe scale. Based on 4 variable combinations, the results show that the current suitable habitats of L. glycinivorella are primarily distributed in most of China, the Korean Peninsula, and Japan. Whereas no suitable area is present in other continents. In future projections, the suitable region shows a slight northward expansion compared with the result predicted with current climatic conditions, and the suitable areas of almost all future projections were stable in size. Among the 9 bioclimatic factors, BIO03 (isothermality) consistently highly contributes to the predictions, indicating that temperature may be a key factor influencing the habitat distribution of L. glycinivorella. Comparative analyses of projections further show that non-climatic factors are informative in the modeling as routinely used bioclimate variables. The spatio-temporal distribution patterns of suitable habitats and the regulatory factors predicted in this study could provide important guidance for L. glycinivorella management.