Spatiotemporal Distribution Characteristics Research Articles

Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO2) and rising temperatures, the near-ground O3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies.

Spatiotemporal Distribution Characteristics of Ground-level-ozone and Its Relationship with Meteorological Conditions in a Representative City in the Bohai Rim from 2017 to 2022

In recent years, ground-level-ozone（O3） pollution in urban areas in the Bohai Rim has attracted wide attention. Based on the analysis of the spatiotemporal distribution characteristics of O3 concentration in Dongying, a representative city in the Bohai Rim from 2017 to 2022, the effects of meteorological factors and sea-land breeze circulation on O3 concentration were evaluated. The results showed that： ① From 2017 to 2022, the annual assessment value of O3 concentration in Dongying showed a fluctuating upward trend, and the pollution days with O3 as the primary pollutant increased. O3 pollution mainly occurred in spring, summer, and autumn, with the most severe O3 pollution episodes typically occurring in May and June, and the duration of O3 pollution season tended to be longer. The monthly variation in the daily maximum 8-h average ozone （MDA8 O3） presented a bimodal distribution, with significant increases in the 5th and 25th percentiles, and the spatial distribution was "high in the north and south, low in the middle." In addition, the nocturnal O3 concentration in recent years in Dongying also showed a significant increase trend. ② Meteorological factors greatly influenced O3 concentration in Dongying. When the temperature was greater than 30℃, the relative humidity was less than 50%, and the wind direction was south-southwest or east-northeast, a high O3 value was more likely to occur. Meteorological factors contributed 30% of the MDA8 O3 variation in Dongying during the study period. In the case of moderate and severe O3 pollution, the contribution of meteorological factors to the change in MDA8 O3 could be as high as 40%. ③ To some extent, sea-land breeze contributed to the occurrence of MDA8 O3 exceeding the secondary standard limit value of the National Ambient Air Quality Standard. In the afternoon, the hourly concentration of O3 during the sea-land breeze days was approximately 20 μg·m-3 higher than that during the non-sea-land breeze days. On the days of moderate and severe O3 pollution, the O3 concentration during the sea-land breeze days from 10：00 to 16：00 was higher than that during non-sea-land breeze days, and the O3 concentration was also at a high level from 20：00 to 23：00 on sea-land breeze days. In the O3 pollution season, the sea-land breeze could significantly affect the O3 level in coastal cities, which could bring significant challenges for O3 pollution prevention and control in this region. In the future, cities in the Bohai Rim need to further strengthen regional joint prevention and control of O3 pollution and increase emission reduction efforts of nitrogen oxides and volatile organic compounds. This strategy could effectively lower pollutant concentrations within the land breeze air mass, consequently reducing the impact of the sea breeze air mass on air quality in cities in the Bohai Rim.

Effects of Meteorological Conditions in Summer and Early Autumn on PM2.5 and O3 Pollution in Beijing-Tianjin-Hebei and Surrounding Areas

PM2.5 pollution remains prominent in autumn, whereas O3 pollution gradually manifests in summer. To understand the dual high characteristics and meteorological effects of PM2.5 and O3 in the summer and early autumn of 2021 in the Beijing-Tianjin-Hebei and surrounding areas, the spatiotemporal distribution characteristics of PM2.5 and O3 concentrations, as well as meteorological conditions, subtropical high index, and weather situation in the Beijing-Tianjin-Hebei and surrounding areas were analyzed. The results showed that PM2.5 concentration and DPO3 （O3 daily maximum 8h mean minus O3 concentration at 06：00） from June to September 2021 decreased compared with those in the same period in 2020 and 2022, and high concentrations were mainly occurring in June. The overall PM2.5 concentration and DPO3 showed a gradual decrease from the middle to the north and south, with synchronous spatiotemporal changes. The hourly value of PM2.5 concentration presented a multimodal distribution, reaching the peak at 07：00-10：00 and 22：00-24：00. O3 concentration showed an opposite trend of change with PM2.5 concentration, reaching their peak from 14：00-16：00. When DPO3 and the concentration of PM2.5 were high, the characteristics of near-surface meteorological elements were characterized by temperatures ranging from 24.0-28.0℃, relative humidity concentrated at 58.4%-76.3%, and wind speeds ranging from 1.5-3 m·s-1. There was a high lag correlation between the subtropical high index and DPO3. When the subtropical high was farther and stronger from the Beijing-Tianjin-Hebei and surrounding areas, DPO3 was higher. The major weather patterns with both high PM2.5 and O3 concentrations in the study area were near surface low-pressure front, high-pressure type, and frontal type. The high altitude was controlled by high-pressure ridges, and the subtropical high ridge line was stable between 21°-28°N.

The response of terrestrial vegetation carbon use efficiency to global environmental changes

Terrestrial vegetation carbon use efficiency (CUE) is a key measure for assessing carbon transfer from the atmosphere to terrestrial biomass, crucial for understanding carbon cycling and allocation in ecosystems. CUE provides valuable insights into how terrestrial ecosystems respond to environmental changes. In this study, we utilized satellite datasets (MODIS and GLASS), MsTMIP models, and TRENDY models to analyze the spatiotemporal distribution characteristics of global vegetation CUE. We found that the average CUE for global land vegetation is 0.44 ± 0.06, with a slight annual increase and significant spatial heterogeneity, characterized by latitude gradients and vegetation types. High-latitude regions demonstrated higher CUE values compared to low-latitude regions. Further employing an integrated attribution approach, we identified the response mechanisms of vegetation CUE to global changes. The comprehensive response of vegetation CUE to climate change, land use change, atmospheric CO2, and nitrogen deposition was found to fluctuate and increase, with a model-averaged CUE increase of approximately 0.01. Land use change was identified as the largest contributor to the annual trend of overall global CUE (48.8%), while climate change was the main factor influencing the interannual variability (IAV) of global CUE (91.7%). Regarding global distribution, the IAV of vegetation CUE is mainly influenced by climate change. CUE annual trends in more regions were influenced by climate change, with 65% and 73% of the ensemble mean of the MsTMIP and TRENDY models, respectively. The results of the MsTMIP and TRENDY models consistently show that, globally, land use change affects about a quarter of the total annual trend of CUE. Land use change affected CUE annual trends to a greater extent than climate change. In addition, the vegetation type most affected by climate change is the deciduous needleleaf forests, and the CUE annual trend of cropland is most affected by land use change. Our findings reveal global patterns and drivers of CUE variability, highlighting the significant impact of climate change and land use change.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Study on the internal waves induced by a submerged body moving in a continuously stratified fluid

This paper studies the characteristics of internal waves induced by the motion of a submerged body through a combination of experimental and numerical methods. First, by deploying an array of conductivity probes in an experiment, the temporal evolution of the internal wave at the plane of the conductivity probe array is obtained and compared with numerical simulation results based on the Navier–Stokes equations, thereby validating the accuracy of the Computational Fluid Dynamics (CFD) model. Subsequently, utilizing CFD calculations, further exploration is conducted on the spatiotemporal distribution characteristics of the internal wave, encompassing its generation and evolution, velocity field distribution, wake angle, and wave profile features. Finally, the variation of the internal wave with the Froude number (Fr) is investigated, revealing that it varies apparently with Fr, including the wave component, wave mode, wake length, wake angle, wave amplitude, and so on. It is found that when Fr is large, the internal wave converges toward the centerline behind the body, until forming a line. The wave magnitude changes with Fr in four stages, i.e., increasing with Fr from zero in the first stage, reaching a peak and decreasing thereafter in the second stage, then changing little in the third stage, and finally increasing approximately linearly in the fourth stage.

Habitat Prediction of Bigeye Tuna Based on Multi-Feature Fusion of Heterogenous Remote-Sensing Data

Accurate habitat prediction of Bigeye Tuna, the main fishing target of tuna pelagic fishery, is of great significance to the fishing operation. In response to the fact that most of the current studies use single-source data for habitat prediction, and the association between spatiotemporal features and habitat distribution is not fully explored and that this has limited the further improvement of prediction accuracy, this paper analyzes the spatiotemporal distribution of the characteristics of Bigeye Tuna’s highly migratory nature. Additionally, it puts forward a method of habitat prediction that utilizes heterosource remote-sensing data for the four-dimensional time–space–environment–spectrum (TSES) for deep-level feature extraction. First, a multi-source heterogeneous dataset was constructed by combining the spatiotemporal distribution characteristics of the product-level environmental remote-sensing data and the L1B-level original spectral remote-sensing data, and then a multi-branch, dynamic spatiotemporal feature extraction, Long Short-Term Memory Network (LSTM) time-series model was constructed to extract the characteristics of the heterogeneous data. This model was constructed to fully explore and utilize the multidimensional deep-level TSES distribution features affecting the habitat prediction. Finally, the two types of heterogeneous data were subjected to the weighted average-based decision-level fusion to obtain the final prediction results. The experimental results show that compared with other methods, the proposed method in this paper outperforms traditional machine-learning models and other single-source, data-based time-series models, with R2 reaching 0.96278 and RMSE decreasing to 0.031361 in the validation experiments of these models. In contrast, the method in this paper demonstrates good generalization ability and achieves accurate prediction of future fishery distribution.

Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors of Vegetation Net Primary Productivity in Shendong Mining Area

Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m2. The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m2; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation.

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to -70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

Spatiotemporal characteristics and influencing factors of network attention to resort hotels in China

An increasing number of people are gathering travel information online prior to their trips as a result of the Internet's rapid expansion. The amount of network attention receives can indicate how many people are looking for something. The orderly development of Chinese resort hotels can be guided by research on the spatiotemporal characteristics of their network attention. Using Chinese resort hotels as the study subject, everyday information about resort hotels in 31 Chinese provinces was gathered via the Baidu Index platform between 2018 and 2022, and mathematical statistics and other methods were used to study the spatiotemporal distribution characteristics and influence of Chinese resort hotels network attention. Findings reveal that, from 2018 to 2022, network attention to resort hotels across the country fluctuated significantly across seasons, and there was a “precursor effect” reaction before the week of network attention. Moreover, the spatial distribution of network attention to Chinese resort hotels was uneven, showing an overall trend of “east–central–west” decline. Level of economic development, degree of network development, leisure time, and population size are the main factors affecting the spatiotemporal distribution of Chinese resort hotels network attention.

Analysis of spatiotemporal variation and relationship to land use – landscape pattern of PM2.5 and O3 in typical arid zone

Land use – landscape pattern act as pollutant sources carries for PM2.5 and ozone, understanding the relationship between them is valuable for the management of atmospheric environment. In this investigation, we examined the spatiotemporal distribution characteristics of PM2.5 and ozone in Xinjiang during 2000 ∼ 2022. Meanwhile, we employed the random forest model under optimal model selection to explore the response of the land use - landscape pattern to the pollutants. The conclusions suggested: (1) PM2.5 in 94.82% of the area showed a significant decrease, with high levels concentrated in the Tarim Basin, Turpan-Hami Basin. Ozone demonstrated a remarkable upward trend in 99.30% of the region, showing high levels in the Tarim Basin and the northern Xinjiang. (2) Upon comparison with the univariate and stepwise linear regression models, random forest model holds significant practical value in quantifying the impact of pollutants on land use-landscapes. (3) In the land use – landscape pattern, the complexity and aggregation of cropland and water, the degree of connectivity in barren and impervious, the diversity and complexity of forest, and the degree of aggregation of grasslands all strongly impact PM2.5. For ozone concentrations, the index characterizing the degree of patchy aggregation is overwhelmingly dominant.

Pollution level and ecological risk assessment of triazine herbicides in Laizhou Bay and derivation of seawater quality criteria

Triazine herbicides are widely used in agriculture and have become common pollutants in marine environments. However, the spatiotemporal distribution characteristics and water quality criteria (WQC) of triazine herbicides are still unclear. This study found that triazine herbicides had a high detection rate of 100 % in surface seawater of Laizhou Bay, China, with average concentrations of 217.61, 225.13, 21.97, and 1296.72 ng/L in March, May, August, and October, respectively. Moreover, estuaries were important sources, and especially the Yellow River estuary exhibited the highest concentrations of 16,115.86 ng/L in October. The 10 triazine herbicides were detected in the sediments of Laizhou Bay, with a concentration ranging from 0.14−1.68 μg/kg. Atrazine and prometryn accounted for 33.41 %−59.10 % and 28.93 %−50.06 % of the total triazine herbicides in the seawater, and prometryn had the highest proportion (63.50 %) in the sediments. Correlation analysis revealed that triazine herbicides led to the loss of plankton biodiversity, which further decreased the dissolved oxygen. In addition, this study collected 45 acute toxicity data and 22 chronic toxicity data of atrazine, 16 acute toxicity data of prometryn, and supplemented with toxicity experiments of prometryn on marine organisms. Based on the toxicity database, the WQCs of atrazine and prometryn were derived using species sensitivity distribution. The overall risk probability of atrazine and prometryn were both less than 1.75 % in the Laizhou Bay, indicating an acceptable risk. This study not only clarified the pollution status and ecological risk of triazine herbicides, but also provided scientific basis for their environmental management standards.

Predicting the Future Distribution and Habitat Suitability of Ilex latifolia Thunb. in China under Climate Change Scenarios

Ilex latifolia Thunb., a plant of significant economic and medicinal value, is both edible and medicinal. Assessing the climate suitability for I. latifolia has profound implications for advancing medical progress and enhancing the quality of human life. This study comprehensively utilized data on the field distribution of I. Latifolia, as well as corresponding climatic, topographical, and soil data at these distribution points, with the aid of future climate data predicted by global climate models, and employed the MaxEnt model to predict and analyze the climate suitability areas of I. latifolia under three greenhouse gas emission scenarios (SSP126, SSP245, and SSP585). The research covers the spatiotemporal distribution characteristics, suitable growth range, and influencing factors from the present to the end of the 21st century (2041–2100). The predictive results of the MaxEnt model indicate that, under current climatic conditions, the main suitable growth areas for I. latifolia are concentrated in the southeastern part of China, especially in the provinces of Fujian and Zhejiang. However, facing the challenges of future climate change, it is expected that the moderately high suitable growth areas for I. latifolia will show a trend of gradual reduction. The primary climatic factors crucial for I. latifolia’s growth are annual precipitation (1469.05 to 4499.50 mm), the lowest temperature in the coldest month (−18.72 to 3.88 °C), seasonal precipitation changes (11.94 to 64.69 mm), and topographic slope (0.37 to 3.00 °), with annual precipitation being the most influential. The findings of this study provide a scientific basis for the introduction of I. latifolia and offer important reference information for the artificial cultivation, resource development, and achievement of sustainable industrial development of this species.

Research on the Spatiotemporal Distribution Characteristics and Accessibility of Traditional Villages Based on Geographic Information Systems—A Case Study of Shandong Province, China

The traditional settlements are of paramount significance as indispensable elements of China’s cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China’s rural revitalization endeavor and Shandong Province’s proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from “southwest to northeast”; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent “four-core” spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility.

Study on the Spatiotemporal Distribution Characteristics of Meiofauna in Baiyangdian Lake and Its Influencing Factors

Study on the Spatiotemporal Distribution Characteristics of Meiofauna in Baiyangdian Lake and Its Influencing Factors

Insight into a single-chamber air-cathode microbial fuel cell for nitrate removal and ecological roles.

Bioelectrochemical systems are sustainable and potential technology systems in wastewater treatment for nitrogen removal. The present study fabricated an air-cathode denitrifying microbial fuel cell (DNMFC) with a revisable modular design and investigated metabolic processes using nutrients together with the spatiotemporal distribution characteristics of dominated microorganisms. Based on the detection of organics and solvable nitrogen concentrations as well as electron generations in DNMFCs under different conditions, the distribution pattern of nutrients could be quantified. By calculation, it was found that heterotrophic denitrification performed in DNMFCs using 56.6% COD decreased the Coulombic efficiency from 38.0% to 16.5% at a COD/NO3 --N ratio of 7. Furthermore, biological denitrification removed 92.3% of the nitrate, while the residual was reduced via electrochemical denitrification in the cathode. Correspondingly, nitrate as the electron acceptor consumed 16.7% of all the generated electrons, and the residual electrons were accepted by oxygen. Microbial community analysis revealed that bifunctional bacteria of electroactive denitrifying bacteria distributed all over the reactor determined the DNMFC performance; meanwhile, electroactive bacteria were mainly distributed in the anode biofilm, anaerobic denitrifying bacteria adhered to the wall, and facultative anaerobic denitrifying bacteria were distributed in the wall and cathode. Characterizing the contribution of specific microorganisms in DNMFCs comprehensively revealed the significant role of electroactive denitrifying bacteria and their cooperative relationship with other functional bacteria.

Spatiotemporal analysis of equine infectious anemia and prediction of risk areas in Europe

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation.

Identifying the Coupling Coordination Relationship between Urbanization and Ecosystem Services Supply–Demand and Its Driving Forces: Case Study in Shaanxi Province, China

Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes.

A novel hydrodynamic-water quality coupling model for high-efficiency and high-resolution simulations of urban NSPs

Nonpoint source pollution (NSP) is one of the main factors leading to black odor in urban rivers. A better understanding and simulation of the formation and transport mechanism of NSP is critical to the development of effective urban rainwater management and pollution control strategies. This work presents a novel hydrodynamic-water quality coupling model to quantitatively simulate the entire physical process of the urban NSP by integrating two-dimensional (2D) NSP build-up, wash-off and transport modules into the source code of the GPU-accelerated surface water and transport (GAST) model. The advantages of this model are that high-resolution terrain is considered, such as key microtopography, such as roads and houses, which will have a diversion effect on the transport of hydrodynamic water quality and affect, and detailed hydrodynamic processes based on physical processes are fully considered. Additionally, a graphical processing unit (GPU)-based acceleration method is introduced to increase the parallel performance of the computing process. Three scales of cases were used to verify the model. First, indoor experimental data from the model roof and measured data from the campus roof under two short-duration rainstorms were used for nonpoint source model verification. The Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), Bayesian information criterion (BIC) and Akaike information criterion (AIC) were utilized to assess the applicability of the different NSP wash-off equations added to this model. Based on this model, the NSP mechanism processes of residential buildings were examined. The results indicate that this model is suitable for various pollutant wash-off formulas and is capable of high-efficiency and robust predictions of the sources, transmission paths, and spatiotemporal distribution characteristics of urban nonpoint sources. We quantitatively simulated the generation and transport of NSP from a single roof and proposed an NSP traceability method based on detailed hydrodynamic processes. This model can provide strong technical support for the precise treatment of urban NSP and the construction of sponge facilities.

Effects of deep coal mining on groundwater hydrodynamic and hydrochemical processes in a multi-aquifer system: Insights from a long-term study of mining areas in ecologically fragile western China

The groundwater hydrodynamic and hydrochemical process of the multi-aquifer system will experience complicated and serious influence under deep coal mining disturbance. There is relatively little research that has integrated hydrodynamic and hydrochemical properties of groundwater to investigate the spatiotemporal distribution characteristics and evolution patterns of hydrogeochemistry and hydrodynamic information in deep multi-aquifer systems. The study of the groundwater hydrodynamic and hydrochemical spatiotemporal coupling response of multi-aquifer systems under the deep and special thick coal seam mining-motivated effect in ecologically fragile western mining areas is of great significance for the safe mining of coal resources and ecological environment protection. In this research, the hydrochemical analysis data composed of 218 groundwater samples from Tangjiahui coalfield, Northwest China with 1526 measurements and a 6-year (2016–2021) sampling period were collected for studying the hydrogeochemical spatiotemporal evolution process and governing mechanism of the multi-aquifer system using hierarchical cluster analysis, ion-ratio method, saturation index and multidimensional statistical analysis. Additionally, wavelet analysis and cross-wavelet coherence analysis were implemented to quantitatively recognize the spatiotemporal variation characteristics of hydrodynamic information and analyze the coherence relationships between time series. The results demonstrate that the hydrochemical characteristics exhibit significant spatial differences, while the temporal variation of hydrochemical characteristics in the Permian Shanxi Formation fractured sandstone aquifer (PSFFA), mine water (MW), and Ordovician karst limestone aquifer (OKA) is not significant. The water-rock interaction is the predominant control mechanism for the spatial evolution of hydrogeochemistry in the research area. Moreover, the large-scale mining of deep coal seams controls the type and degree of water-rock interactions by damaging the structure of aquifers and altering the hydrodynamic conditions of groundwater. The period from 2016 to 2021 exhibits multi-time scale characteristics in time series of precipitation, mine water discharge, and the water level of PSFFA and OKA. The mine water discharge has a positive correlation with the water level of PSFFA and OKA, whereas the significant period of precipitation and the water level of PSFFA coherence is not obvious. The research findings not only provide in-depth insights to protect the groundwater resources in water-shortage mining areas but also promote the secure mining of deep coal resources.