Detecting antimony(III) on-site using novel gel-based techniques: Colorimetric diffusive equilibrium in thin films for two-dimensional imaging and surface-enhanced Raman scattering for sensitive quantification.

Antimony (Sb) pollution has raised increasing public concerns and its rapid on-site screening is central for the risk assessment. Herein, we proposed two gel-based methods based on colorimetric diffusive equilibrium in thin films (DET) and surface-enhanced Raman scattering (SERS), for two-dimensional imaging and sensitive detection of Sb(III) by revisiting the phenylfluorone (PhF) complexation reaction. PhF was well dispersed in the polyvinyl alcohol (PVA) hydrogel and reacted with Sb(III) in the DET gel to form a strong PhF-Sb(III) complex. The distribution of Sb(III) was easily visualized at a submillimeter resolution using computer imaging densitometry, with a detection limit (LOD) of ∼100 nmol L-1. Field application in the Sb mine area reveals limited dissolved Sb(III) penetrating the redox barrier below the sediment-water interface by 20 mm in rivers and tailing pond sediments. To improve the detection sensitivity and apply the principle to trace Sb quantification, a SERS platform was established by anchoring PhF on the hydrogel-stabilized Ag nanoparticles via C-O-Ag bonding to specifically detect Raman-inactive Sb(III). Benefiting from the high SERS activity of PhF and enrichment ability of hydrogel, Sb(III) was quantified with a LOD of 1.2-10.7 nmol L-1 depending on the sample volume. The coexisting ions at a 100-fold higher concentration than Sb(III) resulted in only 3.3-10.4 % variation in SERS intensity, indicating a negligible interference on the SERS platform. The platform exhibited a RSD of 6.6-13.1 % and acceptable recoveries for various environmental matrices, highlighting its promise in on-site application.

Biogeography and community assembly of soil fungi from alpine meadows in southwestern China show the importance of climatic selection.

Soil fungi are pivotal in alpine and arctic ecosystems that are vulnerable to climate changes. Previous studies have shown broad connections between soil fungi in the arctic and alpine regions, but most of these studies are mainly from Europe and North America, with more sporadic studies from East Asia. Currently, little is known about the biogeographic relationships between soil fungi in alpine meadows of southwestern China (AMSC) and other regions of the world. In addition, the regional-scale spatial patterns of fungal communities in the AMSC, as well as their driving factors and ecological processes, are also poorly understood. In this study, we collected roots and surrounding soils of two dominant ectomycorrhizal plants, Bistorta vivipara and B. macrophylla from the AMSC, and performed bioinformatic and statistical analyses based on high-throughput sequencing of ITS2 amplicons. We found that: (1) fungi from the AMSC were closely related with those from boreal forests and tundra, and saprotrophic fungi had higher dispersal potential than ectomycorrhizal fungi; (2) community compositions exhibited clear divergences among geographic regions and between root and soil samples; (3) climate was the predominant factor driving regional-scale spatial patterns but had less explanatory power for saprotrophic and total fungi from roots than those from soils; (4) homogeneous selection and drift were the key ecological processes governing community assembly, but in communities of saprotrophic and total fungi from soil samples, drift contributed less and its role was partially replaced by dispersal limitation. This study highlights the importance of climatic selection and stochastic processes on fungal community assembly in alpine regions, and emphasizes the significance of simultaneously investigating fungi with different trophic modes and from both roots and soils.

Aqueous VOCs in complex water environment of oil exploitation sites: Spatial distribution, migration flux, and risk assessment.

Pollution of the aqueous environment by volatile organic compounds (VOCs) has caused increasing concerns. However, the occurrence and risks of aqueous VOCs in oil exploitation areas remain unclear. Herein, spatial distribution, migration flux, and environmental risks of VOCs in complex surface waters (including River, Estuary, Offshore and Aquaculture areas) were investigated at a typical coastal oil exploitation site. Among these surface waters, River was the most polluted area, and 1,2-Dichloropropane-which emerges from oil extraction activities-was the most prevalent VOC. Positive matrix factorization showed that VOCs pollution sources changed from oil exploitation to offshore disinfection activities along River, Estuary, Offshore and Aquaculture areas. Annual volatilization of VOCs to the atmosphere was predicted to be ∼34.42 tons, and rivers discharge ∼23.70 tons VOCs into the Bohai Sea annually. Ecological risk assessment indicated that Ethylbenzene and Bromochloromethane posed potential ecological risks to the aquatic environment, while olfactory assessment indicated that VOCs in surface waters did not pose an odor exposure risk. This study provides the first assessment of the pollution characteristics of aqueous VOCs in complex aqueous environments of oil exploitation sites, highlighting that oil exploitation activities can have nonnegligible impacts on VOCs pollution profiles.

Temperature-driven and discharge-driven variability of organic micropollutants in a large urban river and its implications for risk-based monitoring.

Urban rivers are exposed to an increasing load of organic micropollutants from wastewater effluent posing an ecological as well as public health hazard. One-off surveys can capture a snapshot of the pollution profile but fail to reveal the full scale of spatial and temporal heterogeneity. In the present study, 41 micropollutants (non-steroid anti-inflammatory drugs (NSAID), antihypertensives, antiepileptic, antidiabetic, antibiotics, iodinated contrast media (ICM), corrosion inhibitors, pesticides) were monitored every two weeks for one-year upstream and downstream of the Budapest metropolitan area in Danube River (336 samples total). ICMs, benzotriazoles and metamizole degradation products were detected in highest concentration regularly exceeding 100 ng/L. Median concentration of other pharmaceuticals ranged from <1 to 26 ng/L, while pesticides were typically below 10 ng/L. Variability of micropollutant concentration was primarily temporal, exhibiting two different patterns: (1) inverse correlation to river discharge, observed for corrosion inhibitors and carbamazepine (r = -0.505 to -0.665) or (2) inverse correlation to water temperature, observed primarily for ICMs, antihypertensives and antibiotics, r = -0.654 to -0.904). Temperature dependence was also significant after correcting for river discharge. Relative increase of pharmaceuticals was 2-134% after the metropolitan area, partially explained by emission estimates calculated from retail data and metabolization rates. The concentration of five ICMs (iopamidol in 100, iodixanol in 96, diatrizoate in 22, iomeprol in 21 and iohexol 13% of the samples) and two NSAIDs (ibuprofen and diclofenac (in 31.5 and 23% of the samples) exceeded the predicted no environmental effect concentration, posing a risk to algae (HQ = 1.2-6) and fish (HQ = 1.4-1.9), respectively. Results suggest that risk-based monitoring and risk management efforts should focus on ICMs, NSAIDs and industrial chemicals, taking into account that sampling in cold periods and during low flow provides the worst-case estimates.

Evaluating rainfall erosivity on the Tibetan Plateau by integrating high spatiotemporal resolution gridded precipitation and gauge data.

High-precision rainfall erosivity mapping is crucial for accurately evaluating regional soil erosion on the Tibetan Plateau (TP) under the backdrop of climate warming and humidification. Although high spatiotemporal resolution gridded precipitation data provides the foundation for rainfall erosivity mapping, the increasing spatial heterogeneity of rainfall with decreasing temporal granularity can lead to greater errors when directly computing rainfall erosivity from gridded precipitation data. In this study, a site-scale conversion coefficient was established so that rainfall erosivity calculated using hourly data can be converted to rainfall erosivity calculated using per-minute data. A revised model was established for calculating the rainfall erosivity based on high-resolution hourly precipitation data from the Third Pole gridded precipitation dataset (TPHiPr). The results revealed a notable underestimation in the original calculation results obtained using the TPHiPr, but strong correlation was observed between the two sets of results. There was a significant improvement in the Nash-Sutcliffe coefficient of efficiency (from -0.39 to 0.80) and the Percent Bias (from -63.95 % to 0.37 %) after model revision. The TPHiPr effectively depict the spatial characteristics of rainfall erosivity on the TP. It accurately reflected the rain shadow area on the northern flank of the Himalayas and the dry-hot valley in the Hengduan Mountains. It also showed high rainfall erosivity values in the tropical rainforest area on the southern flank of the eastern Himalayas. The overall trend of rainfall erosivity has increased on the TP during the period 1981 to 2020, with 65.91 % of the regions exhibiting an increasing trend and 22.25 % showing significant increases, indicating an intensified risk of water erosion. These findings suggest that the 40-year-high spatial resolution rainfall erosivity dataset can provide accurate data support for a quantitative understanding of soil erosion on the TP.

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes.

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

Spatial distribution and health risks assessment of heavy metals in e-waste dumping sites from Pakistan.

The present study focused on to determine the concentration and health risk of heavy metals (Cu, Pb, Zn, Cd, Hg, Cr) in e-waste contaminated soils collected from different provinces of Pakistan. Further, the impact of heavy metals on soil enzyme activities and microbial community was also investigated. The concentration (mg/kg) of Hg, Zn, Fe, Cu, Pb, Cd, and Cr ranged between 0-0.258, 2.284-6.587, 3.005-40.72, 8.67-36.88, 12.05-35.03, 1.03-2.43, and 33.13-60.05, respectively. The results revealed that Lahore site of Punjab province indicated more concentration of heavy metals as compared to other sites. The level of Cr at all sites whereas Hg at only two sites exceeds the World Health Organization standards (WHO) for soil. Soil enzyme activity exhibited dynamic trend among the sites. Maximum enzyme activity was observed for urease followed by phosphatase and catalase. Contamination factor (Cf), Pollution load index (PLI), and geo-accumulation index (Igeo) results showed that all the sites are highly contaminated with Cu, Cd, and Pb. Hazard index (HI) was less than 1 for children and adults suggesting non-carcinogenic health risk. Principle component analysis results depicted relation among Cr, Fr, catalase, and actinomycetes; Cd, OM, urease, and bacteria, and Pb, Cu, Zn, Hg, and phosphatase, suggesting soil enzymes and microbial community profiles were influenced by e-waste pollution. Therefore, there is a dire need to introduce sustainable e-waste recycling techniques as well as to make stringent e-waste management policies to reduce further environmental contamination.

A trawl collected dataset of Johnius (Actinopterygii, Sciaenidae) species in central-western Taiwanese waters.

Background: Sciaenidae is one of the most important coastal fisheries in Taiwan, both in production and economic value. It is also significant as the main targetted diet of Chinese white dolphins, Sousachinensis, especially for the genus Johnius, such as J.taiwanensis, J.belangerii and J.distinctus, which is primarily found in central-western Taiwan coastal waters. Despite an abundance of Johnius species occurrences reported in the Global Biodiversity Information Facility (GBIF) and the Taiwan Biodiversity Information Facility (TaiBIF) data portals (Mozambique, Australia, Taiwan, Korea, India, Indonesia, South Africa, Pakistan, Vietnam and China), there are no specific datasets that properly document the regional distribution of this genus, especially in Taiwanese waters. Thus, this paper describes a dataset of genus Johnius occurrences in waters on the central-western coast of Taiwan. The data collection for the present study was conducted from 2009 until 2020 and comprised 62 sampling events and 133 occurrence records. All fish specimens were collected by trawling in Miaoli, Changhwa and Yunlin Counties, Taiwan and brought back to the lab for identification, individual number count and body weight measurement. These processing data have been integrated and established in the Taiwan Fish Database and published in GBIF. This dataset contains six Johnius species and 2,566 specimens, making it comprehensive Johnius fish fauna and spatial distributional data on the coastal habitat in central-western Taiwanese waters. New information: This dataset contains 133 occurrence records of Johnius species (Sciaenidae) with 2,566 specimens, making it the most extensive public dataset of Johnius distribution records in Taiwan. The publication of this dataset through the TaiBIF and GBIF dataset platforms demonstrated that the number of Johnius spatial and temporal records in Taiwan waters is influenced by the topographical structure of the Changyun Rise (CYR) in combination with the cold current of the China Coastal currents and bound with the warm currents of the Kuroshio and the South China Sea on the central-western coast of Taiwan. The data serve as the foundation for understanding the biogeography and Johnius species ecology in Taiwan's coastal waters, which present a 2°C water temperature difference split at the CYR.

Potential climate change effects on the distribution of urban and sylvatic dengue and yellow fever vectors.

Climate change may increase the risk of dengue and yellow fever transmission by urban and sylvatic mosquito vectors. Previous research primarily focused on Aedes aegypti and Aedes albopictus. However, dengue and yellow fever have a complex transmission cycle involving sylvatic vectors. Our aim was to analyze how the distribution of areas favorable to both urban and sylvatic vectors could be modified as a consequence of climate change. We projected, to future scenarios, baseline distribution models already published for these vectors based on the favorability function, and mapped the areas where mosquitoes' favorability could increase, decrease or remain stable in the near (2041-2060) and distant (2061-2080) future. Favorable areas for the presence of dengue and yellow fever vectors show little differences in the future compared to the baseline models, with changes being perceptible only at regional scales. The model projections predict dengue vectors expanding in West and Central Africa and in South-East Asia, reaching Borneo. Yellow fever vectors could spread in West and Central Africa and in the Amazon. In some locations of Europe, the models suggest a reestablishment of Ae. aegypti, while Ae. albopictus will continue to find new favorable areas. The results underline the need to focus more on vectors Ae. vittatus, Ae. luteocephalus and Ae. africanus in West and Central sub-Saharan Africa, especially Cameroon, Central Africa Republic, and northern Democratic Republic of Congo; and underscore the importance of enhancing entomological monitoring in areas where populations of often overlooked vectors may thrive as a result of climate changes.

Mapping global distributions of clay-size minerals via soil properties and machine learning techniques.

Clay-size mineral is a vital ingredient of soil that influences various environment behaviors. It is crucial to establish a global distribution map of clay-size minerals to improve the recognition of environment variations. However, there is a huge gap of lacking some mineral contents in poorly accessible remote areas. In this work, machine learning (ML) approaches were conducted to predict the mineral contents and analyze their global abundance changes through the relationship between soil properties and mineral distributions. The average content of kaolinite, illite, smectite, vermiculite, chlorite, and feldspar were predicated to be 28.69 %, 22.30 %, 12.42 %, 5.43 %, 5.03 %, and 1.44 % respectively. Model interpretation showed that topsoil bulk density and drainage class were the most significant factors for predicting all six minerals. It could be seen from the feature importance analysis that bulk density notably reflected the distribution of 2:1 layered minerals more than that of 1:1 mineral. High drainage favored secondary minerals development, while low drainage was more benefited for primary minerals. Moreover, the content variation of different minerals aligned with the distribution of corresponding soil properties, which affirmed the accuracy of established models. This study proposed a new approach to predict mineral contents through soil properties, which filled a necessary step of understanding the geochemical cycles of soil-related processes.

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico.

BACKGROUND: This study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico. OBJECTIVES: The primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination. METHODS: Soil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health. RESULTS: The study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children. CONCLUSION: This study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

Assessment of gully influencing factors and susceptibility using remote sensing-based frequency ratio method in Sunshui River Basin, Southwest China.

Gully erosion is a serious global environmental problem associated with land degradation and ecosystem security. Examining the influencing factors of gullies and determining susceptibility hold significance in environmental sustainability. The study evaluates the spatial distribution, influencing factors, and susceptibility of gullies in the Sunshui River Basin in Sichuan Province, Southwest China. The frequency ratio method supported by satellite images and the gully inventory dataset (1614 gully head points) with different influencing factors were applied to assess the distribution and susceptibility of gullies. Additionally, gully head points were grouped into a training set (70%, 1130 points) and a test set (30%, 484 points). Spatial distribution results indicated that most gullies are located in the middle and upper part of the basin, characterized by moderate elevation (2100-3300 m), steep slopes (11.63-27.34°), abandoned farmland, and Cambisols soil, and fewer gullies are located in lower part characterized by lower elevation, gentle slopes, and low vegetation coverage. Land use and land cover influence on susceptibility is significantly greater than other factors with a prediction rate of 33.9, especially farmland abandonment, while the occurrence of gullies is also more often on southwest-orientated slopes. Gully susceptibility highlighted that the study area affected by the very low, low, moderate, high, and very high susceptibilities to these gullies covered an area of about 16%, 23%, 32%, 26%, and 3% of the total basin respectively, which indicates 61% of the study area is susceptible to gully erosion. Moderate to high susceptibility is situated in the upper and middle part, consistent with the spatial distribution of gullies in the basin, and very high susceptibility (3%) is distributed in both the lower and upper parts of the basin. These results have important implications for soil loss control, land planning, and integrated watershed management in the mountainous areas of Southwest China.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Assessment of groundwater fluoride and human health effects in a hard rock province of south India: Implications from Pollution Index Model (PIM) and Geographical Information System (GIS) techniques.

This research examines whether the groundwater in the Sivakasi Region of South India is suitable for consumption, and assesses the possible health hazards for various age demographics including infants, children, teenagers, and adults. A total of 77 groundwater samples were gathered, covering a total area of 580 km2 and analyzed for major and minor ions. The hydrogen ion concentration (pH) of the samples indicates neutral to marginally alkaline. The total dissolved solids (TDS) fluctuate from 255 to 2701 mg/l and electrical conductivity varies from 364 to 3540 µS/cm. A wide range of fluoride concentration was detected (0.1 to 3.2 mg/l) with nearly 38% groundwater samples surpassing the proposed limit (1.5 mg/l) suggested by the World Health Organization in 2017. Gibbs plot analysis suggested that most of the samples were influenced by geogenic factors, primarily rock weathering in this region. Correlation analysis showed that most of the samples were impacted by both natural and human sources. The pollution index of groundwater (PIG) fluctuated from 0.67 to 2.60 with approximately 30% and 53% of samples falling into insignificant and low pollution categories, respectively. Furthermore, 10% and 5% of total samples were characterized as moderate and high pollution levels, and 2% as very high pollution category. Spatial analysis using GIS revealed that 440.63 km2 were within safe fluoride levels according to the WHO standards, while 139.32 km2 were identified as risk zone. The principal component analysis (PCA1) showed strong positive loadings on EC (0.994), TDS (0.905), Mg2+ (0.910), Cl- (0.903) and HCO3- (0.923) indicating rock water interaction. PCA2 accounts the high positive factor loading on HCO3- (0.864) indicating ion exchange and mineral leaching. The PCA1 and PCA2 indicated that variables such as mineral leaching and rock water interaction are the major mechanisms contributing to the chemical signatures in groundwater, which may support for the elevated fluoride levels in certain areas. Risk assessments, including Hazard Quotient results showed that 71%, 61% 38%, and 34% of groundwater samples exceeded the permissible THI limit (THI > 1) for infants, children, teenagers, and adults, respectively. The study recommends implementing measures such as denitrification, defluorination, rainwater harvesting, and improved sanitation infrastructure to enhance the health conditions in the study region. Additionally, it suggests introducing educational programs in rural areas to create awareness about the health dangers due to consumption of water with high fluoride levels.

Characteristics and factors influencing the expansion of urban construction land in China.

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China's new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China's construction land presents a "point-line-plane" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China's construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing-Tianjin-Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land.

Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective.

Free-floating bike sharing (FFBS) attracts increasing research focusing on usage patterns, determining factors, and integrated transportation. However, existing researchers tend to overlook the variation in usage characteristics over various time ranges, particularly the usage pattern at night. This paper is conducted to fill the gap through a series of analysis approaches on FFSB in Beijing. The characteristics of the usage pattern, including time-varying usage and traveling distance distributions, are initially illustrated. Subsequently, the spatial patterns of FFBS are visualized and thoroughly analyzed in different time ranges and origin-destination (O-D) flows. A statistical model evaluating the environmental effects of FFBS trips revealed the source of FFBS usage. In addition to focusing on the nighttime, the usage patterns varying day and night are compared through the analysis. The findings explain the usage pattern variation and the unique pattern at night, providing valuable insight for improving the management of the FFBS system.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau.

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

The Performance and Spatial Distribution of Membrane Fouling in a Sequencing Batch Ceramic Membrane Bioreactor: A Pilot Study for Swine Wastewater Treatment.

The extensive application of ceramic membranes in wastewater treatment draws increasing attention due to their ultra-long service life. A cost-effective treatment for high-strength swine wastewater is an urgent and current need that is a worldwide challenge. A pilot-scale sequencing batch flat-sheet ceramic membrane bioreactor (ScMBR) coupled with a short-cut biological nitrogen removal (SBNR) process was developed to treat high-strength swine wastewater. The ScMBR achieved stable and excellent removal of COD (95.3%), NH4+-N (98.3%), and TN (92.7%), though temperature went down from 20 °C, to 15 °C, to 10 °C stepwise along three operational phases. The COD and NH4+-N concentrations in the effluent met with the discharge standards (GB18596-2001). Microbial community diversity was high, and the genera Pseudomonas and Comamonas were dominant in denitritation, and Nitrosomonas was dominant in nitritation. Ceramic membrane modules of this pilot-scale reactor were separated into six layers (A, B, C, D, E, F) from top to bottom. The total filtration resistance of both the top and bottom membrane modules was relatively low, and the resistance of the middle ones was high. These results indicate that the spatial distribution of the membrane fouling degree was different, related to different aeration scour intensities demonstrated by computational fluid dynamics (CFD). The results prove that the membrane fouling mechanism can be attributed to the cake layer formation of the middle modules and pore blocking of the top and bottom modules, which mainly consist of protein and carbohydrates. Therefore, different cleaning measures should be adopted for membrane modules in different positions. In this study, the efficient treatment of swine wastewater shows that the ScMBR system could be applied to high-strength wastewater. Furthermore, the spatial distribution characteristics of membrane fouling contribute to cleaning strategy formulation for further full-scale MBR applications.

Immune profiling of mouse lung adenocarcinoma paraffin tissues using multiplex immunofluorescence panel: a pilot study.

BACKGROUND: Immune profiling has become an important tool for identifying predictive, prognostic and response biomarkers for immune checkpoint inhibitors from tumor microenvironment (TME). We aimed to build a multiplex immunofluorescence (mIF) panel to apply to formalin-fixed and paraffin-embedded tissues in mice tumors and to explore the programmed cell death protein 1/ programmed cell death 1 ligand 1 (PD-1/PD-L1) axis. RESULTS: An automated eight-color mIF panel was evaluated to study the TME using seven antibodies, including cytokeratin 19, CD3e, CD8a, CD4, PD-1, PD-L1, F4-80 and DAPI, then was applied in six mice lung adenocarcinoma samples. Cell phenotypes were quantified by software to explore the co-localization and spatial distribution between immune cells within the TME. This mice panel was successfully optimized and applied to a small cohort of mice lung adenocarcinoma cases. Image analysis showed a sparse degree of immune cell expression pattern in this cohort. From the spatial analysis we found that T cells and macrophages expressing PD-L1 were close to the malignant cells and other immune cells. CONCLUSIONS: Comprehensive immune profiling using mIF in translational studies improves our ability to correlate the PD-1/PD-L1 axis and spatial distribution of lymphocytes and macrophages in mouse lung cancer cells to provide new cues for immunotherapy, that can be translated to human tumors for cancer intervention.

Epidemiological characteristics of syphilis in mainland China, 2004 to 2019.

OBJECTIVES: Syphilis is a globally prevalent sexually transmitted infection. This study aimed to elucidate the epidemiological characteristics of syphilis in China from 2004 to 2019. METHODS: Incidence data for syphilis across 31 provinces in mainland China were obtained from the Data Center of China Public Health Science for the period from 2004 to 2019. Epidemiological methods and the Chi-squared test were used to analyze the temporal, regional, and disease stage distributions of syphilis. RESULTS: In total, 5,527,399 syphilis cases were reported in China from 2004 to 2019, with an average annual prevalence of 25.7063 per 100,000 population and overall increasing trend. In terms of regional distribution, high-incidence provinces included Shanghai, Zhejiang, Fujian, Guangxi, Guangdong, Xinjiang, Ningxia, and Qinghai. The proportion of latent syphilis increased from 20.41% in 2004 to 82.95% in 2019, with an upward trend each year. CONCLUSIONS: Syphilis incidence exhibited an overall increasing trend in China, and latent syphilis was predominant. Syphilis incidences considerably varied among regions, and syphilis was detected from coastal to inland provinces. Thus, syphilis prevention and control programs should be tailored according to the specific epidemiological characteristics of each region.