Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China.

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.

Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018.

Drought, a complex phenomenon exacerbated by climate change, is influenced by various climate factors. The escalating global temperatures associated with climate change, impact precipitation patterns and water cycle processes, consequently intensifying the occurrence and severity of droughts. To effectively address and adapt to these challenges, it is crucial to identify the dominant climate factors driving drought events. In this study, we utilized the 1979-2018 Chinese meteorological forcing dataset to calculate the daily Standardized Precipitation Evapotranspiration Index (SPEI). The Theil-Sen and Mann-Kendall (M-K) tests were employed to analyze the spatial and temporal trends of drought severity and duration. Additionally, partial correlation analysis was conducted to examine the relationship between climate factors (precipitation and potential evapotranspiration (PET)) and drought characteristic (drought severity and duration). Through this comprehensive analysis, we aimed to identify the primary factors influencing drought severity and duration. The findings revealed the following key results: (1) Over the 40-year period from 1979 to 2018, drought trends in China and its seven climate divisions exhibited an increasing pattern. (2) During drought periods, most regions exhibited a positive correlation between PET and drought severity and duration, while precipitation demonstrated a negative correlation. However, certain areas experiencing severe drought displayed a negative correlation between PET and drought severity and duration, precipitation demonstrated a positive correlation with drought severity and duration. (3) PET emerged as the dominant climatic factor for meteorological drought in the majority of China. These findings contribute valuable insights for policymakers in the development of climate change adaptation and mitigation strategies. By understanding the dominant climate factors driving drought events, policymakers can implement effective measures to mitigate the adverse socioeconomic and environmental impacts associated with climate change.

Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China.

In this study, we simulated the spatial and temporal processes of a particulate matter (PM) pollution episode from December 10-29, 2019, in Zhengzhou, the provincial capital of Henan, China, which has a large population and severe PM pollution. As winter is the high incidence period of particulate pollution, winter statistical data were selected from the pollutant observation stations in the study area. During this period, the highest concentrations of PM2.5 (atmospheric PM with a diameter of less than 2.5 µm) and PM10 (atmospheric PM with a diameter of less than 10 µm) peaked at 283 µg m-3 and 316 µg m-3, respectively. The contribution rates of local and surrounding regional emissions within Henan (emissions from the regions to the south, northwest, and northeast of Zhengzhou) to PM concentrations in Zhengzhou were quantitatively analyzed based on the regional Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). Model evaluation showed that the WRF/Chem can accurately simulate the spatial and temporal variations in the PM concentrations in Zhengzhou. We found that the anthropogenic emissions south of Zhengzhou were the main causes of high PM concentrations during the studied episode, with contribution rates of 14.39% and 16.34% to PM2.5 and PM10, respectively. The contributions of anthropogenic emissions from Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.94% and 7.29%, respectively. The contributions of anthropogenic emissions from the area northeast of Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.42% and 7.18%, respectively. These two areas had similar contributions to PM pollution in Zhengzhou. The area northeast of Zhengzhou had the lowest contributions to the PM2.5 and PM10 concentrations in Zhengzhou (5.96% and 5.40%, respectively).

Quantifying Effects of Ligand-Metal Bond Covalency on Oxygen-Redox Electrochemistry in Layered Oxide Cathodes.

Oxygen-redox electrochemistry is attracting tremendous attention due to its enhanced energy density for layered oxide cathodes. However, quantified effects of ligand-metal bond covalency on the oxygen-redox behaviors are not fully understood, limiting a rational structure design for enhancing the oxygen redox reversibility. Here, using Li2Ru1-xMnxO3 (0 ≤ x ≤ 0.8) which includes both 3d- and 4d-based cations as model compounds, we provide a quantified relation between the ligand-metal bond covalency and oxygen-redox electrochemistry. Supported by theoretical calculations, we reveal a linear positive correlation between the transition metal (TM)-O bond covalency and the overlap area of TM nd and O 2p orbitals. Furthermore, based on the electrochemical tests on the Li2Ru1-xMnxO3 systems, we found that the enhanced TM-O bond covalency can increase the reversibility of oxygen-redox electrochemistry. Due to the strong Ru-O bond covalency, the thus designed Ru-doped Li-rich Li1.2Mn0.54Ni0.13Co0.13O2 cathode shows an enhanced initial coulombic efficiency, increased capacity retention, and suppressed voltage decay during cycling. This systematic study provides a rational structure design principle for the development of oxygen-redox-based layered oxide cathodes.

Effect of agricultural soil wind erosion on urban PM2.5 concentrations simulated by WRF-Chem and WEPS: A case study in Kaifeng, China.

Dust emission induced by agricultural soil wind erosion is one of the main sources of atmospheric particulate matter (PM) in dryland areas. However, most current air quality models do not consider this emission source, resulting in large uncertainties in PM simulations. Here we estimated the agricultural PM2.5 (particulate matter with an aerodynamic diameter <2.5 µm) emission around Kaifeng, a prefecture-level city in central China, using the Wind Erosion Prediction System (WEPS), with the MEIC (Multi-resolution Emission Inventory for China) as an anthropogenic emission source. We then plugged these estimates into the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an air pollution episode in Kaifeng, China. Results showed that the addition of agricultural soil PM2.5 emissions significantly improved the ability of WRF-Chem to accurately simulate PM2.5 concentrations. The PM2.5 concentration mean bias and correlation coefficient of not considered and considered agricultural dust emission were -72.35 µg m-|3 and 3.31 µg m-|3 and 0.3 and 0.58, respectively. The PM2.5 emitted by the agricultural soil wind erosion contributed around 37.79% of the PM2.5 in the Kaifeng municipal district during this pollution episode. This study confirmed that the dust emission caused by agricultural soil wind erosion can significantly impact urban PM2.5 concentrations which surrounded by large areas of farmland, and also indicated that coupling dust emissions from farmland with anthropogenic air pollutant emissions can improve the accuracy of air quality models.

Clipping decreases plant cover, litter mass, and water infiltration rate in soil across six plant community sites in a semiarid grassland.

Water infiltration in the soil is a crucial hydrological function in the land water cycle, especially in the semiarid region where water is relatively scarce. The semiarid grassland in Northern China represents the regional vegetation in the vast area of Eurasian continent and is sensitive to land use change. However, no clear patterns exist regarding the comprehensive examination of water infiltration in relation to clipping across six plant community sites. This study aimed to test the effect of clipping and plant community sites, which were dominated by Agropyron cristatum, Stipa krylovii, Leymus chinensis, Potentilla tanacetifolia, Artemisia frigida, or Lespedeza davurica, on the water infiltration rate in the semiarid grassland. Clipping significantly decreased the water initial, steady, and average infiltration rates by 39.13, 4.36, and 12.46 mm h-1, respectively, across the six plant community sites. Clipping-induced changes in the average infiltration rate positively correlated with the changes in the plant cover (r = 0.60, P < 0.01), litter mass (r = 0.53, P < 0.01), forb functional group ratio (r = 0.46, P = 0.03), and total porosity (r = 0.49, P = 0.02), and negatively with water-holding capacity (r = -0.45, P = 0.03). Further, the water infiltration rate significantly differed among the six plant community sites. The L.davurica site had the highest water initial infiltration rate with a value of 137.63 ± 17.76 mm h-1, while the L. chinensis site had the lowest rate with a value of 74.08 ± 5.26 mm h-1. Principal component analysis showed that the total porosity, litter mass, plant cover, and forb functional group ratio were the main factors affecting water infiltration rates in the control grassland. Overall, our findings suggested that local governments and herders should implement unclipping as a potential sustainable management for improving hydrological function in the semiarid grassland.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin.

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China.

Elucidating the characteristics and influencing mechanisms of PM2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM2.5 concentrations present a U-shaped distribution. The PM2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM2.5 concentrations in winter. The effect of anthropogenic emissions on PM2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Maize yield reduction and economic losses caused by ground-level ozone pollution with exposure- and flux-response relationships in the North China Plain.

Ground-level ozone (O3) has negative effects on agricultural crops. Maize is an important grain crop in China. The North China Plain (NCP) serves as the major crops' production area of China and experiences severe ozone pollution. Using the ground-level ozone simulated by an atmospheric chemistry transport model (WRF-Chem), we quantified the yield reduction and economic losses of maize during 2015-2018 over NCP based on exposure-response AOT40 (accumulation of hourly O3 concentration exceed 40 ppb) and flux-response POD6 (phytotoxic dose of ozone over 6 nmol m-2 s-1). Results showed that the ozone concentration, AOT40, and POD6 clearly increased from 2015 to 2018 in growing season of maize over NCP. The four-year annual mean ozone concentration, AOT40, and POD6 were 0.055 ppm, 18.02 ppm h, and 5.02 mmol m-2, respectively. At county level, the relative loss of maize yield (MRYL) based on AOT40 and POD6 had clearly spatio-temporal differences in NCP. The average MRYLs of AOT40 and of POD6 from 2015 to 2018 were 10.4% and 21.4%, respectively, and these reductions were associated with 2399 million and 5637 million US dollars, respectively. This study suggests that surface ozone increased the yield losses of maize, and indicates that further reductions in ozone concentrations can enhance the food security in China.

Belowground Root Competition Alters the Grass Seedling Establishment Response to Light by a Nitrogen Addition and Mowing Experiment in a Temperate Steppe.

Predicting species responses to climate change and land use practices requires understanding both the direct effects of environmental factors as well as the indirect effects mediated by changes in belowground and aboveground competition. Belowground root competition from surrounding vegetation and aboveground light competition are two important factors affecting seedling establishment. However, few studies have jointly examined the effect of belowground root and light competition on seedling establishment, especially under long-term nitrogen addition and mowing. Here, we examined how belowground root competition from surrounding vegetation and aboveground light competition affect seedling establishment within a long-term nitrogen addition and mowing experiment. Seedlings of two grasses (Stipa krylovii and Cleistogenes squarrosa) were grown with and without belowground root competition under control, nitrogen addition, and mowing treatments, and their growth characteristics were monitored. The seedlings of the two grasses achieved higher total biomass, height, mean shoot and root mass, but a lower root/shoot ratio in the absence than in the presence of belowground root competition. Nitrogen addition significantly decreased shoot biomass, root biomass, and the survival of the two grasses. Regression analyses revealed that the biomass of the two grass was strongly negatively correlated with net primary productivity under belowground root competition, but with the intercept photosynthetic active radiation in the absence of belowground root competition. This experiment demonstrates that belowground root competition can alter the grass seedling establishment response to light in a long-term nitrogen addition and mowing experiment.

Nanocrystalline (AlTiVCr)N Multi-Component Nitride Thin Films with Superior Mechanical Performance.

Multi-component nitride thin films usually show high hardness and good wear resistance due to the nanoscale structure and solid-solution strengthening effect. However, the state of N atoms in the thin film and its effects on the compressive strength is still unclear. In this work, (AlTiVCr)N multi-component nitride thin films with a face-centered cubic (FCC) structure prepared by the direct current magnetron sputtering method exhibit a superior strength of ~4.5 GPa and final fracture at a strain of ~5.0%. The excellent mechanical properties are attributed to the synergistic effects of the nanocrystalline structure, covalent bonding between N and metal atoms, and interstitial strengthening. Our results could provide an intensive understanding of the relationship between microstructure and mechanical performances for multi-component nitride thin films, which may promote their applications in micro- and nano-devices.

Interactive short-term effects of meteorological factors and air pollution on hospital admissions for cardiovascular diseases.

A substantial number of studies have demonstrated the association between air pollution and adverse health effects. However, few studies have explored the potential interactive effects between meteorological factors and air pollution. This study attempted to evaluate the interactive effects between meteorological factors (temperature and relative humidity) and air pollution ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) on cardiovascular diseases (CVDs). Next, the high-risk population susceptible to air pollution was identified. We collected daily counts of CVD hospitalizations, air pollution, and weather data in Nanning from January 1, 2014, to December 31, 2015. Generalized additive models (GAMs) with interaction terms were adopted to estimate the interactive effects of air pollution and meteorological factors on CVD after controlling for seasonality, day of the week, and public holidays. On low-temperature days, an increase of [Formula: see text] in [Formula: see text], [Formula: see text], and [Formula: see text] was associated with increases of 4.31% (2.39%, 6.26%) at lag 2; 2.74% (1.65%, 3.84%) at lag 0-2; and 0.13% (0.02%, 0.23%) at lag 0-3 in CVD hospitalizations, respectively. During low relative humidity days, a [Formula: see text] increment of lag 0-3 exposure was associated with increases of 3.43% (4.61%, 2.67%) and 0.10% (0.04%, 0.15%) for [Formula: see text] and [Formula: see text], respectively. On high relative humidity days, an increase of [Formula: see text] in [Formula: see text] was associated with an increase of 5.86% (1.82%, 10.07%) at lag 0-2 in CVD hospitalizations. Moreover, elderly (≥ 65 years) and female patients were vulnerable to the effects of air pollution. There were interactive effects between air pollutants and meteorological factors on CVD hospitalizations. The risk that [Formula: see text], [Formula: see text], and [Formula: see text] posed to CVD hospitalizations could be significantly enhanced by low temperatures. For [Formula: see text] and [Formula: see text], CVD hospitalization risk increased in low relative humidity. The effects of [Formula: see text] were enhanced at high relative humidity.

Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data.

Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based view, an effective evaluation of the green space exposure inequity at the community level remains challenging. In this study, we presented a green space exposure inequity assessment framework, integrating the Green View Index (GVI), deep learning, spatial statistical analysis methods, and urban rental price big data to analyze green space exposure inequity at the community level toward a "15-minute city" in Zhengzhou, China. The results showed that green space exposure inequality is evident among residential communities. The areas in the old city were with relatively high GVI and the new city districts were with relatively low GVI. Moreover, a spatially uneven association was observed between the degree of green space exposure and housing prices. Especially, the wealthier communities in the new city districts benefit from low green space, compared to disadvantaged communities in the old city. The findings provide valuable insights for policy and planning to effectively implement greening strategies and eliminate environmental inequality in urban areas.

Contribution of local and surrounding area anthropogenic emissions to a high ozone episode in Zhengzhou, China.

This study analyzed an ozone pollution episode that occurred in the summer of 2020 in Zhengzhou, the provincial capital of Henan, China, and quantified the contribution of local and surrounding area anthropogenic emissions to this episode based on the Weather Research and Forecasting with Chemistry (WRF/Chem) model. Simulation results showed that the WRF/Chem model is well suited to simulate the ozone concentrations in this area. In addition, four simulation scenarios (removing the emissions from the northern Zhengzhou, southwestern Zhengzhou, Zhengzhou local and southeastern Zhengzhou) were conducted to explore the specific contributions of local emissions and emissions from surrounding areas within Henan to this ozone pollution episode. We found that contributions from the northern, local, southwestern, and southeastern regions were 6.1%, 5.9%, 1.7%, and 1.5%, respectively. The northern and local emissions of Zhengzhou (only emissions from Zhengzhou) were prominent contributors within the simulation areas. In other words, during this episode, most of the ozone pollution in Zhengzhou appeared to be transported in from regions outside Henan Province.

Rural-urban differences in associations between air pollution and cardiovascular hospital admissions in Guangxi, southwest China.

Epidemiological studies found that exposure to air pollution increases cardiovascular hospitalizations. However, studies on rural-urban differences in associations between hospitalizations for cardiovascular diseases and air pollution are limited. The generalized linear model (GLM) was applied to investigate the associations between cardiovascular hospitalizations and air pollution (SO2, NO2, PM2.5, PM10, CO, and O3) in Guangxi, southwest China, in 2015 (January 1-December 31). The relative risk of pollutants (SO2, NO2) on cardiovascular hospital admissions was significantly different between urban and rural areas. The effect of SO2 on cardiovascular hospitalizations was higher in urban areas than in rural areas at lag0 to lag3 and cumulative lag01 to lag03. In urban areas, there were positive associations between NO2 and cardiovascular hospitalizations at lag0, lag1 and cumulative lag01, lag02. In contrast, the effect of NO2 on cardiovascular hospitalizations was not significant in rural areas. Urban residents were more sensitive than rural residents to SO2 and NO2. Subgroup analyses showed statistically significant differences between rural and urban areas in the association between SO2 and NO2 and cardiovascular hospitalizations for males. For age groups, people aged ≥ 65 years appeared to be more vulnerable to SO2 and NO2 in urban areas. The effects of PM2.5 PM10, CO, and O3 on cardiovascular hospitalizations were consistently negative for all groups. Our findings indicated that there were rural-urban differences in associations between cardiovascular hospitalizations and air pollutants. In rural areas, the risk of cardiovascular hospitalizations was mainly influenced by SO2. Therefore, we expect to pay attention to protecting people from air pollution, particularly for those aged ≥ 65 years in urban areas.

Synergistic effects of gaseous pollutants on hospital admissions for cardiovascular disease in Liuzhou, China.

Previous studies demonstrated that short-term exposure to gaseous pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3)) had a greater adverse effect on cardiovascular disease. However, little evidence exists regarding the synergy between gaseous pollutants and cardiovascular disease (CVD). Therefore, we aimed to estimate the effect of individual gaseous pollutants on hospital admissions for CVD and to explore the possible synergistic effects between gaseous pollutants. Daily hospitalization counts for CVD were collected from January 1, 2014, to December 31, 2015. We also collected daily time series on gaseous pollutants from the Environment of the People's Republic of China, including NO2, SO2, and O3. We used distributed lag nonlinear models (DLNMs) to assess the association of individual gaseous pollutants on CVD hospitalization, after controlling for seasonality, day of the week, public holidays, and weather variables. Then, we explored the variability across age and sex groups. In addition, we analyzed the synergistic effects between gaseous pollutants on CVD. Extremely low NO2 and SO2 increase the risk of CVD in all subgroup at lag 7 days. The greatest effect of high concentration of SO2 was observed in male and the elderly (≥ 65 years) at lag 3 days. Greater effects of high concentration of O3 were more pronounced in the young (< 65 years) and female at lag 3 days, while the effect of low concentration of O3 was greater in male and the young (< 65 years) at lag 0 day. We found a synergistic effect between NO2 and SO2 for CVD, as well as between SO2 and O3. The synergistic effects of NO2 and SO2 on CVD were stronger in the elderly (≥ 65) and female. The female was sensitive to synergistic effects of SO2-O3 and NO2-O3. Interestingly, we found that there was a risk of CVD in the susceptible population even for gaseous pollutant concentrations below the National Environmental Quality Standard. The synergy between NO2 and SO2 was significantly associated with cardiovascular disease hospitalization in the elderly (≥ 65). This study provides evidence for the synergistic effect of gaseous pollutants on hospital admissions for cardiovascular disease.

Flexible, Electrically Conductive, Nanostructured, Asymmetric Aerogel Films for Lithium-Sulfur Batteries.

Lithium-sulfur batteries are afflicted with capacity fading on account of polysulfide shuttling. A novel cost-effective electrode that can hinder the polysulfide shuttling and realize high active material utilization is highly required. Here, we demonstrate a flexible, electrically conductive, nanostructured, and asymmetric hybrid cathode by integrating a high-aspect-ratio wood nanocellulose and a low-cost commercial carbon nanotube (∼$ 0.2 g-1) into an entangled aerogel film. The vacuum filtration combined with lyophilization enables the aerogel film with quite different nanofiber/nanotube packing densities and pore structures at its two sides. The cooperative effects of the entangled building blocks and the asymmetric porous structure of the aerogel film stimulate the simultaneous increase of active sulfur loading, enhancing the electrolyte penetration, alleviating dissolution and shuttling of polysulfide ions, and promoting the fast electron transportation. The as-generated cathode exhibited a capacity fading of 0.01% per cycle over 1000 discharge/charge cycles at a 0.5 C rate (1 C = 1675 mA g-1). The average Coulombic efficiency reached ∼99.7%.

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities.

PM2.5 pollution has gradually attracted people's attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NOX of residential emissions explained 20% (q = 0.2) of the PM2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM2.5 pollution, improve PM2.5 forecasting models, and formulate more precise government control policy.

Identification and Validation of HOTAIRM1 as a Novel Biomarker for Oral Squamous Cell Carcinoma.

ORAL squamous cell carcinoma (OSCC) is a malignant tumor with the highest incidence among tumors involving the oral cavity maxillofacial region, and is notorious for its high recurrence and metastasis potential. Long non-coding RNAs (lncRNAs), which regulate the genesis and evolution of cancers, are potential prognostic biomarkers. This study identified HOTAIRM1 as a novel significantly upregulated lncRNA in OSCC, which is strongly associated with unfavorable prognosis of OSCC. Systematic bioinformatics analyses demonstrated that HOTAIRM1 was closely related to tumor stage, overall survival, genome instability, the tumor cell stemness, the tumor microenvironment, and immunocyte infiltration. Using biological function prediction methods, including Weighted gene co-expression network analysis (WGCNA), Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA), HOTAIRM1 plays a pivotal role in OSCC cell proliferation, and is mainly involved in the regulation of the cell cycle. In vitro, cell loss-functional experiments confirmed that HOTAIRM1 knockdown significantly inhibited the proliferation of OSCC cells, and arrested the cell cycle in G1 phase. At the molecular level, PCNA and CyclinD1 were obviously reduced after HOTAIRM1 knockdown. The expression of p53 and p21 was upregulated while CDK4 and CDK6 expression was decreased by HOTAIRM1 knockdown. In vivo, knocking down HOTAIRM1 significantly inhibited tumor growth, including the tumor size, weight, volume, angiogenesis, and hardness, monitored by ultrasonic imaging and magnetic resonance imaging In summary, our study reports that HOTAIRM1 is closely associated with tumorigenesis of OSCC and promotes cell proliferation by regulating cell cycle. HOTAIRM1 could be a potential prognostic biomarker and a therapeutic target for OSCC.

Satellite-based ground PM2.5 estimation using a gradient boosting decision tree.

Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) is one of the major air pollutants risks to human health worldwide. Satellite-based aerosol optical depth (AOD) products are an effective metric for acquiring PM2.5 information, featuring broad coverage and high resolution, which compensate for the sparse and uneven distribution of existing monitoring stations. In this study, a gradient boosting decision tree (GBDT) model for estimating ground PM2.5 concentration directly from AOD products across China in 2017, integrating human activities and various natural variables was proposed. The GBDT model performed well in estimating temporal variability and spatial contrasts in daily PM2.5 concentrations, with relatively high fitted model (10-fold cross-validation) coefficients of determination of 0.98 (0.81), low root mean square errors of 3.82 (11.57) µg/m3, and mean absolute error of 1.44 (7.45) µg/m3. Seasonal examinations revealed that summer had the cleanest air with the highest estimation accuracies, whereas winter had the most polluted air with the lowest estimation accuracies. The model successfully captured the PM2.5 distribution pattern across China in 2017, showing high levels in southwest Xinjiang, the North China Plain, and the Sichuan Basin, especially in winter. Compared with other models, the GBDT model showed the highest performance in the estimation of PM2.5 with a 3-km resolution. This algorithm can be adopted to improve the accuracy of PM2.5 estimation with higher spatial resolution, especially in summer. In general, this study provided a potential method of improving the accuracy of satellite-based ground PM2.5 estimation.