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The NASA Earth Venture Suborbital (EVS-2) Atmospheric Tomography Mission (ATom) provided rich gas and aerosol measurements over the global oceans. In this study, we investigate the sulfur species of dimethyl sulfide (DMS), sulfur dioxide (SO2), methane sulfonic acid (MSA), and sulfate (SO4) that were measured during the ATom aircraft campaigns and simulated by five AeroCom models. This study focuses on remote regions over the Pacific, Atlantic, and Southern Oceans from near the surface to ~12 km altitude and covers all four seasons. We examine the vertical and seasonal variations of these sulfur species over tropical, mid-, and high latitude regions in both hemispheres. We identify their origins from land versus ocean and from anthropogenic versus natural sources with sensitivity studies by applying tagged tracers linking to emission types and regions. Using the GEOS model, we also investigate impact of cloud simulation (i.e., one-moment bulk cloud module, 1MOM vs two-moment cloud microphysics module, 2MOM) on the sulfur cycle and identify critical mechanisms of cloud impact by performing process-level budget analyses. Generally, SO4 has a better model-observation agreement than DMS, SO2 and MSA, and there are much larger DMS simulated concentrations close to the sea surface than measured, indicating all model DMS emissions may be too high. Anthropogenic emissions are the dominant source (44-60% of the total amount) for atmospheric SO4 simulated along ATom flight tracks in almost every altitude, followed by volcanic eruptions (18-33%) and oceanic sources (16-28%). GEOS SO4 simulations differ significantly between the 1MOM and 2MOM cloud schemes, with the sulfate chemical production via aqueous phase reactions seeming to be the critical process.

Atmospheric transport of iron (Fe) in fine anthropogenic aerosol particles is an important route of soluble Fe supply to remote oceans from continental areas. To investigate Fe properties of aerosol particles over remote oceans, we analyzed atmospheric aerosol particles over the Indian Ocean during the research vessel Hakuho Maru KH-18-6 cruise. Aerosol particles collected using a cascade impactor were analyzed using transmission electron microscopy (TEM) with an energy-dispersive X-ray spectrometry analyzer. The particle shape and composition on the sample stage of 0.3–0.8 μm aerodynamic diameter indicated that most particles collected north of the equator were composed mainly of ammonium sulfate. Regarding the particle number fraction, 0.6–3.0 % of particles contained Fe, which mostly co-existed with sulfate. Of those particles, Fe was found 26 % as metal spheres, often co-existing with Al or Si, regarded as fly ash, 14 % as mineral dust, and 7 % as iron oxide aggregations. Water-dialysis analyses of TEM samples indicated that Fe in spherical fly ash was almost entirely insoluble, whereas Fe in the other morphological-typed particles was partly (65 % Fe mass on average) soluble. Global model simulations mostly reproduce observed Fe mass concentrations in PM2.5 collected using a high-volume air sampler, including their north–south contrast during the cruise. In contrast, a marked difference was found between the simulated mass fractions of Fe mineral sources and the observed Fe types. For example, the model underestimated anthropogenic aluminosilicate Fe contained in matter such as fly ash from coal combustion. Our observations suggest that Fe in particles over remote ocean areas has multiple shapes and minerals, and further suggest that its solubility after aging processes differs depending on their morphological and mineral type. Proper consideration of such Fe types at their sources is necessary for accurate estimation of atmospheric Fe effects on marine biological activity.

The design of artificial supramolecular systems that mimic the structure and functionality of natural enzymes to achieve efficient chemical conversions is a promising subject. In this work, we assembled a novel metal-organic capsule from electron-rich dyes, polyaniline compounds, as ligands by a subcomponent self-assembly strategy. By encapsulating electron-deficient dyes, anthraquinone or 9,10-dicyanoanthracene, into electron-rich pseudo-cubic capsules, we successfully constructed an artificial enzyme-mimicking supramolecular system for the efficient photocatalytic reduction of aryl chlorides with high reduction potentials (

Brain–computer interface (BCI) systems based on electroencephalography (EEG) signals have been extensively used in medical practice. To enhance the BCI performance, improving the classification accuracy of EEG signals is the key, which has always been the focus of research and development. In this article, a novel method integrating complex network and broad learning system (BLS) is proposed for visual evoked potential (VEP)-based BCI research. First, systematic VEP-based brain experiments are conducted for obtaining EEG signals, including steady-state VEP (SSVEP) and steady-state motion VEP (SSMVEP). Then, limited penetrable visibility graph (LPVG) and its degree sequence are employed to implement the preliminary feature extraction. All these features are finally fed into a BLS to study and classify the SSVEP and SSMVEP signals, respectively. The classification results show that our LPVG-based BLS can effectively classify VEP-based EEG signals, with average classification accuracy 96.22% for SSVEP and 74.54% for SSMVEP. These results are significantly better than other comparison methods as well as traditional CCA-based methods. All these open up new venues for studying EEG-based BCI systems via the fusion of network science and BLS.

Enzymes facilitate chemical conversions through the collective activity of aggregated components, but the marriage of aggregation-induced emission (AIE) with molecular containers to emulate enzymatic conversion remains challenging. Herein, we report a new approach to construct a Pt

Black carbon (BC) aerosol particles in the Arctic heat the atmosphere and snow/ice surfaces and may strengthen the snow-albedo feedback that amplifies Arctic warming. Model simulations of BC concentrations in the Arctic depend strongly on the representation of microphysical processes such as aging, activation, and wet removal. Most BC modeling studies have classified BC particles into hydrophobic BC, which cannot form cloud droplets, and hydrophilic BC, which can form cloud droplets, by assuming a globally constant critical supersaturation threshold value (Sthre), without considering its consistency with cloud maximum supersaturation (Smax). Here we show that it is essential to consider the consistency of Sthre with Smax in global model simulations to reduce uncertainties in near-surface ambient BC concentrations in the Arctic. Previous studies often obtained good agreement between simulated and observed near-surface Arctic BC mass concentrations when a low Sthre (~0.1%) was assumed in their models. However, this Sthre may be too low (activation and wet removal of BC may be underestimated) for the Arctic, because some recent observations and our model simulations suggest that Smax may actually be higher (~0.3%) there. We also demonstrate that spatially varying Sthre values and their consistency with Smax, which previous studies did not consider, must be represented in models for more accurate estimation of BC budget in the Arctic. Because both Smax and BC-aging speed depend on climatic conditions, our findings are an important step toward better simulations of BC impacts on past, present, and future Arctic climates.

There is a large gap between the simulated and observed sulfate concentrations during winter haze events in North China. Although multiphase sulfate formation mechanisms have been proposed, they have not been evaluated using chemical transport models. In this study, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to apportion sulfate formation. It was found that Mn-catalyzed oxidation on aerosol surfaces was the dominant sulfate formation pathway, accounting for 92.3 ± 3.5% of the sulfate formation during haze events. Gas-phase oxidation contributed 3.1 ± 0.5% to the sulfate formation due to the low OH levels. The H

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures.

The atmospheric deposition of soluble (bioaccessible) iron enhances ocean primary productivity and subsequent atmospheric CO2 sequestration in iron-limited ocean basins, especially the Southern Ocean. While anthropogenic sources have been recently suggested to be important in some northern hemisphere oceans, the role in the Southern Ocean remains ambiguous. By comparing multiple model simulations with the new aircraft observations for anthropogenic iron, we show that anthropogenic soluble iron deposition flux to the Southern Ocean could be underestimated by more than a factor of ten in previous modeling estimates. Our improved estimate for the anthropogenic iron budget enhances its contribution on the soluble iron deposition in the Southern Ocean from about 10% to 60%, implying a dominant role of anthropogenic sources. We predict that anthropogenic soluble iron deposition in the Southern Ocean is reduced substantially (30‒90%) by the year 2100, and plays a major role in the future evolution of atmospheric soluble iron inputs to the Southern Ocean.

Gas-liquid two-phase flow widely exists in chemical industries and natural gas industries. But characterizing the nonlinear flow behaviors underlying such flow remains a challenge of great importance. In this brief, we first carry out the vertical gas-liquid two-phase flow experiment in a 50mm-inner-diameter pipe to acquire signals via the four-sector conductance measurement system. Then, we develop a multiresolution multiplex network (MMN) to analyze multichannel measurements. In particular, we employ average edge overlap to quantitatively characterize the derived network corresponding to different flow patterns. Results show that our method allows to effectively uncover the evolution from bubble flow to slug flow. In addition, we calculate the approximate entropy of different flow patterns to verify the findings from our network analysis. All the results demonstrate that our proposed method allows characterizing the nonlinear flow behaviors from multichannel measurements and also provides a novel way for multivariate time series analysis.

Gas–liquid two-phase flow widely exits in production and transportation of petroleum industry. Characterizing gas–liquid flow and measuring flow parameters represent challenges of great importance, which contribute to the recognition of flow regime and the optimal design of industrial equipment. In this paper, we propose a novel complex network-based deep learning method for characterizing gas–liquid flow. Firstly, we map the multichannel measurements to multiple limited penetrable visibility graphs (LPVGs) and obtain their degree sequences as the graph representation. Based on the degree distribution, we analyze the complicated flow behavior under different flow structures. Then, we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction. We implement the model with two parallel branches with the same structure, each corresponding to one input. Each branch consists of a channel-projection convolutional part, a spatial–temporal convolutional part, a dense block and an attention module. The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement. At last, our method achieves an accuracy of 95.3% for the classification of flow structures, and a mean squared error of 0.0038 and a mean absolute percent error of 6.3% for the measurement of gas void fraction. Our method provides a promising solution for characterizing gas–liquid flow and measuring flow parameters.

China is one of the largest agricultural countries in the world. Thus, NH3 emission from agricultural activities in China considerably affects the country's regional air quality and visibility. In this study, a high-resolution agricultural NH3 emission inventory compiled on 1 km × 1 km horizontal resolution was applied to calculate the NH3 mass burden in China and reliably estimate the influence of NH3 on agriculture. The key parameter emission factors of this inventory were enhanced by considering many experiment results, and the dynamic data of spatial and temporal information were updated using statistical data of 2015. In addition to fertilizers and husbandry, farmland ecosystems, livestock waste, crop residue burning, wood-based fuel combustion, and other NH3 emission sources were included in this inventory. Furthermore, a source apportionment tool, namely, the Integrated Source Apportionment Method (ISAM) coupled with the air quality modeling system Regional Atmospheric Modeling System and Community Multiscale Air Quality, was applied to capture the contribution of NH3 emitted from total agriculture ( Tagr ) in China. The aerosol mass concentration in 2015 was simulated, and results showed that the high mass concentration of NH3 exceeded 10 µ g m −3 and mainly appeared in the North China Plain, Central China, the Yangtze River Delta, and the Sichuan Basin. Moreover, the annual average contribution of Tagr NH3 to PM 2.5 mass burden was 14 %–22 % in China. Specific to the PM 2.5 components, Tagr NH3 contributed dominantly to ammonium formation (87.6 %) but trivially to sulfate formation (2.2 %). In addition, several brute-force sensitivity tests were conducted to estimate the impact of Tagr NH3 emission reduction on PM 2.5 mass burden. In contrast to the result of ISAM, even though the Tagr NH3 only provided 10.1 % contribution to nitrate under the current emission scenario, the reduction of nitrate could reach 95.8 % upon removal of the Tagr NH3 emission. This deviation occurred because the contribution of NH3 to nitrate should be small under a “rich NH3 ”environment and large under a “poor NH3 ” environment. Thus, the influence of NH3 on nitrate formation would be enhanced with the decrease in ambient NH3 mass concentration.

Satellite observations show a global maximum in ammonia (NH3) over the Indo-Gangetic Plain (IGP), with a peak from June to August. However, it has never been explained explicitly. In this study, we investigated the causes of high NH3 loading over the IGP during the pre-monsoon and monsoon seasons using WRF-Chem (Weather Research and Forecasting model coupled to chemistry). The IGP has relatively high NH3 emission fluxes (0.4 t km−2 month−1) due to intensive agricultural activities and high air temperature from June to August. Additionally, low sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions and high air temperature limit the gas-to-particle conversion of NH3, particularly for ammonium nitrate formation. Moreover, the barrier effects of the Himalayas in combination with the surface convergence weaken the horizontal diffusion of NH3. The high NH3 loading over the IGP mainly results from the low gas-to-particle partitioning of NH3 caused by low SO2 and NOx emissions. It contrasts to those in the North China Plain, where high SO2 and NOx emissions promote the conversion of gaseous NH3 into particulate ammonium.

Gas–liquid two-phase flows widely exist in industrial production, where understanding the dynamics underlying flow patterns is a challenging problem of great significance. In this brief, we propose a complex network framework for analyzing the wire-mesh sensor measurements, aiming to characterize the flow behavior in the transition from bubble flow to slug flow in a 50 mm-inner-diameter vertical pipe. In particular, we design a wire-mesh sensor system and carry out the gas–liquid two-phase flow experiments to acquire the local flow signals from different flow conditions. We infer a complex network from wire-mesh sensor measurements in terms of the mutual information between time series, and then calculate some network measures to quantitatively characterize the network topological features associated with flow behavior. We find that the networks corresponding to different flow patterns show different topological features, which allows us to characterize the transition of flow behavior. Our results suggest that the proposed analytical framework can effectively reveal the complex dynamical behavior of gas–liquid two-phase flows.

Background: The differentiation of microglia from M1 to M2 exerts a pivotal role in the aggression of intracerebral hemorrhage (ICH), and long non-coding RNAs (lncRNAs) are associated with the differentiation of microglia. However, the underlying mechanism had not been fully clarified.Methods: The expression profile of lncRNAs in thrombin-induced primary microglia was analyzed by RNA sequencing. Under thrombin treatment, the effect of lncRNA TCONS_00145741 on the differentiation of microglia was determined by immunofluorescence staining, quantitative real-time PCR, and Western blot. The potential mechanism and related signaling pathways of TCONS_00145741 in the M1 and M2 differentiation of microglia in ICH were assessed by Gene Ontology analysis, flow cytometry, RNA pull-down, RNA Immunoprecipitation, and RNA fluorescence in situ hybridization followed by immunofluorescence analysis.Results: LncRNA TCONS_00145741 expression was elevated in the thrombin-induced primary microglia, and the interference with TCONS_00145741 restrained the M1 differentiation of microglia and facilitated the M2 differentiation under thrombin treatment. The interference with TCONS_00145741 restrained the activation of the JNK pathway in microglia under thrombin treatment and repressed the JNK phosphorylation levels by enhancing the interaction between DUSP6 and JNK. In vivo experiments further illustrated that the interference with TCONS_00145741 alleviated ICH.Conclusion: LncRNA TCONS_00145741 knockdown prevented thrombin-induced M1 differentiation of microglia in ICH by enhancing the interaction between DUSP6 and JNK. This study might provide a promising target for the clinical treatment of ICH.

The development of photocatalysts for efficient tetracycline (TC) degradation under visible light is urgently needed yet remains a great challenge. Most semiconductor photocatalysts with low specific surface area are easy to agglomerate in solution and unfavorable for enriching pollutants. Herein, we present the preparation of pomegranate-shaped zinc oxide@zeolitic imidazolate framework (ZnO@ZIF-8) by in situ growth of ZIF-8 on a petal-shaped ZnO template that enhances the adsorption and photocatalytic degradation of TC. ZnO@ZIF-8 exhibits an excellent photostability and a TC photodegradation efficiency of 91% under visible light (λ420 nm) in 50 min at room temperature, which can be recycled over five times without any loss of activity. Moreover, the plausible photocatalysis reaction mechanism and the degradation intermediates are elucidated with the aid of three-dimensional excitation-emission matrix spectra and liquid chromatography-mass spectrometry system. This study offers new insights into the design of antibiotic degradation photocatalysts and the development of photocatalysts with broad-spectrum responses for efficient TC elimination.

East and South Asia are major hotspots of crop straw burning worldwide, with profound impacts on air quality and climate change. The Northeast China Plain (NECP) and Punjab, India, are two of the most fertile areas for crop production, which have large-scale agricultural fires during post-harvest seasons. Leveraging established fire-emission databases and satellite-retrieved agricultural fire spots, we show that, while the years 2018 and 2019 recorded low agricultural fire emissions in both the NECP and Punjab, probably due to the implementation of crop straw sustainable management, fire emissions markedly rebounded in 2020, reaching about 190% and 150% of 2019 levels, respectively. The COVID-19 lockdown measures somewhat disrupted eco-friendly crop straw management through restrictions on labor and transportation availability, such that farmers may have had to burn off crop wastes to clear up the land. We further demonstrate that the increased fire emissions in the NECP resulted in serious particulate matter pollution during the fire season in spring 2020, as opposed to considerable decreases in particles from fossil fuel emissions caused by the COVID-19 lockdown. This study suggests the unintended impacts of the COVID-19 pandemic on the agricultural sector and human health.

Terrestrial ecosystems in China receive the world’s largest amount of reactive nitrogen (N) deposition. Recent controls on nitrogen oxides (NOx = NO + NO2) emissions in China to tackle air pollution are expected to decrease N deposition, yet the observed N deposition fluxes remain almost stagnant. Here we show that the effectiveness of NOx emission controls for reducing oxidized N (NOy = NOx + its oxidation products) deposition is unforeseen in Eastern China, with one-unit reduction in NOx emission leading to only 55‒76% reductions in NOy-N deposition, as opposed to the high effectiveness (around 100%) in both Southern China and the United States. Using an atmospheric chemical transport model, we demonstrate that this unexpected weakened response of N deposition is attributable to the enhanced atmospheric oxidizing capacity by NOx emissions reductions. The decline in N deposition could bear a penalty on terrestrial carbon sinks and should be taken into account when developing pathways for China’s carbon neutrality.

Temperature inversion tends to inhibit the transfer of momentum, heat and moisture in the atmospheric boundary layer, which is often accompanied by severe air pollution. Recently, severe haze pollution has frequently occurred in North China. In this study, the characteristics of temperature inversion on severe polluted days (SPDs) in Beijing were investigated by using radiosonde data with standard pressure levels from 2011 to 2016. Both surface-based inversion (SI) and elevated inversion (EI) were analyzed. 93% of the SPDs were accompanied by temperature inversion, most of which occurred in wintertime. Annual frequency of SI (FSI) and EI (FEI) showed slight fluctuations with mean value of 0.18 and 0.67, respectively. Overall, the annual SI was stronger and deeper than annual EI. Seasonally, the SI was most frequent (0.39) in autumn, in contrast to EI that occurred most frequently (0.95) in summer. Both SI and EI were weakest in summer and strongest in winter. Average monthly SI strength was about 0.38 °C in summer and 2.40 °C in winter, average monthly EI strength was about 0.64 °C in summer and 2.20 °C in winter. The average monthly SI and EI were deepest in winter and shallowest in summer. SI depth were 778 m and 221 m in winter and summer, EI were 630 m and 336 m in winter and summer. The substantially strong liner relationship was found between seasonal inversion strength and PM2.5 concentration, and the inversion strength was found to be better compared with the inversion depth at predicting the PM2.5 concentration during SPDs. Obvious lower air outflow and turbulent kinetic energy were found in SPDs compared to non-SPDs, which indicated weaker turbulence in SPDs. Future efforts should focus on accurate model simulations of temperature inversions in SPDs.

Inflammatory bowel disease (IBD), including ulcerative colitis and Crohn’s disease (CD), are chronic autoimmune diseases with a high recurrence rate. Epidemiological data have shown that the incidence of IBD increases annually because of improved sanitary conditions and reduced parasitic infection rates. In this experiment, experimental colitis was induced in mice by administering 2,4,6-trinitrobenzene sulfonic acid (TNBS) 28 days after they were infected with Trichinella spiralis to confirm that T. spiralis infection could alleviate the severity of TNBS-induced colitis. Thirty-six male BALB/c mice aged 6–8 weeks were randomly divided into four groups: control group (with 50% ethanol, Control), T. spiralis-infected group (TS-Control), TNBS-induced colitis model group (Colitis), and T. spiralis-pre-infected and TNBS-induced colitis group (TS-Colitis). The mice were sacrificed 3, 7, and 14 days after the model was established. Changes in various colitis indicators to investigate the effect of T. spiralis infection on TNBS-induced murine CD model. Results showed that the weight, DAI score, and macroscopic and microscopic colon damage in the TS-Colitis significantly decreased compared with those in the Colitis. ELISA revealed that the IFN-γ expression decreased and the IL-4 expression increased in the TS-Colitis compared with those in the Colitis. Western Blotting results revealed that the NF-κB expression increased in the Colitis and higher than those in the TS-Colitis. And Flow cytometry results revealed that the percentage of CD4+CD25+Foxp3+ Treg cells significantly increased in the TS-Colitis. T. spiralis-infected mice induced Th2 immune responses and balanced Th1 immune responses stimulated by TNBS to ameliorate intestinal inflammation.

The double revolving fiber positioning technology is one of the key technologies for the success of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The accuracy of fiber positioning will directly affect the observation efficiency of LAMOST. To achieve higher fiber positioning accuracy, the original open-loop controlled fiber positioning system urgently needs to be upgraded into a closed-loop control system. The fiber detection is the most important part of the closed-loop controlled fiber positioning system. The back-illuminated detection method is usually used to detect the fiber position by directly detecting the light spot generated at the fiber end in the multi-fiber spectral surveys. In this paper, we introduce a new method to measure the fiber position based on the image of the front-illuminated LAMOST focal plane. The front-illuminated image does not require lighting devices inside the spectrograph, and it could reduce the instability and light pollution in the spectrograph end. Our method measures the fiber position by fitting the profile of the fiber pinhole with a 2D Gaussian function. A series of tests show that the relative position measurement precision of the front-illuminated method is about 0.″12, and the method could have the same accuracy as the back-illuminated method once the system bias is calibrated by a simple radial correction function. The required fiber positioning accuracy of LAMOST is 0.″4, and the new method satisfies the requirement of LAMOST fiber detection accuracy and could be used in the closed-loop fiber control system.

The North China Plain has been identified as a significant hotspot of ammonia (NH3) due to extensive agricultural activities. Satellite observations suggest a significant increase of about 30 % in tropospheric gas-phase NH3 concentrations in this area during 2008–2016. However, the estimated NH3 emissions decreased slightly by 7 % because of changes in Chinese agricultural practices, i.e., the transition in fertilizer types from ammonium carbonate fertilizer to urea, and in the livestock rearing system from free-range to intensive farming. We note that the emissions of sulfur dioxide (SO2) have rapidly declined by about 60 % over the recent few years. By integrating measurements from ground and satellite, a long-term anthropogenic NH3 emission inventory, and chemical transport model simulations, we find that this large SO2 emission reduction is responsible for the NH3 increase over the North China Plain. The simulations for the period 2008–2016 demonstrate that the annual average sulfate concentrations decreased by about 50 %, which significantly weakens the formation of ammonium sulfate and increases the average proportions of gas-phase NH3 within the total NH3 column concentrations from 26 % (2008) to 37 % (2016). By fixing SO2 emissions of 2008 in those multi-year simulations, the increasing trend of the tropospheric NH3 concentrations is not observed. Both the decreases in sulfate and increases in NH3 concentrations show highest values in summer, possibly because the formation of sulfate aerosols is more sensitive to SO2 emission reductions in summer than in other seasons. Besides, the changes in NOx emissions and meteorological conditions both decreased the NH3 column concentrations by about 3 % in the study period. Our simulations suggest that the moderate reduction in NOx emissions (16 %) favors the formation of particulate nitrate by elevating ozone concentrations in the lower troposphere.

Ag3VO4/Ag2VO2PO4 heterostructure with highly enhanced visible light photocatalytic activity were synthesized by using Ag2VO2PO4 as the sacrificed template in different concentration of ammonia solution. Their crystalline structure, morphology, optical, and electrochemical properties were characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, UV–vis diffuse reflectance spectroscopy, infrared spectroscopy, and electrochemical measurements. The photocatalytic degradation activities of as-prepared samples were evaluated by the photocatalytic of methylene blue (MB), methyl orange (MO) and imidacloprid in the aqueous phase. Compared with bare Ag2VO2PO4, the etched samples Ag3VO4/Ag2VO2PO4 exhibit the significantly enhanced photocatalytic activity under visible light irradiation. The Ag3VO4/Ag2VO2PO4 with 0.15 M etched sample exhibits the highest activity, which are nearly 3.8, 8.6 and 9 times higher than bare Ag2VO2PO4 for removal of MB, MO and imidacloprid, respectively. The improved photocatalytic performance of Ag3VO4/Ag2VO2PO4 can be attributed to the formation of Ag2VO2PO4/Ag3VO4 heterojunction, which suppresses the recombination of photoinduced charges and prolongs the lifetime of the charges. Moreover, the photostability and dominant active species were investigated.

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects’ attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Anthropogenic emissions in China play an important role in altering global radiation budget. Over the recent decade, the clean-air options in China result in substantial reductions in anthropogenic emissions, especially sulfur dioxide (SO2), and improved air quality. However, the associated changes in aerosol climate effects are poorly understood. In this study, we use an advanced global climate model integrated with latest anthropogenic emission inventory to estimate the changes in the aerosol radiative forcings by the emission variation in China between 2008 and 2016. First, our simulations exhibit decreases of 46 % and 25 % for the annual mean surface-level sulfate and black carbon (BC) mass concentrations in East China, respectively, which is the key region subject to stringent emission control options. The decreasing tendency of aerosol optical depth and aerosol absorption optical depth retrieved by satellites is also captured by the model for the period. We find that the resultant net radiative forcing by the changes in the BC and SO2 emissions is −0.04 W m−2 in East China. The substantial reductions in SO2 emissions diminish the scattering effects of sulfate and lead to an overall change of +0.17 W m−2 for the annual mean all-sky shortwave aerosol direct radiative forcing at the top of the atmosphere and +0.13 W m−2 for aerosol-induced cloud radiative forcing. In the meantime, the reduction in BC emissions induces a negative BC radiative forcing of −0.34 W m−2. By accounting for the joint effect of BC and SO2 emissions, our results demonstrate that the positive radiative forcing by the SO2 emission reductions is counteracted by the decrease of BC in China over the recent decade. While the local radiative forcing is small due to the counteracted effects of SO2 and BC emissions, it is relatively larger (+0.16 W m−2) over the north Pacific remote regions for this period, primarily contributed by the reductions in sulfate particles and their effects on cloud properties. With a comprehensive future emission scenario for 2030 and 2050 developed by the recent study, we predict that the strictest environmental policies will induce the change of aerosol radiative forcings of +0.55 and +1.23 W m−2 over East China between 2016–2030 and 2016–2050, respectively. Targeted emission control policies are desirable to improve air quality and mitigate climate change in the future.

Biogenic emission models are developed on the foundation of leaf physiological processes and driven by a set of physical and biological factors. To estimate emissions online, many studies used weat...

China is one of the largest agricultural countries in the world. The NH3 emissions from agricultural activities in China significantly affect regional air quality and horizontal visibility. To reliably estimate the influence of NH3 on agriculture, a high-resolution agricultural NH3 emissions inventory, compiled with a 1 km × 1 km horizontal resolution, was applied to calculate the NH3 mass burden in China. The key emission factors of this inventory were enhanced by considering the results of many native experiments, and the activity data of spatial and temporal information were updated using statistical data from 2015. Fertilizer and husbandry, as well as farmland ecosystems, livestock waste, crop residue burning, fuel wood combustion, and other NH3 emission sources were included in the inventory. Furthermore, a source apportionment tool, ISAM (Integrated Source Apportionment Method), coupled with the air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System and Community Multiscale Air Quality), was applied to capture the contribution of NH3 emitted from total agriculture (Tagr) in China. The aerosol mass concentration in 2015 was simulated, and the results showed that a high mass concentration of NH3, which exceeded 10 μg m−3, appeared mainly in the North China Plain (NCP), Central China (CNC), the Yangtz River Delta (YRD), and the Sichan Basin (SCB), and the annual average contribution of Tagr NH3 to PM2.5 mass burden in China was 14–18 %. Specific to the PM2.5 components, Tagr NH3 provided a major contribution to ammonium formation (87.6 %) but a tiny contribution to sulfate (2.2 %). In addition, several brute-force sensitivity tests were conducted to estimate the impact of Tagr NH3 emissions reduction on the PM2.5 mass burden. Compared with the results of ISAM, it was found that even though the Tagr NH3 only contributed 10.1 % of nitrate under current emissions scenarios, the reduction of nitrate could reach 98.8 % upon removal of the Tagr NH3 emissions. The main reason for this deviation could be that the NH3 contribution to nitrate is small under rich NH3 conditions and large in poor NH3 environments. Thus, the influence of NH3 on nitrate formation could be enhanced with the decrease of ambient NH3 mass concentration.

Biogenic volatile organic compounds (BVOCs) emitted by terrestrial vegetation significantly influence the atmospheric chemistry and global climate. Previous studies calculated BVOC emissions outside the comprehensive terrestrial ecosystem processes and oversimplified the representation of canopy environments which exert substantial impacts on BVOC emissions. The sophisticated land surface model CLM (Community Land Model) reproduces essential ground and canopy characteristics and can calculate BVOC emissions as a step of integrated biogeochemical processes. In this study, the land surface scheme CLM version4 (CLM4) of WRF (Weather Research and Forecasting model) was used to estimate BVOC emissions in China. Based on highly-resolved meteorological outputs derived from WRF, CLM4 computed real-time physical and biological variables to drive MEGAN (Model of Emissions of Gases and Aerosols from Nature), a BVOC estimation algorithm embedded within biogeochemistry component of CLM4, to calculate plant emission flux. MODIS (Moderate Resolution Imaging Spectroradiometer) land-use data with high resolutions was introduced in WRF to replace the outdated land surface parameters. An emission inventory of isoprene and monoterpenes (including α-pinene, β-pinene, 3-carene, t-β-ocimene, limonene, sabinene and myrcene) with high spatiotemporal resolution (12 × 12 km, hourly) was established for the year 2018. The annual BVOC emission in China was 14.7 Tg C, in which isoprene contributes about 78.3 % (11.5 Tg C), followed by α-pinene (1.2 Tg C) and β-pinene (0.7 Tg C). Due to the strong emission capacity and large areas, broadleaf forests contribute to 76.8 % of total isoprene emission and 72.1 % of monoterpenes emission, respectively. BVOC emissions showed marked seasonal and diurnal patterns with the peak emission occurring in summer and midday. Spatially, high emissions of BVOC were mainly concentrated in southern and northeastern China, as well as the Qinling Mountains in central China, accounting for 91.4 % of national emission. Guangxi, Yunnan and Hunan provinces are significant emitters due to large area of vegetation with high emission rate and favored environmental conditions. The emission estimates are compared to past modeling results, field measurements and further evaluated against top-down isoprene emission estimates. Generally, the coupled mode produced a reasonable simulation in both emission amounts and the spatiotemporal distribution of BVOCs. The WRF–CLM–MEGAN coupling framework could be further integrated with atmospheric chemistry model to investigate BVOC chemistry and their effects on regional pollution and climate.

Data for JGR-A submission 2019JD031301R

Satellite observations show a global maximum in ammonia (NH3) over the Indo-Gangetic Plain (IGP), with a peak in summer. However, it has never been explained explicitly. In this study, we investigated the causes of high NH3 loading over the IGP in summer using WRF-Chem (Weather Research and Forecasting model coupled to chemistry). IGP has relatively high NH3 emission fluxes (0.4 t km−2 month−1) due to intensive agricultural activities and high air temperature in summer. Additionally, low sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions and high air temperature limit the gas-to-particle conversion of NH3, particularly for ammonium nitrate formation. Moreover, the barrier effects of the Himalayas in combination with the surface convergence weaken the horizontal diffusion of NH3. The high NH3 loading over the IGP mainly results from the low gas-to-particle partitioning of NH3 caused by low SO2 and NOx emissions. It contrasts to those in the North China Plain, where high SO2 and NOx emissions promote the conversion of gaseous NH3 into particulate ammonium.

Trichinella spiralis (T. spiralis) is widely distributed throughout the world and can cause serious zoonotic parasitic diseases. Serine protease inhibitors (SPIs) have unique enzyme inhibitory activity and occupy an important position in the interaction between parasites and hosts. In order to further understand the immunoprotective effect of SPIs on T. spiralis invasion in vivo, the Kazal and Serpin type SPI of T. spiralis (TsKaSPI and TsAdSPI) were mixed with Freund’s adjuvant in equal volume to immunize mice. The results showed that the expression of IgG1 and IgG2a in serum, the proliferation of spleen cells, and the expression level of cytokines were all increased. The results of flow cytometry showed that the expression of CD4+CD25+Foxp3+ Tregs, CD8+CD28− T cells, CD19+CD5+CD1dhi Bregs in spleen were also increased. Therefore, both TsKaSPI and TsAdSPI could induce strong humoral and cellular immune responses. And the results of adult reduction rate and pathological changes of intestine after adult invasion also indicated that both TsKaSPI and TsAdSPI could prevent T. spiralis from invading intestine. To explore the regulatory effects of TsKaSPI and TsAdSPI on the immune function of macrophage, the results of ELISA showed that the expression of cytokines in cell supernatant were increased. And the results of Western blot showed that both TsKaSPI and TsAdSPI could induce phosphorylation of JAK2 and STAT3 receptors, thereby affecting the signal transduction of macrophages. This experiment demonstrated that SPIs could act as effector molecules affecting the immune function of host when infected with T. spiralis.

SiC ceramic has been successfully joined to Zr4 alloy using TiZrNiCu filler within the temperature range 1173 K–1263 K and 15 min holding time. The morphology of the SiC/Zr4 joints was investigated by SEM, and the phases in the joints were characterized by XRD and TEM. The results indicated that part of the Zr4 substrate dissolved into the molten filler during the brazing process, contributed to the increasing Zr content and the formation of Zr[Ti] solid solution in the joints. In addition, β-Ti phase was discovered in the brazing seam. This might be attributed to the existence of β-Ti stable elements, Cu and Ni. The interface characterization showed that SiC reacted with filler alloy during the brazing process, formed a (Zr, Ti)C reaction layer on SiC surface and Zr 2 Si compound near SiC substrate. Finally, the mechanical properties of the joints, evaluated by a shear strength test, reached a maximum of 95 MPa at the brazing temperature of 1203 K.