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Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

Cooking organic aerosol (COA) is one of the major constituents of particulate matter in urban areas. COA is oxidized by atmospheric oxidants such as ozone, changing its physical, chemical and toxicological properties. However, atmospheric chemical lifetimes of COA and its tracers such as oleic acid are typically longer than those that have been estimated by laboratory studies. We tackled the issue by considering temperature. Namely, we hypothesize that increased viscosity of COA at ambient temperature accounts for its prolonged atmospheric chemical lifetimes in wintertime. Laboratory-generated COA particles from cooking oil were exposed to ozone in an aerosol flow tube reactor for the temperature range of −20 to 35 °C. The pseudo-second-order chemical reaction rate constants (k2) were estimated from the experimental data by assuming a constant ozone concentration in the flow tube. The estimated values of k2 decreased by orders of magnitude for lower temperatures. The temperature dependence in k2 was fit well by considering the diffusion-limited chemical reaction mechanism. The result suggested that increased viscosity was likely the key factor to account for the decrease in chemical reactivity at the reduced temperature range, though the idea will still need to be verified by temperature-dependent viscosity data in the future. In combination with the observed global surface temperature, the atmospheric chemical lifetimes of COA were estimated to be much longer in wintertime (> 1 h) than in summertime (a few minutes) for temperate and boreal regions. Our present study demonstrates that the oxidation lifetimes of COA particles will need to be parameterized as a function of temperature in the future for estimating environmental impacts and fates of this category of particulate matter.

Oxygenated organic molecules (OOMs) produced by the oxidation of aromatic compounds are key components of secondary organic aerosol (SOA) in urban environments. The steric effects of substitutions and rings and the role of key reaction pathways in altering the OOM distributions remain unclear because of the lack of systematic multi-precursor study over a wide range of OH exposure. In this study, we conducted flow-tube experiments and used the nitrate adduct time-of-flight chemical ionization mass spectrometer (NO3--TOF-CIMS) to measure the OOMs produced by the photooxidation of six key aromatic precursors under low-NOx conditions. For single aromatic precursors, the detected OOM peak clusters show an oxygen atom difference of one or two, indicating the involvement of multi-step auto-oxidation and alkoxy radical pathways. Multi-generation OH oxidation is needed to explain the diverse hydrogen numbers in the observed formulae. In particular, for double-ring precursors at higher OH exposure, multi-generation OH oxidation may have significantly enriched the dimer formulae. The results suggest that methyl substitutions in precursor lead to less fragmented OOM products, while the double-ring structure corresponds to less efficient formation of closed-shell monomeric and dimeric products, both highlighting significant steric effects of precursor molecular structure on the OOM formation. Naphthalene-derived OOMs however have lower volatilities and greater SOA contributions than the other-type of OOMs, which may be more important in initial particle growth. Overall, the OOMs identified by the NO3--TOF-CIMS may have contributed up to 30.0 % of the measured SOA mass, suggesting significant mass contributions of less oxygenated, undetected semi-volatile products. Our results highlight the key roles of progressive OH oxidation, methyl substitution and ring structure in the OOM formation from aromatic precursors, which need to be considered in future model developments to improve the model performance for organic aerosol.

Abstract The transcription factor MYB plays a pivotal role in haematopoietic homoeostasis and its aberrant expression is involved in the genesis and maintenance of acute myeloid leukaemia (AML). We have previously demonstrated that not all AML subtypes display the same dependency on MYB expression and that such variability is dictated by the nature of the driver mutation. However, whether this difference in MYB dependency is a general trend in AML remains to be further elucidated. Here, we investigate the role of MYB in human leukaemia by performing siRNA-mediated knock-down in cell line models of AML with different driver lesions. We show that the characteristic reduction in proliferation and the concomitant induction of myeloid differentiation that is observed in MLL-rearranged and t(8;21) leukaemias upon MYB suppression is not seen in AML cells with a complex karyotype. Transcriptome analyses revealed that MYB ablation produces consensual increase of MAFB expression in MYB-dependent cells and, interestingly, the ectopic expression of MAFB could phenocopy the effect of MYB suppression. Accordingly, in silico stratification analyses of molecular data from AML patients revealed a reciprocal relationship between MYB and MAFB expression, highlighting a novel biological interconnection between these two factors in AML and supporting new rationales of MAFB targeting in MLL-rearranged leukaemias.

Fine particulate matter (PM2.5) remains a major air pollutant of significant public health concern in urban areas. Long-term monitoring data of PM2.5 chemical composition and source-specific tracers provide essential information for the identification of major sources as well as evaluation and planning of control measures. In this study, we present and analyze a 10-year dataset of PM2.5 major components and source-specific tracers (e.g., levoglucosan, hopanes, K+, Ni, V, Al, and Si) collected over the period of 2008–2017 in an urban site in Hong Kong, China. The time series of pollutants were analyzed by the seasonal and trend decomposition using the locally estimated scatter plot smoothing (LOESS) method and general least squares with the autoregressive moving average method. Bulk PM2.5 and all its major components displayed a significant decline of varying degrees over the decade. PM2.5 was reduced by 40 % at −1.5 µgm-3yr-1. PM2.5 components that are predominantly influenced by local vehicular emissions showed the steepest decline, with nitrate decreasing by −66 %, elemental carbon by −60 %, and hopanes by −75 %, confirming effective control of local vehicular emissions. For components that are significantly impacted by regional transport and secondary formation, they had a notably lower percentage reduction, with sulfate declining by −40 % and organic carbon by −23 %, reflecting complexity in their region-wide contributing sources and formation chemistry. Levoglucosan and K+, two tracers for biomass burning, differed in their reduction extent, with K+ at −60 % and levoglucosan at −47 %, indicating they likely track different biomass burning types. Dust components in PM2.5 also decreased, by −37 % for Al and −46 % for Si. The year of 2011 was an anomaly in the overall trend in having higher concentrations of PM2.5 and components than its adjacent years, and the long time series analysis attributed the anomaly to unusually lower rainfall associated with strong La Niña events. This 10-year trend analysis based on measurements exemplifies the utility of chemical composition data in support of an evidence-based approach for control policy formulation.

Characterization of the spatial distributions of air pollutants on an intracity scale is important for understanding local sources, secondary formation, and human exposure. In this study, we conducted in situ mobile measurements for the chemical composition of fine particles, volatile organic compounds (VOCs), oxygenated VOCs (OVOCs), and common gas pollutants in winter in the megacity of Beijing. The spatial patterns of these pollutants under different pollution conditions were investigated. During the non-haze days all pollutants showed significant spatial variability. Large spatial variations of secondary species including OVOCs and secondary aerosol species highlight the chemical heterogeneity. In particular, the inorganic chemical composition of fine particles varied greatly on the 65 km urban highway, suggesting a wide range of particle neutralization in the megacity of Beijing. Localized sources such as vehicle, cooking, and industrial emissions led to hot spots and nonuniform distributions of primary pollutants in the city. The spatial heterogeneity of air pollutants under less polluted conditions calls for a future need of using fine-resolution models to evaluate human exposure and to develop pollution control strategies. During the haze day the spatial variabilities of secondary gaseous and particulate pollutants were largely reduced, explained by both the elevated urban background of the polluted air mass and the enhanced secondary formation by elevated precursor concentrations and heterogeneous or aqueous pathways. Although localized primary emissions were accumulated under stagnant haze conditions, the chemical composition of fine particles became relatively homogeneous because of the predominant secondary contributions. A uniform spatial pattern of particle neutralization was observed. The concentrations of hydrocarbons and less oxygenated OVOCs showed good positive spatiotemporal correlations during the haze day, while the concentrations of more oxygenated OVOCs showed good positive correlations among themselves but moderate negative correlations with the concentrations of hydrocarbons, less oxygenated OVOCs, and aerosol species. Our results indicate a spatial chemical homogeneity on the megacity scale to promote efficient secondary organic aerosol (SOA) production under haze conditions.

Graph-based deep learning has been proved a promising approach that has an apparent superiority for learning graph data and modeling spatial topological relations between features. In particular, graph attention networks (GATs) are good at efficiently processing the graph-structured hyperspectral data by leveraging masked self-attention layers to address the known shortcomings of previous frameworks based on graph convolutions or their approximations. In this study, we proposed a novel approach that combines localized spectral filtering and GAT for the hyperspectral image classification task. First, we conducted unsupervised t-SNE (t-distributed stochastic neighbor embedding) manifold learning-based feature dimensionality reduction to create localized hyperspectral data cubes. Then, these feature cubes combined with localized adjacent matrices were fed into a shallow graph attention network in a supervised learning manner. Finally, we obtained credible classification results and promising classification performance in distinguishing diversified land covers through reducing the possible redundancy of spectral information and enhancing the expression of local spatial-spectral information. Experiments on two real hyperspectral data sets (that is, Indian Pines-A (IA) and Huanghekou (HH) data sets) demonstrated that the presented approach offers promising classification performance, that is, the GAT using t-SNE acquires superior performance than that of using PCA (principal component analysis), and also proves the great importance of combining spatial- and spectral information for hyperspectral image classification.

The saturated hydraulic conductivity (Ksat) is a key soil hydraulic property governing agricultural production. However, the influence of the conversion from the conventional tillage (CT) to conservation tillage (CS; including no tillage, NT, and reduced tillage, RT) on the Ksat of soils is not well understood and still debated. In this study, we applied a global meta-analysis method to synthesize 227 paired observations for soil Ksat from 69 published studies and investigated factors influencing the effects of conversion to CS on Ksat. Results showed that soil layer, conservation tillage type, soil texture type, and cropping system management did not have significant effects on the influence of conversion to CS on Ksat. When the Ksat was measured by the rainfall simulator, the conversion to CS significantly (p) increased the surface and subsurface soil Ksat by 41.7 % and 36.9 %, respectively. In addition, the subsurface Ksat also tended to increase under CS practices when the Ksat was measured by a tension disc infiltrometer. However, when the Ksat was measured by a hood infiltrometer, ring infiltrometer, constant/falling head, and Guelph permeameter, the conversion to CS had no significant effects on the Ksat. It is observed that, when the conversion period was less than 15 years, the Ksat under CS showed a greater increase for a longer conversion period. Climatic and topographic factors, including the mean annual temperature (MAT) and the mean annual precipitation (MAP), were statistically related to the responses of Ksat to tillage conversion at the global scale. Quadratic polynomials can describe the relationships between them. These findings suggested that quantifying the effects of tillage conversion on soil Ksat needed to consider experimental conditions, especially the measurement technique and conversion period.

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50 % in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30 % greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

The nitrogen-15 (15N) natural abundance composition (δ15N) in soils or plants is a useful tool to indicate the openness of ecosystem N cycling. This study aimed to evaluate the influence of the experimental warming on soil and plant δ15N. We applied a global meta-analysis method to synthesize 79 and 76 paired observations of soil and plant δ15N from 20 published studies, respectively. Results showed that the mean effect sizes of the soil and plant δ15N under experimental warming were −0.524 (95 % CI (confidence interval): −0.987 to −0.162) and 0.189 (95 % CI: −0.210 to 0.569), respectively. This indicated that soil δ15N had negative response to warming at the global scale, where warming had no significant effect on plant δ15N. Experimental warming significantly (p) decreased soil δ15N in Alkali and medium-textured soils, in grassland/meadow, under air warming, for a 4–10-year warming period and for an increase of >3 ∘C in temperature, whereas it significantly (p) increased soil δ15N in neutral and fine-textured soils and for an increase of 1.5–3 ∘C in temperature. Plant δ15N significantly (p) increased with increasing temperature in neutral and fine-textured soils and significantly (p) decreased in alkali soil. Latitude did not affect the warming effects on both soil and plant δ15N. However, the warming effect on soil δ15N was positively controlled by the mean annual temperature, which is related to the fact that the higher temperature can strengthen the activity of soil microbes. The effect of warming on plant δ15N had weaker relationships with environmental variables compared with that on soil δ15N. This implied that soil δ15N was more effective than plant δ15N in indicating the openness of global ecosystem N cycling.

Oxidation of aromatic volatile organic compounds (VOCs) leads to the formation of tropospheric ozone and secondary organic aerosol, for which gaseous oxygenated products are important intermediates. We show, herein, the experimental results of highly oxygenated organic molecules (HOMs) produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. The results suggest that multigeneration OH oxidation plays an important role in the product distribution, which likely proceeds more preferably via H subtraction than OH addition for early generation products from light aromatics. More oxygenated products present in our study than in previous flow tube studies, highlighting the impact of experimental conditions on product distributions. The formation of dimeric products, however, was suppressed and might be unfavorable under conditions of high OH exposure and low NOx in toluene oxidation. Under high-NOx conditions, nitrogen-containing multifunctional products are formed, while the formation of other HOMs is suppressed. Products containing two nitrogen atoms become more important as the NOx level increases, and the concentrations of these compounds depend significantly on NO2. The highly oxygenated nitrogen-containing products might be peroxyacyl nitrates, implying a prolonged effective lifetime of RO2 that facilitates regional pollution. Our results call for further investigation on the roles of high-NO2 conditions in the oxidation of aromatic VOCs.

As critical precursors of ozone (O3) and secondary organic aerosols, volatile organic compounds (VOCs) play a vital role in air quality, human health, and climate change. In this study, a campaign of comprehensive field observations and VOC grid sampling was conducted in Xi'an, China, from 20 June to 20 July 2019 to identify the spatiotemporal concentration levels, sources, and secondary transformation potential of VOCs. During the observation period, the average VOC concentrations at the Chanba (CB), Di Huan Suo (DHS), Qinling (QL), and gridded sampling sites were 27.8 ± 8.9, 33.8 ± 10.5, 15.5 ± 5.8, and 29.1 ± 8.4 ppb, respectively. Vehicle exhaust was the primary source of VOC emissions in Xi'an, and the contributions of vehicle exhaust to VOCs at the CB, DHS, and QL sites were 41.3 %, 30.6 %, and 23.6 %–41.4 %, respectively. While industrial emissions were the second-largest source of VOCs in urban areas, contributions from aging sources were high in rural areas. High potential source contribution function values primarily appeared in eastern and southern Xi'an near the sampling site, which indicates that Xi'an exhibits a strong local VOC source. Moreover, alkenes, aromatics, and oxygenated VOCs played a dominant role in secondary transformation, which is a major concern in reducing O3 pollution in Xi'an.

A new capture vaporizer (CV) has been developed and used recently in the Aerodyne aerosol mass spectrometer (AMS) and aerosol chemical speciation monitor (ACSM) instead of the standard vaporizer (SV) to reduce the particle bounce. It is important to characterize the CV performance in different environments. In this study, we characterized specific organic aerosols (OAs) from vehicle, cooking, biomass burning, and coal burning emissions by a time-of-flight ACSM (TOF-ACSM) with the CV. Their corresponding marker ions that have been defined in the previous SV-based analysis are still valid in the CV mass spectra. Spectra of OAs from cooking and vehicle exhaust show similarities in distinct alkyl fragments but different ratios of m∕z 55 and 57. Ions related to polycyclic aromatic hydrocarbons are present in the OA spectra obtained from burning lignite and bituminous coal but not in the spectra obtained from burning anthracite. Although the relative intensities of m∕z 60 and 73 are much lower in the CV spectra than in the SV spectra for biomass burning OA, they are still relatively greater compared with the spectra for other sources. Our data suggest an atmospheric background of f60 of below 0.03 % for CV. Moreover, we deployed the CV TOF-ACSM along with a SV AMS in urban Beijing during the winter of 2017 to characterize ambient OA with strong anthropogenic influences. The CV TOF-ACSM shows a collection efficiency (CE) of about unity. The CV and SV data show consistent mass concentrations of sulfate, nitrate, ammonium, and OA. Six OA factors are identified by the positive matrix factorization (PMF) analysis for both the CV and the SV data. The SV and CV PMF factors show good correlations in mass concentrations. The SV and CV factors related to coal combustion and cooking differ significantly in loadings, explained by the PMF uncertainty and the lack of understanding of the relative ionization efficiency and CE for primary OA. The CV factors related to secondary sources show greater loadings than the SV factors, which may be associated with the changes in signal-to-noise ratios of various ions in the PMF analysis. Our results support improved mass quantification and useful source identification by the CV for ambient particles in the polluted urban environment. The difference in factor loadings between SV and CV should be considered when interpreting or comparing the PMF results among studies.

Superconducting rf cavities are used in particle accelerators to provide energy to the particle beam. Such cavities are mostly fabricated in niobium and often operated in superfluid helium. One of their limits of operation is the appearance of a local quench, initiated by a local field enhancement due to a defect, which leads to a normal conducting transition of the cavity. Localizing the quench area can be achieved with temperature mapping systems. Another method is the use of second sound wave propagation in superfluid helium. Measuring the time of propagation of these waves from quench location to special sensors, called oscillating superleak transducers (OSTs), and using their well-known velocity should allow trilateration. However, most of the experimental measurements on cavities show premature signals, i.e., the second sound signals arrive earlier on the OSTs than expected. This paper presents several quench experiments on cavities equipped with OSTs and temperature mapping quench detection systems. Two hypotheses can explain the observed premature signals. The first one assesses faster propagation in helium. An experimental setup has been developed for testing this hypothesis, where second sound is created by a localized heater in a controlled environment up to 4.3 kW/cm^{2} and 2.8 J. Premature signals could not be verified in this setup. A second hypothesis based on a simple model including several processes in niobium and second sound propagation in helium is discussed. The model improves significantly the prediction of the times of arrival of the second sound waves. The overall study shows that the processes in niobium play a prominent role in the second sound detection for superconducting cavities.