Global-regional nested simulation of particle number concentration by combing microphysical processes with an evolving organic aerosol module

Aerosol microphysical processes are essential for the next generation of global and regional climate and air quality models to determine particle size distribution. The contribution of organic aerosols (OAs) to particle formation, mass, and number concentration is one of the major uncertainties in current models. A new global–regional nested aerosol model was developed to simulate detailed microphysical processes. The model combines an advanced particle microphysics (APM) module and a volatility basis set (VBS) OA module to calculate the kinetic condensation of low-volatility organic compounds and equilibrium partitioning of semi-volatile organic compounds in a 3-D framework using global–regional nested domain. In addition to the condensation of sulfuric acid, the equilibrium partitioning of nitrate and ammonium, and the coagulation process of particles, the microphysical processes of the OAs are realistically represented in our new model. The model uses high-resolution size bins to calculate the size distribution of new particles formed through nucleation and subsequent growth. The multi-scale nesting enables the model to perform high-resolution simulations of the particle formation processes in the urban atmosphere in the background of regional and global environments. By using the nested domains, the model reasonably reproduced the OA components obtained from the analysis of aerosol mass spectrometry measurements through positive matrix factorization and the particle number size distribution in the megacity of Beijing during a period of approximately a month. Anthropogenic organic species accounted for 67 % of the OAs of secondary particles formed by nucleation and subsequent growth, which is considerably larger than that of biogenic OAs. On the global scale, the model well predicted the particle number concentration in various environments. The microphysical module combined with the VBS simulated the universal distribution of organic components among the different aerosol populations. The model results strongly suggest the importance of anthropogenic organic species in aerosol particle formation and growth at polluted urban sites and over the whole globe.Aerosol microphysical processes are essential for the next generation of global and regional climate and air quality models to determine particle size distribution. The contribution of organic aerosols (OAs) to particle formation, mass, and number concentration is one of the major uncertainties in current models. A new global-regional nested aerosol model was developed to simulate detailed microphysical processes. The model combines an advanced particle microphysics (APM) module and a volatility basis set (VBS) OA module to calculate the kinetic condensation of low-volatility organic compounds and equilibrium partitioning of semi-volatile organic compounds in a 3-D framework using global-regional nested domain In addition to the condensation of sulfuric acid, the equilibrium partitioning of nitrate and ammonium, and the coagulation process of particles, the microphysical processes of the OAs are realistically represented in our new model. The model uses high-resolution size bins to calculate the size distribution of new particles formed through nucleation and subsequent growth. The multi-scale nesting enables the model to perform high-resolution simulations of the particle formation processes in the urban atmosphere in the background of regional and global environments. By using the nested domains, the model reasonably reproduced the OA components obtained from the analysis of aerosol mass spectrometry measurements through positive matrix factorization and the particle number size distribution in the megacity of Beijing during a period of approximately a month. Anthropogenic organic species accounted for 67 % of the OAs of secondary particles formed by nucleation and subsequent growth, which is considerably larger than that of biogenic OAs. On the global scale, the model well predicted the particle number concentration in various environments. The microphysical module combined with the VBS simulated the universal distribution of organic components among the different aerosol populations. The model results strongly suggest the importance of anthropogenic organic species in aerosol particle formation and growth at polluted urban sites and over the whole globe.Peer reviewe

A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks

IntroductionEarly identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method.MethodsPreterm infants with gestational age  < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.ResultsIn total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.ConclusionA support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age  < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population

An interlaboratory comparison of aerosol inorganic ion measurements by ion chromatography : Implications for aerosol pH estimate

Water-soluble inorganic ions such as ammonium, nitrate and sulfate are major components of fine aerosols in the atmosphere and are widely used in the estimation of aerosol acidity. However, different experimental practices and instrumentation may lead to uncertainties in ion concentrations. Here, an intercomparison experiment was conducted in 10 different laboratories (labs) to investigate the consistency of inorganic ion concentrations and resultant aerosol acidity estimates using the same set of aerosol filter samples. The results mostly exhibited good agreement for major ions Cl-, SO2-4, NO-3, NHC4 and KC. However, F-, Mg2C and Ca2C were observed with more variations across the different labs. The Aerosol Chemical Speciation Monitor (ACSM) data of nonrefractory SO2-4, NO-3 and NHC4 generally correlated very well with the filter-analysis-based data in our study, but the absolute concentrations differ by up to 42 %. Cl-from the two methods are correlated, but the concentration differ by more than a factor of 3. The analyses of certified reference materials (CRMs) generally showed a good detection accuracy (DA) of all ions in all the labs, the majority of which ranged between 90 % and 110 %. The DA was also used to correct the ion concentrations to showcase the importance of using CRMs for calibration check and quality control. Better agreements were found for Cl-, SO2-4, NO-3, NHC4 and KC across the labs after their concentrations were corrected with DA; the coefficient of variation (CV) of Cl-, SO2-4, NO-3, NHC4 and KC decreased by 1.7 %, 3.4 %, 3.4 %, 1.2 % and 2.6 %, respectively, after DA correction. We found that the ratio of anion to cation equivalent concentrations (AE/CE) and ion balance (anions-cations) are not good indicators for aerosol acidity estimates, as the results in different labs did not agree well with each other. In situ aerosol pH calculated from the ISORROPIA II thermodynamic equilibrium model with measured ion and ammonia concentrations showed a similar trend and good agreement across the 10 labs. Our results indicate that although there are important uncertainties in aerosol ion concentration measurements, the estimated aerosol pH from the ISORROPIA II model is more consistent

Impact of HO2 aerosol uptake on radical levels and O3 production during summertime in Beijing

The impact of heterogeneous uptake of HO2 on aerosol surfaces on radical concentrations and the O3 production regime in Beijing in summertime was investigated. The uptake coefficient of HO2 onto aerosol surfaces, γHO2 , was calculated for the AIRPRO campaign in Beijing, in summer 2017, as a function of measured aerosol soluble copper concentration, [Cu2+]eff, aerosol liquid water content, [ALWC], and particulate matter concentration, [PM]. An average γHO2 across the entire campaign of 0.070 ± 0.035 was calculated, with values ranging from 0.002 to 0.15, and found to be significantly lower than the value of γHO2 = 0.2, commonly used in modelling studies. Using the calculated γHO2 values for the summer AIRPRO campaign, OH, HO2 and RO2 radical concentrations were modelled using a box model incorporating the Master Chemical Mechanism (v3.3.1), with and without the addition of γHO2 , and compared to the measured radical concentrations. The rate of destruction analysis showed the dominant HO2 loss pathway to be HO2 + NO for all NO concentrations across the summer Beijing campaign, with HO2 uptake contributing < 0.3 % to the total loss of HO2 on average. This result for Beijing summertime would suggest that under most conditions encountered, HO2 uptake onto aerosol surfaces is not important to consider when investigating increasing O3 production with decreasing [PM] across the North China Plain. At low [NO], however, i.e. < 0.1 ppb, which was often encountered in the afternoons, up to 29 % of modelled HO2 loss was due to HO2 uptake on aerosols when calculated γHO2 was included, even with the much lower γHO2 values compared to γHO2 = 0.2, a result which agrees with the aerosol-inhibited O3 regime recently proposed by Ivatt et al. (2022). As such it can be concluded that in cleaner environments, away from polluted urban centres where HO2 loss chemistry is not dominated by NO but where aerosol surface area is high still, changes in PM concentration and hence aerosol surface area could still have a significant effect on both overall HO2 concentration and the O3 production regime. Using modelled radical concentrations, the absolute O3 sensitivity to NOx and volatile organic compounds (VOCs) showed that, on average across the summer AIRPRO campaign, the O3 production regime remained VOC-limited, with the exception of a few days in the afternoon when the NO mixing ratio dropped low enough for the O3 regime to shift towards being NOx -limited. The O3 sensitivity to VOCs, the dominant regime during the summer AIRPRO campaign, was observed to decrease and shift towards a NOx -sensitive regime both when NO mixing ratio decreased and with the addition of aerosol uptake. This suggests that if [NOx ] continues to decrease in the future, ozone reduction policies focussing solely on NOx reductions may not be as efficient as expected if [PM] and, hence, HO2 uptake to aerosol surfaces continue to decrease. The addition of aerosol uptake into the model, for both the γHO2 calculated from measured data and when using a fixed value of γHO2 = 0.2, did not have a significant effect on the overall O3 production regime across the campaign. While not important for this campaign, aerosol uptake could be important for areas of lower NO concentration that are already in a NOx -sensitive regime

Effects of Ca and Mg levels on colony formation and EPS content of cultured M. aeruginosa

AbstractColony formation of Microcystis plays a significant role in Microcystis blooms. Calcium (Ca) and magnesium (Mg) are important nutrient elements during algae growth. In this study, the influences of Ca and Mg concentrations on extracellular polysaccharides (EPS) content and colony formation of Microcystis were investigated, and then the effects of EPS content and specific growth rate on colony formation were discussed. The results showed that Ca had a direct observable influence on the colony formation of Microcystis; however, Mg was not obvious. More specifically, when Ca concentration was lower than 20 mg•L-1, the Microcystis was dominated by single cells, when the concentration was over 20 mg•L-1, Microcystis colonies were found and took up more than 50%, and the size of colony was increased with the increasing Ca concentration. Moreover, it showed that the mean size of Microcystis had significant correlation with EPS content, and lower specific growth rate was in favor of colony formation

RNA–CTMA Dielectrics in Organic Field Effect Transistor Memory

In recent years, biopolymers are highly desired for their application in optic electronic devices, because of their unique structure and fantastic characteristics. In this work, a non-volatile memory (NVM) device based on the bio thin-film transistor (TFT) was fabricated through applying a new RNA&ndash;CTMA (cetyltrimethylammonium) complex as a gate dielectric. The physicochemical performance, including UV, CD spectral, thermal stability, surface roughness, and microstructure, has been investigated systematically. The RNA&ndash;CTMA complex film exhibits strong absorption with a well-defined absorption peak around 260 nm, the RMS roughness is ~2.1 nm, and displayed excellent thermal stability, up to 240 &deg;C. In addition, the RNA&ndash;CTMA complex-based memory device shows good electric performance, with a large memory window up to 52 V. This demonstrates that the RNA&ndash;CTMA complex is a promising candidate for low cost, low-temperature processes, and as an environmentally friendly electronic device

Fractional fourier image transformer for multimodal remote sensing data classification

With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, the limited receptive field restricted the convolutional neural networks (CNNs) to represent global contextual and sequential attributes, while visual image transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier image transformer (FrIT) as a backbone network to extract both global and local contexts effectively. In the proposed FrIT framework, HSI and LiDAR data are first fused at the pixel level, and both multisource feature and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contextual and sequential feature extraction. Unlike the attention-based representations in classic VIT, FrIT is capable of speeding up the transformer architectures massively and learning valuable contextual information effectively and efficiently. More significantly, to reduce redundancy and loss of information from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. Five HSI and LiDAR scenes including one newly labeled benchmark are utilized for extensive experiments, showing improvement over both CNNs and VITs

Diagnosis and classification in MRI of brucellar spondylitis

Objective: To explore the magnetic resonance imaging (MRI) features of patients with brucellar spondylitis and try to classify them depending on the MRI findings. Material and methods: 67 patients (male&female: 50&17) with brucellar spondylitis were recruited in this study. MRI examinations were performed in all patients. Firstly, MRI data were analyzed by two senior radiologists. Secondly, according to the imaging findings, patients were divided into different types. Results: In all 67 patients with spinal brucellosis, 5 cases only had paravertebral soft tissue involved, 62 cases showed abnormal signal in single or multiple adjacent vertebrae. Thirty-five patients focused on the L4 vertebral involvement. 18 cases had appendage involvement. 27 cases hand intervertebral disc narrowing and cystic signal. Paravertebral, epidural and psoas abscesses were detected in 35, 20 and 8 cases. Patients were grouped according to MRI findings. The vertebral inflammatory type was the most frequently type with the rate of 35.8%, followed by discitis type 32.9%, adnexitis type 11.9%, paravertebral and psoas abscess type 11.9% and paravertebral soft tissue type 7.5%. Conclusion: It is not difficult to diagnose brucellar spondylitis in MRI findings based on clinical background and laboratory tests. According to the performance of MRI, five types can be classified