On the mode-segregated aerosol particle number concentration load : contributions of primary and secondary particles in Hyytiälä and Nanjing

Aerosol particle concentrations in the atmosphere are governed by their sources and sinks. Sources include directly-emitted (primary) and secondary aerosol particles formed from gas-phase precursor compounds. The relative importance of primary and secondary aerosol particles varies regionally and with time. In this work, we investigated primary and secondary contributions to mode-segregated particle number concentrations by using black carbon as a tracer for the primary aerosol number concentration. We studied separately nucleation, Aitken and accumulation mode concentrations at a rural boreal forest site (Hyytiala, Finland) and in a rather polluted megacity environment (Nanjing, China) using observational data from 2011 to 2014. In both places and in all the modes, the majority of particles were estimated to be of secondary origin. Even in Nanjing, only about half of the accumulation mode particles were estimated to be of primary origin. Secondary particles dominated particularly in the nucleation and Aitken modes.Peer reviewe

Estimating cloud condensation nuclei number concentrations using aerosol optical properties : role of particle number size distribution and parameterization

The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between NCCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (σsp) of PM10 particles. The analysis first showed that the dependence of NCCN on supersaturation (SS) can be described by a logarithmic fit in the range SS 4. At SS >0.4 % the average bias ranged from ∼0.7 to ∼1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, ∼1.4–1.9. In other words, at SS >0.4 % NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured NCCN varied from ∼0.5 to ∼0.8. To study the physical explanation of the relationships between NCCN and AOPs, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation showed that the relationships of NCCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of NCCN and AOPs were similar to those of the observed ones.The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol-cloud interactions within warm clouds. Long-term CCN number concentration (N-CCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating N-CCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between N-CCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Angstrom exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (sigma(sp)) of PM10 particles. The analysis first showed that the dependence of N-CCN on supersaturation (SS) can be described by a logarithmic fit in the range SS 4. At SS > 0 :4% the average bias ranged from similar to 0.7 to similar to 1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, similar to 1.4-1.9. In other words, at SS > 0:4% N-CCN was estimated with an average uncertainty of approximately 30% by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Angstrom exponent (SAE). The squared correlation coefficients between the AOP-derived and measured N-CCN varied from similar to 0.5 to similar to 0.8. To study the physical explanation of the relationships between N-CCN and AOPs, lognormal unimodal particle size distributions were generated and N-CCN and AOPs were calculated. The simulation showed that the relationships of N-CCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of N-CCN and AOPs were similar to those of the observed ones.Peer reviewe

Comprehensive Analysis of Peripheral Exosomal circRNAs in Large Artery Atherosclerotic Stroke

Exosomes are crucial vehicles in intercellular communication. Circular RNAs (circRNAs), novel endogenous noncoding RNAs, play diverse roles in ischemic stroke. Recently, the abundance and stability of circRNAs in exosomes have been identified. However, a comprehensive analysis of exosomal circRNAs in large artery atherosclerotic (LAA) stroke has not yet been reported. We performed RNA sequencing (RNA-Seq) to comprehensively identify differentially expressed (DE) exosomal circRNAs in five paired LAA and normal controls. Further, quantitative real-time PCR (qRT-PCR) was used to verify the RNA-Seq results in a cohort of stroke patients (32 versus 32). RNA-Seq identified a total of 462 circRNAs in peripheral exosomes; there were 25 DE circRNAs among them. Additionally, circRNA competing endogenous RNA (ceRNA) network and translatable analysis revealed the potential functions of the exosomal circRNAs in LAA progression. Two ceRNA pathways involving 5 circRNAs, 2 miRNAs, and 3 mRNAs were confirmed by qRT-PCR. In the validation cohort, receiver operating characteristic (ROC) curve analysis identified two circRNAs as possible novel biomarkers, and a logistic model combining two and four circRNAs increased the area under the curve compared with the individual circRNAs. Here, we show for the first time the comprehensive expression of exosomal circRNAs, which displayed the potential diagnostic and biological function in LAA stroke

Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi-sensor satellite observations

Aerosol effects on low warm clouds over the Yangtze River Delta (YRD, eastern China) are examined using co-located MODIS, CALIOP and CloudSat observations. By taking the vertical locations of aerosol and cloud layers into account, we use simultaneously observed aerosol and cloud data to investigate relationships between cloud properties and the amount of aerosol particles (using aerosol optical depth, AOD, as a proxy). Also, we investigate the impact of aerosol types on the variation of cloud properties with AOD. Finally, we explore how meteorological conditions affect these relationships using ERA-Interim reanalysis data. This study shows that the relation between cloud properties and AOD depends on the aerosol abundance, with a different behaviour for low and high AOD (i.e. AOD0.35). This applies to cloud droplet effective radius (CDR) and cloud fraction (CF), but not to cloud optical thickness (COT) and cloud top pressure (CTP). COT is found to decrease when AOD increases, which may be due to radiative effects and retrieval artefacts caused by absorbing aerosol. Conversely, CTP tends to increase with elevated AOD, indicating that the aerosol is not always prone to expand the vertical extension. It also shows that the COT-CDR and CWP (cloud liquid water path)-CDR relationships are not unique, but affected by atmospheric aerosol loading. Furthermore, separation of cases with either polluted dust or smoke aerosol shows that aerosol-cloud interaction (ACI) is stronger for clouds mixed with smoke aerosol than for clouds mixed with dust, which is ascribed to the higher absorption efficiency of smoke than dust. The variation of cloud properties with AOD is analysed for various relative humidity and boundary layer thermodynamic and dynamic conditions, showing that high relative humidity favours larger cloud droplet particles and increases cloud formation, irrespective of vertical or horizontal level. Stable atmospheric conditions enhance cloud cover horizontally. However, unstable atmospheric conditions favour thicker and higher clouds. Dynamically, upward motion of air parcels can also facilitate the formation of thicker and higher clouds. Overall, the present study provides an understanding of the impact of aerosols on cloud properties over the YRD. In addition to the amount of aerosol particles (or AOD), evidence is provided that aerosol types and ambient environmental conditions need to be considered to understand the observed relationships between cloud properties and AOD.Peer reviewe

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Submicron particles in polluted regions have received much attention because of their influences on human health and climate. A k-means clustering technique was performed on a data set of particle number size distributions (PNSD) that was obtained over more than 3 years in the Yangtze River Delta (YRD) region of East China. With simultaneous measurements of meteorological conditions, trace gases and aerosol compositions, seven clusters were categorized and interpreted. Cluster 1 and cluster 2, which accounted for 9.9% of the total PNSD data, were attributed to new particle formation (NPF) and vehicle exhaust emissions with different intensities; Cluster 3 and Cluster 4, which accounted for 10.5% of the total PNSD data, were related to the growth of nucleation mode particles; Cluster 5, which accounted for 37.9% of the total data, was attributed to the humid YRD background; and Cluster 6 and Cluster 7, which accounted for 41.6% of the total data set, were both pollution-related clusters with similar mass concentrations but completely different PNSD. Although the PM2.5 mass concentrations were somewhat similar, the particle number concentrations of the accumulation mode particles could vary by more than one order of magnitude from the urban background cluster to the pollution-related clusters. The cluster proximity diagram and conversion flow chart of clusters clearly show the influence of NPF and growth on haze, as well as the conversion between background and polluted conditions. This study highlights the importance of PNSD for understanding urban air quality and recommends the clustering technique for analyzing complex PNSD datasets. Plain Language Summary Submicron particles in polluted regions have significant influences on human health and climate. Based on long-term field measurements, we used the k-means clustering technique to characterize the number size distributions of submicron particles in the Yangtze River Delta (YRD) of China. Seven clusters were categorized and interpreted. New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in the YRD. The influences of NPF and growth on haze, as well as the conversion between background and polluted conditions, were found. Key Points New particle formation (NPF), fossil fuel combustion and biomass burning are the main sources of submicron particles in Nanjing The influences of NPF and growth on haze, and the conversion between background and pollution conditions were found The k-means cluster technique is an effective tool to categorize particle number size distribution data setPeer reviewe

Measurements of sub-3nm particles using a particle size magnifier in different environments : from clean mountain top to polluted megacities

The measurement of sub-3 nm aerosol particles is technically challenging. Therefore, there is a lack of knowledge about the concentrations of atmospheric sub-3 nm particles and their variation in different environments. In this study, the concentrations of similar to 1-3 nm particles measured with a particle size magnifier (PSM) were investigated at nine sites around the world. Sub-3 nm particle concentrations were highest at the sites with strong anthropogenic influence. In boreal forest, measured particle concentrations were clearly higher in summer than in winter, suggesting the importance of biogenic precursor vapors in this environment. At all sites, sub-3 nm particle concentrations had daytime maxima, which are likely linked to the photochemical production of precursor vapors and the emissions of precursor vapors or particles from different sources. When comparing ion concentrations to the total sub-3 nm particle concentrations, electrically neutral particles were observed to dominate in polluted environments and in boreal forest during spring and summer. Generally, the concentrations of sub-3 nm particles seem to be determined by the availability of precursor vapors rather than the level of the sink caused by preexisting aerosol particles. The results also indicate that the formation of the smallest particles and their subsequent growth to larger sizes are two separate processes, and therefore studying the concentration of sub-3 nm particles separately in different size ranges is essential.Peer reviewe

Toward Building a Physical Proxy for Gas-Phase Sulfuric Acid Concentration Based on Its Budget Analysis in Polluted Yangtze River Delta, East China

Gaseous sulfuric acid (H2SO4) is a crucial precursor for secondary aerosol formation, particularly for new particle formation (NPF) that plays an essential role in the global number budget of aerosol particles and cloud condensation nuclei. Due to technology challenges, global-wide and long-term measurements of gaseous H2SO4 are currently very challenging. Empirical proxies for H2SO4 have been derived mainly based on short-term intensive campaigns. In this work, we performed comprehensive measurements of H2SO4 and related parameters in the polluted Yangtze River Delta in East China during four seasons and developed a physical proxy based on the budget analysis of gaseous H2SO4. Besides the photo-oxidation of SO2, we found that primary emissions can contribute considerably, particularly at night. Dry deposition has the potential to be a non-negligible sink, in addition to condensation onto particle surfaces. Compared with the empirical proxies, the newly developed physical proxy demonstrates extraordinary stability in all the seasons and has the potential to be widely used to improve the understanding of global NPF fundamentally.Peer reviewe

Comprehensive modelling study on observed new particle formation at the SORPES station in Nanjing, China

New particle formation (NPF) has been investigated intensively during the last 2 decades because of its influence on aerosol population and the possible contribution to cloud condensation nuclei. However, intensive measurements and modelling activities on this topic in urban metropolitan areas in China with frequent high-pollution episodes are still very limited. This study provides results from a comprehensive modelling study on the occurrence of NPF events in the western part of the Yangtze River Delta (YRD) region, China. The comprehensive modelling system, which combines the WRF-Chem (the Weather Research and Forecasting model coupled with Chemistry) regional chemical transport model and the MALTE-BOX sectional box model (the model to predict new aerosol formation in the lower troposphere), was shown to be capable of simulating atmospheric nucleation and subsequent growth. Here we present a detailed discussion of three typical NPF days, during which the measured air masses were notably influenced by either anthropogenic activities, biogenic emissions, or mixed ocean and continental sources. Overall, simulated NPF events were generally in good agreement with the corresponding measurements, enabling us to get further insights into NPF processes in the YRD region. Based on the simulations, we conclude that biogenic organic compounds, particularly monoterpenes, play an essential role in the initial condensational growth of newly formed clusters through their low-volatility oxidation products. Although some uncertain-ties remain in this modelling system, this method provides a possibility to better understand particle formation and growth processes.Peer reviewe

Polluted dust promotes new particle formation and growth

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