Episode-Based Analysis of Size-Resolved Carbonaceous Aerosol Compositions in Wintertime of Xinxiang: Implication for the Haze Formation Processes in Central China

To provide a comprehensive understanding of carbonaceous aerosol and its role in the haze formation in the Central Plains Urban Agglomeration of China, size-segregated particulate matter samples (PM1, PM2.5 and PM10) were continually collected from 20 December 2017, to 17 January 2018, in Xinxiang, the third largest city of Henan province. The results showed that the mean mass concentrations of PM1, PM2.5 and PM10 were 63.20, 119.63 and 211.95 μg·m−3, respectively, and the organic carbon (OC) and elemental carbon (EC) were 11.37 (5.87), 19.24 (7.36), and 27.04 (10.27) μg·m−3, respectively. Four pollution episodes that were categorized by short evolution patterns (PE1 and PE3) and long evolution patterns (PE2 and PE4) were observed. Meteorological condition was attributed to haze episodes evolution pattern. Carbonaceous components contributed to PE1 and PE2 under drier condition through transportation and local combustion emission, while they were not main species in PE3 and PE4 for haze explosive growth under suitable RH, whatever for the short or long evolution pattern. The atmospheric self-cleaning processes were analyzed by a case study, which showed the wet scavenging effectively reduced the coarse particles with a removal rate of 73%, while it was not for the carbonaceous components in fine particles that is hydrophobic in nature. These results highlight that local primary emissions such as biomass combustion were the important sources for haze formation in Central China, especially in dry conditions

Monitoring Ice Phenology in Lake Wetlands Based on Optical Satellite Data: A Case Study of Wuliangsu Lake

It is challenging to obtain the ice phenology for a lake covered with a vast area of aquatic (shallow lake wetlands) using optical satellite data because possible clouds above the lake could contaminate the result. We developed a new method to tackle this challenge. Our target was Wuliangsu Lake, a large (330 km2) and shallow (1.6 m average depth) lake wetland in the Inner Mongolia Plateau. We used Landsat and Sentinel-2 imageries to extract the lake water boundary. The MOD09GQ/MYD09GQ dataset, having the highest spatial resolution among MODIS reflectivity products, was first selected to differentiate water and ice pixels. Then, we used the reflectivity state parameters containing cloud information in the dataset to filter out the cloud pixels. The ice phenology characteristics, such as freeze-up, break-up dates, and ice cover duration (ICD) between 2013 and 2022 were obtained. We further applied the air temperature correction technique to remove the outliers. The average of ICD in Wuliangsu Lake was about 127 ± 6 days. The freeze-up start and break-up end occurred on 17 November ± 5 days and 25 March ± 4 days, respectively. The remote sensing results agree well with the field observation, with a mean absolute error of 2 days. The algorithm can effectively remove the influence of aquatic plants and clouds on lake ice identification, thereby satisfying the needs of daily monitoring and ice phenology research in the lake wetlands

An improved Yolov5s based on transformer backbone network for detection and classification of bronchoalveolar lavage cells

Biological tissue information of the lung, such as cells and proteins, can be obtained from bronchoalveolar lavage fluid (BALF), through which it can be used as a complement to lung biopsy pathology. BALF cells can be confused with each other due to the similarity of their characteristics and differences in the way sections are handled or viewed. This poses a great challenge for cell detection. In this paper, An Improved Yolov5s Based on Transformer Backbone Network for Detection and Classification of BALF Cells is proposed, focusing on the detection of four types of cells in BALF: macrophages, lymphocytes, neutrophils and eosinophils. The network is mainly based on the Yolov5s network and uses Swin Transformer V2 technology in the backbone network to improve cell detection accuracy by obtaining global information; the C3Ghost module (a variant of the Convolutional Neural Network architecture) is used in the neck network to reduce the number of parameters during feature channel fusion and to improve feature expression performance. In addition, embedding intersection over union Loss (EIoU_Loss) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher accuracy of the algorithm. The experiments showed that our model could achieve mAP of 81.29% and Recall of 80.47%. Compared to the original Yolov5s, the mAP has improved by 3.3% and Recall by 3.67%. We also compared it with Yolov7 and the newly launched Yolov8s. mAP improved by 0.02% and 2.36% over Yolov7 and Yolov8s respectively, while the FPS of our model was higher than both of them, achieving a balance of efficiency and accuracy, further demonstrating the superiority of our model

Improved Mechanical and Electrochemical Properties of XNBR Dielectric Elastomer Actuator by Poly(dopamine) Functionalized Graphene Nano-Sheets

In this work, graphene nano-sheets (GNS) functionalized with poly(dopamine) (PDA) (denoted as GNS-PDA) were dispersed in a carboxylated nitrile butadiene rubber (XNBR) matrix to obtain excellent dielectric composites via latex mixing. Because hydrogen bonds were formed between ⁻COOH groups of XNBR and phenolic hydroxyl groups of PDA, the encapsulation of GNS-PDA around XNBR latex particles was achieved, and led to a segregated network structure of filler formed in the GNS-PDA/XNBR composite. Thus, the XNBR composite filled with GNS-PDA showed improved filler dispersion, enhanced dielectric constant and dielectric strength, and decreased conductivity compared with the XNBR composite filled with pristine GNS. Finally, the GNS-PDA/XNBR composite displayed an actuated strain of 2.4% at 18 kV/mm, and this actuated strain was much larger than that of pure XNBR (1.3%) at the same electric field. This simple, environmentally friendly, low-cost, and effective method provides a promising route for obtaining a high-performance dielectric elastomer with improved mechanical and electrochemical properties

Dispersion and Preparation of Nano-AlN/AA6061 Composites by Pressure Infiltration Method

Nanomaterials play an important role in metal matrix composites (MMC). In this study, 3.0 wt.%, 6.0 wt.%, and 9.0 wt.% nano-AlN-particles-reinforced AA6061 (nano-AlN/AA6061) composites were successfully prepared by pressure infiltration technique and then hot extruded (HE) at 500 °C. The microstructural characterization of the composites after HE show that the grain structure of the Al matrix is significantly refined, varying from 2 to 20 μm down to 1 to 3 μm. Nano-AlN particles in the composites are agglomerated around the matrix, and the distribution of nano-AlN is improved after HE. The interface between AA6061 and nano-AlN is clean and smooth, without interface reaction products. The 3.0 wt.% nano-AlN/AA6061 composite shows an uppermost yield and supreme tensile strength of 333 MPa and 445 MPa, respectively. The results show that the deformation procedure of the composite is beneficial to the further dispersion of nano-AlN particles and improves the strength of nano-AlN/AA6061 composite. At the same time, the strengthening mechanism active in the composites was discussed

Ambient Volatile Organic Compound Characterization, Source Apportionment, and Risk Assessment in Three Megacities of China in 2019

In order to illustrate pollution characterization, source apportionment, and risk assessment of VOCs in Beijing, Baoding, and Shanghai, field observations of CO, NO, NO2, O3, and volatile organic compounds (VOCs) were conducted in 2019. Concentrations of VOCs were the highest in Beijing (105.4 ± 52.1 ppb), followed by Baoding (97.1 ± 47.5 ppb) and Shanghai (91.1 ± 41.3 ppb). Concentrations of VOCs were the highest in winter (120.3 ± 61.5 ppb) among the three seasons tested, followed by summer (98.1 + 50.8 ppb) and autumn (75.5 + 33.4 ppb). Alkenes were the most reactive VOC species in all cities, accounting for 56.0%, 53.7%, and 39.4% of ozone formation potential in Beijing, Baoding, and Shanghai, respectively. Alkenes and aromatics were the reactive species, particularly ethene, propene, 1,3,5-trimethylbenzene, and m/p-xylene. Vehicular exhaust was the principal source in all three cities, accounting for 27.0%, 30.4%, and 23.3% of VOCs in Beijing, Baoding, and Shanghai, respectively. Industrial manufacturing was the second largest source in Baoding (23.6%) and Shanghai (21.3%), and solvent utilization was the second largest source in Beijing (25.1%). The empirical kinetic modeling approach showed that O3 formation was limited by both VOCs and nitric oxides at Fangshan (the suburban site) and by VOCs at Xuhui (the urban site). Acrolein was the only substance with an average hazard quotient greater than 1, indicating significant non-carcinogenic risk. In Beijing, 1,2-dibromoethane had an R-value of 1.1 × 10−4 and posed a definite carcinogenic risk