Sources and processes of organic aerosol in non-refractory PM1 and PM2.5 during foggy and haze episodes in an urban environment of the Yangtze River Delta, China.

Organic aerosol (OA) generally accounts for a large fraction of fine particulate matter (PM2.5) in the urban atmosphere. Despite significant advances in the understanding their emission sources, transformation processes and optical properties in the submicron aerosol fraction (PM1), larger size fractions - e.g., PM2.5 - still deserve complementary investigations. In this study, we conducted a comprehensive analysis on sources, formation process and optical properties of OA in PM1 and PM2.5 under haze and foggy environments in the Yangtze River Delta (eastern China), using two aerosol chemical speciation monitors, as well as a photoacoustic extinctiometer at 870 nm. Positive matrix factorization analysis - using multilinear engine (ME2) algorithm - was conducted on PM1 and PM2.5 organic mass spectra. Four OA factors were identified, including three primary OA (POA) factors, i.e., hydrocarbon-like OA (HOA), cooking OA (COA), and biomass burning OA (BBOA), and a secondary OA (SOA) factor, i.e., oxidized oxygenated OA (OOA). An enhanced PM1-2.5 COA concentration was clearly observed during cooking peak hours, suggesting important contribution of fresh cooking emissions on large-sized particles (i.e., PM1-2.5). The oxidation state and concentration of PM2.5 HOA were higher than that in PM1, suggesting that large-sized HOA particles might be linked to oxidized POA. High contribution (44%) of large-sized OOA to non-refractory PM2.5 mass was observed during haze episodes. During foggy episodes, PM1 and PM2.5 OOA concentrations increased as a positive relationship over time, along with an exponential increase in the PM2.5-OOA to PM1-OOA ratio. Meanwhile, OOA loadings increased with the aerosol liquid water content (ALWC) during foggy episodes. Random forest cross-validation analysis also supported the important influence of ALWC on OOA variations, supporting substantial impact of aqueous process on SOA formation during haze and/or foggy episodes. Obtained results also indicated high OOA contributions (21%-36%) and low POA contributions (6%-14%) to the PM2.5 scattering coefficient during haze and foggy episodes, respectively. Finally, we could illustrate that atmospheric vertical diffusion and horizontal transport have important but different effects on the concentrations of different primary and secondary OA factors in different particle size fractions.

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO-v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.