This study investigates the impact of the aerosol hygroscopic growth effect on haze events in Xingtai, a heavily polluted city in the central part of the North China Plain (NCP), using a large array of instruments measuring aerosol optical, physical, and chemical properties. Key instruments used and measurements made include the Raman lidar for atmospheric water vapor content and aerosol optical profiles, the PC-3016A GrayWolf six-channel handheld particle and mass meter for atmospheric total particulate matter (PM) that has diameters less than 1 and 2.5 µm (PM₁ and PM_2.5, respectively), the aerosol chemical speciation monitor (ACSM) for chemical components in PM₁, and the hygroscopic tandem differential mobility analyzer (H-TDMA) for aerosol hygroscopicity. The changes in PM₁ and PM_2.5 agreed well with that of the water vapor content due to the aerosol hygroscopic growth effect. Two cases were selected to further analyze the effects of aerosol hygroscopic growth on haze events. The lidar-estimated hygroscopic enhancement factor for the aerosol backscattering coefficient during a relatively clean period (Case I) was lower than that during a pollution event (Case II) with similar relative humidity (RH) levels of 80 %–91 %. The Kasten model was used to fit the aerosol optical hygroscopic growth factor (GF) whose parameter b differed considerably between the two cases, i.e., 0.1000 (Case I) versus 0.9346 (Case II). The aerosol acidity value calculated from ACSM data for Case I (1.35) was less than that for Case II (1.50) due to different amounts of inorganics such as NH₄NO₃, NH₄HSO₄, and (NH₄)₂SO₄. Model results based on H-TDMA data showed that aerosol hygroscopic growth factors in each size category (40, 80, 110, 150, and 200 nm) at different RH levels (80 %–91 %) for Case I were lower than those for Case II. For similar ambient RH levels, the high content of nitrate facilitates the hygroscopic growth of aerosols, which may be a major factor contributing to heavy haze episodes in Xingtai.

Salinity is a major abiotic stress affecting plant growth and development. Understanding the molecular mechanisms of salt response and defense in plants will help in efforts to improve the salt tolerance of crops. Brachypodium distachyon is a new model plant for wheat, barley, and several potential biofuel grasses. In the current study, proteome and phosphoproteome changes induced by salt stress were the focus. The Bd21 leaves were initially treated with salt in concentrations ranging from 80 to 320 mm and then underwent a recovery process prior to proteome analysis. A total of 80 differentially expressed protein spots corresponding to 60 unique proteins were identified. The sample treated with a median salt level of 240 mm and the control were selected for phosphopeptide purification using TiO₂ microcolumns and LC-MS/MS for phosphoproteome analysis to identify the phosphorylation sites and phosphoproteins. A total of 1509 phosphoproteins and 2839 phosphorylation sites were identified. Among them, 468 phosphoproteins containing 496 phosphorylation sites demonstrated significant changes at the phosphorylation level. Nine phosphorylation motifs were extracted from the 496 phosphorylation sites. Of the 60 unique differentially expressed proteins, 14 were also identified as phosphoproteins. Many proteins and phosphoproteins, as well as potential signal pathways associated with salt response and defense, were found, including three 14-3-3s (GF14A, GF14B, and 14-3-3A) for signal transduction and several ABA signal-associated proteins such as ABF2, TRAB1, and SAPK8. Finally, a schematic salt response and defense mechanism in B. distachyon was proposed.

Airborne dust, which contains high levels of toxic metals, is recognized as one of the most harmful environment component. The purpose of this study was to evaluate heavy metals pollution in dustfall from bus stations in Beijing, and to perform a risk assessment analysis for adult passengers. The concentrations of Cd, Co, Cr, Cu, Mo, Ni, Pb, V and Zn were determined by inductively coupled plasma mass spectroscopy (ICP-MS). The spatial distribution, pollution level and potential health risk of heavy metals were analyzed by Geographic Information System (GIS) mapping technology, geo-accumulation index and health risk assessment model, respectively. The results indicate that dust samples have elevated metal concentrations, especially for Cd, Cu, Pb and Zn. The nine metals can be divided into two categories in terms of spatial distribution and pollution level. Cd, Cr, Cu, Mo, Pb and Zn reach contaminated level and have similar spatial patterns with hotspots distributed within the Fifth Ring Road. While the hot spot areas of Co and V are always out of the Fifth Ring Road. Health risk assessment shows that both carcinogenic and non-carcinogenic risks of selected metals were within the safe range.

Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM_2.5) is a key air quality parameter. A real-time knowledge of PM_2.5 is highly valuable for lowering the risk of detrimental impacts on human health. To achieve this goal, we developed a new deep learning model-EntityDenseNet to retrieve ground-level PM_2.5 concentrations from Himawari-8, a geostationary satellite providing high temporal resolution data. In contrast to the traditional machine learning model, the new model has the capability to automatically extract PM_2.5 spatio-temporal characteristics. Validation across mainland China demonstrates that hourly, daily and monthly PM_2.5 retrievals contain the root-mean-square errors of 26.85, 25.3, and 15.34 μg/m³, respectively. In addition to a higher accuracy achievement when compared with various machine learning inversion methods (backpropagation neural network, extreme gradient boosting, light gradient boosting machine, and random forest), EntityDenseNet can "peek inside the black box" to extract the spatio-temporal features of PM_2.5. This model can show, for example, that PM_2.5 levels in the coastal city of Tianjin were more influenced by air from Hebei than Beijing. Further, EntityDenseNet can still extract the seasonal characteristics that demonstrate that PM_2.5 is more closely related within three month groups over mainland China: (1) December, January and February, (2) March, April and May, (3) July, August and September, even without meteorological information. EntityDenseNet has the ability to obtain high temporal resolution satellite-based PM_2.5 data over China in real-time. This could act as an important tool to improve our understanding of PM_2.5 spatio-temporal features.