The current understanding of the climate effects of mixed-type aerosols is an open question. The optical and radiative properties of the anthropogenic, mixed-type, and dust aerosols were studied using simultaneous observations of a sun photometer and a depolarization lidar over the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL), northwestern China. The aerosol radiative effect was calculated using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model and was in good agreement with the Aerosol Robotic Network (AERONET) product. The anthropogenic, mixed-type, and dust aerosols were identified mainly based on the lidar-measured depolarization ratio, which was supported by the airmass back trajectories. The mixed-type aerosols exhibit lower (higher) extinctions below (above) 1.5 km above the ground, indicating anthropogenic pollution from the atmospheric boundary layer and dust aerosols above. The dust aerosols exhibit the highest absolute radiative effect because of the highest aerosol loading. However, the mixed-type aerosols are effective in both scattering and absorbing solar radiation, leading to the highest cooling efficiency at the bottom of the atmosphere (BOA), 7.4% and 6.5% higher than those of the anthropogenic and dust aerosols, respectively. The mixed-type aerosols exhibit the highest warming efficiency in the atmosphere (ATM), 20.8% and 28.2% higher than the anthropogenic and dust aerosols, respectively. The mixed-type aerosols also show the lowest cooling efficiency at the top of the atmosphere (TOA). The results suggest the necessity of carefully characterizing the mixed-type aerosols in atmospheric numerical models to more precisely assess the energy budget of the Earth–atmosphere system.

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The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

BackgroundType II alveolar epithelial cell (AEC II), in addition to its roles in maintaining lung homeostasis, takes an active role in inflammatory response during acute lung injury (ALI). Ca2+/calmodulin-dependent protein kinase IV (CaMK4) activated by Ca2+/calmodulin signaling, has been implicated in immune responses. This study was to investigate the roles of CaMK4 in the development of ALI and the underlying mechanisms.MethodsCaMK4 inhibitor KN-93 was used to investigate the effects of CaMK4 on NLRP3 inflammasome activation. The effects of KN-93 on disease development of lipopolysaccharide (LPS)-induced ALI were also evaluated. The role of CaMK4 on NLRP3 inflammasome activation was explored in human AEC II cell line A549 using KN-93 or CaMK4 siRNA. NLRP3 inflammasome activation was measured by histology immunofluorescence and Western blot. IL-1β and IL-18 were measured by ELISA.ResultsPhosphorylation of CaMK4 and the expression of NLRP3 and Caspase-1 p20 were increased in the lungs of LPS-induced ALI mice, which was suppressed by KN-93 as measured by Western blot. Further, the activation of NLRP3 inflammasome was detected in AEC II from patients with acute respiratory distress syndrome (ARDS) and LPS-induced ALI mice. In vitro, inhibition or silencing CaMK4 in AEC II significantly inhibited NLRP3 inflammasome activation, resulting in reduced IL-1β production. The inhibition of NLRP3 inflammasome and decreased IL-1β/IL-18 production by KN-93 led to reduced inflammatory infiltration and ameliorated lung injury in LPS-induced ALI mice.ConclusionCaMK4 controls the activation of NLRP3 inflammasome in AEC II during LPS-induced ALI. CaMK4 inhibition could be a novel therapeutic approach for the treatment of ALI.