Background: The current focus is largely on whole course medical management of coronavirus disease-19 (COVID-19) with real-time polymerase chain reaction (RT-PCR) and radiological features, while the mild cases are usually missed. Thus, combination of multiple diagnostic methods is urgent to understand COVID-19 fully and to monitor the progression of COVID-19. Methods: laboratory variables of 40 mild COVID-19 patients, 30 patients with community-acquired pneumonia (CAP) and 32 healthy individuals were analyzed by principal component analysis (PCA), Kruskal test, Procrustes test, the vegan package in R, CCA package and receiver operating characteristic to investigate the characteristics of the laboratory variables and their relationships in COVID-19. Results: The correlations between the laboratory variables presented a variety of intricate linkages in the COVID-19 group compared with the healthy group and CAP patient group. The prediction probability of the combination of lymphocyte count (LY), eosinophil (EO) and platelets (PLT) was 0.847, 0.854 for the combination of lactate (LDH), creatine kinase isoenzyme (CK-MB), and C-reactive protein (CRP), 0.740 for the combination of EO, white blood cell count (WBC) and neutrophil count (NEUT) and 0.872 for the combination of CK-MB and P. Conclusions: The correlations between the laboratory variables in the COVID-19 group could be a unique characteristic showing promise as a method for COVID-19 prediction and monitoring progression of COVID-19 infection.

To explore the impact of climate change on snow cover and spring soil moisture (SM) in areas with seasonally frozen soil, snow cover parameters and spring SM for different land use types in Northeast China are extracted based on remote sensing snow cover and SM products. Snow cover parameters include snow days (SD), first day of snow cover (FSD), last day of snow cover (LSD), maximum snow depth (MSD) and average snow depth (ASD). The spatiotemporal variations and correlations between snow cover parameters and spring SM for different land use types are analyzed. The results showed that the average spring SM for different land use types was ordered woodland > farmland > grassland, with obvious woodland and farmland increases. Woodlands had many SD and large snow depths (MSD and ASD) that eventually decreased. Farmland SM increased significantly in spring, which aided crop development. The decrease in grassland spring SM was not obvious, but the snow cover parameters of certain areas decreased notably. Snow cover significantly impacted farmland SM, and correlation coefficients were highest between all snow cover parameters and SM in spring. The correlations between grassland snow cover parameters and SM in April were higher than those in May, but woodland snow cover parameters and spring SM were not correlated. Among the five snow parameters, FSD had the lowest correlation with spring SM, and SD had the greatest impact on SM. These results show the significant relationship between snow cover and SM and reveal relevant patterns. As future climate warming may introduce drought risk to woodland and grassland areas, advance preparations should be made. Farmland areas will continue to maintain appropriate SM, which is beneficial for agricultural development.

Based on their interaction with solar radiations, aerosols may be categorized as absorbing or scattering in nature. The absorbing aerosols are coarser and influence precipitation mainly due to microphysical effect (participating in the formation of Cloud Condensation Nuclei) and radiative forcing (by absorbing electromagnetic radiations). The prominent absorbing aerosols found in India are Black Carbon, soil dust, sand and mineral dust. Their size, distribution, and characteristics vary spatially and temporally. This paper aims at showing the spatio-temporal variation of Absorbing Aerosol Index (AAI) and precipitation over the four most polluted zones of Indian sub-continent (Indo-Gangetic plains 1, Indo-Gangetic plains 2, Central and Southern India) for monsoon season (June, July, August, September) during the last decade (2005 to 2014). Zonal averages AAI have been found to be exhibiting an increasing trend, hence region-wise correlations have been computed between AAI and precipitation during monsoon. Daily Absorption Aerosol Index (AAI) obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) and monthly precipitation from TRMM 3B42-V7 gridded data have been used