Impact of climate change on SARS-CoV-2 epidemic in China.

The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible. China is selected as the study area, and six weather factors that receive the most attention from January 20, 2020 to April 30, 2020 are selected to investigate the correlation between weather and SARS-CoV-2 to provide a theoretical basis for long-term epidemic prevention and control. The results show that (1) the average growth rate (GR) of SARS-CoV-2 in each province is logarithmically distributed with a mean value of 5.15%. The GR of the southeastern region is higher than that of the northwestern region, which is consistent with the Hu Line. (2) The specific humidity, 2-m temperature (T), ultraviolet (UV) radiation, and wind speed (WS) adversely affect the GR. By contrast, the total precipitation (TP) and surface pressure (SP) promote the GR. (3) For every 1 unit increase in UV radiation, the GR decreases by 0.30% in 11 days, and the UV radiation in China is higher than that worldwide (0.92% higher per day). Higher population aggregation and urbanization directly affect the epidemic, and weather is an indirect factor.

[Effect of Vegetation Coverage on the Temporal and Spatial Distribution of PM2.5 Concentration in China's Eight Major Economic Regions from 1998 to 2016].

The study researched the relationship between vegetation cover and PM2.5 pollution. The raster NDVI dataset from 1998 to 2016 were reclassified into low, medium, and high vegetation coverage area, and the corresponding PM2.5 concentration in eight economic regions in China were then calculated. On this basis, the temporal and spatial characteristics of PM2.5 pollution were analyzed and Pearson correlation coefficient was used to explore its correlation with NDVI landscape pattern indexes separately from landscape and class level NDVI. The preliminary results showed that:①The northern, eastern, southern coastal, middle reaches of the Yangtze River, and the northeast economic zones have relatively low vegetation coverage in areas with relatively serious PM2.5 pollution. However, the middle reaches of the Yellow River, the southwestern and the Northwestern Economic Zones in areas with relatively low vegetation coverage showed lighter PM2.5 pollution. ②PM2.5 increased in most areas between 1998 and 2016. ③A significant correlation between PM2.5 and NDVI landscape pattern indexes was not found for all areas. ④Therefore, the impacts of the landscape shape index(LSI), percent of landscape(PLAND), number of patches(NP), largest patch index(LPI), and aggregation index(AI) on PM2.5 are heterogeneous.