Identification of bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei reveals distinct temperature-driven patterns.

Change in temperature is often a major environmental factor in triggering waterborne disease outbreaks. Previous research has revealed temporal and spatial patterns of bacterial population in several aquatic ecosystems. To date, very little information is available on aquaculture environment. Here, we assessed environmental temperature effects on bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei (FASFL). Water samples were collected over a one-year period, and aquatic bacteria were characterized by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and 16S rDNA pyrosequencing. Resulting DGGE fingerprints revealed a specific and dynamic bacterial population structure with considerable variation over the seasonal change, suggesting that environmental temperature was a key driver of bacterial population in the FASFL. Pyrosequencing data further demonstrated substantial difference in bacterial community composition between the water at higher (WHT) and at lower (WLT) temperatures in the FASFL. Actinobacteria, Proteobacteria and Bacteroidetes were the highest abundant phyla in the FASFL, however, a large number of unclassified bacteria contributed the most to the observed variation in phylogenetic diversity. The WHT harbored remarkably higher diversity and richness in bacterial composition at genus and species levels when compared to the WLT. Some potential pathogenenic species were identified in both WHT and WLT, providing data in support of aquatic animal health management in the aquaculture industry.

Long-term variation of satellite-based PM2.5 and influence factors over East China.

With the explosive economic development of China over the past few decades, air pollution has attracted increasing global concern. Using satellite-based PM2.5 data from 2000 to 2015, we found that the available emissions of atmospheric compositions show similar yearly variation trends to PM2.5, even if the synchronization is not met for each composition, implying that the intensity of anthropogenic emissions dominates the temporal variation of PM2.5 in East China. Empirical orthogonal function analysis demonstrates that the dominant variability in the seasonal PM2.5 is closely associated with climate circulation transformation, incarnated as the specific climate index such as the Asia Polar Vortex intensity in spring, the Northern Hemisphere Subtropical High Ridge Position for the leading mode and the Kuroshio Current SST for the second mode in summer, the Asia Polar Vortex Area for the leading mode and the Pacific Polar Vortex Intensity for the second mode in autumn, the NINO A SSTA for the leading mode and the Pacific Decadal Oscillation for the second mode in winter. Therefore, apart from anthropogenic emissions effects, our results also provide robust evidence that over the past 16 years the climate factor has played a significant role in modulating PM2.5 in eastern China.

Long-term characteristics of satellite-based PM2.5 over East China.

With the explosive economic development of China over the past few decades, air pollution has become a serious environmental problem and has attracted increasing global concern. Using satellite-based PM2.5 data from 2000 to 2015, we found that the temporal-spatial variation of PM2.5 in East China is characterized by high concentrations in the northern part and low concentrations in the southern part of East China, and by being seasonally high in autumn and winter but low in spring and summer. We also found that the regional average PM2.5 concentration shows an approximative peak pattern over the last 16years, with the highest, 60.13µgm-3, and the lowest, 46.18µgm-3, occurring in 2007 and 2000, respectively. Despite obviously diminishing heavy polluted regions with a PM2.5 of >80µgm-3 after 2011, those cells dominated by natural background have still not recovered back to the clean level of 2000. These characteristics are valuable information to analyze the relative contributions of anthropogenic emissions and atmospheric conditions to the temporal-spatial variation characteristics of PM2.5.