谷歌Chrome浏览器插件
订阅小程序
在清言上使用

Denitrifier abundance and community composition linked to denitrification potential in river sediments

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH(2021)

引用 12|浏览12
暂无评分
摘要
Denitrification in river sediments plays a very important role in removing nitrogen in aquatic ecosystem. To gain insight into the key factors driving denitrification at large spatial scales, a total of 135 sediment samples were collected from Huaihe River and its branches located in the northern of Anhui province. Bacterial community composition and denitrifying functional genes ( nirS , nirK , and nosZ ) were measured by high-throughput sequencing and real-time PCR approaches. Potential denitrification rate (PDR) was measured by acetylene inhibition method, which varied from 0.01 to 15.69 μg N g −1 h −1 . The sequencing results based on 16S rRNA gene found that the main denitrification bacterial taxa included Bacillus , Thiobacillus , Acinetobacter , Halomonas , Denitratisoma , Pseudomonas , Rhodanobacter , and Thauera . Therein, Thiobacillus might play key roles in the denitrification. Total nitrogen and N:P ratio were the only chemical factors related with all denitrification genes. Furthermore, nirS gene abundance could be more susceptible to environmental parameters compared with nirK and nosZ genes. Canonical correspondence analysis indicated that NO 3 − , NO 2 − , NH 4 + and IP had the significant impacts on the nirS -encoding bacterial community and spatial distributions. There was a significantly positive correlation between Thiobacillus and nirS gene. We considered that higher numbers of nosZ appeared in nutrient rich sediments. More strikingly, PDR was positively correlated with the abundance of three functional genes. Random forest analysis showed that NH 4 + was the most powerful predictor of PDR. These findings can yield practical and important reference for the bioremediation or evaluation of wetland systems.
更多
查看译文
关键词
River sediment, Denitrifier, Functional gene, Denitrification potential, Nutrient level, Factor
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要