Literature DB >> 30032368

Different pollutant removal efficiencies of artificial aquatic plants in black-odor rivers.

Qingfeng Chen1, Zhao Yang2, Kemei Qi2, Changsheng Zhao3.   

Abstract

Black-odor rivers have become a prominent environmental problem, especially for developing countries. A laboratory experiment was conducted to determine the optimum operating parameters of artificial aquatic plants (AAP) to provide a theoretical and scientific basis for their application in black-odor rivers. The purification mechanism of operating parameters for AAP was also explored at the micro-organic and genetic levels by high-throughput sequencing. Chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) were measured in systems with different AAP lengths and pH. After 24 days, the best removal efficiencies of APP for COD and NH4+-N were 90.07 and 82.40% for 100 cm and 90.70 and 91.90% for pH values of 8.0-9.0, respectively. High-throughput sequencing analysis revealed that the relative abundance of Flavobacterium in the AAP was 7.80% at 50 cm, while the proportion increased to 29.30% at 100 cm. The abundance of microorganisms improved continuously with increased length, and the ratio of Acinetobacter increased obviously at pH 8.0-9.0 relative to pH 6.0-7.0. Furthermore, the AAP were used in Qihe Artificial Wetland in Shandong Province, China. The results revealed that the average removal efficiencies of AAP for COD and NH4+-N were 27.75 and 14.34%, respectively, in the artificial wetland. Therefore, AAP was beneficial to the growth of bacteria and could be used in the treatment of black-odor rivers.

Entities:  

Keywords:  Artificial aquatic plants; Black-odor rivers; High-throughput sequencing; Purification mechanism; Removal efficiency

Year:  2018        PMID: 30032368     DOI: 10.1007/s11356-018-2696-5

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  5 in total

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Authors:  Yafan Cai; Binbin Hua; Lijuan Gao; Yuegao Hu; Xufeng Yuan; Zongjun Cui; Wanbin Zhu; Xiaofen Wang
Journal:  Bioresour Technol       Date:  2017-04-21       Impact factor: 9.642

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Authors:  Hong-ying Yuan; Hua-xing Zhang; Yin-guang Chen; Qi Zhou
Journal:  Huan Jing Ke Xue       Date:  2006-07

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Authors:  Marina G Kalyuzhnaya; Sarah Bowerman; Jimmie C Lara; Mary E Lidstrom; Ludmila Chistoserdova
Journal:  Int J Syst Evol Microbiol       Date:  2006-12       Impact factor: 2.747

  5 in total

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