Effects of sludge discharge stage on SBBR biofilm cultivation

doi:10.16085/j.issn.1000-6613.2018-0921

Abstract

Abstract:

Sequencing biofilm batch reactor (SBBR) is applied to sewage treatment. In order to investigate the effects of different sludge drainage stages on the pollutant removal and microbial community structure, three reactors were installed to treat domestic sewage. Activated sludge was removed in reactors at the initial, middle, and late culturing stages, respectively. Meanwhile, the microbial community structure and traditional microbial dominant species were analyzed by 16S rDNA high-throughput sequencing technology. Moreover, machine learning （ML） also played a role in excavating the sequencing data in depth and finding key species which led to the differences among groups. The water quality analysis results showed that there was no significant difference in COD removal efficiency among reactors with different sludge drainage stages, and the COD of effluent was all lower than 30mg/L. The NH₃-N removal efficiency of SBBR in sludge discharge in the middle stage was the first to reach a stable level and higher than that in the early and late stage of sludge discharge system. The results of high-throughput sequencing showed that the dominant microbial species in SBBR were mainly those that with high organic-pollutants-degrading capability. In SBBR, the NH₃-N removal related species (Hydrogenophaga, Gemmata and Nitrospira) screened by ML had higher abundance and stronger stability of microbial community structure, which could explain the difference of pollutant removal efficiency of SBBR from the microbial level.

Key words: wastewater, biofilm, high-throughput sequencing, microbial community structure, machine learning

摘要：

序批式生物膜反应器（sequencing biofilm batch reactor, SBBR）是应用广泛的污水处理方法。为探究不同时期排泥对SBBR污染物去除效果与微生物群落结构的影响，本研究设置了挂膜初期、中期和后期进行排泥的反应器处理生活污水，同时结合16S rDNA高通量测序技术对微生物群落结构进行分析，并采用机器学习（machine learning, ML）的方法，在传统的微生物优势种分析基础上，对测序数据进行深度挖掘，寻找造成组间差异的关键物种。水质测定结果显示，COD去除效果在不同时期排泥的SBBR间没有明显差异，出水COD均低于30mg/L。挂膜中期排泥的SBBR的NH₃-N去除率先达到稳定且高于前期和后期排泥的系统。高通量测序结果显示，各SBBR中微生物优势种均以降解有机物的物种为主。挂膜中期排泥的SBBR中，ML筛选得到的NH₃-N去除相关物种（Hydrogenophaga、Gemmata和Nitrospira）与差异关键物种丰度更高，微生物群落结构稳定性更强，可从微生物层面解释分析SBBR污染物去除效果的差异。

关键词: 废水, 生物膜, 高通量测序, 微生物群落结构, 机器学习

CLC Number:

X703

Shaolin WU, Wenwen LIU, Zhenhuan WEI, Ying CHEN, Dong ZHANG. Effects of sludge discharge stage on SBBR biofilm cultivation[J]. Chemical Industry and Engineering Progress, 2019, 38(02): 1134-1139.

吴绍琳, 刘文文, 魏镇欢, 陈滢, 张东. 不同时期排泥对培养SBBR生物膜的影响[J]. 化工进展, 2019, 38(02): 1134-1139.

Figures/Tables 6

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菌属	功能	A1	A2	A3
Zoogloea （动胶菌属）	降解有机物、脱氮除磷^[17]	6.77%	10.01%	10.42%
Ferruginibacter （铁锈菌）	降解有机物^[18]、脱氮[^19]	6.21%	9.50%	4.62%
Hydrogenophaga （氢噬胞菌属）	异养硝化，好氧反硝化^[20]	1.16%	3.25%	2.26%
Acidovorax （嗜酸菌属）	降解有机物^[21]	3.17%	2.97%	4.47%
Neisseria （奈瑟菌属）	降解有机物^[22]	2.63%	2.85%	1.15%
Pelomonas （嗜糖假单胞菌属）	降解有机物^[23]	4.18%	2.54%	4.70%
Sphaerotilus （球衣细胞属）	降解有机物^[24]	1.33%	1.91%	0.40%

菌属	功能	A1	A2	A3
Zoogloea （动胶菌属）	降解有机物、脱氮除磷^[17]	6.77%	10.01%	10.42%
Ferruginibacter （铁锈菌）	降解有机物^[18]、脱氮[^19]	6.21%	9.50%	4.62%
Hydrogenophaga （氢噬胞菌属）	异养硝化，好氧反硝化^[20]	1.16%	3.25%	2.26%
Acidovorax （嗜酸菌属）	降解有机物^[21]	3.17%	2.97%	4.47%
Neisseria （奈瑟菌属）	降解有机物^[22]	2.63%	2.85%	1.15%
Pelomonas （嗜糖假单胞菌属）	降解有机物^[23]	4.18%	2.54%	4.70%
Sphaerotilus （球衣细胞属）	降解有机物^[24]	1.33%	1.91%	0.40%

反应器	OTUs	Chao1	Shannon	InvSimpson
A1	553±17	758±84	4.20±0.12	27.5±4.6
A2	615±32	933±99	4.47±0.05	36.9±3.6
A3	577±23	770±132	4.41±0.06	36.4±2.3

反应器	OTUs	Chao1	Shannon	InvSimpson
A1	553±17	758±84	4.20±0.12	27.5±4.6
A2	615±32	933±99	4.47±0.05	36.9±3.6
A3	577±23	770±132	4.41±0.06	36.4±2.3

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