Abstract
Microbial communities of activated sludge (AS) play a key role in the performance of wastewater treatment processes. However, seasonal variability of microbial population in varying AS-based processes has been poorly correlated with operation of full-scale wastewater treatment systems (WWTSs). In this paper, significant seasonal variability of AS microbial communities in eight WWTSs located in the city of Guangzhou were revealed in terms of 16S rRNA-based Miseq sequencing. Furthermore, variation redundancy analysis (RDA) demonstrated that the microbial community compositions closely correlated with WWTS operation parameters such as temperature, BOD, NH4+-N and TN. Consequently, support vector regression models which reasonably predicted effluent BOD, SS and TN in WWTSs were established based on microbial community compositions. This work provided an alternative tool for rapid assessment on performance of full-scale wastewater treatment plants.
Highlights
Activated sludge (AS) process has been most widely used to treat domestic sewage for a century due to its high treatment efficiency and low cost
After filtering the low quality sequences, a total of 2,197,507 effective sequences were yielded for the 33 samples, and clustered to 5,409 operational taxonomic units (OTUs) with 3% of nucleotide cutoff
A total of 58 phyla were detected in the 33 samples from 8 wastewater treatment systems (WWTSs)
Summary
Activated sludge (AS) process has been most widely used to treat domestic sewage for a century due to its high treatment efficiency and low cost. Biological wastewater treatment, as the largest application area of biotechnology, accelerates the beneficial activities of naturally occurring microorganisms, removing organic pollutants and nutrients via metabolism [1]. Microbial communities in AS ecosystems are crucial for well-performing bioreactors. Maintaining municipal wastewater treatment systems (WWTSs) is still based on empirical relationships between physicochemical and operational parameters and reactor performance, which is not reliable enough for stable performance [2]. A systematic understanding of bacterial communities as a function of environmental factors and how they influence the performance is vital to improve process performance stability and provide important guidance in diagnosis and prognosis.
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