Linkages between microbial functional potential and wastewater constituents in large-scale membrane bioreactors for municipal wastewater treatment
Graphical abstract
Introduction
Membrane bioreactor (MBR) has been widely applied to treat municipal wastewater (Huang et al., 2010). With a combination of biological treatment process and membrane technology, MBR has remarkable advantages in producing high-quality reclaimed water over conventional activated sludge process. The high biomass retained in the systems by membrane rejection and the long sludge retention time (SRT) were thought to be important for the stable performance of MBRs (Drews et al., 2005). However, it remains unclear whether and how microbial community in activated sludge is linked to the MBR performance. Most of the current studies focused on the understanding of the influence of specific influent components, single operational conditions or the reactor configuration on microbial communities. Furthermore, most of them were conducted in lab-scale or pilot-scale plants (Xia et al., 2012), which did not reflect actual conditions in large-scale municipal MBRs due to substantial differences in design, scale, operational time and parameters, as well as influent components and the fluctuations (Table S1) (Shen et al., 2012). In addition to differences in influent alkalinity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), BOD/COD and C/N/P ratios, real wastewater often contains antibiotics, heavy metals and other organic pollutants from domestic and industrial discharges, which seldom appears in the synthetic wastewater treated by lab-scale MBRs. To date, few studies were done with large-scale, operational MBR plants except for two recent studies (Wan et al., 2011, Hu et al., 2012). These two studies targeted bacterial phylogenetic or ammonia-oxidizing community composition. Thus, microbial functional structure and metabolic potentials remained elusive.
High-throughput functional gene array (e.g., GeoChip) technology has been proven to be powerful in examining microbial functional potentials, since it targets a wide range of functional genes involved in C, N, P, S cycling, metal resistance and organic contaminant degradation (such as aromatics, herbicides and pesticides related compounds) and so on. To date, it has been used to profile microbial communities in various habitats, including soil, marine sediments, contaminated groundwater and lab-scale bioreactors (Van Nostrand et al., 2009, Yang et al., 2013, Zhong et al., 2012). However, its utility in examining microbial communities of wastewater treatment plants is yet to be demonstrated.
In this study, we applied GeoChip to examine four large-scale, in-operation MBRs located in Beijing, China. These plants, each with a capacity over 10,000 m3/d, were combined with a common nutrient-removal anaerobic-anoxic-oxic process (Table 1). Considering that different tanks might have similar microbial profiles due to activated sludge circulated between each tanks by return sludge pumping system, and on the other hand membrane tank has unique features enduring “harsh” environment such as intense aeration and high shear force performed for fouling control, higher sludge concentration and soluble microbial products (SMPs) retained by membrane interception, activated sludge in membrane tank was focused in this study. We hypothesize that (1) microbial community functional structures in the sludge were different among different MBRs, considering that these MBRs differ in process, operation and wastewater constituents; (2) key functional gene categories (C, N, P and S cycling) are related to influent constituents; and (3) microbial genes present in all four MBRs were consistent with wastewater constituents. To test these hypotheses, activated sludge samples in membrane tanks of MBRs were collected in triplicates and analyzed by GeoChip 4.0. To our knowledge, this is the first study to examine microbial community functional structures in large-scale, in-operation MBRs.
Section snippets
MBRs and sample analysis
The activated sludge samples were obtained from four large-scale MBRs (B1, B2, B3 and B4) located in Beijing, China. The four plants share a common nutrient-removal anaerobic-anoxic-oxic process enhanced with membrane bioreactor (A1/A2/O-MBR). Three replicate sludge samples were collected in the membrane tanks from each plant during October to December, 2010. All precipitated sludge samples were sealed into sterile sampling tubes, stored in a portable dry ice container, directly shipped to the
Process and environmental variables of MBRs
Process and environmental variables of MBRs are detailed in Table 1. The process of four plants in Beijing was similar, which was an A2O process with a membrane bioreactor, but they varied in commission time and wastewater treatment capacities. Substantial differences in operation and influent condition were notable, HRT, SRT, influent –N, COD, TN and TP. B1 and B2 had higher influent COD than other two plants. The influent –N was the highest in B4, and the TP concentration was highest
Microbial heterogeneity in MBRs
The microbial functional structure and metabolic potentials of large-scale, in-operation MBR plants have been previously examined. In this study, we showed that microbial functional structures were distinct in different MBR plants, and changes of N, C, P, S-cycling, antibiotics resistance, metal resistance, organic remediation genes were in consistency with changes of influent COD, –N, TP, sulfate and toxic compounds expected in wastewater, which generally supports our hypotheses.
The
Conclusions
To the best of our knowledge, this is the first study to profile functional gene compositions and explore major environmental parameters that shape microbial communities in large-scale, operational MBRs. The results showed that functional gene heterogeneity was present in MBRs. Notably, the abundance of genes present in all of the MBRs was much higher than unique genes and functional gene categories involved in antibiotic resistance, metal resistance and organic remediation were abundant. Key
Acknowledgments
This work was supported by the Science Fund for Creative Research Groups (No.21221004) and the Major Science and Technology Program for Water Pollution Control and Treatment (No.2011ZX07301-002).
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