Institute of Functional Interfaces

Insights into the variability of microbial community composition and micropollutant degradation in diverse biological wastewater treatment systems

  • chair:

    Wolff, D. / Krah, D. / Dötsch, A. / Ghattas, A.K. / Wick, A. / Ternes, T.A. (2018)

  • place:

    Water Research, 2018, 143,  313-324

  • Date: Juni 2018

Abstract

The biological potential of conventional wastewater treatment plants to remove micropollutants mainly depends on process conditions and the predominant microbial community. To explore this dependence and to connect the occurrence of genera with operating conditions, five pilot-scale reactors with different process conditions were combined into two reactor cascades and fed with the effluent of the primary clarifier of a municipal WWTP. All reactors and the WWTP were analyzed for the removal of 33 micropollutants by LC-MS/MS and the presence of the microbial community using 16S rRNA gene sequencing. The overall removal of the micropollutants was slightly improved (ca. 20%) by the reactor cascades in comparison to the WWTP while certain compounds such as diatrizoate, venlafaxine or diclofenac showed an enhanced removal (ca. 70% in one or both cascades). To explore the diverse bacteria in more detail, the general community was divided into a core and a specialized community. Despite their profoundly different operating parameters (especially redox conditions), the different treatments share a core community consisted of 143 genera (9% of the overall community). Furthermore, the alpha- and beta-biodiversity as well as the occurrence of several genera belonging to the specialized microbial community could be linked to the prevalent process conditions of the individual treatments. Members of the specialized community also correlated with the removal of certain groups of micropollutants. Hence, the comparison of the specialized community with micropollutant removal and operating conditions via correlation analysis is a valuable tool for an extended evaluation of prevalent process conditions. Based on an extended data set this approach could also be used to identify organisms as indicators for operating conditions which are beneficial for an improved removal of specific micropollutants.

 

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