Optimization of methane-dependent oxygenic denitrification in sequencing batch reactors by insights into the microbial interactions

Abstract

Methane-dependent oxygenic denitrification (O2DN) is a promising technology used for reducing greenhouse gas emissions of nitrous oxide (N2O) during wastewater treatment. Heterotrophic bacteria are associated with methane-dependent O2DN bacteria, and it has been proposed that metabolic cross-feeding occurs between the two populations above. In this study, a mathematical model was developed to describe the microbial processes and interactions between methane-dependent O2DN bacteria and associated heterotrophic bacteria in a sequencing batch reactor (SBR). A growth factor-dependent decoupling of metabolism and growth of methane-dependent O2DN bacteria was introduced into the model. Effects of influent substrates, operating parameters, and initial biomass on microbial community and reactor performance were then investigated, and the above parameters were optimized using the model. Results surprisingly show that organic matter in the influent greatly stimulated the growth of methane-dependent O2DN bacteria but slightly limited the increase of heterotrophic bacteria. This effect could be explained by the increased excretion of growth factors by heterotrophic bacteria and the intensified competition for nitrite when methane-dependent O2DN bacteria increased. These results will assist in providing a new understanding of microbial interactions in methane-dependent O2DN systems and offer a new and efficient strategy for operating methane-dependent O2DN reactors.