Abstract

Despite aerobic granular sludge wastewater treatment plants operating around the world, our understanding of internal granule structure and its relation to treatment efficiency remains limited. This can be attributed in part to the drawbacks of time-consuming, labor-intensive, and invasive microscopy protocols which effectively restrict samples sizes and may introduce artefacts. Time-domain nuclear magnetic resonance (NMR) allows non-invasive measurements which describe internal structural features of opaque, complex materials like biofilms. NMR was used to image aerobic granules collected from five full-scale wastewater treatment plants in the Netherlands and United States, as well as laboratory granules and control beads. T1 and T2 relaxation-weighted images reveal heterogeneous structures that include high- and low-density biofilm regions, water-like voids, and solid-like inclusions. Channels larger than approximately 50 μm and connected to the bulk fluid were not visible. Both cluster and ring-like structures were observed with each granule source having a characteristic structural type. These structures, and their NMR relaxation behavior, were stable over several months of storage. These observations reveal the complex structures within aerobic granules from a range of sources and highlight the need for non-invasive characterization methods like NMR to be applied in the ongoing effort to correlate structure and function.

HIGHLIGHTS

  • Ultra-high field NMR imaging shows complex and heterogeneous structures in intact aerobic granules treating municipal wastewater.

  • The structures were comprised of high- and low-density biofilm regions, water-like voids, and solid-like inclusions.

  • Internal structural characteristics varied by granule source and were stable over at least 2 months of storage.

  • Nuclear magnetic resonance (NMR) is sensitive to different physico-chemical parameters than traditional microscopy and can provide a new research perspective.

  • NMR allows for non-invasive screening of larger sample sizes to explore the structure – function relationship.

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