Safe drinking water is essential for public health, and biofiltration offers a natural, sustainable approach to its production. However, the biological mechanisms underpinning indoor slow sand filtration (SSF), often the final treatment stage, are not fully understood. This thesis investigates the microbial ecology of these filters, focusing specifically on the "Schmutzdecke" (the biologically active surface layer) to enhance understanding of SSF performance.

Using full-scale and laboratory experiments, the research compared total microbial presence (DNA) with metabolically active populations (RNA). The findings highlighted a significant difference: while DNA analysis identified many members, RNA profiling narrowed this down to the specific active groups driving bioactivities. This confirms that monitoring active communities provides deeper insight than presence alone. Additionally, the study revealed that influent water quality affects the Schmutzdecke microbial community, determining whether the SSF acts as a simple polisher or an active modifier.

The research showed that a filter’s age alone does not reliably predict its maturity. Instead, biochemical markers, particularly the protein-to-carbohydrate ratio, emerged as a potentially accurate predictor of removal efficiency and stability. Further experiments demonstrated that sand grain size and type influence initial Schmutzdecke formation, while inoculation with mature Schmutzdecke may accelerate ripening. Modelling of Escherichia coli removal indicated that in indoor SSFs, pathogens are removed primarily through physical attachment, enhanced by Schmutzdecke activity, rather than by direct biological predation.

In conclusion, this thesis supports a shift towards activity-based monitoring. By integrating biochemical and microbial analyses with multi-scale experiments, it offers new strategies for performance prediction and design optimisation, reinforcing SSF as a resilient technology for modern drinking water treatment

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