Creating a sustainable future for preparation of drinking water, using slow sand filters

Project: Research

Project Details


Slow sand filters (SSFs) are a nature-based solution that for hundreds of years have produced safe water for human societies. In Sweden more than 2.5 million people depend on SSFs for drinking water. SSFs use very little energy, no chemicals, generate minimal waste and increase the efficiency of other technologies, so ensuring their optimal function will contribute to achieving multiple UN Sustainability goals.

Microbial biofilm ecosystems living in SFF remove undesirable molecules including organic matter, pathogens and micropollutants. The microbes experience stress due to, for example, flow rate changes, which may increasingly impact SSF in the future. Predicting how SSF will respond to these stresses is difficult, as the genetic and metabolic diversity of the microbes make full scale processes difficult to mimic in the laboratory. In this project, drinking water practitioners, microbiologists, engineers and mathematicians will unite their expertise to explain, and predict, SSF responses at full scale. DNA-based microbial community profiling, water quality data will describe SSFs from across Sweden that experience changes in flow rates, temperatures, weather and source water. These descriptions will be integrated in a mathematical model with which a variety of tests and extrapolations can be made to achieve the following goals: optimize current operation of SSFs; predict changes for the future; and suggest strategies for mitigation and protection of SSF water production.
Effective start/end date2020/01/012023/08/31

Collaborative partners

  • Lund University (lead)
  • Sydvatten AB
  • Stockholm Vatten och Avfall
  • Sweden Water Research AB
  • Tekniska verken


  • FORMAS, The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning

Free keywords

  • slow sand filter
  • drinking water
  • microbiology
  • DNA sequencing
  • metagenomics
  • mathematical modelling
  • microbial communities