@misc{9224251,
  abstract     = {{Quality assurance is crucial for producers of drinking water, and several steps are taken to ensure a safe and aesthetically acceptable drinking water. Monitoring the microbiological quality of drinking water, both finished and within the drinking water treatment plants, is done through cultivation-based techniques. These techniques can take up several days before results can be seen, and quicker results would allow drinking water producers to act on potential quality issues before the water reaches the distribution system.

Flow cytometry offers quick and high-throughput results, measuring total and intact cell counts along with other parameters depending on which reagents are used. It also allows for mapping the cells detected to two-dimensional histograms based on, for example, fluorescence. This project studies the possibility of relating traditional cultivation-based techniques to results from flow cytometric measurements on water effluent of slow sand filters, first through exploratory data analysis and then with machine learning. Machine learning was used to find patterns in the fingerprints to determine if heterotrophic plate counts or coliform content were above or below a set threshold.

Data was provided by three different drinking water treatment plants: Norsborg’s drinking water treatment plant (Stockholm vatten och avfall), Ringsjöverket (Sydvatten) and Berggården (Tekniska verken). Correlations were found between flow cytometric data and heterotrophic plate counts at Norsborg’s drinking water treatment plant. The machine learning models also showed promise at determining if the heterotrophic plate count would be above or below 20 colony forming units/mL by analysing the fingerprints. Correlations were also found between total cell counts and head loss in the filters at Norsborg’s drinking water treatment plant and Ringsjöverket. No relationships between flow cytometric data and cultivation-based techniques were observed at Berggården or Ringsjöverket, and the machine learning models were less effective at these sites.}},
  author       = {{Westberg, Agnes}},
  issn         = {{1101-9824}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{TVVR 5000}},
  title        = {{Evaluating flow cytometry for investigating slow sand filter function in drinking water treatment}},
  year         = {{2026}},
}