Sewage effluent is a major ongoing threat to water quality and biodiversity in freshwater environments. It can cause outbreaks of sewage fungus (filamentous bacteria which form macroscopic masses) but, until now, these were only qualitatively recorded from visual inspection, ignoring microscopic forms. Here, we used an innovative method which combines machine learning, microscopy and flow cytometry, to rapidly and efficiently quantify the presence and abundance of sewage fungus in rivers. Our study involved 11 rivers with (n=6) and without (n=5) sewage input in the south-west of England over four sampling occasions. We were able to detect and enumerate the filaments before masses became visible to the naked eye and, as expected, we found a higher number of filaments downstream of sites where treated sewage was offloaded into the river. Therefore, our detection method could be used as a ‘canary in the coal mine’ for future outbreaks allowing early intervention. Combining our quantitative data on filaments with data on the physical and chemical parameters of the rivers, we found that high conductivity, sulphate, nitrates and TDS were associated with the presence and proliferation of sewage fungus. This information can be extremely useful for regulatory bodies and water companies to develop mitigating strategies and action to prevent future outbreaks.

We collected samples in UK rivers, upstream, downstream and in the final effluent. We collected physical and chemical data.
Physical parameters of the rivers were recorded for all sampling months, while the chemical parameters were tested in August, October, and November 2021. At each sampling site, we measured: water temperature, pH, total dissolved solids, total dissolved oxygen and electrical conductivity. We used a hand Combo pH and EC Hanna probe or a LT Lutron probe.

Three replicates of 100mL of water were collected to analyse fluoride, chloride, bromide, nitrite, nitrate, phosphate, sulphate and total organic carbon. Water samples were done in the laboratory of the School of Geography and the Environment at the University of Oxford.

R studio was used to produce all the statistical analysis and the graphs.