Early detection and environmental drivers of sewage fungus outbreaks in rivers

Abstract

Abstract Sewage effluent is a major ongoing threat to water quality and biodiversity in freshwater environments. It can cause outbreaks of sewage fungus (fungus‐like 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 that 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 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.

Keywords

• automated technique
• freshwater ecology
• machine learning
• river pollution
• sewage fungus
• wastewater