Early detection and environmental drivers of sewage fungus outbreaks in rivers

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.

污水排放是持续威胁淡水生态系统水质与生物多样性的主要风险之一。其可引发污水真菌（sewage fungus）——即形成宏观聚集体的丝状细菌——暴发，但此前仅通过目视检查开展定性记录，忽略了其微观形态。本研究结合机器学习、显微成像与流式细胞术（flow cytometry），开发了一种创新方法，可快速高效地定量检测河流中污水真菌的存在与丰度。本研究于英格兰西南部开展，共选取11条河流，其中6条受污水排放影响（n=6）、5条未受影响（n=5），累计进行4次采样。研究团队可在丝状聚集体肉眼可见前完成检测与计数；且正如预期，经处理污水排入河道的点位下游，丝状真菌的数量显著更高。因此，本检测方法可作为未来暴发事件的“煤矿金丝雀（canary in the coal mine）”，助力早期干预措施的实施。本研究将丝状真菌的定量数据与河流物理化学参数数据相结合后发现，高电导率、硫酸盐、硝酸盐及总溶解固体（Total Dissolved Solids, TDS）与污水真菌的存在及增殖显著相关。该研究结果可为监管机构与水务公司制定缓解策略及行动方案、预防未来暴发事件提供关键参考。