The Impact of Extreme Weather Events on Bacterial Communities and Opportunistic Pathogens in a Drinking Water Treatment Plant

• Published in: Water (Basel) Vol. 14; no. 1; p. 54
• Main Authors: Tang, Wei, Liu, Yunsi, Li, Qiuyan, Chen, Ling, Li, Qi, Li, Pan, Xia, Shengji
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Activated carbon; Analysis; Bacteria; Bayesian analysis; Biofilms; biological activated carbon tank; Chemical contaminants; China; Chlorine; Climate change; Community structure; Cyanobacteria; Downstream effects; Drinking water; Effluents; Extreme weather; extreme weather event; Genes; Investigations; Microorganisms; Opportunist infection; opportunistic pathogen; Pathogenic microorganisms; Pathogens; Plant communities; Precipitation; Public health; Rain; Rain and rainfall; Rainfall; Relative abundance; Rivers; RNA; rRNA 16S; Sedimentation & deposition; Temperature; Treated water; Typhoons; Water analysis; Water quality; Water sampling; Water treatment; Water treatment plants; Weather; China; Yangtze River
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14010054

Drinking water treatment processes are highly effective at improving water quality, but pathogens can still persist in treated water, especially after extreme weather events. To identify how extreme weather events affected bacterial populations in source and treated water, water samples were collected from the Yangtze River Delta area and a local full-scale drinking water treatment plant. Bacterial community structure and the occurrence of pathogens were investigated in samples using 16S rRNA sequencing and qPCR techniques. In this study, the results show that intense rainfall can significantly increase levels of bacteria and opportunistic pathogens in river and drinking water treatment processes (p < 0.05); in particular, the relative abundance of Cyanobacteria increased after a super typhoon event (p < 0.05). The biological activated carbon (BAC) tank was identified as a potential pathogen reservoir and was responsible for 52 ± 6% of the bacteria released downstream, according to Bayesian-based SourceTracker analysis. Our results provide an insight into the challenges faced by maintaining finished water quality under changing weather conditions.