Spatiotemporal Dynamics and Drivers of Microbial Contaminants in Hurricane Florence Floodwaters
To strengthen our understanding regarding the signatures and drivers of floodwater contamination, this study aimed to investigate spatiotemporal patterns of pathogens, fecal indicator bacteria, and fecal biomarker detections, and identify the watershed characteristics that best explain water quality signatures in floodwaters. To accomplish this, we collected water samples across 51 sites during and after Hurricane Florence impacted North Carolina in 2018. Each site was visited four times, and samples were assessed for Arcobacter, Salmonella, Listeria, Escherichia coli, and source-specific biomarkers HF183 and Pig2Bac. Water quality responses were explained using a multivariate Bayesian logistic regression model incorporating land characteristics, pollution point sources, and hydroclimatic factors contributing to water quality degradation. Model results suggested that during flood conditions, pollution point sources were the dominant contributors to surface water contamination, likely due to direct connectivity with floodwaters. In contrast, nonpoint sources and rainfall-driven processes played a greater role in pollutant transport during nonflooded conditions. Overall, the high prevalence of contaminants during flood conditions underscores public health concerns associated with floodwater exposure. In particular, modeling results reveal potential drivers and sources of water quality contamination across spatiotemporal scales and can inform targeted strategies for improved water quality management and enhanced public health protection.
