Automated In-situ E.coli Monitoring for Reliable Risk Assessments in Presence of Free and Aggregated Bacteria

Presented by Dan Angelescu, Fluidion US Inc.
Contact Information: [email protected]

ABSTRACT

Current criteria for evaluating human health risks associated with agricultural, recreational and stormwater rely on manual sampling and regulatory-approved culture-based bacterial enumeration methods, such as Most Probable Number (MPN) or Membrane Filtration (MF), which are widely used but have important limitations in distinguishing between planktonic and aggregate-bound E.coli. These limitations are more pronounced in water matrices subjected to recent sewage pollution, potentially resulting in an underestimation of the total bacteria and associated pathogens in the sample, and therefore, to skewed public health risk assessments.

Using the proven Fluidion ALERT method and automated rapid microbiology instrumentation, we investigate the abundance of free and aggregate-bound fecal indicators in diverse water matrices and geographies. ALERT technology is based on real-time optical measurements of enzymatic reaction byproducts in a target-specific growth medium, which provides full E.coli quantification results within 2 to 12 hours. By contrast with the traditional MPN and MF methods, where aggregates containing high E.coli counts provide identical responses to isolated planktonic E.coli, the ALERT method provides comprehensive counts of all bacteria present, regardless of whether they are in planktonic or aggregate-bound form. As a result, the ALERT method ensures a more accurate and comprehensive enumeration of E.coli, providing potentially improved risk assessment capabilities.

We use size fractionation alongside parallel ALERT and MPN assays to study the E.coli distribution among aggregates of different sizes. We find aggregate-bound E.coli in typical stormwater runoff in Southern California to be more than double the planktonic forms, this prevalence increasing to 2.4 times in a major urban river in Europe and exceeding a hundredfold in some irrigation canals of California's Central Valley. We show that ALERT technology can further enhance risk monitoring in remote areas via automated sampling and in-situ measurements or be used for prioritizing samples requiring further molecular analyses to determine human-associated contributions. Multiple case studies in the US and Europe will be discussed.