New paper from ENSM PhD student Ale Rossi and colleagues
We acknowledge that 55% of the investigated rivers in the U.S. are impaired and that the leading contamination comes from pathogens.
Posted in: Student Research
On top of this, the results of water samples collected 24 hours earlier, might not be representing the current microbiological status of a river, if a disturbance event happened during those 24 hours of sample incubation (e.g. heavy rainfall). The idea for this project, which is part of my doctoral dissertation, was born considering that the greater focus of research in predicting near-real time E. coli concentrations is on coastal beaches. This makes the results less applicable to rivers and streams, where variation in flow discharge makes it more difficult to estimate the relationship between pathogens and water quality parameters.
We therefore decided to conduct the project at the confluence of two major rivers in NJ (Passaic and Pompton) where a USGS facility is regularly maintained. This allowed us to use the available environmental variables continuously recorded at the facility and study their relationship with the microbiological information from the water samples. Operators from the United State Geological Survey (USGS) and the Passaic Valley Sewerage Commission (PVSC) provided valuable help with data and sample collection. Statistical analysis produced equations predicting the probability of finding ourselves in safe waters or not. The results were presented in a manuscript that was submitted to the journal “Science of the Total Environment” in early December 2019 and accepted in early January 2020.
About the Paper
Monitoring operations of public health at recreational freshwater ecosystems commonly use Escherichia coli (E. coli) as a Fecal Indicator Bacteria (FIB) in order to estimate the potential contaminations from pathogenic forms and prevent the development of waterborne diseases. The traditional techniques of bacteria detection can be costly and are not time-sensitive enough to alarm recreational users. In addition, the predictive models available to produce real-time predictions also have various methodological challenges, including the heavy reliance on correlation instead of the more rigorous multivariate regression analyses, among others. In this study, we addressed these challenges and developed a cost-effective and timely alternative for estimating pathogen indicators using real-time water quality and quantity data for the Passaic and Pompton rivers. We used the Membrane Filtration Method and mColiblue24 media to enumerate E. coli in water samples collected from April to November 2016 from the two rivers. We also collected data on environmental variables concurrently to determine the variables significantly predicting the water safety conditions for recreation activities. The results show that source water, higher specific conductance, lower pH, and cumulative rainfall for the 72 hours antecedent the sampling significantly impacted the density of E. coli. Through this approach we can deliver a probabilistic estimation that a waterbody’s E. coli count violates an established threshold value, providing a new way to determine the health of a river in near-real time.
Citation: Rossi, A., Wolde, B.T., Lee, L.H., and Wu, M. (2020). Prediction of recreational water safety using Escherichia coli as an indicator: case study of the Passaic and Pompton rivers, New Jersey, Science of the Total Environment, Volume 714, 2020,136814, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2020.136814.