Sediment re-suspension as a potential mechanism for viral and bacterial contaminants.

Pathogenic enteric viruses and bacteria tend to occur in higher concentrations and survive longer in aquatic sediments than suspended in the water column. Re-suspension of these organisms can result in a significant degradation of overlying water quality. Additionally, the re-suspension of microbial pathogens in artificial irrigation canals could endanger the consumption of fresh and ready-to-eat produce. Irrigation water has been implicated in numerous fresh produce outbreaks over the last 30 years. This study aimed to quantify the proportions of bacterial and viral re-suspension from sediment in a recirculating flume with varying velocities. MS2 coliphage and Escherichia coli were found to re-suspend at rates that were not significantly different, despite organism size differences. However, E. coli re-suspension rates from sand and clay were significantly different. This suggests that likely sediment-associated particles were recovered with the organisms attached. Similar re-suspension rates are hypothesized to be due to the dynamics of sediment transport, rather than that of the organisms themselves. This study also indicated that the re-suspension of sediment at very low velocities (e.g., less than 10 cm/s), could impact the microbiological quality of the overlaying water. Results from this study conclude that sediment could be a viable mechanism for irrigation water contamination.

Evaluation of E. coli in sediment for assessing irrigation water quality using machine learning.

Fresh produce irrigated with contaminated water poses a substantial risk to human health. This study evaluated the impact of incorporating sediment information on improving the performance of machine learning models to quantify E. coli level in irrigation water. Field samples were collected from irrigation canals in the Southwest U.S., for which meteorological, chemical, and physical water quality variables as well as three additional flow and sediment properties: the concentration of E. coli in sediment, sediment median size, and bed shear stress. Water quality was classified based on E. coli concentration exceeding two standard levels: 1 E. coli and 126 E. coli colony forming units (CFU) per 100 ml of irrigation water. Two series of features, including (FIS) and excluding (FES) sediment features, were selected using multi-variant filter feature selection. The correlation analysis revealed the inclusion of sediment features improves the correlation with the target standards for E. coli compared to the models excluding these features. Support vector machine, logistic regression, and ridge classifier were tested in this study. The support vector machine model performed the best for both targeted standards. Besides, incorporating sediment features improved all models' performance. Therefore, the concentration of E. coli in sediment and bed shear stress are major factors influencing E. coli concentration in irrigation water.