ABSTRACT:

E. Coli and Listeria monocytogenes (or L. monocytogenes) are bacteria affecting fresh produce that is harmful for health of humans and animals. If these bacteria are present in surface waterbody (e.g., irrigation canals), they will impair irrigation water quality and threaten produce safety. This paper studied the resuspension of E. Coli and Listeria from bed sediment into irrigation water through several sets of laboratory experiments in an open channel flume. We studied three types of sediments using several flow rates in different velocities and shear stress. Bacteria’s concentration in water increases with the bed shear stress. Two empirical relations were derived to correlate the concentration of E. coli and L. monocytogenes with the dimensionless bed shear stress. The experimental data favorably verified the relationships for sandy loam, loamy sand and loam. The results showed that both bacteria could entrain from sand more efficiently comparing to other sediments (i.e. sandy loam or loam). These relationships can be applied to water quality models for simulating E. coli and L. monocytogenes transport in irrigation canals for better managing irrigation water quality.

ABSTRACT:

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 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.