Experimental Study of Pathogenic Microorganism Resuspension from Mobile Bed Sediment in Irrigational Canals

Pathogens in an interactive water-sediment environment are present as free agents in water or attachments to sediment particles. The resuspension of pathogens from bed sediment in irrigation canals impairs the quality of overlaying water and can result in the contamination of fresh produces. Sediments are known to be potential reservoirs of pathogenic microorganisms. Pathogens’ concentration in the water is associated with sediment through two key processes: resuspension and attachment. This dissertation reports two experimental and one field studies to quantify the role of sediment in microbial water quality.The first experimental study focused on the viral pathogen resuspension from sediment in irrigation canal. PhiX174, a spherical single-stranded DNA bacteriophage, used as a surrogate and its resuspension was evaluated through a series of laboratory experiments. Different flow conditions and three types of sediment mixtures (i.e., loam, sand, sandy loam) were investigated. Results revealed that the resuspension rate increases with the dimensionless bed shear stress. Based on these results, for the first time, two models were proposed, for correlating the concentration of PhiX174 with the dimensionless bed shear stress for different sediments. One model was proposed for sandy loam and loam, and the other for sand. Models were verified favorably by the experimental data. The second experiment focused on the mechanism governing the viral pathogens’ resuspension and their attachment to suspended sediment. Experimental results of the first research were utilized, and a conceptual model was developed to quantify the resuspension flux and attachment ratio. Two resuspension flux models were derived for the sediments with and without cohesive material. The resulting models correlated the resuspension flux with non-dimensional bed shear stress. Unlike previous reports on attachment as an irreversible and static process, the attachment ratio, in fact, increases with bed shear stress until reaching the critical shear stress, and then decreases with a further increase of bed shear stress. Results shed light on the key processes governing the fate and transport of viral pathogens in sediment laden flow. At last, field data were analyzed to evaluate the impact of incorporating sediment information on the prediction of E. Coli level in water. Field samples of water and sediment were collected from irrigation canals in the Southwest U.S. Environmental variables, such as water and air temperature, PH, salinity, were measured at the sampling sites. The samples were analyzed in WEST center to obtain other 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. Three machine learning methods, support vector machine (SVM), logistic regression (LR), and ridge classifier (RC), were used to classify irrigation water quality based on E. coli concentration exceeding two standard levels: 1 E. coli and 126 E. coli E. coli count(s) per 100 ml of irrigation water. Models were trained and validated over two sets of features: including and excluding sediment features. All models were tuned through 5-fold cross validation. SVM performed the best among three methods, and incorporating sediment features have considerably improved the performance of all models. These results signify the importance of incorporating sediment properties for assessing E. coli contamination in irrigation water