This study was conducted to compare subsurface drip irrigation (SDI) with furrow irrigation (FI) in crop contamination with microbial-contaminated water irrigation. Escherichia coli, Clostridium perfringens, and coliphage PRD-1 were added to water used to irrigate cantaloupe, lettuce, and bell pepper. Samples of produce, surface, and subsurface (10 cm) soil for each irrigation system were collected on Days 1, 3, 5, 7, 10, and 14 after the application of the study microorganisms. Overall, greater contamination of produce occurred in FI plots than in SDI plots. The microorganisms were detected on the surfaces of cantaloupe and lettuce, but were never recovered on the bell peppers. The greatest amount of contamination occurred with PRD-1 on cantaloupe. The study microorganisms survived longer in the subsurface soil than the soil surface. PRD-1 showed greater persistence than E. coli in soil, while C. perfringens experienced little inactivation during the experiment periods. This study showed that subsurface ...

The present experimental study focuses on the characterization of complex mixing phenomena at pipe intersections within pressurized water distribution networks. To examine the complete mixing assumption at a cross junction, a series of experiments were conducted in the turbulent regime (R>10,000) . The experimental setup consists of a cross junction with various sensors, pumps, and a data acquisition system to accurately measure solute concentration. Selected experimental results are compared to computational fluid dynamics (CFD) results. In addition, the water quality model associated with a standard water distribution network simulator (EPANET) was reevaluated based on CFD and experimental data. Corrections based on experimental results are incorporated into EPANET (AZRED 1.0) for use in a case study. The study concludes that the complete mixing assumption can potentially create considerable errors in water quality modeling. Further, severe errors are likely to occur in systems with many cross type junc...

The present study investigates solute mixing phenomena at various flow rates within a cross junction, which is commonly found in municipal drinking water distribution systems. Simulations using computational fluid dynamics are employed to model the solute concentrations leaving the junction when one inlet is comprised of clean water while the other inlet carries a solute at $\mathsf{R}>\mathrm{10,000}$ . For a few exemplary cases, the resulting velocity vectors and contours of dimensionless concentration are presented to explain the detailed mixing mechanisms at the impinging interface. The turbulent Schmidt number $\left({\mathsf{Sc}}_t\right)$ , an important scaling parameter, is also evaluated. Experimental results were used to assess values of ${\mathsf{Sc}}_t$ for various flow conditions that accurately captured the detailed mixing processes within the junction. The present study clearly indicates that mixing at pipe cross junctions is far from “perfect.” Incomplete mixing results from bifurcating inlet flows that reflect off one another with minimal contact time. Improving the existing water quality model based on accurate mixing data and simulations is important not only to predict concentrations of chemical species such as chlorine in water distribution systems, but also to prepare for potential intentional and accidental contamination events.

Because longitudinal dispersion is becoming increasingly germane to the problem of accurately determining water quality, water-resource managers and engineers seeking to represent solute transport in drinking-water systems must be able to compute relevant dispersion coefficients. Accordingly, the present study was undertaken to develop and experimentally verify a modified advection-dispersion-reaction transport equation as well as the formulas used to calculate the axial dispersion coefficient. The analysis assumes laminar flows, constant mean velocities, and short travel times (dimensionless time, T<0.01). With regard to the modified transport equation, the dispersion term was assumed to be direction-dependent. Thus, two distinct dispersion rates (forward and backward) were recognized and quantified as opposed to the single value used in conventional dispersion models. With the dimensionless travel time taken to be the independent variable, the developed dispersion coefficients increased at about one-fou...

Optimal water distribution system (WDS) design including the layout and pipe sizes is invariably complex, even when hydraulic constraints alone are considered. The addition of water quality (WQ) constraints adds to the computational demands. Using conventional WQ models to evaluate the feasibility of the many networks that must be analyzed can extend the optimization process beyond acceptable limits. Artificial neural networks (ANNs) can approximate disinfectant concentrations in a fraction of the time required by deterministic WQ models, and thus have been used for WDS pipe optimization as fast surrogates of these models. This study seeks to improve the performance of ANNs applied to the optimal design of WDSs by comparing their outcomes on the basis of ANN architecture and data used for ANN training, two factors that affect their speed and accuracy. ANNs were trained to forecast the disinfectant concentration at the relevant nodes of two case studies: A simple WDS (common in the literature of WD...