Conservative chemicals (such as sodium chloride) have been utilized to perform tracer studies within drinking water distribution systems. The resulting signals from a tracer study can provide significant quantitative information to assess the ability of a given network model to represent the underlying hydraulic and transport characteristics of the network. Often, however, the resulting observed water quality time-series data are simply visually inspected to assess the ability of the network model to accurately predict water quality transport. The use of standard quantitative metrics, such as arrival times, sum of squared errors (SSE), and correlation analysis at different time lags to assess the differences between the observed and predicted time-series, can provide some useful information but are not sufficient for paired data signals. In this study, the use of dynamic time warping (DTW)—an approach for estimating the similarity between two time series of data—is presented as a method for quantitative analysis of observed and model-predicted conservative chemical time-series data. DTW uses dynamic programming to match the elements of two time series, in a sequential approach, to minimize the SSE of the two signals. Whereas the SSE provides one goodness-of-fit metric, the resulting length of the warping path also provides additional information as to the degree of the alignment between the two data streams.
The efficacy of germicidal ultraviolet (UV-C) light emitting diodes (LEDs) was evaluated for inactivating human enteroviruses included on the United States Environmental Protection Agency (EPA)'s Contaminant Candidate List (CCL). A UV-C LED device, emitting at peaks of 260 nm and 280 nm and the combination of 260∣280 nm together, was used to measure and compare potential synergistic effects of dual wavelengths for disinfecting viral organisms. The 260 nm LED proved to be the most effective at inactivating the CCL enteroviruses tested. To obtain 2-log10 inactivation credit for the 260 nm LED, the fluences (UV doses) required are approximately 8 mJ/cm2 for coxsackievirus A10 and poliovirus 1, 10 mJ/cm2 for enterovirus 70, and 13 mJ/cm2 for echovirus 30. No synergistic effect was detected when evaluating the log inactivation of enteroviruses irradiated by the dual-wavelength UV-C LEDs.
Microbial electrochemical treatment was integrated with phycoremediation and photolytic oxidation (UV/H2O2) for wastewater reuse, achieving successful removal of contaminants.
Contaminants are retained on the internal pore surfaces of granular activated carbon (GAC) and periodic regeneration and/or replacement of GAC is mandatory for robust water treatment.Alternatively, the chemical washing can be acceptable for adsorptivity recovery of short-term fouled GAC.In this study, Fenton's reagent was employed to restore adsorptivity of saturated GAC, focusing on the removal of organic components from surface water after cleaning in comparison with fresh GAC.The chemical washing with Fenton's reagent was carried out for 10 min, which was identical to that applied for the hydraulic backwash of GAC.Four different GAC beds were prepared, that is, fresh, saturated, saturated/hydraulically washed, and saturated/chemically cleaned GACs, and their performance was compared during the treatment of surface water.An innovative suite of analytical tools was applied to better elucidate the changes in compositional and functional properties of organic matter during GAC filtration.In addition to the characterization of organic matter, this study also investigated the fate of biomass-derived organic matter during GAC filtration.The results indicate that the use of in situ chemical washing is a highly feasible technology to effectively remove extracellular polymeric substances that are retained on activated carbon materials and to accelerate GAC saturation.
The lead contamination of drinking water in homes and buildings remains an important public health concern. In order to assess strategies to measure and reduce exposure to lead from drinking water, models are needed that incorporate the multiple factors affecting lead concentrations in premise plumbing systems (PPS). In this study, the use of EPANET, a commonly used hydraulic and water quality model for water distribution systems, was assessed for its ability to predict lead concentrations in PPS. The model was calibrated and validated against data collected from multiple experiments in the EPA's Home Plumbing Simulator that contained a lead service line and other lead sources. The EPANET's first-order saturation kinetics model was used to simulate the dissolution of lead in the lead service line. A version of EPANET was developed to include one-dimensional mass dispersion. Modeling results were compared to experimental data, and recommendations were made to improve the EPANET-based modeling framework for predicting lead concentrations in PPS.
A Lagrangian method to simulate the advection, dispersion, and reaction of a single chemical, biological, or physical constituent within drinking water pipe networks is presented. This Lagrangian approach removes the need for fixed computational grids typically required in Eulerian and Eulerian-Lagrangian methods and allows for nonuniform computational segments. This makes the method fully compatible with the advection-reaction water quality engine currently used in EPANET. An operator splitting approach is used, in which the advection-reaction process is modeled before the dispersion process for each water quality step. The dispersion equation is discretized using a segment-centered finite-difference scheme, and flux continuity boundary conditions are applied at network junctions. A staged approach is implemented to solve the dispersion equation for interconnected pipe networks. First, a linear relationship between the boundary and internal concentrations is established for every pipe. Second, a symmetric and positive definite linear system of equations is constructed to calculate the concentrations at network junctions. Last, pipe internal concentrations are updated based on the junction concentrations. The solution generates exact results when the analytical solutions are available and leads to more accurate water quality simulations than advection-reaction-only water quality models, especially in the areas where dispersion dominates advection.
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