Optimization of disinfectant dosage for simultaneous control of lead and disinfection-byproducts in water distribution networks.

Drinking water utilities are increasingly facing the challenges of maintaining water quality, and simultaneously complying with conflicting regulatory standards. One such challenge is the dosage of chlorine-based disinfectants which is typically regulated to prevent microbial growth in the water distribution systems, while limiting disinfection by-products (DBPs). On the other hand, chlorine residuals also influence the dissolution of lead into drinking water from corrosion scales in the pipe internals, as has been shown by previous studies. Hence, it is important to consider water lead levels (WLLs) while determining the appropriate chlorine dosage. This study proposes a multi-objective optimization framework to understand and balance the trade-offs between (i) minimizing total disinfectant dosage to reduce DBPs formation potential, and (ii) maximizing the volume of safe drinking water supply with respect to both WLLs and residual free chlorine concentrations. The approach comprises of the development of a dynamic simulation-optimization framework to account for the impact of spatial and temporal variations of network hydraulics and water chemistry on WLLs and residual free chlorine. The framework couples dynamic multi-species water quality simulations (pH, residual free chlorine, dissolved inorganic carbon, and orthophosphate concentration) with a multi-objective genetic optimization algorithm in an integrated MATLAB-EPANET-MSX platform. The application of the optimization formulation is demonstrated on a real-world distribution network case study. The resulting optimal solution set on pareto-plots are discussed for sensitivity of the trade-offs between the two objective functions, under various water chemistry conditions that have been suggested in earlier studies for minimizing lead release from plattnerite (PbO2) corrosion scales in lead service lines. The resulting optimal disinfectant dosage schedule, for pumping and booster station nodes in each case, provided insights on maintaining disinfectant residuals throughout the distribution system so as to prevent microbial growth as well as lead contamination events while limiting DBPs formation. Further, environmental implications related to the use of the proposed framework are also discussed.

Modeling Soluble and Particulate Lead Release into Drinking Water from Full and Partially Replaced Lead Service Lines.

Partial replacement of lead service lines (LSLs) often results in the excessive long-term release of lead particulates due to the disturbance of pipe scale and galvanic corrosion. In this study, a modeling approach to simulate the release and transport of particulate and dissolved lead from full and partially replaced LSLs is developed. A mass-transfer model is coupled with a stochastic residential water demand generator to investigate the effect of normal household usage flow patterns on lead exposure. The model is calibrated by comparing simulation results against experimental measurements from pilot-scale setups where lead release under different flow rates and water chemistry scenarios was reported. Applying the model within a Monte Carlo simulation framework, partial replacement of the LSL was predicted to result in releasing spikes with significantly high concentrations of particulate lead (1011.9 ± 290.3 µg/L) that were five times higher than those released from the simulated full LSL. Sensitivity analysis revealed that the intensity of flow demands significantly affects particulate lead release, while dissolved lead levels are more dependent on the lengths of the stagnation periods. Preflushing of the LSL prior to regulatory sampling was found to underestimate the maximum monthly exposure to dissolved lead by 19%, while sampling at low flow rates (<5.2 LPM) was found to consistently suppress the high spikes induced by particulate lead mobilization.

Disinfectant control in drinking water networks: Integrating advection-dispersion-reaction models and byproduct constraints.

Effective disinfection is essential for maintaining water quality standards in distribution networks. Chlorination, as the most used technique, ensures safe water by maintaining sufficient chlorine residuals but also leads to the formation of disinfection byproducts (DBPs). These DBPs pose health risks, highlighting the need for chlorine injection control (CIC) by booster stations to balance safety and DBPs formation. Prior studies have followed various approaches to address this research problem. However, most of these studies overlook the changing flow conditions and their influence on the evolution of the chlorine and DBPs concentrations by integrating simplified transport-reaction models into CIC. In contrast, this paper proposes a novel CIC method that: (i) integrates multi-species dynamics, (ii) allows for a more accurate representation of the reaction dynamics of chlorine, other substances, and the resulting DBPs formation, and (iii) optimizes for the regulation of chlorine concentrations subject to EPA mandates thereby mitigating network-wide DBPs formation. The novelty of this study lies in its incorporation of time-dependent controllability analysis that captures the control coverage of each booster station. The effectiveness of the proposed CIC method is demonstrated through its application and validation via numerical case studies on different water networks with varying scales, initial conditions, and parameters.

Adsorption of per- and poly-fluoroalkyl substances (PFAS) on Ni: A DFT investigation.

Electrocatalytic destruction of per- and polyfluoroalkyl substances (PFAS) is an emerging approach for treatment of PFAS-contaminated water. In this study, a systematic ab initio investigation of PFAS adsorption on Ni, a widely used electrocatalyst, was conducted by means of dispersion-corrected Density Functional Theory (DFT) calculations. The objective of this investigation was to elucidate the adsorption characteristics and charge transfer mechanisms of different PFAS molecules on Ni surfaces. PFAS adsorption on three of the most thermodynamically favorable Ni surface facets, namely (001), (110), and (111), was investigated. Additionally, the role of PFAS chain length and functional group was studied by comparing the adsorption characteristics of different PFAS compounds, namely perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorobutanesulfonic acid (PFBS), and perfluorobutanoic acid (PFBA). For each PFAS molecule-Ni surface facet pair, different adsorption configurations were considered. Further calculations were carried out to reveal the effect of solvation, pre-adsorbed atomic hydrogen (H), and surface defects on the adsorption energy. Overall, the results revealed that the adsorption of PFAS on Ni surfaces is energetically favorable, and that the adsorption is primarily driven by the functional groups. The presence of preadsorbed H and the inclusion of solvation produced less exothermic adsorption energies, while surface vacancy defects showed mixed effects on PFAS adsorption. Taken together, the results of this study suggest that Ni is a promising electrocatalyst for PFAS adsorption and destruction, and that proper control for the exposed facets and surface defects could enhance the adsorption stability.

Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks.

Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel.

Spatiotemporal trends of recovery from lead contamination in Flint, MI as revealed by crowdsourced water sampling.

In the aftermath of the lead contamination crisis that plagued the water system in Flint, MI, more than 35,000 water samples were collected from the city's premises. The majority of these samples (>85%) were collected through a voluntary crowdsourced sampling campaign. The samples were analyzed for lead and copper concentrations by the Michigan Department of Environmental Quality (MDEQ). In this study, the crowdsourced sampling data was analyzed by means of spatial autocorrelation analysis to reveal the locations of statistically significant hotspot regions of high water lead levels (WLLs), and to track the spatiotemporal evolution of WLLs as the system recovered from lead contamination. The results showed that hotspot regions that experienced high WLLs were consistent with the areas where lead service line (LSL) density was the highest. Additionally, galvanized service lines and other lead-containing plumbing components could have also contributed to lead release in hotspot regions. The temporal trend exhibited by the crowdsourced sampling data did not reflect a consistent decrease in WLLs despite the interventions implemented by MDEQ and EPA. Instead, sampled WLLs remained high for several months after boosting the orthophosphate dose and launching a city-wide residential flushing campaign. The findings of this study suggest that this could be partially attributed to disproportionate sampling from premises in hotspot regions of high WLLs and LSL density.

SnO2 Nanostructured Thin Films for Room-Temperature Gas Sensing of Volatile Organic Compounds.

We demonstrated room-temperature gas sensing of volatile organic compounds (VOCs) using SnO2 nanostructured thin films grown via the aerosol chemical vapor deposition process at deposition temperatures ranging from 450 to 600 °C. We investigated the film's sensing response to the presence of three classes of VOCs: apolar, monopolar, and biopolar. The synthesis process was optimized, with the most robust response observed for films grown at 550 °C as compared to other temperatures. The role of film morphology, exposed surface planes, and oxygen defects were explored using experimental techniques and theoretical calculations to improve the understanding of the room-temperature gas sensing mechanism, which is proposed to be through the direct adsorption of VOCs on the sensor surface. Overall, the improved understanding of the material characteristics that enable room-temperature sensing gained in this work will be beneficial for the design and application of metal oxide gas sensors at room temperature.

Investigating the role of biofilms in trihalomethane formation in water distribution systems with a multicomponent model.

Biofilms are ubiquitous in the pipes of drinking water distribution systems (DWDSs), and recent experimental studies revealed that the chlorination of the microbial carbon associated with the biofilm contributes to the total disinfection by-products (DBPs) formation with distinct mechanisms from those formed from precursors derived from natural organic matter (NOM). A multiple species reactive-transport model was developed to explain the role of biofilms in DBPs formation by accounting for the simultaneous transport and interactions of disinfectants, organic compounds, and biomass. Using parameter values from experimental studies in the literature, the model equations were solved to predict chlorine decay and microbial regrowth dynamics in an actual DWDS, and trihalomethanes (THMs) formation in a pilot-scale distribution system simulator. The model's capability of reproducing the measured concentrations of free chlorine, suspended biomass, and THMs under different hydrodynamic and temperature conditions was demonstrated. The contribution of bacteria-derived precursors to the total THMs production was found to have a significant dependence on the system's hydraulics, seasonal variables, and the quality of the treated drinking water. Under system conditions that promoted fast bacterial re-growth, the transformation of non-microbial into microbial carbon DBP precursors by the biofilms showed a noticeable effect on the kinetics of THMs formation, especially when a high initial chlorine dose was applied. These conditions included elevated water temperature and high concentrations of nutrients in the influent water. The fraction of THMs formed from microbial sources was found to reach a peak of 12% of the total produced THMs under the investigated scenarios. The results demonstrated the importance of integrating bacterial regrowth dynamics in predictive DBPs formation models.

Water quality modeling in the dead end sections of drinking water distribution networks.

Dead-end sections of drinking water distribution networks are known to be problematic zones in terms of water quality degradation. Extended residence time due to water stagnation leads to rapid reduction of disinfectant residuals allowing the regrowth of microbial pathogens. Water quality models developed so far apply spatial aggregation and temporal averaging techniques for hydraulic parameters by assigning hourly averaged water demands to the main nodes of the network. Although this practice has generally resulted in minimal loss of accuracy for the predicted disinfectant concentrations in main water transmission lines, this is not the case for the peripheries of the distribution network. This study proposes a new approach for simulating disinfectant residuals in dead end pipes while accounting for both spatial and temporal variability in hydraulic and transport parameters. A stochastic demand generator was developed to represent residential water pulses based on a non-homogenous Poisson process. Dispersive solute transport was considered using highly dynamic dispersion rates. A genetic algorithm was used to calibrate the axial hydraulic profile of the dead-end pipe based on the different demand shares of the withdrawal nodes. A parametric sensitivity analysis was done to assess the model performance under variation of different simulation parameters. A group of Monte-Carlo ensembles was carried out to investigate the influence of spatial and temporal variations in flow demands on the simulation accuracy. A set of three correction factors were analytically derived to adjust residence time, dispersion rate and wall demand to overcome simulation error caused by spatial aggregation approximation. The current model results show better agreement with field-measured concentrations of conservative fluoride tracer and free chlorine disinfectant than the simulations of recent advection dispersion reaction models published in the literature. Accuracy of the simulated concentration profiles showed significant dependence on the spatial distribution of the flow demands compared to temporal variation.