Toward Digital Twins for Emerging Contaminants in Water Distribution Systems

Emerging contaminants (ECs) are natural or manufactured chemical compounds that are hard to remove through water treatment; hence, they accumulate in the environment. Such contaminants have already been detected in wastewater, aquatic environments, and water distribution systems (WDSs). Consequently, researchers are developing sensors tailored explicitly to new contaminants. By combining those new sensors with real-time simulation models, digital twins are within reach to assess system-wide water quality. In the future, such twins will build the cornerstone for early warning or real-time control systems concerning these new pollutants. However, realistic simulation tools competent enough to create such digital twins are lacking, mainly because of two reasons: (1) hydraulic models are unable to account for the spatiotemporal dynamics of customer demand, and (2) water quality models are not equipped to simulate the fate and transport of ECs. Our work aims to close this gap by proposing a novel way to model water quality that combines realistic water demand models (i.e., by using the stochastic water demand end-use model, pySIMDEUM), hydraulic solvers (i.e., using the object-oriented Python NETwork analysis tool, OOPNET), and water quality solvers (i.e., using EPyT-C). Extending the state-of-the-art for hydraulic and water quality modeling by incorporating the water demand stochasticity and the uncertainties associated with the imperfect understanding of the formation and transmission of ECs in WDSs is expected to advance the digital twins technology for detecting ECs' formation and evaluating health-related exposure risks in WDSs.