Prediction of lead concentration in random daytime (RDT) samples of high rise buildings by coupled 3D-1D modeling

Abstract

The drinking water supply system in many high rise buildings in densely populated cities consists of a complex labyrinth of copper pipes and brass fixtures (valves, meters, couplings). Lead contamination in these non-lead pipe systems can occur due to the presence of lead-soldered connections, and lead containing brass fixtures. The prediction of lead concentration characteristics of random daytime (RDT) samples in these high rise buildings has hitherto not been studied. The stochastic variation of lead concentration of RDT samples is studied by a coupling of 3-D and 1-D models and the Monte-Carlo Method. A 3-dimensional CFD model based on an equilibrium concentration ($E_0$) approach is used to simulate the leaching process from different lead sources. With the calibrated $E_0$ for different materials obtained from leaching experiments, the lead source strengths of leaded components in a water supply chain can be predicted by the 3D model as a function of stagnation time. Using the predicted distributed lead sources, the transport and mixing of lead in the turbulent pipe flow can be accurately simulated by a 1D advection–diffusion model. Using the Monte-Carlo method, a large number of simulations of consumer tap water Pb concentrations is performed using randomly sampled stagnation time, inter-use time, and flushing time. The computations are performed for two representative prototypes: (i) a full scale lead contaminated water supply chain; and (ii) a chain with only clean pipes and brass fixtures. The effect of stagnation time and flushing time before water use on tap Pb levels are investigated. The predicted range and distribution of RDT sample concentrations are validated by a three-year field data set (2017–2020) of the Hong Kong Water Supplies Department.