Computational modelling to investigate the sampling of lead in drinking water

摘要

The monitoring of lead in drinking water is beset by difficulties relating to the inherent temporal variation of lead emissions at individual premises. Such difficulties are compounded by spatial variation when considering an entire water supply area (e.g., City or Town), which is necessary to determine compliance with regulatory standards and to judge the efficacy of corrective measures. A computational modelling system, that uses a Monte Carlo probabilistic framework for simulating lead emissions within a water supply area, has been successfully validated in a range of UK case studies and enabled corrective treatment measures to be optimised for a range of water types. This modelling system includes the simulation of a range of sampling methods, and has made it possible to undertake an exhaustive comparison between daily average lead emissions (DAC - which are equivalent to weekly average lead concentrations as a consequence of the modelling system used), random daytime sampling (RDT), 30 min stagnation sampling (30MS) and 6 h stagnation sampling (6HS). It is concluded that: (a) the stringency of UK and US compliance assessment methods for lead in drinking water is fairly similar for waters of reduced plumbosolvency, despite different sampling approaches; (b) RDT sampling is equivalent to random DAC for waters of moderate plumbosolvency; (c) RDT sampling is more stringent than random DAC for waters of low plumbosolvency; (d) all random sampling methods suffer from poor reproducibility, albeit less so for low plumbosolvency water; and (e) fixed point stagnation sampling may not be representative.