Models for predicting heavy metal concentrations in residential plumbing pipes and hot water tanks
Supply water is an important source of human exposure to heavy metals through the oral pathway. Due to stagnation of water in plumbing systems, exposure concentrations of heavy metals from tap water can be higher than water distribution systems (WDS), which is often ignored by the regulatory agencie...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c4d1cf9df6c64225ad580fa861506187 |
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| Sumario: | Supply water is an important source of human exposure to heavy metals through the oral pathway. Due to stagnation of water in plumbing systems, exposure concentrations of heavy metals from tap water can be higher than water distribution systems (WDS), which is often ignored by the regulatory agencies. In this study, concentrations of a few heavy metals (arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), magnesium (Mg), zinc (Zn) and iron (Fe)) and water quality parameters were monitored in WDS, plumbing pipe (PP) and hot water tanks (HWT). Multiple models were trained for predicting metal concentrations in PP and HWT, which were validated. Heavy metal concentrations in HWT were 1.2–8.1 and 1.4–6.7 times the concentrations in WDS and PP respectively. Concentrations of As, Cr, Cu, Hg and Zn were in the increasing order of WDS, PP and HWT. Concentrations of Cr and Fe were higher during summer while Cu and Zn were higher in winter. The models showed variable performances for PP and HWT (R2: PP = 0.61–0.99; HWT = 0.71–0.99). The validation data demonstrated variable correlation coefficients (r: PP = 0.45–0.99; HWT = 0.83–0.99). Few models can be used for predicting heavy metals in tap water to reduce the cost of expensive sampling and analysis. HIGHLIGHTS
Heavy metal (HM) concentrations were in the increasing order of WDS, PP and HWT.;
Few HM were higher in summer than winter while others were higher in winter.;
Multiple models were trained and validated to predict HM in PP and HWT.;
Models showed varying performances (R2 for PP: 0.61–0.99; HWT: 0.71–0.99).;
Few models were useful to predict HM in PP and HWT with very good accuracy.; |
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