A mathematical model for removal of human pathogenic viruses and bacteria by slow sand filtration under variable operational conditions
| Autor: | W.A.M. Hijnen, Alexandra Magic-Knezev, Yolanda Dullemont, Wim A. Oorthuizen, Michel Colin, Jack Schijven, Harold H.J.L. van den Berg, Gerhard Wubbels |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Time Factors
Environmental Engineering Aardwetenschappen Microorganism medicine.disease_cause Slow sand filter Water Purification Bacteriophage MS2 medicine Escherichia coli Humans Waste Management and Disposal Levivirus Water Science and Technology Civil and Structural Engineering Sewage biology Ecological Modeling Temperature Environmental engineering Models Theoretical Silicon Dioxide Pulp and paper industry biology.organism_classification Schmutzdecke Pollution Filter (aquarium) Kinetics Biodegradation Environmental Pilot plant Slow sand filtration model Salts Water Microbiology Filtration Bacteria |
| Zdroj: | Water Research, 47(7), 2592. Elsevier |
| ISSN: | 0043-1354 |
| Popis: | Slow sand filtration (SSF) in drinking water production removes pathogenic microorganisms, but detection limits and variable operational conditions complicate assessment of removal efficiency. Therefore, a model was developed to predict removal of human pathogenic viruses and bacteria as a function of the operational conditions. Pilot plant experiments were conducted, in which bacteriophage MS2 and Escherichia coli WR1 were seeded as model microorganisms for pathogenic viruses and bacteria onto the filters under various temperatures, flow rates, grain sizes and ages of the Schmutzdecke. Removal of MS2 was 0.082–3.3 log10 and that of E. coli WR1 0.94–4.5 log10 by attachment to the sand grains and additionally by processes in the Schmutzdecke. The contribution of the Schmutzdecke to the removal of MS2 and E. coli WR1 increased with its ageing, with sticking efficiency and temperature, decreased with grain size, and was modelled as a logistic growth function with scale factor f0 and rate coefficient f1. Sticking efficiencies were found to be microorganism and filter specific, but the values of f0 and f1 were independent of microorganism and filter. Cross-validation showed that the model can be used to predict log removal of MS2 and ECWR1 within ±0.6 log. Within the range of operational conditions, the model shows that removal of microorganisms is most sensitive to changes in temperature and age of the Schmutzdecke. |
| Databáze: | OpenAIRE |
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