Interest in water reuse for potable purposes has heightened the significance of evaluating the potential presence of microbial agents in treated water. Evaluating the public health risk from microbial agents in water after advanced water treatment (AWT) requires an estimate of the effectiveness and reliability of the treatment system in removing microbial agents. Indicator organisms such as total and fecal coliform do not provide sufficient basis to characterize the performance of the treatment system relative to the removal of pathogenic organisms such as enteric viruses and parasitic agents. Seeding studies provide an alternative experimental approach. However, when treatment systems are challenged using more specific indicators for enteric viruses, the results are often inconclusive because the organisms are reduced to non-detectable levels after the first few unit processes. In this study we provide a mathematical approach for estimating the effectiveness of the entire treatment train with respect to removal and/or inactivation of microbial agents. These estimates are insightful since standard monitoring of final effluent consistently yielded non-detectable results.
Estimation of Pathogen Removal in an Advanced Water Treatment Facility Using Monto Carlo Simulation
A. Olivieri, D. Eisenberg, J. Soller, J. Eisenberg, R. Cooper, G. Tchobanoglous, R. Trussell, P. Gagliardo; Estimation of Pathogen Removal in an Advanced Water Treatment Facility Using Monto Carlo Simulation. Water Sci Technol 1 August 1999; 40 (4-5): 223–233. doi: https://doi.org/10.2166/wst.1999.0595
Download citation file:
Close
A. Olivieri, D. Eisenberg, J. Soller, J. Eisenberg, R. Cooper, G. Tchobanoglous, R. Trussell, P. Gagliardo; Estimation of Pathogen Removal in an Advanced Water Treatment Facility Using Monto Carlo Simulation. Water Sci Technol 1 August 1999; 40 (4-5): 223–233. doi: https://doi.org/10.2166/wst.1999.0595
Download citation file:
Close
Impact Factor 1.638
CiteScore 2.9 • Q2
Cited by
Subscribe to Open
This paper is Open Access via a Subscribe to Open model. Individuals can help sustain this model by contributing the cost of what would have been author fees. Find out more here.