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• W2766238041 abstract "Abstract Understanding pathogen risks is a critically important consideration in the design of water treatment, particularly for potable reuse projects. As an extension to our published microbial risk assessment methodology to estimate infection risks associated with Direct Potable Reuse (DPR) treatment train unit process combinations, herein, we (1) provide an updated compilation of pathogen density data in raw wastewater and dose-response models; (2) conduct a series of sensitivity analyses to consider potential risk implications using updated data; (3) evaluate the risks associated with log credit allocations in the United States; and (4) identify reference pathogen reductions needed to consistently meet currently applied benchmark risk levels. Sensitivity analyses illustrated changes in cumulative annual risks estimates, the significance of which depends on the pathogen group driving the risk for a given treatment train. For example, updates to norovirus (NoV) raw wastewater values and use of a NoV dose-response approach, capturing the full range of uncertainty, increased risks associated with one of the treatment trains evaluated, but not the other. Additionally, compared to traditional log-credit allocation approaches, our results indicate that the risk methodology provides more nuanced information about how consistently public health benchmarks are achieved. Our results indicate that viruses need to be reduced by 14 logs or more to consistently achieve currently applied benchmark levels of protection associated with DPR. The refined methodology, updated model inputs, and log credit allocation comparisons will be useful to regulators considering DPR projects and design engineers as they consider which unit treatment processes should be employed for particular projects." @default.
• W2766238041 created "2017-11-10" @default.
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• W2766238041 date "2018-01-01" @default.
• W2766238041 modified "2023-10-17" @default.
• W2766238041 title "Direct potable reuse microbial risk assessment methodology: Sensitivity analysis and application to State log credit allocations" @default.
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• W2766238041 doi "https://doi.org/10.1016/j.watres.2017.10.034" @default.
• W2766238041 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6816270" @default.
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