open access

https://hdl.handle.net/2115/61970

Bayesian modeling of virus removal efficiency in wastewater treatment processes

英語

wastewater treatment

2015-11

IWA Publishing

Water Science and Technology

72(10)

1789-1795

Left-censored datasets of virus density in wastewater samples make it difficult to evaluate the virus removal efficiency in wastewater treatment processes. In the present study, we modeled the probabilistic distribution of virus removal efficiency in a wastewater treatment process with a Bayesian approach, and investigated how many detect samples in influent and effluent are necessary for accurate estimation. One hundred left-censored data of virus density in wastewater (influent and effluent) were artificially generated based on assumed log-normal distributions and the posterior predictive distribution of virus density, and the log-ratio distribution were estimated. The estimation accuracy of distributions was quantified by Bhattacharyya coefficient. When it is assumed that the accurate estimation of posterior predictive distributions is possible when a 100% positive rate is obtained for 12 pairs of influent and effluent, 11 out of 144, 60 out of 324, and 201 out of 576 combinations of detect samples gave an accurate estimation at the significant level of 0.01 in a Kruskal-Wallis test when the total sample number was 12, 18, and 24, respectively. The combinations with the minimum number of detect samples were (12, 9), (16, 10), and (21, 8) when the total sample number was 12, 18, and 24, respectively.

学術雑誌論文

Accepted Manuscript

©IWA Publishing 2015. The definitive peer-reviewed and edited version of this article is published in Water Science and Technology 72 (10) 1789-1795 2015 DOI: 10.2166/wst.2015.402 and is available at www.iwapublishing.com.

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