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Bayesian modeling of virus removal efficiency in wastewater treatment processes
Title: | Bayesian modeling of virus removal efficiency in wastewater treatment processes |
Authors: | Ito, T. Browse this author | Kato, T. Browse this author | Takagishi, K. Browse this author | Okabe, S. Browse this author →KAKEN DB | Sano, D. Browse this author |
Keywords: | Bayesian model | left-censored data | truncated log-normal distribution | virus removal efficiency | wastewater treatment |
Issue Date: | Nov-2015 |
Publisher: | IWA Publishing |
Journal Title: | Water Science and Technology |
Volume: | 72 |
Issue: | 10 |
Start Page: | 1789 |
End Page: | 1795 |
Publisher DOI: | 10.2166/wst.2015.402 |
PMID: | 26540540 |
Abstract: | 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. |
Rights: | ©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. |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/61970 |
Appears in Collections: | 工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 佐野 大輔
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