Regularized regression analysis for the prediction of virus inactivation efficiency by chloramine disinfection

Kadoya, Syun suke; Nishimura, Osamu; Kato, Hiroyuki; Sano, Daisuke

doi:10.1039/d0ew00539h

Cited by 7 publications

(2 citation statements)

References 55 publications

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“…Many techniques of artificial intelligence (AI), including multiple linear regression (MLR), 7 the artificial neural network (ANN), [8][9][10] and the support vector machine (SVM), [11][12][13][14] have been applied to the process of water treatment. 8,[15][16][17][18][19][20][21][22][23] Many hybrid AI technologies used to determine the quality of water have also emerged in recent years, and have been used for making observations based on remote sensing, predicting the distribution of the water plant, and decision making. 24,25 A number of models have been applied to predict the turbidity of the effluent [26][27][28][29][30][31] and the requisite coagulant dosage.…”

Section: Introductionmentioning

confidence: 99%

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Wang

et al. 2023

Environ. Sci.: Water Res. Technol.

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Section: Introductionmentioning

confidence: 99%

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Wang

et al. 2023

Environ. Sci.: Water Res. Technol.

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“… Kadoya et al. (2019 and 2020) previously proposed the concept of predictive water virology and reported that the predictive inactivation models, based on hierarchical Bayesian modeling (HBM) and regularized regressions, flexibly correspond to the changes and variety in water quality parameters among WWTPs ( e.g., seasonal variation). Predictive water virology can help us understand the required operational conditions to achieve the target LRV, in which there is no gap between the predicted and target LRVs.…”

Section: Introductionmentioning

confidence: 99%

Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection

et al. 2021

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Wastewater reclamation and reuse have been practically applied to water-stressed regions, but waterborne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Planning adopts the hazard analysis and critical control point (HACCP) approach to manage human health risks upon exposure to reclaimed wastewater. HACCP requires a predetermined reference value (critical limit: CL) at critical control points (CCPs), in which specific parameters are monitored and recorded in real time. A disinfection reactor of a wastewater treatment plant (WWTP) is regarded as a CCP, and one of the CCP parameters is the disinfection intensity ( e.g. , initial disinfectant concentration and contact time), which is proportional to the log reduction value (LRV) of waterborne pathogens. However, the achievable LRVs are not always stable because the disinfection intensity is affected by water quality parameters, which vary among WWTPs. In this study, we established models for projecting virus LRVs using ozone, in which water quality and operational parameters were used as explanatory variables. For the model construction, we used five machine learning algorithms and found that automatic relevance determination with interaction terms resulted in better prediction performances for norovirus and rotavirus LRVs. Poliovirus and coxsackievirus LRVs were predicted well by a Bayesian ridge with interaction terms and lasso with quadratic terms, respectively. The established models were relatively robust to predict LRV using new datasets that were out of the range of the training data used here, but it is important to collect LRV datasets further to make the models more predictable and flexible for newly obtained datasets. The modeling framework proposed here can help WWTP operators and risk assessors determine the appropriate CL to protect human health in wastewater reclamation and reuse.

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Virus Disinfection and Population Genetics: Toward the Control of Waterborne Virus Diseases by Water Engineering

2021

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Purpose of Review Major waterborne viruses comprise numerous variants rather than only a master sequence and form a genetically diverse population. High genetic diversity is advantageous for adaptation to environmental changes because the highly diverse population likely includes variants resistant to an adverse effect. Disinfection is a broadly employed tool to inactivate pathogens, but due to virus evolvability, waterborne viruses may not be inactivated sufficiently in currently applied disinfection conditions. Here, by focusing on virus population genetics, we explore possibility and factor of emergence of disinfection sensitivity change. Recent Findings To test whether virus population obtains disinfection resistance, the evolutionary experiment developed in the field of population genetics has been applied, indicating the change in disinfection sensitivity. It has been also confirmed that the sensitivity of environmental strains is lower than that of laboratory strains. In some of these studies, genetic diversity within a population less sensitive to disinfection is higher. Researches in virus population genetics have shown the contribution of intra-population genetic diversity to virus population phenotype, so disinfection sensitivity change may attribute to the genetic diversity. Summary The research elucidating a relationship between virus evolution and disinfection has only recently begun, but significant information about the relationship has been accumulated. To develop an effective disinfection strategy for the control of waterborne virus spread, we need to clarify whether disinfection practice truly affects virus outbreaks by refining both laboratory and field experiments related to virus evolution in the disinfection-exerted environment.

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Regularized regression analysis for the prediction of virus inactivation efficiency by chloramine disinfection

Abstract: The sparse modeling methods using water quality information as explanatory variables enable us to appropriately predict virus inactivation efficiency in wastewater treatment plants.

Cited by 7 publications

References 55 publications

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection

Virus Disinfection and Population Genetics: Toward the Control of Waterborne Virus Diseases by Water Engineering

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