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A Simple Approach to Predicting the Reliability of Small Wastewater Treatment Plants

Lookup NU author(s): Joshua Bunce, Professor David Graham

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

The treatment performance of small wastewater treatment plants (WWTPs) is not well understood, and their ecological impact may be underestimated. Growing evidence suggests they play a critical role in ensuring sustainable wastewater management, meaning they can no longer be neglected. The aim of this study was to provide new data, understanding, and analytical approaches to improve the management of existing small WWTPs. A one-year sampling campaign was performed in the rural UK, and we found the effluent quality from twelve small versus three larger WWTPs was significantly poorer (p < 0.05) across a range of performance parameters. Specifically, mean removal rates at the small plants were 67.3 ± 20.4%, 80 ± 33.9%, and 55.5 ± 30.4% for soluble chemical oxygen demand (sCOD), total suspended solids (TSS), and NH4-N (± standard deviation), respectively, whereas equivalent rates for larger plants were 73.3 ± 17.6%, 91.7 ± 4.6%, and 92.9 ± 3.7%. A random forest classification model was found to accurately predict the likelihood of smaller WWTPs becoming unreliable. Importantly, when condensed to the three most ‘important’ predictors, the classifier retained accuracy, which may reduce the data requirements for effective WWTP management. Among the important predictors was population equivalence, suggesting the smallest WWTPs may require particularly stringent management. Growing awareness of the need for sustainable wastewater and water resources management makes this new approach both timely and widely relevant.


Publication metadata

Author(s): Bunce JT, Graham DW

Publication type: Article

Publication status: Published

Journal: Water

Year: 2019

Volume: 11

Issue: 11

Print publication date: 15/11/2019

Online publication date: 15/11/2019

Acceptance date: 08/11/2019

Date deposited: 19/11/2019

ISSN (print): 2073-4441

ISSN (electronic): 2073-4441

Publisher: MPDI

URL: https://doi.org/10.3390/w11112397

DOI: 10.3390/w11112397


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