Machine learning approach to predict quality parameters for bacterial consortium-treated hospital wastewater and phytotoxicity assessment on radish, cauliflower, hot pepper, rice and wheat crops

Rashid, A., Mirza, S. A., Keating, C. , Ijaz, U. Z. , Ali, S. and Campos, L. C. (2022) Machine learning approach to predict quality parameters for bacterial consortium-treated hospital wastewater and phytotoxicity assessment on radish, cauliflower, hot pepper, rice and wheat crops. Water, 14(1), 116. (doi: 10.3390/w14010116)

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Abstract

Raw hospital wastewater is a source of excessive heavy metals and pharmaceutical pollutants. In water-stressed countries such as Pakistan, the practice of unsafe reuse by local farmers for crop irrigation is of major concern. In our previous work, we developed a low-cost bacterial consortium wastewater treatment method. Here, in a two-part study, we first aimed to find what physico-chemical parameters were the most important for differentiating consortium-treated and untreated wastewater for its safe reuse. This was achieved using a Kruskal–Wallis test on a suite of physico-chemical measurements to find those parameters which were differentially abundant between consortium-treated and untreated wastewater. The differentially abundant parameters were then input to a Random Forest classifier. The classifier showed that ‘turbidity’ was the most influential parameter for predicting biotreatment. In the second part of our study, we wanted to know if the consortium-treated wastewater was safe for crop irrigation. We therefore carried out a plant growth experiment using a range of popular crop plants in Pakistan (Radish, Cauliflower, Hot pepper, Rice and Wheat), which were grown using irrigation from consortium-treated and untreated hospital wastewater at a range of dilutions (turbidity levels) and performed a phytotoxicity assessment. Our results showed an increasing trend in germination indices and a decreasing one in phytotoxicity indices in plants after irrigation with consortium-treated hospital wastewater (at each dilution/turbidity measure). The comparative study of growth between plants showed the following trend: Cauliflower > Radish > Wheat > Rice > Hot pepper. Cauliflower was the most adaptive plant (PI: −0.28, −0.13, −0.16, −0.06) for the treated hospital wastewater, while hot pepper was susceptible for reuse; hence, we conclude that bacterial consortium-treated hospital wastewater is safe for reuse for the irrigation of cauliflower, radish, wheat and rice. We further conclude that turbidity is the most influential parameter for predicting bio-treatment efficiency prior to water reuse. This method, therefore, could represent a low-cost, low-tech and safe means for farmers to grow crops in water stressed areas.

Item Type:Articles
Keywords:Hospital wastewater, bacterial consortium treatment, machine learning, Random Forest classifier, phytotoxicity, crop irrigation.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Keating, Dr Ciara and Ijaz, Dr Umer
Creator Roles:
Keating, C.Software, Validation, Writing – review and editing
Ijaz, U. Z.Software, Validation, Writing – review and editing
Authors: Rashid, A., Mirza, S. A., Keating, C., Ijaz, U. Z., Ali, S., and Campos, L. C.
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Water
Publisher:MDPI
ISSN:2073-4441
ISSN (Online):2073-4441
Published Online:05 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Water 14(1): 116
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
170256Understanding microbial community through in situ environmental 'omic data synthesisUmer Zeeshan IjazNatural Environment Research Council (NERC)NE/L011956/1ENG - Infrastructure & Environment
300451Optimising decentralised low-cost wastewater infrastructure by managing the microbesWilliam SloanEngineering and Physical Sciences Research Council (EPSRC)EP/P029329/1ENG - Infrastructure & Environment
309846Decentralised water technologiesWilliam SloanEngineering and Physical Sciences Research Council (EPSRC)EP/V030515/1ENG - Infrastructure & Environment