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Article

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

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Department of Botany, GC University Lahore, Lahore 54000, Pakistan
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Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK
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Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, UK
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Division of Infrastructure and Environment, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK
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Institute of Industrial Biotechnology (IIB), GC University Lahore, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Paola Verlicchi
Water 2022, 14(1), 116; https://doi.org/10.3390/w14010116
Received: 20 October 2021 / Revised: 16 December 2021 / Accepted: 20 December 2021 / Published: 5 January 2022
(This article belongs to the Section Wastewater Treatment and Reuse)
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. View Full-Text
Keywords: hospital wastewater; bacterial consortium treatment; machine learning; Random Forest classifier; phytotoxicity; crop irrigation hospital wastewater; bacterial consortium treatment; machine learning; Random Forest classifier; phytotoxicity; crop irrigation
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MDPI and ACS Style

Rashid, A.; Mirza, S.A.; Keating, C.; Ijaz, U.Z.; Ali, S.; Campos, L.C. 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 2022, 14, 116. https://doi.org/10.3390/w14010116

AMA Style

Rashid A, Mirza SA, Keating C, Ijaz UZ, Ali S, Campos LC. 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. 2022; 14(1):116. https://doi.org/10.3390/w14010116

Chicago/Turabian Style

Rashid, Aneeba, Safdar A. Mirza, Ciara Keating, Umer Z. Ijaz, Sikander Ali, and Luiza C. Campos. 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, no. 1: 116. https://doi.org/10.3390/w14010116

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