Rashid, A;
Mirza, SA;
Keating, C;
Ijaz, UZ;
Ali, S;
Campos, LC;
(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)
, Article 116. 10.3390/w14010116.
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Abstract
Raw hospital wastewater is a source of excessive heavy metals and pharmaceutical pol-lutants. 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.
| Type: | Article |
|---|---|
| Title: | 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 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/w14010116 |
| Publisher version: | https://doi.org/10.3390/w14010116 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited |
| Keywords: | hospital wastewater; bacterial consortium treatment; machine learning; Random Forest classifier; phytotoxicity; crop irrigation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142117 |
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