Abstract
The present study aimed to assess the efficiency of the water hyacinth (Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and artificial neural networks (ANN) methods were adopted to evidence the optimization and prediction performances of E. crassipes for total Kjeldahl’s nitrogen (TKN) and total phosphorus (TP) removal. The control parameters, i.e., GIE concentration (0, 50, and 100%) and plant density (1, 3, and 5 numbers) were used to optimize the best reduction conditions of TKN and TP. A quadratic model of RSM and feed-forward backpropagation algorithm-based logistic model (input layer: 2 neurons, hidden layer: 10 neurons, and output layer: 1 neuron) of ANN showed good fitness results for experimental optimization. Optimization results showed that maximum reduction of TKN (93.86%) and TP (87.43%) was achieved by using 60% of GIE concentration and nearly five plants. However, coefficient of determination (R2) values showed that ANN models (TKN: 0.9980; TP: 0.9899) were superior in terms of prediction performance as compared to RSM (TKN: 0.9888; TP: 0.9868). Therefore, the findings of this study concluded that E. crassipes can be effectively used to remediate nitrogen and phosphorus loads of GIE and minimize environmental hazards caused by its unsafe disposal.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Acknowledgements
The authors are thankful to the Gurukula Kangri (Deemed to be University) for providing the necessary experimental facilities. Mostafa A. Taher extends his appreciation to King Khalid University for funding this work through the Research Group Project under grant number RGP. 1/182/43; The author (Hanan E. M. Osman) would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4320730DSR05), Makkah, Saudi Arabia.
Funding
This research was funded by King Khalid University (grant number RGP. 1/182/43), Abha, Saudi Arabia; and Umm-Al-Qura University (grant code 22UQU4320730DSR05), Makkah, Saudi Arabia.
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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Jogendra Singh and Pankaj Kumar. The first draft of the manuscript was written by Jogendra Singh and Pankaj Kumar and all authors commented on previous versions of the manuscript. Vinod Kumar supervised the work and provided resources. Ebrahem M. Eid managed the project administration, visualization, data curation and supervision. Mostafa A. Taher, Mohamed H.E. El-Morsy, Hanan E.M. Osman, and Dhafer A. Al-Bakre validated the work. All authors read and approved the final manuscript.
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Statement of novelty
Being extremely toxic, there are limited adopted methods for the glass industry effluent treatment. The current study emphasized plant-assisted phyto-treatment of nitrogen and phosphorus pollutants from glass industry effluent. Work focuses on the design and development of response surface methodology (RSM) and artificial neural network (ANN) models-based phytoremediation experiments for efficient reduction and optimization of total Kjeldahl nitrogen (TKN) and total phosphorus (TP) pollutants. The findings reported in this study are novel that could help in the sustainable upcycling of industrial effluent using green technology.
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Singh, J., Kumar, P., Eid, E.M. et al. Phytoremediation of nitrogen and phosphorus pollutants from glass industry effluent by using water hyacinth (Eichhornia crassipes (Mart.) Solms): Application of RSM and ANN techniques for experimental optimization. Environ Sci Pollut Res 30, 20590–20600 (2023). https://doi.org/10.1007/s11356-022-23601-9
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DOI: https://doi.org/10.1007/s11356-022-23601-9