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

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 a...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 30; H. 8; S. 20590 - 20600
Hauptverfasser:	Singh, Jogendra, Kumar, Pankaj, Eid, Ebrahem M., Taher, Mostafa A., El-Morsy, Mohamed H. E., Osman, Hanan E. M., Al-Bakre, Dhafer A., Kumar, Vinod
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Schlagworte:	Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Biodegradation, Environmental Earth and Environmental Science Ecotoxicology Eichhornia Eichhornia crassipes Environment Environmental Chemistry Environmental Health Environmental Pollutants glass industry logit analysis Neural Networks, Computer Nitrogen Phosphorus phytoremediation plant density Plants prediction Research Article response surface methodology total phosphorus Waste Water Technology Water Management Water Pollutants, Chemical - analysis Water Pollution Control Artificial neural network Response surface method Glass industry effluent Phytoremediation Eichhornia crassipes
ISSN:	1614-7499, 1614-7499
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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 (R 2 ) 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.
AbstractList	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 (R ) 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. 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.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. 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 (R 2 ) 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. 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.
Author	Osman, Hanan E. M. Eid, Ebrahem M. Kumar, Pankaj Al-Bakre, Dhafer A. Taher, Mostafa A. Kumar, Vinod Singh, Jogendra El-Morsy, Mohamed H. E.
Author_xml	– sequence: 1 givenname: Jogendra surname: Singh fullname: Singh, Jogendra organization: Agro-Ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri (Deemed to Be University) – sequence: 2 givenname: Pankaj surname: Kumar fullname: Kumar, Pankaj organization: Agro-Ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri (Deemed to Be University) – sequence: 3 givenname: Ebrahem M. surname: Eid fullname: Eid, Ebrahem M. organization: Biology Department, College of Science, King Khalid University, Botany Department, Faculty of Science, Kafrelsheikh University – sequence: 4 givenname: Mostafa A. surname: Taher fullname: Taher, Mostafa A. organization: Biology Department, Faculty of Science and Arts, King Khalid University, Botany Department, Faculty of Science, Aswan University – sequence: 5 givenname: Mohamed H. E. surname: El-Morsy fullname: El-Morsy, Mohamed H. E. organization: Deanship of Scientific Research, Umm Al-Qura University, Plant Ecology and Range Management Department, Desert Research Center – sequence: 6 givenname: Hanan E. M. surname: Osman fullname: Osman, Hanan E. M. organization: Biology Department, Faculty of Science, Umm-Al-Qura University, Botany and Microbiology Department, Faculty of Science, Al-Azhar University – sequence: 7 givenname: Dhafer A. surname: Al-Bakre fullname: Al-Bakre, Dhafer A. organization: Biology Department, College of Science, Tabuk University – sequence: 8 givenname: Vinod surname: Kumar fullname: Kumar, Vinod email: drvksorwal@gkv.ac.in organization: Agro-Ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri (Deemed to Be University)
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Cites_doi	10.1061/(asce)hz.2153-5515.0000290 10.1016/j.talanta.2004.09.006 10.1016/j.nbt.2014.01.001 10.1007/s11356-020-08045-3 10.1007/s11356-019-05019-y 10.1080/15226514.2020.1768513 10.1007/s13762-021-03433-9 10.1016/j.bcab.2019.101288 10.18639/rabm.2016.02.341789 10.1007/s11356-020-10940-8 10.1016/j.chemosphere.2020.125966 10.1080/10408347.2012.751787 10.1007/s13201-018-0790-y 10.5755/j01.erem.72.3.14120 10.1080/15226514.2019.1566885 10.4236/jwarp.2019.113017 10.1007/s11356-011-0530-4 10.1088/1757-899x/1058/1/012068 10.1016/j.ecoleng.2013.09.058 10.1007/s11356-020-11458-9 10.1038/s41597-020-0478-7 10.3969/j.issn.1673-288X.2015.01.016 10.3390/su12051927 10.1016/j.jhazmat.2020.123658 10.1016/j.asej.2020.05.009 10.1007/s13280-011-0159-z 10.1002/wer.1599 10.3389/fevo.2017.00070 10.18805/ijare.a-5122 10.1007/s11356-020-07839-9 10.1016/j.eti.2019.100327 10.1016/j.chemosphere.2019.124486 10.1016/j.biortech.2020.124430 10.1016/j.scitotenv.2020.142540 10.1080/15226514.2017.1393391 10.1080/15226514.2021.1895719 10.1016/j.jwpe.2021.102152 10.1007/s11356-017-0813-5 10.1002/wer.1536 10.4324/9781315161549 10.1016/B978-0-323-91180-1.00007-7 10.3138/9781442679634-006 10.1007/978-981-32-9664-0_4 10.1016/B978-0-12-821729-0.00005-1 10.1016/B978-0-12-820318-7.00007-1 10.1201/9780367461362-6 10.1201/9781003004684-11
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Keywords	Artificial neural network Response surface method Glass industry effluent Phytoremediation Eichhornia crassipes
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References	Alam, Khan, Basheer, Farooqi (CR3) 2021; 1058 CR39 Malyan, Yadav, Sonkar (CR26) 2021; 93 Sáez-Plaza, Navas, Wybraniec (CR33) 2013; 43 CR30 OdedishemiAjibade, Wang, Guadie (CR31) 2021; 322 Mohammadi, Samaei, Azhdarpoor (CR27) 2019; 237 Goala, Yadav, Alam (CR13) 2021; 42 Shi (CR37) 2015; 1 Webb, Quintã, Papadimitriou (CR45) 2013; 61 CR6 CR5 Ceschin, Crescenzi, Iannelli (CR7) 2020; 27 Kumar, Singh, Kumar, Kumar (CR21) 2019; 14 Worsfold, Gimbert, Mankasingh (CR47) 2005; 66 Singh, Kumar, Kumar, Kumar (CR40) 2021; 19 Rathi, Madan (CR32) 2019; 53 Kumar, Singh, Chopra (CR20) 2018; 20 Sharma, Saini, Paul, Chaudhary, Nehra (CR35) 2021; 28 Xu, Zhu, Mei (CR48) 2021; 28 Adeniran, Adelodun, Ogunshina (CR2) 2016; 72 Yang, Lien, Huang (CR49) 2017; 21 Gavrilescu, Demnerová, Aamand (CR11) 2015; 32 Nwankwoala, Omofuophu (CR29) 2019; 2 Eid, Galal, Sewelam (CR9) 2020; 27 Varun, D’Souza, Pratas, Paul (CR44) 2012; 19 Mustafa, Hayder (CR28) 2021; 12 CR17 Gholipour, Zahabi, Stefanakis (CR12) 2020; 247 CR15 Kumar, Singh (CR19) 2019; 26 CR10 Sen, Nandi, Dutta (CR34) 2018; 8 Varma, Gupta, Ghosal, Majumder (CR43) 2021; 755 Kumar, Sharma (CR18) 2019; 11 Malyan, Singh, Rawat (CR24) 2019; 21 Ali, Abbas, Rizwan (CR4) 2020; 12 Eid, Shaltout, Moghanm (CR8) 2019; 21 Sricoth, Meeinkuirt, Pichtel, Taeprayoon, Saengwilai (CR42) 2018; 25 Adelodun, Olajire, Afolabi (CR1) 2021; 23 Shikha, Gauba (CR38) 2016; 2 Guignard, Leitch, Acquisti (CR14) 2017; 5 CR25 CR23 CR22 Wei, van Le, Peng (CR46) 2021; 403 Sood, Uniyal, Prasanna, Ahluwalia (CR41) 2012; 41 Shen, Amatulli, Sethi (CR36) 2020; 7 Jaskulak, Grobelak, Vandenbulcke (CR16) 2020; 22 23601_CR15 V Kumar (23601_CR20) 2018; 20 R Sharma (23601_CR35) 2021; 28 23601_CR17 H-S Xu (23601_CR48) 2021; 28 S Shikha (23601_CR38) 2016; 2 JM Webb (23601_CR45) 2013; 61 23601_CR10 EM Eid (23601_CR9) 2020; 27 ZH Yang (23601_CR49) 2017; 21 T Sricoth (23601_CR42) 2018; 25 S Ceschin (23601_CR7) 2020; 27 M Goala (23601_CR13) 2021; 42 A Sood (23601_CR41) 2012; 41 J Singh (23601_CR40) 2021; 19 23601_CR22 23601_CR23 23601_CR25 P Sáez-Plaza (23601_CR33) 2013; 43 23601_CR5 A Gholipour (23601_CR12) 2020; 247 23601_CR6 PM Rathi (23601_CR32) 2019; 53 F Mohammadi (23601_CR27) 2019; 237 HM Mustafa (23601_CR28) 2021; 12 P Worsfold (23601_CR47) 2005; 66 Z Wei (23601_CR46) 2021; 403 23601_CR39 MS Guignard (23601_CR14) 2017; 5 M Varma (23601_CR43) 2021; 755 S Ali (23601_CR4) 2020; 12 23601_CR30 V Kumar (23601_CR21) 2019; 14 SK Malyan (23601_CR26) 2021; 93 SK Malyan (23601_CR24) 2019; 21 M Varun (23601_CR44) 2012; 19 EM Eid (23601_CR8) 2019; 21 M Gavrilescu (23601_CR11) 2015; 32 R Alam (23601_CR3) 2021; 1058 LQ Shen (23601_CR36) 2020; 7 F OdedishemiAjibade (23601_CR31) 2021; 322 KA Adeniran (23601_CR2) 2016; 72 M Jaskulak (23601_CR16) 2020; 22 H Shi (23601_CR37) 2015; 1 D Kumar (23601_CR18) 2019; 11 AA Adelodun (23601_CR1) 2021; 23 M Kumar (23601_CR19) 2019; 26 H Nwankwoala (23601_CR29) 2019; 2 S Sen (23601_CR34) 2018; 8
References_xml	– volume: 21 start-page: 15003 year: 2017 end-page: 15010 ident: CR49 article-title: Development of the risk assessment and management strategies for TPH-contaminated sites using TPH fraction methods publication-title: J Hazard Toxic Radioact Waste doi: 10.1061/(asce)hz.2153-5515.0000290 – ident: CR22 – volume: 66 start-page: 273 year: 2005 end-page: 293 ident: CR47 article-title: Sampling, sample treatment and quality assurance issues for the determination of phosphorus species in natural waters and soils publication-title: Talanta doi: 10.1016/j.talanta.2004.09.006 – volume: 32 start-page: 147 year: 2015 end-page: 156 ident: CR11 article-title: Emerging pollutants in the environment: Present and future challenges in biomonitoring, ecological risks and bioremediation publication-title: New Biotechnol doi: 10.1016/j.nbt.2014.01.001 – volume: 27 start-page: 15806 issue: 13 year: 2020 end-page: 15814 ident: CR7 article-title: Phytoremediation potential of the duckweeds and to remove nutrients from treated waters publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-08045-3 – volume: 26 start-page: 18421 year: 2019 end-page: 18433 ident: CR19 article-title: Assessment of pollutant removal processes and kinetic modelling in vertical flow constructed wetlands at elevated pollutant loading publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-019-05019-y – ident: CR39 – volume: 22 start-page: 1321 year: 2020 end-page: 1330 ident: CR16 article-title: Modeling and optimizing the removal of cadmium by L. from contaminated soil via response surface methodology and artificial neural networks during assisted phytoremediation with sewage sludge publication-title: Int J Phytorem doi: 10.1080/15226514.2020.1768513 – volume: 19 start-page: 5481 year: 2021 end-page: 5492 ident: CR40 article-title: Kinetics and prediction modeling of heavy metal phytoremediation from glass industry effluent by water hyacinth ( ) publication-title: Int J Environ Sci Technol doi: 10.1007/s13762-021-03433-9 – volume: 21 start-page: 101288 year: 2019 ident: CR24 article-title: An overview of carcinogenic pollutants in groundwater of India publication-title: Biocatal Agric Biotechnol doi: 10.1016/j.bcab.2019.101288 – volume: 2 start-page: 113 year: 2016 ident: CR38 article-title: Phytoremediation of industrial and pharmaceutical pollutants publication-title: Recent Adv Biol Med doi: 10.18639/rabm.2016.02.341789 – volume: 28 start-page: 7116 issue: 6 year: 2021 end-page: 7122 ident: CR35 article-title: Removal of organic dyes from wastewater using : A potential phytoremediation option publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-10940-8 – volume: 247 year: 2020 ident: CR12 article-title: A novel pilot and full-scale constructed wetland study for glass industry wastewater treatment publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.125966 – ident: CR25 – volume: 43 start-page: 224 year: 2013 end-page: 272 ident: CR33 article-title: An overview of the Kjeldahl method of nitrogen determination. Part II. Sample Preparation, Working Scale, Instrumental Finish, and Quality Control publication-title: Crit Rev Anal Chem doi: 10.1080/10408347.2012.751787 – ident: CR15 – volume: 2 start-page: 1 year: 2019 end-page: 7 ident: CR29 article-title: Modeling and assessment of human health risks from exposure to hydrocarbon contaminated soils in parts of Bonny, Rivers State, Nigeria publication-title: SF J Env Earth Sci – volume: 8 start-page: 148 year: 2018 ident: CR34 article-title: Application of RSM and ANN for optimization and modeling of biosorption of chromium(VI) using cyanobacterial biomass publication-title: Appl Water Sci doi: 10.1007/s13201-018-0790-y – volume: 72 start-page: 59 year: 2016 end-page: 74 ident: CR2 article-title: Biocheminio deguonies suvartojimo ir ištirpusio deguonies kiekio upėse modeliavimas naudojant dirbtinius neuroninius tinklus: Asos upės atvejo studija publication-title: Environ Res Eng Manag doi: 10.5755/j01.erem.72.3.14120 – volume: 21 start-page: 821 year: 2019 end-page: 830 ident: CR8 article-title: Bioaccumulation and translocation of nine heavy metals by in Nile delta, Egypt: Perspectives for phytoremediation publication-title: Int J Phytorem doi: 10.1080/15226514.2019.1566885 – volume: 11 start-page: 296 year: 2019 end-page: 310 ident: CR18 article-title: Remediation of pulp and paper industry effluent using electrocoagulation process publication-title: J Water Resour Prot doi: 10.4236/jwarp.2019.113017 – volume: 19 start-page: 269 year: 2012 end-page: 281 ident: CR44 article-title: Metal contamination of soils and plants associated with the glass industry in North Central India: Prospects of phytoremediation publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-011-0530-4 – ident: CR5 – volume: 1058 year: 2021 ident: CR3 article-title: Nutrients and organics removal from slaughterhouse wastewater using phytoremediation: A comparative study on different aquatic plant species publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899x/1058/1/012068 – volume: 61 start-page: 145 year: 2013 end-page: 153 ident: CR45 article-title: The effect of halophyte planting density on the efficiency of constructed wetlands for the treatment of wastewater from marine aquaculture publication-title: Ecol Eng doi: 10.1016/j.ecoleng.2013.09.058 – volume: 28 start-page: 9475 year: 2021 end-page: 9483 ident: CR48 article-title: Effects of high levels of nitrogen and phosphorus on perennial ryegrass ( L.) and its potential in bioremediation of highly eutrophic water publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-11458-9 – volume: 7 start-page: 161 year: 2020 ident: CR36 article-title: Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework publication-title: Scientific Data doi: 10.1038/s41597-020-0478-7 – volume: 1 start-page: 68 year: 2015 end-page: 71 ident: CR37 article-title: Practice and innovation of discharge standard of water pollutants in North China publication-title: Environ Sustain Dev doi: 10.3969/j.issn.1673-288X.2015.01.016 – ident: CR30 – ident: CR10 – volume: 12 start-page: 1927 year: 2020 ident: CR4 article-title: Application of floating aquatic plants in phytoremediation of heavy metals polluted water: A review publication-title: Sustainability (switzerland) doi: 10.3390/su12051927 – volume: 403 year: 2021 ident: CR46 article-title: A review on phytoremediation of contaminants in air, water and soil publication-title: J Hazard Mater doi: 10.1016/j.jhazmat.2020.123658 – ident: CR6 – volume: 12 start-page: 355 year: 2021 end-page: 365 ident: CR28 article-title: Recent studies on applications of aquatic weed plants in phytoremediation of wastewater: a review article publication-title: Ain Shams Eng J doi: 10.1016/j.asej.2020.05.009 – ident: CR23 – volume: 41 start-page: 122 year: 2012 end-page: 137 ident: CR41 article-title: Phytoremediation potential of aquatic macrophyte, publication-title: Ambio doi: 10.1007/s13280-011-0159-z – volume: 93 start-page: 1882 year: 2021 end-page: 1909 ident: CR26 article-title: Mechanistic understanding of the pollutant removal and transformation processes in the constructed wetland system publication-title: Water Environ Res doi: 10.1002/wer.1599 – volume: 5 start-page: 70 year: 2017 ident: CR14 article-title: Impacts of nitrogen and phosphorus: From genomes to natural ecosystems and agriculture publication-title: Front Ecol Evol doi: 10.3389/fevo.2017.00070 – volume: 53 start-page: 468 year: 2019 end-page: 472 ident: CR32 article-title: Effect of glass industry effluent on seed germination and biochemical parameters of (Soyabean) publication-title: Agric Rev doi: 10.18805/ijare.a-5122 – volume: 27 start-page: 12138 year: 2020 end-page: 12151 ident: CR9 article-title: Phytoremediation of heavy metals by four aquatic macrophytes and their potential use as contamination indicators: A comparative assessment publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-07839-9 – volume: 14 start-page: 100327 year: 2019 ident: CR21 article-title: Response surface methodology based electro-kinetic modeling of biological and chemical oxygen demand removal from sugar mill effluent by water hyacinth ( ) in a Continuous Stirred Tank Reactor (CSTR) publication-title: Environ Technol Innov doi: 10.1016/j.eti.2019.100327 – ident: CR17 – volume: 237 start-page: 6 year: 2019 ident: CR27 article-title: Modelling and optimizing pyrene removal from the soil by phytoremediation using response surface methodology, artificial neural networks, and genetic algorithm publication-title: Chemosphere doi: 10.1016/j.chemosphere.2019.124486 – volume: 322 start-page: 124430 year: 2021 ident: CR31 article-title: Total nitrogen removal in biochar amended non-aerated vertical flow constructed wetlands for secondary wastewater effluent with low C/N ratio: Microbial community structure and dissolved organic carbon release conditions publication-title: Biores Technol doi: 10.1016/j.biortech.2020.124430 – volume: 755 start-page: 14254 year: 2021 ident: CR43 article-title: A review on performance of constructed wetlands in tropical and cold climate: Insights of mechanism, role of influencing factors, and system modification in low temperature publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2020.142540 – volume: 20 start-page: 507 year: 2018 end-page: 521 ident: CR20 article-title: Assessment of plant growth attributes, bioaccumulation, enrichment, and translocation of heavy metals in water lettuce ( L.) grown in sugar mill effluent publication-title: Int J Phytorem doi: 10.1080/15226514.2017.1393391 – volume: 23 start-page: 1333 year: 2021 end-page: 1341 ident: CR1 article-title: Phytoremediation potentials of for nutrients and organic pollutants from textile wastewater publication-title: Int J Phytorem doi: 10.1080/15226514.2021.1895719 – volume: 42 year: 2021 ident: CR13 article-title: Phytoremediation of dairy wastewater using : Application of image processing technique for leaflet growth simulation publication-title: Journal of Water Process Engineering doi: 10.1016/j.jwpe.2021.102152 – volume: 25 start-page: 5344 issue: 6 year: 2018 end-page: 5358 ident: CR42 article-title: Synergistic phytoremediation of wastewater by two aquatic plants ( and ) and potential as biomass fuel publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-017-0813-5 – volume: 53 start-page: 468 year: 2019 ident: 23601_CR32 publication-title: Agric Rev doi: 10.18805/ijare.a-5122 – volume: 28 start-page: 7116 issue: 6 year: 2021 ident: 23601_CR35 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-10940-8 – ident: 23601_CR39 doi: 10.1002/wer.1536 – volume: 12 start-page: 1927 year: 2020 ident: 23601_CR4 publication-title: Sustainability (switzerland) doi: 10.3390/su12051927 – ident: 23601_CR17 doi: 10.4324/9781315161549 – volume: 27 start-page: 15806 issue: 13 year: 2020 ident: 23601_CR7 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-08045-3 – volume: 2 start-page: 1 year: 2019 ident: 23601_CR29 publication-title: SF J Env Earth Sci – ident: 23601_CR15 doi: 10.1016/B978-0-323-91180-1.00007-7 – volume: 12 start-page: 355 year: 2021 ident: 23601_CR28 publication-title: Ain Shams Eng J doi: 10.1016/j.asej.2020.05.009 – volume: 8 start-page: 148 year: 2018 ident: 23601_CR34 publication-title: Appl Water Sci doi: 10.1007/s13201-018-0790-y – volume: 403 year: 2021 ident: 23601_CR46 publication-title: J Hazard Mater doi: 10.1016/j.jhazmat.2020.123658 – volume: 247 year: 2020 ident: 23601_CR12 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.125966 – volume: 7 start-page: 161 year: 2020 ident: 23601_CR36 publication-title: Scientific Data doi: 10.1038/s41597-020-0478-7 – volume: 61 start-page: 145 year: 2013 ident: 23601_CR45 publication-title: Ecol Eng doi: 10.1016/j.ecoleng.2013.09.058 – volume: 42 year: 2021 ident: 23601_CR13 publication-title: Journal of Water Process Engineering doi: 10.1016/j.jwpe.2021.102152 – volume: 755 start-page: 14254 year: 2021 ident: 23601_CR43 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2020.142540 – volume: 20 start-page: 507 year: 2018 ident: 23601_CR20 publication-title: Int J Phytorem doi: 10.1080/15226514.2017.1393391 – ident: 23601_CR10 doi: 10.3138/9781442679634-006 – volume: 41 start-page: 122 year: 2012 ident: 23601_CR41 publication-title: Ambio doi: 10.1007/s13280-011-0159-z – volume: 21 start-page: 15003 year: 2017 ident: 23601_CR49 publication-title: J Hazard Toxic Radioact Waste doi: 10.1061/(asce)hz.2153-5515.0000290 – volume: 21 start-page: 101288 year: 2019 ident: 23601_CR24 publication-title: Biocatal Agric Biotechnol doi: 10.1016/j.bcab.2019.101288 – volume: 25 start-page: 5344 issue: 6 year: 2018 ident: 23601_CR42 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-017-0813-5 – volume: 93 start-page: 1882 year: 2021 ident: 23601_CR26 publication-title: Water Environ Res doi: 10.1002/wer.1599 – volume: 66 start-page: 273 year: 2005 ident: 23601_CR47 publication-title: Talanta doi: 10.1016/j.talanta.2004.09.006 – volume: 27 start-page: 12138 year: 2020 ident: 23601_CR9 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-07839-9 – ident: 23601_CR5 doi: 10.1007/978-981-32-9664-0_4 – volume: 2 start-page: 113 year: 2016 ident: 23601_CR38 publication-title: Recent Adv Biol Med doi: 10.18639/rabm.2016.02.341789 – volume: 11 start-page: 296 year: 2019 ident: 23601_CR18 publication-title: J Water Resour Prot doi: 10.4236/jwarp.2019.113017 – volume: 14 start-page: 100327 year: 2019 ident: 23601_CR21 publication-title: Environ Technol Innov doi: 10.1016/j.eti.2019.100327 – ident: 23601_CR25 doi: 10.1016/B978-0-12-821729-0.00005-1 – ident: 23601_CR23 doi: 10.1016/B978-0-12-820318-7.00007-1 – volume: 28 start-page: 9475 year: 2021 ident: 23601_CR48 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-11458-9 – ident: 23601_CR22 doi: 10.1201/9780367461362-6 – volume: 32 start-page: 147 year: 2015 ident: 23601_CR11 publication-title: New Biotechnol doi: 10.1016/j.nbt.2014.01.001 – volume: 19 start-page: 269 year: 2012 ident: 23601_CR44 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-011-0530-4 – volume: 43 start-page: 224 year: 2013 ident: 23601_CR33 publication-title: Crit Rev Anal Chem doi: 10.1080/10408347.2012.751787 – volume: 19 start-page: 5481 year: 2021 ident: 23601_CR40 publication-title: Int J Environ Sci Technol doi: 10.1007/s13762-021-03433-9 – volume: 26 start-page: 18421 year: 2019 ident: 23601_CR19 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-019-05019-y – volume: 23 start-page: 1333 year: 2021 ident: 23601_CR1 publication-title: Int J Phytorem doi: 10.1080/15226514.2021.1895719 – ident: 23601_CR6 – volume: 72 start-page: 59 year: 2016 ident: 23601_CR2 publication-title: Environ Res Eng Manag doi: 10.5755/j01.erem.72.3.14120 – volume: 1058 year: 2021 ident: 23601_CR3 publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899x/1058/1/012068 – volume: 322 start-page: 124430 year: 2021 ident: 23601_CR31 publication-title: Biores Technol doi: 10.1016/j.biortech.2020.124430 – volume: 21 start-page: 821 year: 2019 ident: 23601_CR8 publication-title: Int J Phytorem doi: 10.1080/15226514.2019.1566885 – volume: 1 start-page: 68 year: 2015 ident: 23601_CR37 publication-title: Environ Sustain Dev doi: 10.3969/j.issn.1673-288X.2015.01.016 – ident: 23601_CR30 doi: 10.1201/9781003004684-11 – volume: 237 start-page: 6 year: 2019 ident: 23601_CR27 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2019.124486 – volume: 22 start-page: 1321 year: 2020 ident: 23601_CR16 publication-title: Int J Phytorem doi: 10.1080/15226514.2020.1768513 – volume: 5 start-page: 70 year: 2017 ident: 23601_CR14 publication-title: Front Ecol Evol doi: 10.3389/fevo.2017.00070
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Snippet	The present study aimed to assess the efficiency of the water hyacinth ( Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus... The present study aimed to assess the efficiency of the water hyacinth (Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus...
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SubjectTerms	Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Biodegradation, Environmental Earth and Environmental Science Ecotoxicology Eichhornia Eichhornia crassipes Environment Environmental Chemistry Environmental Health Environmental Pollutants glass industry logit analysis Neural Networks, Computer Nitrogen Phosphorus phytoremediation plant density Plants prediction Research Article response surface methodology total phosphorus Waste Water Technology Water Management Water Pollutants, Chemical - analysis Water Pollution Control
Title	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
URI	https://link.springer.com/article/10.1007/s11356-022-23601-9 https://www.ncbi.nlm.nih.gov/pubmed/36253577 https://www.proquest.com/docview/2725650954 https://www.proquest.com/docview/2887614194
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