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Singh NS, Mukherjee I. Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:55676-55694. [PMID: 39240431 DOI: 10.1007/s11356-024-34902-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/30/2024] [Indexed: 09/07/2024]
Abstract
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network (BN), quantitative structure-activity relationship-density functional theory (QSAR-DFT), artificial neural network (ANN), molecular docking (MD), and molecular dynamics stimulation (MS) of PCB biodegradation, i.e., PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCB biodegradation best fits the first-order kinetics (R2 ≥ 0.93). The consortium treatment (29.44-74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii sp. MN69 (27.09-71.25%), Corynespora cassiicola sp. MN69 (23.76-57.37%), and Corynespora cassiicola sp. MN70 (23.09-54.98%). 3'-Methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6 to 72.9%. The ANN approach showed the importance of chemical descriptors in decreasing order, i.e., LUMO > MW > IP > polarity no. > no. of chlorine > Wiener index > Zagreb index > HOMU > Pogliani index > APE in PCB removal. Furthermore, the QSAR-DFT model between the chemical descriptors and rate constant (log K) exhibited a high fit and good robustness of R2 = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through normal mode analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCB contamination within the environment.
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Affiliation(s)
- Ningthoujam Samarendra Singh
- Division of Agricultural Chemicals, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, 110012, India
| | - Irani Mukherjee
- Division of Agricultural Chemicals, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, 110012, India.
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Yu F, Bobashev G, Bienkowski PR, Sayler GS. Artificial Neural Network Modeling on Trichloroethylene Biodegradation in a Packed-Bed Biofilm Reactor and Its Comparison with Response Surface Modeling Approach. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method. Sci Rep 2022; 12:19662. [PMID: 36385121 PMCID: PMC9669037 DOI: 10.1038/s41598-022-21996-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/07/2022] [Indexed: 11/17/2022] Open
Abstract
Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.
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Olawoyin R. Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil. CHEMOSPHERE 2016; 161:145-150. [PMID: 27424056 DOI: 10.1016/j.chemosphere.2016.07.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 07/01/2016] [Indexed: 06/06/2023]
Abstract
The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants.
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Affiliation(s)
- Richard Olawoyin
- Environmental Health and Safety, School of Health Sciences, Oakland University, Rochester, MI, USA.
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The influence of different modes of bioreactor operation on the efficiency of phenol degradation by Rhodococcus UKMP-5M. RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI 2016. [DOI: 10.1007/s12210-016-0567-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Modeling of Severe Shot Peening Effects to Obtain Nanocrystalline Surface on Cast Iron Using Artificial Neural Network. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.matpr.2016.04.126] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Movafeghi A, Khataee AR, Moradi Z, Vafaei F. Biodegradation of direct blue 129 diazo dye by Spirodela polyrrhiza: An artificial neural networks modeling. INTERNATIONAL JOURNAL OF PHYTOREMEDIATION 2016; 18:337-347. [PMID: 26540563 DOI: 10.1080/15226514.2015.1109588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Phytoremediation potential of the aquatic plant Spirodela polyrrhiza was examined for direct blue 129 (DB129) azo dye. The dye removal efficiency was optimized under the variable conditions of the operational parameters including removal time, initial dye concentration, pH, temperature and amount of plant. The study reflected the significantly enhanced dye removal efficiency of S. polyrrhiza by increasing the temperature, initial dye concentration and amount of plant. Intriguingly, artificial neural network (ANN) predicted the removal time as the most dominant parameter on DB129 removal efficiency. Furthermore, the effect of dye treatment on some physiologic indices of S. polyrrhiza including growth rate, photosynthetic pigments content, lipid peroxidation and antioxidant enzymes were studied. The results revealed a reduction in photosynthetic pigments content and in multiplication of fronds after exposure to dye solution. In contrast, malondialdehyde content as well as catalase (CAT) and peroxidase (POD) activities significantly increased that was probably due to the ability of plant to overcome oxidative stress. As a result of DB129 biodegradation, a number of intermediate compounds were identified by gas chromatography-mass spectroscopy (GC-MS) analysis. Accordingly, the probable degradation pathway of DB129 in S. polyrrhiza was postulated.
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Affiliation(s)
- A Movafeghi
- a Department of Plant Biology , Faculty of Natural Sciences, University of Tabriz , Tabriz , Iran
| | - A R Khataee
- b Research Laboratory of Advanced Water and Wastewater Treatment Processes , Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz , Tabriz , Iran
| | - Z Moradi
- a Department of Plant Biology , Faculty of Natural Sciences, University of Tabriz , Tabriz , Iran
| | - F Vafaei
- a Department of Plant Biology , Faculty of Natural Sciences, University of Tabriz , Tabriz , Iran
- b Research Laboratory of Advanced Water and Wastewater Treatment Processes , Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz , Tabriz , Iran
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Buenno LH, Rocha JC, Leme J, Caricati CP, Tonso A, Fernández Núñez EG. Use of uniform designs in combination with neural networks for viral infection process development. Biotechnol Prog 2015; 31:532-40. [DOI: 10.1002/btpr.2051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Revised: 12/20/2014] [Indexed: 11/09/2022]
Affiliation(s)
- Laís Hara Buenno
- Departamento de Ciências Biológicas; Universidade Estadual “Júlio de Mesquita Filho” Campus-Assis; Avenida Dom Antonio 2100 19806-900 Assis SP-Brasil
| | - José Celso Rocha
- Departamento de Ciências Biológicas; Universidade Estadual “Júlio de Mesquita Filho” Campus-Assis; Avenida Dom Antonio 2100 19806-900 Assis SP-Brasil
| | - Jaci Leme
- Laboratório Especial de Pesquisa e Desenvolvimento em Imunológicos Veterinários, Instituto Butantan; Av. Vital Brasil, 1500 05503-900 São Paulo SP-Brazil
| | - Celso Pereira Caricati
- Laboratório Especial de Pesquisa e Desenvolvimento em Imunológicos Veterinários, Instituto Butantan; Av. Vital Brasil, 1500 05503-900 São Paulo SP-Brazil
| | - Aldo Tonso
- Laboratório de Células Animais, Departamento de Engenharia Química, Escola Politécnica; Universidade de São Paulo; Av. Prof. Luciano Gualberto, trav. 3, 380 05508-900 São Paulo SP-Brazil
| | - Eutimio Gustavo Fernández Núñez
- Departamento de Ciências Biológicas; Universidade Estadual “Júlio de Mesquita Filho” Campus-Assis; Avenida Dom Antonio 2100 19806-900 Assis SP-Brasil
- Laboratório de Células Animais, Departamento de Engenharia Química, Escola Politécnica; Universidade de São Paulo; Av. Prof. Luciano Gualberto, trav. 3, 380 05508-900 São Paulo SP-Brazil
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Hassani A, Vafaei F, Karaca S, Khataee A. Adsorption of a cationic dye from aqueous solution using Turkish lignite: Kinetic, isotherm, thermodynamic studies and neural network modeling. J IND ENG CHEM 2014. [DOI: 10.1016/j.jiec.2013.10.049] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Pal S, Mukherjee S, Ghosh S. Estimation of the phenolic waste attenuation capacity of some fine-grained soils with the help of ANN modeling. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2014; 21:3524-3533. [PMID: 24271727 DOI: 10.1007/s11356-013-2315-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Accepted: 10/31/2013] [Indexed: 06/02/2023]
Abstract
In the present investigation, batch experiments were undertaken in the laboratory for different initial phenol concentration ranging from 10 to 40 mg/L using various types of fine-grained soils namely types A, B, C, D, and E based on physical compositions. The batch kinetic data were statistically analyzed with a three-layered feed-forward artificial neural network (ANN) model for predicting the phenol removal efficiency from the water environment. The input parameters considered were the adsorbent dose, initial phenol concentration, contact time, and percentage of clay and silt content in soils. The response output of the ANN model was considered as the phenol removal efficiency. The predicted results of phenol removal efficiency were compared with the experimental values as obtained from batch tests and also tests for goodness of fitting in ANN model with experimental results. The estimated values of coefficient of correlation (R = 0.99) and mean squared error (MSE = 0.006) reveals a reasonable closeness of experimental and predicted values. Out of five different types of soil, type E exhibited the highest removal efficiency (31.6 %) corresponding to 20 mg/L of initial phenol concentration. A sensitivity analysis was also carried out on the ANN model to ascertain the degree of effectiveness of various input variables.
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Affiliation(s)
- Supriya Pal
- Department of Civil Engineering, National Institute of Technology (NIT), Durgapur, 713209, West Bengal, India,
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Vafaei F, Movafeghi A, Khataee AR, Zarei M, Salehi Lisar SY. Potential of Hydrocotyle vulgaris for phytoremediation of a textile dye: Inducing antioxidant response in roots and leaves. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2013; 93:128-134. [PMID: 23660490 DOI: 10.1016/j.ecoenv.2013.03.035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Revised: 03/09/2013] [Accepted: 03/11/2013] [Indexed: 06/02/2023]
Abstract
The potential of Hydrocotyle vulgaris as an aquatic plant species was evaluated for phytoremediation of C.I. Basic Red 46 (BR46) from nutrient solution. Under the optimized experimental conditions, BR46 was removed up to 95% from incubation medium by H. vulgaris. The ability of the plant in consecutive removal under long term repetitive experiments confirmed the biodegradation process. Accordingly, a number of produced intermediate compounds were identified. An artificial neural network (ANN) model was developed to predict the biodegradation efficiency. A predictive performance (R(2)=0.974) was obtained based on the network results. Interestingly, dye stress enhanced the activity of antioxidant enzymes including superoxide dismutase, peroxidase and catalase in H. vulgaris roots and leaves. Enzymatic responses found to be highly depended on the plant organ and dye concentration in the liquid medium. Overall, the increase in the activity of antioxidant enzymes was much higher in the roots than in the leaves. Nevertheless, no significant increase in the malondialdehyde (MDA) content was detected in both roots and leaves which reflects the high efficiency of antioxidant system in the elimination of reactive oxygen species.
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Affiliation(s)
- F Vafaei
- Department of Plant Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
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Shahryari Z, Mohebbi A, Soltani Goharrizi A, Forghani AA. Application of artificial neural networks for formulation and modeling of dye adsorption onto multiwalled carbon nanotubes. RESEARCH ON CHEMICAL INTERMEDIATES 2012. [DOI: 10.1007/s11164-012-0865-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Perpetuo EA, Silva DN, Avanzi IR, Gracioso LH, Baltazar MPG, Nascimento CAO. Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling. ENVIRONMENTAL TECHNOLOGY 2012; 33:1739-1745. [PMID: 22988635 DOI: 10.1080/09593330.2011.644585] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L(-1), 2.8 g L(-1), 4.2 g L(-1)), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency ofbiodegradation.
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Affiliation(s)
- Elen Aquino Perpetuo
- Environmental Microbiology Laboratory, CEPEMA-POLI-USP, University of São Paulo, São Paulo-SP, CEP 11573-000, Brazil.
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Nourouzi MM, Chuah TG, Choong TSY, Rabiei F. Modeling biodegradation and kinetics of glyphosate by artificial neural network. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART. B, PESTICIDES, FOOD CONTAMINANTS, AND AGRICULTURAL WASTES 2012; 47:455-65. [PMID: 22424071 DOI: 10.1080/03601234.2012.663603] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (< 8) exhibited the inhibitory effect of glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.
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Affiliation(s)
- Mohsen M Nourouzi
- Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, Selangor, Malaysia.
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