IJEP 44(9): 833-840 : Vol. 44 Issue. 9 (September 2024)
1. Graphic Era (Deemed to be University), Department of Environmental Science, Dehradun – 248 002, Uttarakhand, India
2. Graphic Era (Deemed to be University), Department of Computer Science, Dehradun – 248 002, Uttarakhand, India
Abstract
Phytoremediation is an emerging technology that involves plants to decontaminate soil, water and air. Plants are natural purifiers that purify air pollution by tackling chemicals and biological pollutants. There are certain special plants, such as spider plants, bamboo palms, etc., that can help to get clean indoor air in the city. Alternatively, the introduction of an intelligent processing system for malodorous gas monitoring devices, like artificial neural networks, could provide a reliable solution to the problem. This study tries to fill in the gaps left by previous research on how to choose the optimal design, as well as its benefits and drawbacks. If implemented, this method could open up a new door in the field of environmental odour management by making use of a robust mathematical computing tool to produce more accurate and consistent results. This research article also provides a summary of the ways in which artificial neural networks have been used to control unpleasant odours in the built environment. It has been determined that the elements, structure and learning algorithms are the primary variables in creating the best artificial neural network. The performance of the proposed neural network is examined rigorously, with strengths and faults highlighted for each metric.
Keywords
Phytoremediation, Artificial intelligence, Odours, Pollutants, Technology, Environment
References
- Hayes, J.E., et al. 2017. Survey of th effect of odour impact on communities. J. Env. Manage., 204(1): 349-354.
- Brancher, M., et al. 2017. A review of odour impact criteria in slected countries around the world. Chemosphere. 168: 1531-1570.
- Suen, L.K., et al. 2019. The public washroom- Friend or foe? An observational study of washroom cleanliness combined with microbiological investigation of hand hygiene facilities. Antimicrob. Resist. Infect. Cont., 8: 47.
- Craven, M.A., J. W. Gardner and P.N. Bartlett. 1966. Electronic noses: Development and future prospects. Trends Anal. Chem., 15: 486-493.
- Rappert, S. and R. Muller. 2005. Odour compounds in waste gas emissions from agricultural operations and food industry. Waste Manage., 25(9): 887-907.
- Bar, D. and K. Cohen-Ilattab. 2003. A new kind of pilgrimage: The modern tourist pilgrim of nineteenth-century and early twentieth-century Palestine. Middle Eastern Studies. 39(2): 131-148.
- Chaney, R.L. 1983. Plant uptake of inorganic waste constituents. In Land tratment of hazardous waste. Ed J.F. Parr, P.B. Marsh and J.M. Kla. Noyes Data Corporation, Park Ridge, New Jersey. pp 50-76.
- Hooda, V. 2007. Phytoremediation of toxic metals from soil and wastewater. J. Env. Biol., 28(2 Suppl.): 367-376.
- Mott, J.A., et al. 2002. Wildland forest fire smoke: Health effects and intervention evaluation, Hoopa, California, 1999. Western J. Med., 176(3): 157–162.
- Adeniran, J.A. and A.S. Atanda. 2023. Carbon monoxide formation from total volatile organic compounds from the use of household spray products. J. Air Poll. Health. 8(3): 361-380.
- Park, S., et al. 2018. Predicting PM10concentration in Seoul metropolitan subway stations using artificial neural network (ANN). J. Hazard. Mater., 341: 75-82.
- Pelosi, P., J. Zhu and W. Knoll. 2018. From gas sensors to biomimetric artificial noses. Chemosen-sers. 6: 32.
- Nozaki, Y. and T. Nakamoto. 2016. Odour impression prediction from mass spectra. PLoS One. 11: e0157030.
- Shang, L., et al. 2017. Machine-learning based olfactometer: Prediction of odour perception from photochemical features of odorant molecules. Anal. Chem., 89: 11999-12005.
- Zarra, T., et al. 2019. Environmental odour management artificial neural network- A review. Env. Int., 133: 105189.
- Bylinski, H., A. Sobecki and J. Gebicki. 2019. The use of artifical neural networks and decision trees to predict the degree of odour nuisance of post-digestion sludge in the sewage treatment plant process. Sustain., 11: 4407.
- Mirshahi, M., V. Partovi Nia and L. Adjengue. 2018. Automatic odour prediction for electronic nose. J. Appl. Stat., 15: 2788-2799.
- Onkal-Engin, G., I. Demirand and S.N. Engin. 2005. Determination of the relationship between sewage odour and BOD by neural networks. Env. Model. Softw., 20: 843-850.
- Kang, J.H., et al. 2020. Prediction of odour concentration emitted from wastewater treatment plant using an artifical neural network (ANN). Atmos., 11(8): 784.
- Fomunyam, K.G. 2019. Health, metal and emotional impacts of odour producing industrial emissions on man. Int. J. Civil Eng. Tech., 10(10): 402-414.
- Zara, T., et al. 2021. Environmental odour quantification by IOMS: Parametric vs non-parametric prediction techniques. Chemosensor. 9(7): 183.
- Yang, F., et al. 2021. Advanced machine learning application for odour and corrosion control at a water resource recovery facility. Water Env. Res., 93(11): 2316-2359.
- Lee, D.H., et al. 2022. Prediction of complex odour from pig barn using machine learning and identifying the influence of variables using explainable artificial intelligence. Appl. Sci., 12(24): 12943.
- Rincon, C.A., et al. 2019. Odour concentration (OC) prediction based on odour activity values (OAVs) during composting of solid wastes and digestates. Atmos. Env., 201: 1-12.
- Barczak, R.J., et al. 2022. Odour concentration prediction based on odourants concentrations from biosolids emissions. Env. Res., 214: 113871.
- Cangialosi, F., E. Bruno and G. De Santis. 2021. Application of machine learning for fenceline monitoring of odour classes and concentrations at a wastewater treatment plant. Sensor. 21: 4716.
- Mulrow, J., et al. 2020. Prediction of odour complaints at a large composite reservoir in a highly urbanized area: A machine learning approach. Water Env. Res., 92(3): 418-429.
- Zhu, N., et al. 2020. A novel Coronavirus from patients with pneumonia in China, 2019. New England J. Med., 382(8): 727-733.
- Qui, M., et al. 2022. Favourable vaccine-induced SARS-CoV-2–specific T cell response profile in patients undergoing immune-modifying therapi-es. J. Clin. Investig., 132(12): e159500.
- Wojtuch, A., R. Jankowski and S. Podlewska. 2021. How can SHAP values help to shape metabolic stability of chemical compounds? J. Cheminf., 13: 1-20.
- Chakkingal, A., et al. 2021. Unravelling the influence of catalyst properties on light olefin production via Fischer–Tropsch synthesis: A descriptor space investigation using single-event micro-kinetics. Chem. Eng. J., 419: 129633.
- Grimmig, R., et al. 2021. Analyses of used engine oils via atomic spectroscopy– Influence of sample pre-treatment and machine learning for engine type classification and lifetime assessment. Talanta. 232: 122431.
- Kim, K. R. and G. Owens. 2010. Potential for enhanced phytoremediation of landfills using biosolids – A review. J. Env. Manage., 91(4): 791-797.
- Wolverton, B.C., A. Johnson and K. Bounds. 1989. Interior landscape plants for indoor air pollution abatement (patent no. NASA-TM-101766). NASA Technical Reports Server.
- Levei, L., et al. 2021. Use of black Poplar leaves for the biomonitoring of air pollution in an urban agglomeration. Plants. 10(3): 548.
