Abstract
In recent decades, the application of machine learning methods as a powerful tool supporting accurate and representative models has become common in various fields, including pollution assessment in soil and sediment. Widespread contamination in these areas, causing severe impacts on ecosystems and living beings, has resulted in the development of numerous models based on machine learning techniques. These models have been used to detect, trace, and predict the extent of contamination levels and create specified management plans. This paper provides a bibliometric analysis of the evaluation of soil and sediment contamination and treatment strategies using machine learning studies from 1986 to 2022. Meaningful analysis has been done on research trends, publishing activity of journals, most active countries, subject areas, top authors, and author keywords. The research showed that China with the highest number of publications has made extensive investments and has put a special focus on this area. The most studied contaminants are heavy metals, followed by polycyclic aromatic hydrocarbons, and persistent organic pollutants. The artificial neural network followed by cluster analysis and principal component analysis are the most widely used methods.
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References
Agyeman PC et al (2021) Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review. Environ Geochem Health 43(5):1715–1739. https://doi.org/10.1007/s10653-020-00742-9
Cerniglia CE (1992) Biodegradation of polycyclic aromatic hydrocarbons. Biodegradation 3(2–3):351–368. https://doi.org/10.1007/BF00129093
de Boer J, Wagelmans M (2016) Polycyclic aromatic hydrocarbons in soil - practical options for remediation. Clean (Weinh) 44(6):648–653. https://doi.org/10.1002/clen.201500199
Fu HZ, Wang MH, Ho YS (2013) Mapping of drinking water research: a bibliometric analysis of research output during 1992–2011. Sci Total Environ 443:757–765. https://doi.org/10.1016/j.scitotenv.2012.11.061
Guo K, Liu YF, Zeng C, Chen YY, Wei XJ (2014) Global research on soil contamination from 1999 to 2012: a bibliometric analysis. Acta Agric Scand B Soil Plant Sci 64(5):377–391. https://doi.org/10.1080/09064710.2014.913679
Lee H, Kim HK, Noh HJ, Byun YJ, Chung HM, Kim JI (2021) Source identification and assessment of heavy metal contamination in urban soils based on cluster analysis and multiple pollution indices. J Soils Sediments 21(5):1947–1961. https://doi.org/10.1007/s11368-020-02716-x.
Hou D, Bolan NS, Tsang DCW, Kirkham MB, O’Connor D (2020) Sustainable soil use and management: an interdisciplinary and systematic approach. Sci Total Environ, vol 729. https://doi.org/10.1016/j.scitotenv.2020.138961.
Kirkok SK, Kibet JK, Kinyanjui TK, Okanga FI (2020) A review of persistent organic pollutants: dioxins, furans, and their associated nitrogenated analogues. SN Appl Sci 2(10). https://doi.org/10.1007/s42452-020-03551-y.
Lei Y, Liu Z (2019) The development of artificial intelligence: a bibliometric analysis, 2007–2016. J Phys Conf Ser 1168(2). https://doi.org/10.1088/1742-6596/1168/2/022027.
Li Z, Ma Z, van der Kuijp TJ, Yuan Z, Huang L (2014) A review of soil heavy metal pollution from mines in China: pollution and health risk assessment. Sci Total Environ 468–469:843–853. https://doi.org/10.1016/j.scitotenv.2013.08.090
Li T, Liu Y, Lin S, Liu Y, Xie Y (2019) Soil pollution management in China: a brief introduction. Sustainability (Switzerland) 11(3):1–15. https://doi.org/10.3390/su11030556
Ljung K, Maley F, Cook A, Weinstein P (2009) Acid sulfate soils and human health-a millennium ecosystem assessment. Environ Int 35(8):1234–1242. https://doi.org/10.1016/j.envint.2009.07.002
Manzoor B, Othman I, Durdyev S, Ismail S, Wahab MH (2021) Influence of artificial intelligence in civil engineering toward sustainable development—a systematic literature review. Appl Syst Innovation 4(3):1–17. https://doi.org/10.3390/asi4030052
Mao G, Shi T, Zhang S, Crittenden J, Guo S, Du H (2018) Bibliometric analysis of insights into soil remediation. J Soils Sediments 18(7):2520–2534. https://doi.org/10.1007/s11368-018-1932-4
Najah Ahmed A et al. (2019) Machine learning methods for better water quality prediction. J Hydrol (Amst), vol 578. https://doi.org/10.1016/j.jhydrol.2019.124084.
Rachna MR, Shanker U (2019) Degradation of tricyclic polyaromatic hydrocarbons in water, soil and river sediment with a novel TiO2 based heterogeneous nanocomposite. J Environ Manage 248:109340. https://doi.org/10.1016/j.jenvman.2019.109340.
Sabour MR, Derhamjani G, Akbari M, Hatami AM (2021) Global trends and status in waste foundry sand management research during the years 1971–2020: a systematic analysis. Environ Sci Pollut Res 28(28):37312–37321. https://doi.org/10.1007/s11356-021-13251-8
M. R. Sabour, M. A. Jafari, and S. M. Hosseini Gohar, “Si-based Solar Cells’ Conversion Efficiency Related Publications Bibliometric Review During 2000–2017,” Silicon, vol. 12, no. 11, pp. 2705–2720, 2020, https://doi.org/10.1007/s12633-019-00366-4.
Sall ML, Diaw AKD, Gningue-Sall D, Efremova Aaron S, Aaron JJ (2020) Toxic heavy metals: impact on the environment and human health, and treatment with conducting organic polymers, a review. Environ Sci Pollut Res 27(24):29927–29942. https://doi.org/10.1007/s11356-020-09354-3.
Song B et al (2017) Evaluation methods for assessing effectiveness of in situ remediation of soil and sediment contaminated with organic pollutants and heavy metals. Environ Int 105(January):43–55. https://doi.org/10.1016/j.envint.2017.05.001
Vácha R, Čechmánková J, Skála J (2010) Soil and Plant Absorption.Pdf. 9:434–443.
Xu Y, Shi H, Fei Y, Wang C, Mo L, Shu M (2021) Article identification of soil heavy metal sources in a large-scale area affected by industry. Sustainability (Switzerland) 13(2):1–18. https://doi.org/10.3390/su13020511
Yaseen ZM (2021) An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere 277:130126. https://doi.org/10.1016/j.chemosphere.2021.130126
Zhang S, Mao G, Crittenden J, Liu X, Du H (2017) Groundwater remediation from the past to the future: A bibliometric analysis. Water Res 119:114–125. https://doi.org/10.1016/j.watres.2017.01.029
Zhang Y, Lei M, Li K et al (2023) Spatial prediction of soil contamination based on machine learning: a review. Front Environ Sci Eng 17:93. https://doi.org/10.1007/s11783-023-1693-1
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Sabour, M.R., Sakhaie, P. & Sharifian, F. Trend analysis of machine learning application in the study of soil and sediment contamination. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05575-y
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DOI: https://doi.org/10.1007/s13762-024-05575-y