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Research Article

Machine learning models for occurrence form prediction of heavy metals in tailings

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Pages 978-995 | Received 18 Apr 2023, Accepted 22 Jun 2023, Published online: 04 Jul 2023

References

  • E.E. Medina Tripodi, J.A. Gamboa Rueda, C. Aguirre Céspedes, J. Delgado Vega, and C. Collao Gómez, et al., Characterization and geostatistical modelling of contaminants and added value metals from an abandoned Cu–Au tailing dam in Taltal (Chile), J. S. Am. Earth Sci. 93 (2019), pp. 183–202. doi:10.1016/j.jsames.2019.05.001.
  • C. Wang, D. Harbottle, Q. Liu, Z. Xu, et al., Current state of fine mineral tailings treatment: A critical review on theory and practice, Min. Eng. 58 (2014), pp. 113–131. doi:10.1016/j.mineng.2014.01.018.
  • C. Falagán, B.M. Grail, and D.B. Johnson, New approaches for extracting and recovering metals from mine tailings, Min. Eng. 106 (2017), pp. 71–78. doi:10.1016/j.mineng.2016.10.008.
  • L. Jiang, H. Sun, T. Peng, W. Ding, B. Liu, Q. Liu, et al., Comprehensive evaluation of environmental availability, pollution level and leaching heavy metals behavior in non-ferrous metal tailings, J. Environ. Manage. 290 (2021), pp. 112639. doi:10.1016/j.jenvman.2021.112639.
  • P. Quevauviller, R. Lavigne, and L. Cortez, Impact of industrial and mine drainage wastes on the heavy metal distribution in the drainage basin and estuary of the Sado River (Portugal), Environ. Pollut. 59 (4) (1989), pp. 267–286. doi:10.1016/0269-7491(89)90155-3.
  • A. Ordóñez, R. Álvarez, and J. Loredo, Soil pollution related to the mercury mining legacy at Asturias (Northern Spain), Int. J. Min. Reclam. Environ. 28 (6) (2014), pp. 389–396. doi:10.1080/17480930.2014.967920.
  • L.C. Kon, S. Durucan, and A. Korre, The development and application of a wind erosion model for the assessment of fugitive dust emissions from mine tailings dumps, Int. J. Min. Reclam. Environ. 21 (3) (2007), pp. 198–218. doi:10.1080/17480930701365547.
  • C. Qi, M. Wu, H. Liu, Y. Liang, X. Liu, Z. Lin, et al., Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes, J. Clean. Prod. 401 (2023), pp. 136771. doi:10.1016/j.jclepro.2023.136771.
  • J. Nyamangara, Use of sequential extraction to evaluate zinc and copper in a soil amended with sewage sludge and inorganic metal salts, Agr. Ecosyst. Environ. 69 (2) (1998), pp. 135–141. doi:10.1016/S0167-8809(98)00101-7.
  • C.B. Opara, S. Kutschke, and K. Pollmann, Fractionation of Metal(loid)s in three European mine wastes by sequential extraction, Separations 9 (3) (2022), pp. 67. doi:10.3390/separations9030067.
  • B. Pérez-Cid, I. Lavilla, and C. Bendicho, Application of microwave extraction for partitioning of heavy metals in sewage sludge, Anal. Chim. Acta 378 (1) (1999), pp. 201–210. doi:10.1016/S0003-2670(98)00634-5.
  • M. Kersten and U. Förstner, Chemical fractionation of heavy metals in anoxic estuarine and coastal sediments, Water Sci. Technol. 18 (4–5) (1986), pp. 121–130. doi:10.2166/wst.1986.0187.
  • G. Rauret, J.F. López-Sánchez, A. Sahuquillo, R. Rubio, C. Davidson, A. Ure, P. Quevauviller, Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials, J. Environ. Monitor. 1 (1) (1999), pp. 57–61. doi:10.1039/a807854h.
  • A.M. Ure, P. Quevauviller, H. Muntau, B. Griepink, Speciation of Heavy Metals in Soils and Sediments. An Account of the Improvement and Harmonization of Extraction Techniques Undertaken Under the Auspices of the BCR of the Commission of the European Communities, Int J Environ Anal Chem 51 (1–4) (1993), pp. 135–151. doi:10.1080/03067319308027619.
  • H. Kennedy, V. et al., Use of single and sequential chemical extractants to assess radionuclide and heavy metal availability from soils for root uptake, Analyst (Lond). 1228 (1997), 89R–100R. 10.1039/a704133k.
  • R.I.O.R.S.M. Balaniuk and O. Isupova, Mining and tailings dam detection in satellite imagery using deep learning, Sensors 20 (23) (2020), pp. 6936. doi:10.3390/s20236936.
  • J.H.Y.R.S.Y.Y.D.D.G.Q.T.L.E.T.T.P.F.H.S.R.R.S.I.B.I.A.D.L.-B. Lyu, Remote Sensing, 2021, 13 10.3390/rs13040743.
  • B. Salman and M.M. Kadhum, Predicting of load carrying capacity of reactive powder concrete and normal strength concrete column specimens using artificial neural network, Know. Eng. Sci. 3 (1) (2022), pp. 45–53.
  • E.F. AlHares and C. Budayan, Estimation at completion simulation using the potential of soft computing models: Case study of construction engineering projects, Symmetry 11 (2) (2019), pp. 190. doi:10.3390/sym11020190.
  • B. Xiao, S. Miao, and Q. Gao, Quantifying particle size and size distribution of mine tailings through deep learning approach of autoencoders, Pow. Technol. 397 (2022), pp. 117088. doi:10.1016/j.powtec.2021.117088.
  • Z. Cheng, Y. Yang, and H. Zhang, Interpretable ensemble machine-learning models for strength activity index prediction of iron ore tailings, Case Stud. Cons. Mater. 17 (2022), pp. e01239. doi:10.1016/j.cscm.2022.e01239.
  • C.A.L. Davies, K. Tomlinson, and T. Stephenson, Heavy metals in River Tees estuary sediments, Environ Technol 12 (11) (1991), pp. 961–972. doi:10.1080/09593339109385095.
  • F. Moore, M.J. Nematollahi, and B. Keshavarzi, Heavy metals fractionation in surface sediments of Gowatr bay–Iran, Environ. Monit. Assess. 187 (1) (2015), pp. 4117. doi:10.1007/s10661-014-4117-7.
  • S.R. Claff, L.A. Sullivan, E.D. Burton, and R.T. Bush, et al., A sequential extraction procedure for acid sulfate soils: Partitioning of iron, Geoderma 155 (3–4) (2010), pp. 224–230. doi:10.1016/j.geoderma.2009.12.002.
  • S. Sachdeva, T. Bhatia, and A.K. Verma, GIS-based evolutionary optimized gradient boosted decision trees for forest fire susceptibility mapping, Nat. Hazards 92 (3) (2018), pp. 1399–1418. doi:10.1007/s11069-018-3256-5.
  • L. Wang, Y. Zhang, Y. Yao, Z. Xiao, K. Shang, X. Guo, J. Yang, S. Xue, J. Wang, GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets, Remote Sens. 13 (6) (2021), pp. 1054. doi:10.3390/rs13061054.
  • Hastie, T., R. Tibshirani, and J. Friedman, Boosting and Additive Trees, in The Elements of Statistical Learning: Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani, and J. Friedman, Editors., Springer New York: New York, NY (2009). p. 337-387.
  • N. Doebelin and R. Kleeberg, Profex: A graphical user interface for the Rietveld refinement program BGMN, J. Appl. Crystallogr. 48 (5) (2015), pp. 1573–1580. doi:10.1107/S1600576715014685.
  • A. Grobelak and A. Napora, The chemophytostabilisation process of heavy metal polluted soil, PLoS One 10 (6) (2015), pp. e0129538. doi:10.1371/journal.pone.0129538.
  • D.E. Booth, V. Gopalakrishna-Remani, M.L. Cooper, F.R. Green, M.P. Rayman, et al., Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium, Sci Rep 11 (1) (2021), pp. 17877. doi:10.1038/s41598-021-97412-2.
  • R. Baieta, M. Mihaljevič, V. Ettler, A. Vaněk, V. Penížek, J. Trubač, B. Kříbek, J. Ježek, M. Svoboda, O. Sracek, and I. Nyambe, et al., Depicting the historical pollution in a Pb–Zn mining/smelting site in Kabwe (Zambia) using tree rings, J. Afr. Earth Sci. 181 (2021), pp. 104246. doi:10.1016/j.jafrearsci.2021.104246.
  • W.Q. Pu, J. Sun, F. Zhang, X. Wen, W. Liu, and C. Huang, et al., Effects of copper mining on heavy metal contamination in a rice agrosystem in the Xiaojiang River Basin, southwest China, Acta Geoch. 38 (5) (2019), pp. 753–773. doi:10.1007/s11631-019-00321-5.
  • Z.Y. Hseu, Geochemical Fractionation of Chromium and Nickel in Serpentine Soil Profiles Along a Temperate to Tropical Climate Gradient, GEODERMA, Vol. 327, 2018, pp. 97–106.
  • V. Memoli, E. Eymar, C. García-Delgado, F. Esposito, L. Santorufo, A. De Marco, R. Barile, G. Maisto, et al., Total and fraction content of elements in volcanic soil: Natural or anthropogenic derivation, Sci. Total Environ. 625 (2018), pp. 16–26. doi:10.1016/j.scitotenv.2017.12.223.
  • F. Pedregosa, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12 (2011), pp. 2825–2830.
  • J. Zhou, X. Li, and S. Mitri Hani, Classification of rockburst in underground projects: Comparison of ten supervised learning methods, J. Comput. Civil Eng. 30 (5) (2016), pp. 04016003. doi:10.1061/(ASCE)CP.1943-5487.0000553.
  • J. Fan, W. Yue, L. Wu, F. Zhang, H. Cai, X. Wang, X. Lu, Y. Xiang, et al., Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China, Agric. For. Meteorol. 263 (2018), pp. 225–241. doi:10.1016/j.agrformet.2018.08.019.
  • S.H. Samadi, B. Ghobadian, and M. Nosrati, Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method. Energy sources, Part A: Recovery, Uti. Environ. Effec. 43 (6) (2021), pp. 672–681. doi:10.1080/15567036.2019.1630521.
  • F. Yang, D. Wang, F. Xu, Z. Huang, K.-L. Tsui, et al., Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model, J Power Sources 476 (2020), pp. 476. doi:10.1016/j.jpowsour.2020.228654.
  • J. Elith, J.R. Leathwick, and T. Hastie, A working guide to boosted regression trees, J Anim Ecol 77 (4) (2008), pp. 802–813. doi:10.1111/j.1365-2656.2008.01390.x.
  • V. Chandwani, V. Agrawal, and R. Nagar, Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert systems with applications, Expert Syst Appl 42 (2) (2015), pp. 885–893. doi:10.1016/j.eswa.2014.08.048.
  • E.M. Golafshani, A. Behnood, and M. Arashpour, Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer, Constr. Build. Mater. 232 (2020), pp. 117266. doi:10.1016/j.conbuildmat.2019.117266.
  • M.Z. Naser and A.H. Alavi, Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences, Architecture, Structures and Construction, 2021.
  • T. Hastie, The Elements of Statistical Learning: Data Mining, Inference, and Prediction Vol. 2, New York, Springer, (2009).
  • G.Z. Espinoza, R. Stoean, Evaluating Deep Learning models for predicting ALK-5 inhibition, PLoS One 16 (1) (2021), pp. e0246126. doi:10.1371/journal.pone.0246126.
  • H.B. Ly, T.-A. Nguyen, H.-V. Thi Mai, V.Q. Tran, et al., Development of deep neural network model to predict the compressive strength of rubber concrete. CONSTRUCTION and BUILDING MATERIALS, 2021, Constr. Build. Mater. 301 (2021), pp. 124081. doi:10.1016/j.conbuildmat.2021.124081.
  • M. Vega García and J.L. Aznarte, Shapley additive explanations for NO2 forecasting, Ecol. Inform 56 (2020), pp. 101039. doi:10.1016/j.ecoinf.2019.101039.
  • S.M. Lundberg and S.I. Lee, A unified approach to interpreting model predictions, Adv. NEUR. INFORM. PROCESS.SYS. 30 (NIPS 2017), 2017, pp. 4768–4777.
  • J. Li, L. Pan, M. Suvarna, Y.W. Tong, and X. Wang, et al., Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning, Appl. Energ. 269 (2020), pp. 115166. doi:10.1016/j.apenergy.2020.115166.
  • B. Gregorutti, B. Michel, and P. Saint-Pierre, Correlation and variable importance in random forests, Stat. Comput. 27 (3) (2017), pp. 659–678. doi:10.1007/s11222-016-9646-1.
  • R.T. Sanderson, Electronegativity and bond energy, J. Am. Chem. Soc. 105 (8) (1983), pp. 2259–2261. doi:10.1021/ja00346a026.
  • J.E. Huheey, Inorganic Chemistry: Principles of Structure and Reactivity, (2006), New Delhi, India, Pearson Education India.
  • T.L. Tra Ho and K. Egashira, Heavy metal characterization of river sediment in Hanoi, Vietnam, Commun. Soil Sci. Plan. 31 (17–18) (2000), pp. 2901–2916. doi:10.1080/00103620009370637.
  • T.X. Guan, H.B. He, X.D. Zhang, and Z. Bai, et al., Cu fractions, mobility and bioavailability in soil-wheat system after Cu-enriched livestock manure applications, Chemosphere 82 (2) (2011), pp. 215–222. doi:10.1016/j.chemosphere.2010.10.018.
  • M. Wu, C. Qi, Q. Chen, H. Liu, et al., Evaluating the metal recovery potential of coal fly ash based on sequential extraction and machine learning, Environ. Res. 224 (2023), pp. 115546. doi:10.1016/j.envres.2023.115546.
  • G.B. Kaufman, Inorganic chemistry: Principles of structure and reactivity, 4th ed. (Huheey, James E.; Keiter, Ellen A.; Keiter, Richard L.), J. Chem. Educ. 70 (10) (1993), pp. A279. doi:10.1021/ed070pA279.1.
  • R. Chang and K. Goldsby, Chemistry, in Reactions in Aqueous Solutions, 10th, R. Chang, ed. McGraw-Hill, New York, 2010, pp. 120–159.

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