Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 03004
Number of page(s) 10
Section Environment Science
DOI https://doi.org/10.1051/e3sconf/202344803004
Published online 17 November 2023
  1. X. Wang, C. Zhang, C. Wang, Y. Zhu, and Y. Cui, “Probabilistic-fuzzy risk assessment and source analysis of heavy metals in soil considering uncertainty: A case study of Jinling Reservoir in China,” Ecotoxicol. Environ. Saf., vol. 222, p. 112537, 2021, doi: 10.1016/j.ecoenv.2021.112537. [CrossRef] [Google Scholar]
  2. L. Yang et al., “Phytoremediation of heavy metal pollution: Hotspots and future prospects,” Ecotoxicol. Environ. Saf., vol. 234, no. September 2021, p. 113403, 2022, doi: 10.1016/j.ecoenv.2022.113403. [CrossRef] [Google Scholar]
  3. S. Mathur, “Modeling phytoremediation of soils,” Pract. Period. Hazardous, Toxic, Radioact. Waste Manag., vol. 8, no. 4, pp. 286–297, 2004, doi: 10.1061/(ASCE)1090-025X(2004)8:4(286). [CrossRef] [Google Scholar]
  4. H. R. Hadad, M. A. Maine, and C. A. Bonetto, “Macrophyte growth in a pilot-scale constructed wetland for industrial wastewater treatment,” Chemosphere, vol. 63, no. 10, pp. 1744–1753, 2006, doi: 10.1016/j.chemosphere.2005.09.014. [CrossRef] [PubMed] [Google Scholar]
  5. A. Valipour, V. Kalyan Raman, and V. S. Ghole, “A new approach in wetland systems for domestic wastewater treatment using Phragmites sp.,” Ecol. Eng., vol. 35, no. 12, pp. 1797–1803, 2009, doi: 10.1016/j.ecoleng.2009.08.004. [CrossRef] [Google Scholar]
  6. E. Jahantab, M. Jafari, R. Roohi, M. S. Aman, M. Moameri, and S. Zare, “Application of artificial neural network model for the identification the effect of municipal waste compost and biochar on phytoremediation of contaminated soils,” J. Geochemical Explor., vol. 208, no. September 2019, p. 106399, 2020, doi: 10.1016/j.gexplo.2019.106399. [Google Scholar]
  7. H. Teiri, Y. Hajizadeh, M. R. Samaei, H. Pourzamani, and F. Mohammadi, “Modelling the phytoremediation of formaldehyde from indoor air by Chamaedorea Elegans using artificial intelligence, genetic algorithm and response surface methodology,” J. Environ. Chem. Eng., vol. 8, no. 4, p. 103985, 2020, doi: 10.1016/j.jece.2020.103985. [CrossRef] [Google Scholar]
  8. A. Shahsavar, S. Khanmohammadi, A. Karimipour, and M. Goodarzi, “A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: A new approach of GMDH type of neural network,” Int. J. Heat Mass Transf., vol. 131, pp. 432–441, 2019, doi: 10.1016/j.ijheatmasstransfer.2018.11.069. [CrossRef] [Google Scholar]
  9. O. Omoregbe, A. N. Mustapha, R. Steinberger-Wilckens, A. El-Kharouf, and H. Onyeaka, “Carbon capture technologies for climate change mitigation: A bibliometric analysis of the scientific discourse during 1998–2018,” Energy Reports, vol. 6, pp. 1200–1212, 2020, doi: 10.1016/j.egyr.2020.05.003. [CrossRef] [Google Scholar]
  10. T. T. George, A. O. Obilana, A. B. Oyenihi, and F. G. Rautenbach, “Moringa oleifera through the years: a bibliometric analysis of scientific research (2000-2020),” South African J. Bot., vol. 141, pp. 12–24, 2021, doi: 10.1016/j.sajb.2021.04.025. [CrossRef] [Google Scholar]
  11. K. C. Obileke, H. Onyeaka, O. Omoregbe, G. Makaka, N. Nwokolo, and P. Mukumba, “Bioenergy from bio-waste: a bibliometric analysis of the trend in scientific research from 1998–2018,” Biomass Convers. Biorefinery, vol. 12, no. 4, pp. 1077–1092, 2022, doi: 10.1007/s13399-020-00832-9. [CrossRef] [Google Scholar]
  12. J. F. Burnham, “Scopus database: A review,” Biomed. Digit. Libr., vol. 3, pp. 1–8, 2006, doi: 10.1186/1742-5581-3-1. [CrossRef] [Google Scholar]
  13. S. I. Abdelwahab, M. M. E. Taha, S. M. E. Taha, and A. A. Alsayegh, “Fifty-year of Global Research in Calendula Officinalis L. (1971−2021): A Bibliometric Study,” Clin. Complement. Med. Pharmacol., vol. 2, no. 4, p. 100059, 2022, doi: 10.1016/j.ccmp.2022.100059. [CrossRef] [Google Scholar]
  14. Z. M. Yaseen, “An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions,” Chemosphere, vol. 277, 2021, doi: 10.1016/j.chemosphere.2021.130126. [CrossRef] [PubMed] [Google Scholar]
  15. N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, no. April, pp. 285–296, 2021, doi: 10.1016/j.jbusres.2021.04.070. [CrossRef] [Google Scholar]
  16. Z. M. Yaseen, “An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions,” Chemosphere, vol. 277, p. 130126, 2021, doi: 10.1016/j.chemosphere.2021.130126. [CrossRef] [PubMed] [Google Scholar]
  17. D. L. Sobariu et al., “Rhizobacteria and plant symbiosis in heavy metal uptake and its implications for soil bioremediation,” N. Biotechnol., vol. 39, pp. 125–134, 2017, doi: 10.1016/j.nbt.2016.09.002. [CrossRef] [Google Scholar]
  18. Z. Chen et al., “Optimizing co-combustion synergy of soil remediation biomass and pulverized coal toward energetic and gas-to-ash pollution controls,” Sci. Total Environ., vol. 857, 2023, doi: 10.1016/j.scitotenv.2022.159585. [Google Scholar]
  19. T. He, M. Zhang, and B. Jin, “Insight into the synergistic effect and products distribution during co-pyrolysis of phytoremediation residue and municipal sewage sludge through experiment and reaction force field simulation,” Fuel, vol. 333, 2023, doi: 10.1016/j.fuel.2022.126326. [Google Scholar]
  20. E. Jahantab, M. Jafari, R. Roohi, M. S. Aman, M. Moameri, and S. Zare, “Application of artificial neural network model for the identification the effect of municipal waste compost and biochar on phytoremediation of contaminated soils,” J. Geochemical Explor., vol. 208, no. June 2019, p. 106399, 2020, doi: 10.1016/j.gexplo.2019.106399. [Google Scholar]
  21. L. Polechońska, A. Klink, and M. Dambiec, “Trace element accumulation in Salvinia natans from areas of various land use types,” Environ. Sci. Pollut. Res., vol. 26, no. 29, pp. 30242–30251, 2019, doi: 10.1007/s11356-019-06189-5. [CrossRef] [PubMed] [Google Scholar]
  22. M. Złoch, T. Kowalkowski, J. Tyburski, and K. Hrynkiewicz, “Modeling of phytoextraction efficiency of microbially stimulated Salix dasyclados L. in the soils with different speciation of heavy metals,” Int. J. Phytoremediation, vol. 19, no. 12, pp. 1150–1164, 2017, doi: 10.1080/15226514.2017.1328396. [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.