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Phytoremediation potential of Miscanthus sinensis And. in organochlorine pesticides contaminated soil amended by Tween 20 and Activated carbon.

Aigerim MamirovaValentina PidlisnyukAday AmirbekovAlena ŠevcůAsil Nurzhanova
Published in: Environmental science and pollution research international (2020)
The organochlorine pesticides (OCPs) have raised concerns about being persistent and toxic to the environment. Phytoremediation techniques show promise for the revitalization of polluted soils. The current study focused on optimizing the phytoremediation potential of Miscanthus sinensis And. (M. sinensis), second-generation energy crop, by exploring two soil amendments: Tween 20 and activated carbon (AC). The results showed that when M. sinensis grew in OCP-polluted soil without amendments to it, the wide range of compounds, i.e., α-HCH, β-HCH, γ-HCH, 2.4-DDD, 4.4-DDE, 4.4-DDD, 4.4-DDT, aldrin, dieldrin, and endrin, was accumulated by the plant. The introduction of soil amendments improved the growth parameters of M. sinensis. The adding of Tween 20 enhanced the absorption and transmigration to aboveground biomass for some OCPs; i.e., for γ-HCH, the increase was by 1.2, for 4.4-DDE by 8.7 times; this effect was due to the reduction of the hydrophobicity which made pesticides more bioavailable for the plant. The adding of AC reduced OCPs absorption by plants, consequently, for γ-HCH by 2.1 times, 4.4-DDD by 20.5 times, 4.4-DDE by 1.4 times, 4.4-DDT by 8 times, α-HCH was not adsorbed at all, and decreased the translocation to the aboveground biomass: for 4.4-DDD by 31 times, 4.4-DDE by 2.8 times, and γ-HCH by 2 times; this effect was due to the decrease in the bioavailability of pesticides. Overall, the amendment of OCP-polluted soil by Tween 20 speeds the remediation process, and incorporation of AC permitted to produce the relatively clean biomass for energy.
Keyphrases
  • heavy metals
  • plant growth
  • risk assessment
  • wastewater treatment
  • gas chromatography
  • human health
  • climate change
  • mass spectrometry
  • sewage sludge
  • machine learning