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Phytoremediation potential of heavy metals by two native pasture plants (Eucalyptus grandis and ailanthus altissima) assisted with AMF and fibrous minerals in contaminated mining regions
2017
Abbaslou, Hakime | Bakhtiari, Somayeh
The current study assesses the effect of fibrous clay minerals’ amendments and arbuscular mycorrhiza incubation on heavy metal uptake and translocation in Eucalyptus grandis and Ailanthus altissima plants. For doing so, Eucalyptus and ailanthus trees have been grown in a soil sample, contaminated with heavy metal iron ore mining and collected from southern Iran. The area under study is arid, with the majority of trees being ailanthus and eucalyptus. Amounts of Cd, Pb, Zn, Cu, and Mn have initially been at toxic levels which declined after cultivation. Fibrous clay minerals have been added to soils as a natural adsorbent to adsorb heavy metals like Pb, Cd, Zn, and Mn. Accumulation of the elements in the roots and shoots has been in the following order: Cu>Zn>Mn>Cd>Pb>Fe. The organ metal concentrations have not statistically translocated from roots to shoots of plants, except for Zn and Cu whose concentrations have been significantly higher in roots. Eucalyptus is well capable of extracting elements from contaminated soils, compared to ailanthus, particularly in case of Cu and Cd. The percentage of mycorrhizal colonization proves to be more in pots with ailanthus plants grown in contaminated soil, suggesting enhanced effect of high metal concentrations on plant infection by G. mosseae. AMF assists soil remediation by enhancing the growth and retention of toxic elements by ailanthus, while no substantial change has been observed between inoculated and non-inoculated eucalyptus plants by AFM, regarding translocation of elements to plants. The possibility of increasing metal accumulation in roots is interesting for phytoremediation purposes, since most high-producing biomass plants, such as eucalyptus, retain heavy metals in roots.
Show more [+] Less [-]Investigating the effects of plant growth promoting bacteria and Glomus Mosseae on cadmium phytoremediation by Eucalyptus camaldulensis L.
2017
Motesharezadeh, Babak | kamal-poor, Sama | Alikhani, Hossein Ali | Zariee, Mehdi | Azimi, Sina
This research aims to study the effect of Mycorrizal fungus and Plant-Growth-Promoting Bacteria (PGPB) on Cadmium (Cd) uptake by one-year-old Eucalyptus Camaldulensis seedlings. The treatments have involved three levels of heavy metal (0, 30, and 60 mg/kg) for Cd, and three bacterial levels (no bacteria (B0), Bacillus (Ba105), and Pseudomonas (Ps36, Ps448)), inoculated with mycorrhizal fungus Glomus mosseae (M1) and non-inoculated with fungus (M0). Results show that absorption of these elements in plant increased as Cd concentration in soil became more. Inoculation by Ps448 bacteria had an incremental effect on Cd uptake by 90%, compared to the non-inoculated (control) samples. Moreover, inoculation of the plants with mycorrhizal fungus increased Cd uptake by 24%, compared to the control. Also, it has been observed that plant resistance to metal stress and plant growth under such conditions ascended in treatments wherein inoculation happened with mycorrhizal fungus and bacteria. The highest Cd heavy metal uptake has been observed in Eucalyptus (shoots and roots), treatment (C2B2M1) with 648.19 micrograms per one seedling in pot. According to the obtained results, Eucalyptus with biological factors (fungi and bacteria) has the ability to clean and purify the contaminated soil with Cd heavy metal.
Show more [+] Less [-]Thresholds Value of Soil Trace Elements for the Suitability of Eucalyptus (The Case Study of Guadiamar Green Corridor)
2023
Blanco-Velázquez, Francisco José | Anaya-Romero, María | Pino-Mejías, Rafael
The development of suitability species models look for the availability to growth in a study area. These models can be used for different targets. In this research, a suitability model of Eucalyptus has been developed to soils contaminated by trace elements management. Guadiamar Green Corridor has been selected due to the huge data available regarding trace elements, forestry species and so on. Logistic regression (LR) and Random Forest (RF), as popular machine learning model, were applied in a geodatabase from Guadiamar Green Corridor with more of 20 years of data. This database is composed by soil physical and chemical variables, climate (temperature min and max, annual precipitation), forestry species. The results show the poor performance of LR and RF applied directly over the unbalanced training set. However, when Up-sampling or SMOTE are applied, both procedures improve its sensitivity, however, RF show more improve that LR. The methodology applied can help to determine the potential distribution of Eucalyptus in similar Mediterranean areas and extended to different areas according to Soil, Climate and Trace Elements data. Finally, the models developed under this research work can be used to reduce human and environmental health by trace elements taking into account local conditions but also climate change scenarios.
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