Rhizobacteria and Plant Symbiosis in Heavy Metal Uptake and Its Implications for Soil Bioremediation
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Certain species of plants can benefit from synergistic effects with plant growth-promoting rhizobacteria (PGPR) that improve plant growth and metal accumulation, mitigating toxic effects on plants and increasing their tolerance to heavy metals. The application of PGPR as biofertilizers and atm. nitrogen fixators contributes considerably to the intensification of the phytoremediation process. In this paper, we have built a system consisting of rhizospheric Azotobacter microbial populations and Lepidium sativum plants, growing in solns. contg. heavy metals in various concns. We examd. the ability of the organisms to grow in symbiosis so as to stimulate the plant growth and enhance its tolerance to Cr(VI) and Cd(II), to ultimately provide a reliable phytoremediation system. The study was developed at the lab. level and, at this stage, does not assess the inherent interactions under real conditions occurring in contaminated fields with autochthonous microflora and under different pedoclimatic conditions and environmental stresses. Azotobacter sp. bacteria could indeed stimulate the av. germination efficiency of Lepidium sativum by almost 7%, av. root length by 22%, av. stem length by 34% and dry biomass by 53%. The growth of L. sativum has been affected to a greater extent in Cd(II) solns. due its higher toxicity compared to that of Cr(VI). The reduced tolerance index (TI, %) indicated that plant growth in symbiosis with PGPR was however affected by heavy metal toxicity, while the tolerance of the plant to heavy metals was enhanced in the bacteria-plant system. A methodol. based on artificial neural networks (ANNs) and differential evolution (DE), specifically a neuro-evolutionary approach, was applied to model germination rates, dry biomass and root/stem length and proving the robustness of the exptl. data. The errors assocd. with all four variables are small and the correlation coeffs. higher than 0.98, which indicate that the selected models can efficiently predict the exptl. data.
DOI der Originalausgabehttps://doi.org/10.1016/j.nbt.2016.09.002