İbrahim İlker ÖZYİĞİT

Article | 2021 | International Journal of Phytoremediation23 ( 12 )

In this study, to determine whether having potential to be used as hyperaccumulator for Cd and Ni, numerous experiments were designed for conducting assessments for physiological and genotoxic changes along with defining possible alterations on mineral nutrient status of Lemna minor L. by applying Cd-Ni binary treatments (0, 100, 200 and 400 mu M). Our study revealed that there were increases in the concentrations of B, Cr, Fe, K, Mg, and Mn whereas decreases were noticed in the concentrations of Na and Zn and the levels of Ca were inversely proportional to Cd-Ni applications showing tendency to increase at the low concentration and . . . to decrease at the high concentration. Randomly Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR) analyses revealed that rather than band losses and new band formations, mostly intensity changes in the band profiles, and low polymorphism and high genomic template stability (GTS) were observed. Although, to date, L. minor was defined as an efficient hyperaccumulator/potential accumulator or competent phytoremedial agent by researchers. Our research revealed that L. minor showing high accumulation capability for Cd and having low polymorphism rate and high genomic template stability is a versatile hyperaccumulator, especially for Cd; therefore, highly recommended by us for decontamination of water polluted with Cd. NOVELTY STATEMENT Many studies have been focused on the effects of individual metal ions. However, heavy metal contaminants usually exist as their mixtures in natural aquatic environments. Especially, Cd and Ni coexist in industrial wastes. In this study, the accumulation properties of Lemna minor for both Cd and Ni were investigated and the effects of Cd and Ni on the bioaccumulation of B, Ca, Cu, Fe, Mg, K, Mn, Na, Pb and Zn in L. minor were also determined. This study furthermore aimed to assess the genotoxic effects of Cd and Ni found in being extended concentrations on DNA using the Randomly Amplified Polymorphic DNA-Polymerase Chain Reaction (RAPD-PCR) method More less

İbrahim İlker ÖZYİĞİT

Article | 2022 | International Journal of Phytoremediation25 ( 1 )

This research is to predict heavy metal levels in plants, particularly in Robinia pseudoacacia L., and soils using an effective artificial intelligence approach with some ecological parameters, thereby significantly eliminating common defects such as high cost and seriously tedious and time-consuming laboratory procedures. In this respect, the artificial neural network (ANN) is employed to estimate the concentrations of essential heavy metals such as Fe, Mn and Ni, depending on the Cu and Zn concentrations of plant and soil samples collected from five different locations. The derived relative errors for the constructed ANN model hav . . .e been computed within the ranges 0.041-0.051, 0.017-0.025, and 0.026-0.029 for the training, testing and holdout data regarding Fe, Mn, and Ni, respectively. In addition, it has been realized that the relative errors could be diminished up to 0.007 for Fe, 0.014 for Mn and 0.022 for Ni by considering the Cu, Zn, location and plant parts as independent variables during the analysis. The results produced seem instructive and pioneering for environmentalists and scientists to design optimal study programs to leave a livable ecosystem.Novelty statement The levels of essential heavy metals, Fe, Mn, Ni, based on Zn and Cu in plant and soil samples have been predicted through an AI-based prediction model, a class of feedforward artificial neural networks (ANNs) with a multilayer perceptron (MLP). Thereby common drawbacks such as high cost and severely time-consuming laboratory procedures have been significantly eradicated. In the evaluation of different pollution levels at locations, it has been shown that the ANN method can overcome several disadvantages of analytical element analyzers to monitor the amounts of heavy metals such as Fe, Mn, and Ni in soil and plants More less