RESUMO
The present study was designed to determine the glaucine, quercetin and kaempferol contents in the root and shoot parts of Nigella arvensis, treated for 21 days with different concentrations of the nanoparticles (NPs), including titanium dioxide (TiO2), alumina (Al2O3) and nickel oxide (NiO) by high-performance liquid chromatography (HPLC). Results showed a significant increase in the total flavonoid and total alkaloid content in treated plants. Glaucine content in shoot parts was significantly higher than the root parts. The highest amount of glaucine was obtained in shoots and roots exposed to NiONPs at concentrations of 1000 and 2500 mg/L with up to 3.2 and 2.6 fold increase compared to the control group, respectively. The maximum content of quercetin was observed in the shoot and root parts under 50 mg/L NiONPs with 2.2 and 1.8 fold increase compared to the control group, respectively. The kaempferol content was significantly decreased in all treatment, except for 1000 mg/L NiONPs treatment in the root parts, which was 2.9 fold higher than the control group. Apart from the toxic effects of some NPs, our findings suggest that the NPs at specific levels can be considered as new and appropriate elicitors for in vitro production and increasing the biosynthesis of secondary metabolites to use in pharmaceutical applications.
RESUMO
The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.