Uptake prediction of nine heavy metals by Eichhornia crassipes grown in irrigation canals: A biomonitoring approach

Highlights: • All HMs were more concentrated in the E. crassipes roots than in any other organ. • HM levels in E. crassipes organs correlate significantly with HM levels in water. • E. crassipes was documented by a bioconcentration factor > 1.0 for all HMs. • The translocation of HMs by E. crassipes was below 1 for all examined HMs. The principal objective of this study is to generate mathematical regression equations that facilitate the estimation of the extent to which Eichhornia crassipes (C. Mart.) Solms, water hyacinth, absorbs heavy metals (HMs) into four plant organs (laminae, petioles, roots, and stolons). This study considers the absorption of nine HMs (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn), and the E. crassipes evaluated in this study were located in three irrigation canals in the North Nile Delta in Egypt, with sampling being conducted in both monospecific and homogenous E. crassipes. Samples of both E. crassipes and water were collected on a monthly basis during one growing season. Analysis of the water samples showed that the HM concentrations ranged from 1.1 μg/l for Cd to 2079.8 μg/l for Fe. All HMs were more concentrated in the E. crassipes roots than in any other organ. Typically, there was a significant correlation between the HM levels in the water and the HM levels in the E. crassipes organs. E. crassipes was documented by a bioconcentration factor > 1.0 for all HMs. The translocation factor in this study was t-values that referred to the discrepancies between the measured and predicted values of the HMs in the four E. crassipes organs were not significant. This finding can be considered to be an indication of the goodness of fit with respect to the ability of the equations to forecast HM uptake. Therefore, the developed equations will benefit the prediction of HM uptake by E. crassipes grown in irrigation canals in the Nile Delta. The efficacy of E. crassipes as a metric for gauging the aggregate impact of environmental pollution in water sources and its potential application in biomonitoring are confirmed in this study.