Bioaccumulation of Heavy Metals in Mangrove (Avicennia marina): Predictive Uptake Modeling and Phytoremediation Potential

Abstract

Globally, mangrove forests have been impacted by several environmental stressors including overloading with heavy metal pollution. The objective of the current study was to develop a predictive model for estimating heavy metal accumulation at Avicennia marina populations based on sediment characteristics. A transect (170 km) along Saudi Arabia’s southern coast of the Red Sea was selected and three major regions were sampled, and both sediment and plant organs (aerial roots, stems, and leaves) were collected. For both sediment and plant materials, the following metals (mg kg⁻¹) were analyzed: Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, and Zn. Metal Bioaccumulation Factors (BAFs) and Translocation Factors (TFs) were calculated. Moreover, model efficiency (ME) and validation parameters were calculated including coefficient of determination (R²), mean normalized average error (MNAE), and mean normalized bias (MNB). On average, A. marina sediment was moderately to heavily polluted with Pb and Zn (311.2 ± 56.34 and 141.9 ± 19.11 mg kg⁻¹, respectively). Cu, Zn, Mo, Cr, and Pb were translocated from A. marina’s aerial roots to the stems (TF > 1), while Cr, Cu, Zn, Fe, Mo, and Co were translocated from A. marina’s aerial roots to the leaves (TF > 1). The statistical analysis using t-tests showed no significant differences between the observed heavy metal contents and the model-estimated contents within the mangrove's leaves, stems (except for Cd), and aerial roots. Our predictive model to estimate heavy metals in different tissues of A. marina based on sediment characteristics was significantly valid (with exception for stem Cd content). Our results confirm the efficacy of A. marina as a bioindicator of toxic metal for monitoring pollution and application of A. marina as a natural phytoremediation tool.