Chemometrics in biomonitoring: Distribution and correlation of trace elements in tree leaves

Abstract

The concentrations of 15 elements were measured in the leaf samples of Aesculus hippocastanum, Tilia spp., Betula pendula and Acer platanoides collected in May and September of 2014 from four different locations in Belgrade, Serbia. The objective was to assess the chemical characterization of leaf surface and in-wax fractions, as well as the leaf tissue element content, by analyzing untreated, washed with water and washed with chloroform leaf samples, respectively. The combined approach of self-organizing networks (SON) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) aided by Geometrical Analysis for Interactive Aid (GAIA) was used in the interpretation of multiple element loads on/in the tree leaves. The morphological characteristics of the leaf surfaces and the elemental composition of particulate matter (PM) deposited on tree leaves were studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) detector. The results showed that the amounts of retained and accumulated element concentrations depend on several parameters, such as chemical properties of the element and morphological properties of the leaves. Among the studied species, Tilia spp. was found to be the most effective in the accumulation of elements in leaf tissue (70% of the total element concentration), while A. hippocastanum had the lowest accumulation (54%). After water and chloroform washing, the highest percentages of removal were observed for Al, V, Cr, Cu, Zn, As, Cd and Sb (>40%). The PROMETHEE/SONranking/classifying results were in accordance with the results obtained from the GAIA clustering techniques. The combination of the techniques enabled extraction of additional information from datasets. Therefore, the use of both the ranking and clustering methods could be a useful tool to be applied in biomonitoring studies of trace elements.