Background: Herbaceous plants are common vegetal species generally exposed, for a limited period of time, to bioavailable environmental pollutants. Heavy metals contamination is the most common form of environmental pollution. Herbaceous plants have never been used as natural bioindicators of environmental pollution, in particular to monitor the amount of heavy metals in soil. In this study, we aimed at assessing the usefulness of using three herbaceous plants (Plantago major L., Taraxacum officinale L. and Urtica dioica L.) and one leguminous (Trifolium pratense L.) as alternative indicators to evaluate soil pollution by heavy metals. Results: We employed Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) to assess the concentration of selected heavy metals (Cu, Zn, Mn, Pb, Cr and Pd) in soil and plants and we employed statistical analyses to describe the linear correlation between the accumulation of some heavy metals and selected vegetal species. We found that the leaves of Taraxacum officinale L. and Trifolium pratense L. can accumulate Cu in a linearly dependent manner with Urtica dioica L. representing the vegetal species accumulating the highest fraction of Pb. Conclusions: In this study we demonstrated that common plants can be used as an alternative analytical tool for monitoring selected heavy metals in soil.

Recent studies have shown the potential of food waste materials as low cost adsorbents for the removal of heavy metals and toxic elements from wastewater. However, the adsorption experiments have been performed in heterogeneous conditions, consequently it is difficult to compare the efficiency of the individual adsorbents. In this study, the adsorption capacities of 12 food waste materials were evaluated by comparing the adsorbents’ efficiency for the removal of 23 elements from complex multi-element solutions, maintaining homogeneous experimental conditions. The examined materials resulted to be extremely efficient for the adsorption of many elements from synthetic multi-element solutions as well as from a heavy metal wastewater. The 12 adsorbent surfaces were analyzed by Fourier transform infrared spectroscopy and showed different types and amounts of functional groups, which demonstrated to act as adsorption active sites for various elements. By multivariate statistical computations of the obtained data, the 12 food waste materials were grouped in five clusters characterized by different elements’ removal efficiency which resulted to be in correlation with the specific adsorbents’ chemical structures. Banana peel, watermelon peel and grape waste resulted the least selective and the most efficient food waste materials for the removal of most of the elements.

The aim of this work was to study the potential of food waste materials (banana peel, potato peel, apple peel, lemon peel, coffee waste, decaf coffee waste, grape waste, and carob peel) as low-cost adsorbents for the removal of aliphatic and aromatic volatile organic compounds (VOCs) from wastewater. The ability of examined food waste materials to adsorb VOCs from synthetic multi-component standard solutions was evaluated and the examined food waste materials showed high removal efficiency. Performances of coffee waste, grape waste, and lemon peel were detailed by using Trichloroethylene and p-Xylene in mono-component standard solutions. The adsorption capacity of the three selected food wastes was determined by using linear Langmuir and Freundlich isotherm models. Two errors functions, average percentage error (APE) and the chi-square test (χ²), were used for isotherm optimization prediction. Freundlich isotherm well described the adsorption of VOCs on the considered materials. According to the obtained results, a multilayer, physical, and cooperative adsorption process was hypothesized, particularly evident when the VOCs’ concentrations are high. This was confirmed by the high adsorption efficiency percentages (E% > 80%) of VOCs from a real polluted matrix (urban solid waste leachate), containing high concentrations of total organic content.