Biomagnetic monitoring of atmospheric heavy metal pollution using pine needles: the case study of Isfahan, Iran.

Abstract

Atmospheric deposition particles are fine-sized having a high adsorption capacity. Therefore, they can easily transfer the contamination to other areas. Plants can absorb certain pollutants using their leaves and then accumulate them in their biomass. In this study, the spatial and temporal variability of air pollution was assessed using pine needles as the bioindicators of atmospheric pollution. The magnetic susceptibility (MS) at low and high frequencies (χlf, χhf) and the concentration of selected heavy metals of pine needles (Pinus mugo) were estimated in order to address the possible relationships between needles' MS and the heavy metal concentration in the city of Isfahan, central Iran. In addition, the relationship between the heavy metal concentration of pine needles and that of the atmospheric dust was examined using the published data. Tree pine needles were monthly sampled, from April to December 2015 (T1-T9), during 9 months, from 30 different sites in the Isfahan city. There were two treatments including washed + unwashed (WU) and washed + washed (WW). The heavy metal total concentrations including Zn, Fe, Cu, Co, Pb, and Ni were measured. The mean concentrations of Fe, Zn, Pb, Cu, Ni, and Co were 80.4, 3.9, 1.8, 1.4, 0.6, and 0.3 mg kg-1, respectively. The results revealed that the concentration of heavy metals and MS in the pine needles followed the order Fe > Zn > Pb > Cu > Ni > Co. Also, the heavy metal concentration in the pine needles with different treatments had the following trend: WU > WW. It was shown that there was a significant correlation (p < 0.01) between the heavy metal concentrations and the leaf MS values of the pine needles and the concentration of heavy metals in atmospheric dust. Besides, similar trends were detected for the spatial variability of heavy metals and the pine needles' MS. In general, it could be concluded that the biomagnetic approach could serve as a comparatively fast and low-cost method to detect highly polluted urban areas with selected heavy metals, particularly the areas which are under the influence of anthropogenic and other traffic-related sources.