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Tree leaves are considered as one of the best biogenic dust collectors due to their ability to trap and retain particulate matter on their surfaces. In this study, the magnetic susceptibility (MS) and the concentration of selected heavy metals of plane tree (Platanus orientalis L.) leaves and deposited atmospheric dust, sampled by an indirect and a direct method, respectively, were determined to investigate the relationships between leaf magnetic parameters and the concentration of heavy metals in deposited atmospheric dust. The objective was to develop a biomagnetic method as an alternative to the common ones used for determining atmospheric heavy metal contaminations. Plane tree leaves were monthly sampled on the 19th of May to November, 2012 (T1-T7), for seven months from 21 different sites in the city of Isfahan, central Iran. Deposited atmospheric dust samples were also collected using flat glass surfaces from the same sites on the same dates, except for T1. MS (χlf, χhf) values in washed (WL) and unwashed leaves (UL) as well as Cu, Fe, Mn, Ni, Pb, and Zn concentrations in UL and deposited atmospheric dust samples were determined. The results showed that the MS content with a biogenic source was low with almost no significant change during the sampling period, while an increasing trend was observed in the MS content of UL samples due to the deposition of heavy metals and magnetic particles on leaf surfaces throughout the plant growth. The latter type of MS content could be reduced through washing off by rain. Most heavy metals examined, as well as the Tomlinson pollution load index (PLI) in UL, showed statistically significant correlations with MS values. The correlation between heavy metals content in atmospheric dust deposited on glass surfaces and leaf MS values was significant for Cu, Fe, Pb, and Zn. Moreover, the similarity observed between the spatial distribution maps of leaf MS and deposited atmospheric dust PLI provided convincing evidence regarding the suitability of the biomagnetic approach as a relatively rapid and inexpensive method for identifying highly polluted urban areas with selected heavy metals, especially those subjected to anthropogenic and other traffic related sources., (Copyright © 2016 Elsevier Ltd. All rights reserved.)