To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.
Keywords: fugitive dust; heavy metals; positive matrix factorization; principal component analysis; source identification.