The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Hongling Chen, Dandan Wu, Qiao Wang, Lihu Fang, Yanan Wang, Changlin Zhan, Jiaquan Zhang, Shici Zhang, Junji Cao, Shihua Qi and Shan Liu ()
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Hongling Chen: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
Dandan Wu: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
Qiao Wang: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
Lihu Fang: Research Center of Ecological Environment Restoration and Resources Comprehensive Utilization, The First Geological Brigade of Hubei Geological Bureau, Huangshi 435000, China
Yanan Wang: Research Center of Ecological Environment Restoration and Resources Comprehensive Utilization, The First Geological Brigade of Hubei Geological Bureau, Huangshi 435000, China
Changlin Zhan: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
Jiaquan Zhang: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
Shici Zhang: School of Environment and Health, Jianghan University, Wuhan 430056, China
Junji Cao: Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Shihua Qi: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Shan Liu: School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China

IJERPH, 2022, vol. 19, issue 20, 1-16

Abstract: 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: heavy metals; fugitive dust; principal component analysis; positive matrix factorization; source identification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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