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Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models

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Abstract

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5−10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m3 for PM2.5 and 331.36, 190.01, and 184.60 μg/m3 for PM2.5−10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5−10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5−10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.

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Acknowledgment

The authors thank the Centre for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA for the facilities used in this work.

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Authors and Affiliations

  1. Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife, 220005, Nigeria

    Lasun T. Ogundele, Oyediran K. Owoade & Felix S. Olise

  2. Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, 13699-5808, USA

    Philip K. Hopke

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  1. Lasun T. Ogundele
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  2. Oyediran K. Owoade
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  3. Felix S. Olise
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  4. Philip K. Hopke
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Correspondence to Felix S. Olise.

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Ogundele, L.T., Owoade, O.K., Olise, F.S. et al. Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models. Environ Monit Assess 188, 574 (2016). https://doi.org/10.1007/s10661-016-5585-8

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  • DOI: https://doi.org/10.1007/s10661-016-5585-8

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