ArticleEnvironmental monitoring and assessment2016
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.
Article in Environmental monitoring and assessment, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Assessment of radiological risks in soils from different land use types within southwest Nigeria.Scientific reports · 2026Article
- Levels of polycyclic aromatic hydrocarbons around a scrap iron and steel recycling industry and the associated health risk assessment.RSC advances · 2025Article
- Source identification and health risk assessment of PMEnvironmental science and pollution research international · 2024Article
- Article
- Source identification and health risk assessments of heavy metals in indoor dusts of Ilorin, North central Nigeria.Journal of environmental health science & engineering · 2022Article
- Water quality assessment and pollution source apportionment in a highly regulated river of Northeast China.Environmental monitoring and assessment · 2020Article
- PM2.5 monitoring during a 10-year period: relation between elemental concentration and meteorological conditions.Environmental monitoring and assessment · 2020Article
- Trace element characterization of fine particulate matter and assessment of associated health risk in mining area, transportation routes and institutional area of Dhanbad, India.Environmental geochemistry and health · 2019Article
- Biomonitoring of environmental pollution in the vicinity of iron and steel smelters in southwestern Nigeria using transplanted lichens and mosses.Environmental monitoring and assessment · 2019Article
- Trends on PMEnvironmental science and pollution research international · 2018Article
- Chemical characteristics and source apportionment of PMEnvironmental science and pollution research international · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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/m(3) for PM2.5 and 331.36, 190.01, and 184.60 μg/m(3) 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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