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Source Apportionment of Atmospheric PM(10) in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

In this paper, the emission sources of PM(10) are characterised by analysing its trace elements (TE) and ions contents. PM(10) samples were collected for a year (2019–2020) at five sites and analysed. PM(10) speciated data were analysed using graphical visualization, correlation analysis, generalise...

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Detalles Bibliográficos
Autores principales: Habeebullah, Turki M., Munir, Said, Zeb, Jahan, Morsy, Essam A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950437/
https://www.ncbi.nlm.nih.gov/pubmed/35324744
http://dx.doi.org/10.3390/toxics10030119
Descripción
Sumario:In this paper, the emission sources of PM(10) are characterised by analysing its trace elements (TE) and ions contents. PM(10) samples were collected for a year (2019–2020) at five sites and analysed. PM(10) speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM(10) concentrations (µg/m(3)) were 304.68 ± 155.56 at Aziziyah, 219.59 ± 87.29 at Misfalah, 173.90 ± 103.08 at Abdeyah, 168.81 ± 82.50 at Askan, and 157.60 ± 80.10 at Sanaiyah in Makkah, which exceeded WHO (15 µg/m(3)), USEPA (50 µg/m(3)), and the Saudi Arabia national (80 µg/m(3)) annual air quality standards. A GAM model was developed using PM(10) as a response and ions and TEs as predictors. Among the predictors Mg, Ca, Cr, Al, and Pb were highly significant (p < 0.01), Se, Cl, and NO(2) were significant (p < 0.05), and PO(4) and SO(4) were significant (p < 0.1). The model showed R-squared (adj) 0.85 and deviance explained 88.1%. PMF identified four main emission sources of PM(10) in Makkah: (1) Road traffic emissions (explained 51% variance); (2) Industrial emissions and mineral dust (explained 27.5% variance); (3) Restaurant and dwelling emissions (explained 13.6% variance); and (4) Fossil fuel combustion (explained 7.9% variance).