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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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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 |
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author | Habeebullah, Turki M. Munir, Said Zeb, Jahan Morsy, Essam A. |
author_facet | Habeebullah, Turki M. Munir, Said Zeb, Jahan Morsy, Essam A. |
author_sort | Habeebullah, Turki M. |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-8950437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89504372022-03-26 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 Habeebullah, Turki M. Munir, Said Zeb, Jahan Morsy, Essam A. Toxics Article 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). MDPI 2022-03-02 /pmc/articles/PMC8950437/ /pubmed/35324744 http://dx.doi.org/10.3390/toxics10030119 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Habeebullah, Turki M. Munir, Said Zeb, Jahan Morsy, Essam A. 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950437/ https://www.ncbi.nlm.nih.gov/pubmed/35324744 http://dx.doi.org/10.3390/toxics10030119 |
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