Abstract
In this paper, the emission sources of PM10 are characterised by analysing its trace elements (TE) and ions contents. PM10 samples were collected for a year (2019–2020) at five sites and analysed. PM10 speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM10 concentrations (µg/m3) 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/m3), USEPA (50 µg/m3), and the Saudi Arabia national (80 µg/m3) annual air quality standards. A GAM model was developed using PM10 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 NO2 were significant (p < 0.05), and PO4 and SO4 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 PM10 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).
Highlights
Air pollution is one of the major public health concerns causing cardiovascular, pulmonary, respiratory, and cognitive problems
positive matrix factorization (PMF) was employed for the source apportionment analysis to identify the major sources of PM10 in Makkah, which explained over 80% variation in PM10 concentrations
For a given response variable, Y regressed over ‘m’ explanatory variables X1, X2, ..., Xm, a Generalised Additive Model (GAM) in a general form can be described as shown in Equation (2): Y = s1 (X1) + s2 (X2) + . . . + sm (Xm) where Y is the response (PM10 concentrations in Equation (3)) variable and ‘s’ is the smoothing term, which corresponds to an associated explanatory variable (X)
Summary
Air pollution is one of the major public health concerns causing cardiovascular, pulmonary, respiratory, and cognitive problems. Urban air quality has caught the interest of researchers worldwide, analysing emission sources, levels of various air pollutants, and predicting future concentrations and their impacts on human health [2–5]. Saudi Arabia has large scale desert areas, arid or semi-arid climatic conditions, little rain, and strong winds, which play a positive role in enhancing the atmospheric burden of particulates. These natural conditions are supported by anthropogenic activities including busy roads, large and small scales industries, construction-and-demolition projects, and oil and coal burnings [11,18,19]. Farahat et al [10] monitored air quality in Makkah, Madinah, and Jeddah during the Hajj seasons of 2019 and 2020 and compared the levels in the two years. The range of chemical species is increased, and advanced modelling approaches are employed, namely Generalised Additive Model (GAM), Positive Matrix Factorisation (PMF), and Enrichment Factor (EF), along with graphical visualization and correlation analysis to identify the main emission sources of PM10 and investigate the linear and nonlinear association between the various constituents of PM10
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