Abstract
Particulate Matter (PM10) has been one of the main air pollutants exceeding the ambient standards in most of the major cities in India. During last few years, receptor models such as Chemical Mass Balance, Positive Matrix Factorization (PMF), PCA–APCS and UNMIX have been used to provide solutions to the source identification and contributions which are accepted for developing effective and efficient air quality management plans. Each site poses different complexities while resolving PM10 contributions. This paper reports the variability of four sites within Mumbai city using PMF. Industrial area of Mahul showed sources such as residual oil combustion and paved road dust (27%), traffic (20%), coal fired boiler (17%), nitrate (15%). Residential area of Khar showed sources such as residual oil combustion and construction (25%), motor vehicles (23%), marine aerosol and nitrate (19%), paved road dust (18%) compared to construction and natural dust (27%), motor vehicles and smelting work (25%), nitrate (16%) and biomass burning and paved road dust (15%) in Dharavi, a low income slum residential area. The major contributors of PM10 at Colaba were marine aerosol, wood burning and ammonium sulphate (24%), motor vehicles and smelting work (22%), Natural soil (19%), nitrate and oil burning (18%).
Highlights
Urbanization has resulted in high levels of ground level deterioration of air quality
Different models including principal component analysis/absolute principal component scores (PCA-APCS) [5, 6], edge analysis (UNMIX, [7]), chemical mass balance (CMB) [8], and positive matrix factorization (PMF) [9] have been applied by several researchers to identify and establish the sources contributing to ambient air
Callen et al [13] carried out source apportionment of PM10 in Zaragoza, Spain by three multivariate receptor models based on factor analysis: PCA-APCS, UNMIX, and PMF
Summary
Urbanization has resulted in high levels of ground level deterioration of air quality. Different models including principal component analysis/absolute principal component scores (PCA-APCS) [5, 6], edge analysis (UNMIX, [7]), chemical mass balance (CMB) [8], and positive matrix factorization (PMF) [9] have been applied by several researchers to identify and establish the sources contributing to ambient air. Rizzo and Scheff [12] compared the magnitude of source contributions resolved by each model and examined correlations between PMF and CMB-resolved contributions They found that the major factors correlated well and were similar in magnitude. Callen et al [13] carried out source apportionment of PM10 in Zaragoza, Spain by three multivariate receptor models based on factor analysis: PCA-APCS, UNMIX, and PMF. Ambient concentration data used in the source-receptor modeling include PM10 mass, anions and cations, total organic carbon (OC), elemental carbon (EC), and elements
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