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Partic. vol. 37 pp. 107-118 (April 2018)
doi: 10.1016/j.partic.2017.05.009

Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF

Srishti Jaina,b, S.K. Sharmaa,b,*, T.K. Mandala,b, Mohit Saxenaa

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    • Comparisons of receptor models, i.e. PCA/APCS, UNMIX and PMF, were conducted. • Source profiles and contributions of PM10 were achieved by receptor models. • VE, SA and SD were dominant contributors to PM10 in Delhi. • PMF allowed to obtain better results than PCA/APCS and UNMIX.


Source apportionment of particulate matter (PM10) measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores (PCA/APCS) and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization (PMF) receptor model to investigate each model’s source-apportioning capability. All models used the PM10 chemical composition (organic carbon (OC), elemental carbon (EC), water soluble inorganic ions (WSIC), and trace elements) for source apportionment. The average PM10 concentration during the study period was 249.7 ± 103.9 μg/m3 (range: 61.4–584.8 μg/m3). The UNMIX model resolved five sources (soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), a mixed source of biomass burning (BB) and sea salt (SS), and industrial emissions (IE)). The PCA/APCS model also resolved five sources, two of which also included mixed sources (SD, VE, SD+SS, (SA+BB+SS) and IE). The PMF analysis differentiated seven individual sources (SD, VE, SA, BB, SS, IE, and fossil fuel combustion (FFC)). All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.

Graphical abstract


Receptor model; PCA/APCS; UNMIX; PMF; Source apportionment