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Comparison of Two Receptor Model Techniques for the Size Fractionated Particulate Matter Source Apportionment

Category: Air Pollution Modeling

Volume: 16 | Issue: 6 | Pages: 1497-1508
DOI: 10.4209/aaqr.2015.06.0416
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Kothai Parthasarathy, Sanjay Kumar Sahu, Gauri Girish Pandit

  • Environmental Monitoring and Assessment Section, Bhabha Atomic Research Centre, Trombay, Mumbai – 400 085, India


A three year particulate monitoring program was carried out at a hot spot of Mumbai.
Potential sources were identified using receptor models like FA-MLR and PMF.
The model results were compared and validated in four distinguished steps.
Study revealed crustal and sea salt as the major sources for coarse Particulates.
Vehicular and industrial emissions found to be the dominant contributors for fine PM.


The goal of the study is to investigate elemental concentrations and source signatures of particulate matter at a selected receptor site in an urban area Navi Mumbai which is one of the hot spots of India with heavy industrialization. To achieve the objective, particulate matter samples were collected from 2008–2010 using Gent sampler with the automatic dichotomous size segregation unit. It was observed that the three year average concentrations of coarse ((PM10–2.5 µm) and fine (PM2.5µm) fractions of PM were 89.92 (µg m–3) and 42.25 (µg m–3) respectively which were higher than the national standards prescribed by Central Pollution Control Board (CPCB), India. Subsequent elemental analysis of air filters using INAA and EDXRF showed marginally higher levels of anthropogenic derived elements. Furthermore, in the present study potentially contributing sources of coarse and fine PM were identified using two different receptor model techniques Factor Analysis (FA) and Positive Matrix Factorization (PMF). Six possible contributing sources of coarse fraction and seven probable sources of fine PM were identified by both the techniques. Further, sources identified by the receptor techniques and the comparability between the two techniques were also evaluated.


Coarse Fine Receptor model FA-MLR PMF

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