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Source Apportionment of PM10 at an Urban Site of a South Asian Mega City

Category: Urban Air Quality

Article In Press
DOI: 10.4209/aaqr.2017.07.0237
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Imran Shahid 1, Muhammad Usman Alvi2,3, Muhammad Zeeshaan Shahid4, Khan Alam5, Farrukh Chishtie6

  • 1 Institute of Space Technology, Islamabad 44000, Pakistan
  • 2 Institute of Chemistry, University of the Punjab, Lahore 54590, Pakistan
  • 3 University of Education, Okara Campus, Okara 57000, Pakistan
  • 4 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
  • 5 Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
  • 6 SERVIR-Mekong, Asian Disaster Preparedness Center, Bangkok 10400, Thailand

Highlights

A very high PM10 concentration was observed during the study period i.e., 793 µg m–3.
Backward trajectory analysis exhibited local contribution and long range transport.
Maximum contribution of Ca, Al and Fe was found in PM10 concentrations.
PMF used for the source apportionment of PM10 at Karachi.
A strong correlation was observed between the observed and predicted PM10 mass.


Abstract

In the present study, elemental composition of PM10 and source apportionment was conducted in the urban atmosphere of Karachi. Trace elements such as Ni, Ba, Cd, Ca, Mg, Cr, Mn, Fe, Co, Cu, Sr and Ti were measured. The PM10 concentration ranged from 255 µg m–3 to 793 µg m–3 with an average of 438 ± 161 µg m–3. Among the various elements analyzed, concentrations of Ca, Al and Fe were highest (> 10 000 ng m–3), followed by Mg and S (> 1000 ng m–3). Elements like Zn, P, Cu, Pb, Mn, Ti, Sr and Ba demonstrated medium concentrations (> 100 ng m–3), whereas lowest concentrations were measured for elements like Cr, Ni and Se (> 10 ng m–3). The Positive Matrix Factorization (PMF) model identified five possible factors contributing towards PM10, including biomass burning, coal combustion, re-suspended road/soil dust, vehicular emission and industrial dust. Industrial dust as major contributor (23.2%) to PM10 followed by Biomass burning (23%), Vehicular emissions (22.2%), Coal combustion (21.7%) and Re-suspended dust (9.9%). A strong positive correlation (R2 = 0.98) was observed between the model predicted PM10 mass and gravimetrically measured mass collected on filters.

Keywords

Particulate matter Air pollution Urban air quality Elemental analysis Source apportionment Positive Matrix Factorization


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