Articles online

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
PDF | Supplemental material | RIS | BibTeX

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


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.


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.


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

Related Article

Health Risk of Ambient PM10-Bound PAHs at Bus Stops in Spring and Autumn in Tianjin, China

Taosheng Jin , Miao Han, Kun Han, Xuemei Fu, Limin Xu, Xiaohong Xu
Accepted Manuscripts
DOI: 10.4209/aaqr.2017.11.0461

Evolution of Key Chemical Components in PM2.5 and Potential Formation Mechanisms of Serious Haze Events in Handan, China

Chengyu Zhang, Litao Litao Wang , Mengyao Qi, Xiao Ma, Le Zhao, Shangping Ji, Yu Wang, Xiaohan Lu, Qing Wang, Ruiguang Xu, Yongliang Ma
Accepted Manuscripts
DOI: 10.4209/aaqr.2017.10.0386

Investigation of Diurnal Pattern of Generation and Resuspension of Particles Induced by Moving Subway Trains in an Underground Tunnel

Sang-Hee Woo, Jong Bum Kim, Gwi-Nam Bae , Moon Se Hwang, Gil Hun Tahk, Hwa Hyun Yoon, Se-Jin Yook
Accepted Manuscripts
DOI: 10.4209/aaqr.2017.11.0444

Elemental Composition and Source Apportionment of Fine and Coarse Particles at Traffic and Urban Background Locations in Athens, Greece

Georgios Grivas , Stavros Cheristanidis, Archontoula Chaloulakou, Petros Koutrakis, Nikos Mihalopoulos