Screening of urban aerosol particulate composites for selected metal distribution and their dependence on meteorological parameters

• Published in: Annali di chimica Vol. 94; no. 11; p. 805
• Main Authors: Shah, Munir H, Shaheen, Nazia, Jaffar, M
• Format: Journal Article
• Language: English
• Published: Italy 01-11-2004
• Subjects: Aerosols - analysis; Air Pollutants - analysis; Cities; Environmental Monitoring - standards; Environmental Monitoring - statistics & numerical data; Metals - analysis; Multivariate Analysis; Pakistan; Particle Size; Spectrophotometry, Atomic; Pakistan
• ISSN: 0003-4592
• DOI: 10.1002/adic.200490101

Local atmospheric aerosol particulate samples, collected as composites on daily 6-12 hour basis, at Quaid-i-Azam University campus, Islamabad, Pakistan, using high volume sampling technique, were analysed for Pb, Na, K, Fe, Mn, Cd, Cr, Ni, Zn and Co by FAAS method. The monitoring period ran from October, 2001 through March, 2002, with a total of 105 samples collected on cellulose filters, treated in part with the HNO3-based wet digestion method for metal quantification, and for particle size distribution separately. The metal content of the aerosols was examined in relation to dependence on meteorological parameters, such as temperature, relative humidity, wind speed, sun shine and pan evaporation. Statistical correlation analysis was conducted for multiple metal pairs in aerosols, and the data were examined in relation to meteorological parameters and relevant aerosol particle size fractions. The study revealed no viable strong correlation between the meteorological parameters and metal levels; in general, however, a significant positive correlation was found for temperature. A strong positive correlation was observed for PM<25 and PM2.5-10. For coarse particles (PM10-100 and PM>100), however, a negative correlation was observed. The levels of Na, K, Fe and Zn were found in the range of 1-5 microg/m3 while those for the rest of the metals in the sub microg/m3 range. Principal component analysis and cluster analysis were performed on dataset for source identification and appointment. Largest contribution (33%) was shown by the industrial emissions followed by traffic/road dust (16.7%).