Please use this identifier to cite or link to this item:
|Title:||Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants.|
|Citation:||Chan, Y. C., Hawas, O., Hawker, D., Vowles, P., Cohen, D. D., Stelcer, E., Simpson, R., Golding, G., & Christensen, E. (2011). Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants. Atmospheric Environment, 45(2), 439-449. doi:10.1016/j.atmosenv.2010.09.060|
|Abstract:||In this study a small but comprehensive data set from a 24-hourly sampling program carried out during June 2001 in an industrial area in Brisbane was chosen to investigate the effect of inclusion of multiple type composition data and wind data on source apportionment of air pollutants using the Positive Matrix Factorisation model, EPA PMF 3.0. The combined use of aerosol, VOC, main gaseous pollutants composition data and wind data resulted in better values of statistical indicators and diagnostic plots, and source factors which could be more easily related to known sources. The number of source factors resolved was similar to those reported in the literature where larger data sets were used. Three source factors were identified for the coarse particle samples, including ‘crustal matter’, ‘vehicle emissions’ and ‘sea spray’. Seven source factors were identified for the fine particle and VOC samples, including ‘secondary and biogenic’, ‘petroleum refining’, ‘vehicle emissions’, ‘petroleum product wholesaling’, ‘evaporative emissions’, ‘sea spray’ and ‘crustal matter’. The factor loadings of the 16 wind sectors and the calm wind sector from the PMF analysis were also used to quantify the directional contribution of the source factors. While the contributions were higher in the prevailing wind directions as expected, calm winds were also found to contribute up to 17% of the pollutant mass on average. The factor loadings, normalised by the overall abundance of the wind sectors, were also used to assess the directional dependences of the source factors. The results matched well with the location of known sources in the area. There was also a higher contribution potential from calm winds for local sources compared to that for distant sources. The results of directional effect using the PMF factor loading approach were similar to those by using the other approaches. This approach, however, also provides estimates of the mass contribution of source factors by wind sector and also the uncertainty of the results. © 2011, Elsevier Ltd.|
|Gov't Doc #:||3323|
|Appears in Collections:||Journal Articles|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.