| View Larger Image | Characterization of atmospheric concentrations and partitioning of PAHs in the Chicago atmosphere [An article from: Science of the Total Environment, The] | Digitalby N. Vardar (Author), Y. Tasdemir (Author), M. Odabasi (Author), K.E. Noll (Author)
| List Price: | $8.95 | | | Available: | Available for download now |
| | Binding: | Digital | | Publisher: | Elsevier | | Publication Date: | July 05, 2004 |
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Product Description
This digital document is a journal article from Science of the Total Environment, The, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.Description: An intensive sampling program has been undertaken in the absence of precipitation at an urban site, Chicago, to characterize the atmospheric concentration and partitioning of PAHs. Two different sampling programs have been carried out with a large number of samples. Measured ambient concentrations of PAHs were classified as Land and Lake samples based on wind direction and back trajectory calculations. Differences in ambient concentrations of PAHs were observed between Land and Lake samples. The concentrations of PAHs when air originated over the Land were approximately two-four times higher than the concentrations measured when air originated over the Lake. It has been demonstrated that partitioning of PAHs shows a consistent difference between samples taken when wind came from off the land rather than off the water. This was most evident by more shallow slopes for Lake samples compared to the slopes for Land samples, when partition coefficient (K"p) is plotted on a log-log scale vs. the subcooled liquid vapor pressure (P"L^0). Experimentally, determined K"p values were compared with the results obtained using two different models, one based on absorption into aerosol organic matter and the other adsorption onto soot carbon. Experimental K"p values generally agreed well with the soot+octanol based model predictions. |
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