Knowledge gaps related to mass transfer processes involved in passive air sampling of semivolatile organic compounds and factors potentially influencing passive sampling rates (PSRs) were addressed with controlled laboratory experiments, mass transfer modeling, and a field sampling campaign. The observed non-uniform SVOC distributions within porous passive sampling media (PSMs) contradict an assumption in an earlier passive air sampling theory and proved the existence of a kinetic resistance on the PSM side. This resistance can affect PSRs as revealed by a new PAS model which is based on fundamental laws of mass transfer in air and porous media. By considering mass transfer processes within the PSM, the model is able to explain the large variations of field calibrated PSRs with temperature and between SVOC species and the two-stage uptake process, which cannot be addressed by the earlier PAS theory. Because the PSM side kinetic resistance invalidates the assumption that depuration compounds added to the PSM prior to deployment are subject to the same kinetic resistance as the sampled SVOCs, PSRs derived from the loss rates of depuration compounds can differ from the actual PSRs of the sampled SVOCs. Using such PSRs could thus introduce additional uncertainty to PAS-derived air concentrations.
Experiments using XAD-resin and silica-gel filled mesh cylinder as PSMs for the uptake of SVOCs and water vapor respectively revealed that sorbent in the inner portion of the PSM does not take part in chemical uptake; PSRs are thus proportional to the interfacial transfer area but not the amount of the sorbent. Accordingly, thinner PSM can be used to reduce the amount of sorbent while keeping or even increasing the PSRs. Optimized designs of PASs could be tested time efficiently using the gravimetrical approach based on water vapor uptake by silica gel.
Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/43404 |
Date | 16 December 2013 |
Creators | Zhang, Xianming |
Contributors | Wania, Frank |
Source Sets | University of Toronto |
Language | en_ca |
Detected Language | English |
Type | Thesis |
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