Laboratory Testing of Cabin Air Filters for the Removal of Reduced-Sulfur Odors

Paper #:
  • 980873

Published:
  • 1998-02-23
Citation:
Ostojic, N., Siegl, W., Lee, S., Marano, R. et al., "Laboratory Testing of Cabin Air Filters for the Removal of Reduced-Sulfur Odors," SAE Technical Paper 980873, 1998, https://doi.org/10.4271/980873.
Pages:
17
Abstract:
The next generation of cabin air filters will include the ability to remove not only particulate matter, but odors as well. A key element in the development of odor removal filters is the design of laboratory tests to predict in-service performance. The studies described in this report used a combination of subjective and objective test methods to evaluate a series of odor-removal filters for their ability to remove environmentally significant reduced sulfur compounds. The work was performed in two parts. In the first part the detection, recognition, and annoyance thresholds for hydrogen sulfide and methyl mercaptan were measured using a 37-member odor panel. The second part consisted of a group of tests in which the contaminant concentrations upstream and downstream of six types of filters were measured using an instrumental method. A series of short term contaminant removal tests were conducted with a range of inlet contaminant concentrations reflecting the high end of the range of concentrations likely to be encountered in the real world (50 - 200 ppb). The average removal efficiency ranged from 81-96% for hydrogen sulfide and 78-95% for methyl mercaptan. This study shows that because of the shapes of the concentration versus annoyance curves, a filter becomes effective in reducing annoyance only if it is able to reduce the odorant concentration to a level below the threshold level. In certain concentration ranges, little change in the annoyance level would result from use of low efficiency filters.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$27.00
Mail
$27.00
Members save up to 40% off list price.
Share
HTML for Linking to Page
Page URL

Related Items