Process studies of odour emissions from effluent ponds using machine-based odour measurement

Sohn, Jae Ho and Smith, R. J. and Yoong, Ernest (2006) Process studies of odour emissions from effluent ponds using machine-based odour measurement. Atmospheric Environment, 40 (7). pp. 1230-1241. ISSN 1352-2310

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Abstract

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

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Item Type:	Article (Commonwealth Reporting Category C)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Deposited in acordance with the copyright policy of the publisher.
Faculty / Department / School:	Historic - Faculty of Engineering and Surveying - Department of Agricultural, Civil and Environmental Engineering
Date Deposited:	11 Oct 2007 00:48
Last Modified:	02 Jul 2013 22:39
Uncontrolled Keywords:	odour, electronic nose, effluent pond, olfactometry, swine, manure, neural network
Fields of Research :	09 Engineering > 0907 Environmental Engineering > 090703 Environmental Technologies
Identification Number or DOI:	doi: 10.1016/j.atmosenv.2005.10.035
URI:	http://eprints.usq.edu.au/id/eprint/1616

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