High Performance Cost Effective Miniature Sensor for Continuous Network Monitoring of H<sub>2</sub>S
Zaouak, O.
Ben Daoud, A.
Fages, M.
Fanlo, J.L.
Aubert, B.
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How to Cite

Zaouak O., Ben Daoud A., Fages M., Fanlo J., Aubert B., 2012, High Performance Cost Effective Miniature Sensor for Continuous Network Monitoring of H2S, Chemical Engineering Transactions, 30, 325-330.
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Abstract

H2S is frequently encountered in the field of odour monitoring because of its high odorous power. Hence, different plants, such as water treatment or composting plants, are expected to monitor their emissions.
A novel high performance cost effective miniature sensor, named Cairclip, has been developed to detect H2S at the low ppbv level. Based on amperometric detection, it is mainly composed of a dynamic air sampling system, a special filter and a high sensitive electronic circuit containing a data logger.
It is able to detect a minimum of 6 ppbv with a RSD of only 1 % (n=5) and a T10-90 response time shorter than 60s. Analytical performances of Cairclips has been validated with a gas chromatography coupled with flame photoionization detector (GC-FPD) for laboratory experiments and total reduced sulphur compounds analyzer (CTRS) for field analyses. Experiments performed with Cairclips were in agreement with both methods. Hence, average sensitivity of sensors differs of less than 9 % compared to the sensitivity of the GC-FPD. Then, field analyses have been performed in a waste water treatment plant (WWTP) in the south of France using the CTRS and Cairclips. These experiments have permitted to determine, using both methods, reduced sulphur compounds contents in the atmosphere in several measurement points of the site.
In order to confront Cairclip to a standard apparatus of odour generation, three sensors have been exposed to H2S via an olfactometer. The linear correlation observed between concentrations generated and sensors measurements illustrates the potential of such apparatus in the field of olfactory pollutions and more widely in air quality monitoring.
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