350-25 Comparison of Green House Gas Fluxes Monitored with Photoacoustic Spectroscopy and Gas Chromatograph.
Poster Number 309
See more from this Division: ASA Section: Climatology & ModelingSee more from this Session: Agroclimatology and Agronomic Modeling: III
Wednesday, October 24, 2012
Duke Energy Convention Center, Exhibit Hall AB, Level 1
Greenhouse gas (GHG) fluxes are spatially and temporally variable and hence, monitoring of these fluxes from different environmental conditions and at several times is strongly encouraged. Conventionally, the GHG fluxes are monitored with gas chromatography (GC) method, however, one disadvantage of GC is time and labor intensive. Photoacoustic spectroscopy (PAS) has been developed for in-situ monitoring of almost any gas that absorbs infrared light, especially carbon dioxide (CO2), nitrous oxide (N2O) and ammonium (NH3). The PAS uses a measurement system based on “The Photoacoustic infrared detection method”. This instrument can take less than 2 minutes for one reading to monitor concentrations of CO2, NH3 and N2O simultaneously. Further, PAS is easy to carry, and to move around in the field. Therefore, this study was conducted to compare the GHG fluxes monitored with PAS and GC. The experimental site was established during 1994 at the Waterman Farm of The Ohio State University, Columbus. The study design was a factorial experiment with two tillage systems: no-tillage (NT), chisel tillage (CT), and two drainage systems: drainage (D) and non-drainage (ND). The GHG fluxes were monitored bi-weekly since March 2012 and still an ongoing experiment. Data indicate that the GHG fluxes monitored with PAS was comparable with that of GC having R2 value is always greater than 0.90. It can be concluded that GHG fluxes can be monitored easily with the PAS since this instrument saves time, labor and energy as compared to the other traditional and expensive instruments such as GC.
See more from this Division: ASA Section: Climatology & ModelingSee more from this Session: Agroclimatology and Agronomic Modeling: III
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