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Inferring gas fluxes from point or line- averaged concentrations Tom Denmead Fellow, CSIRO Land and Water & University of Melbourne Ozflux Conference,

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Presentation on theme: "Inferring gas fluxes from point or line- averaged concentrations Tom Denmead Fellow, CSIRO Land and Water & University of Melbourne Ozflux Conference,"— Presentation transcript:

1 Inferring gas fluxes from point or line- averaged concentrations Tom Denmead Fellow, CSIRO Land and Water & University of Melbourne Ozflux Conference, 4 February 2008

2 CSIRO. Inferring gas fluxes from point or line-averaged concentrations A backward Lagrangian stochastic (bLs) dispersion model The model traces particles backwards from sensor to origin using a Lagrangian dispersion model Surface fluxes calculated from number of touchdowns inside and outside source area in many simulations: (C/Q)sim = (1/N) Σ |2/w 0 | C is downwind concentration Q is the surface flux N is the number of trajectories commonly, 50,000 w 0 is the vertical velocity of particles at touchdown Q = (C-Cbackground) / (C/Q)sim Micromet. Source area wind Point concentration sensor Touchdowns

3 CSIRO. Inferring gas fluxes from point or line-averaged concentrations A backward Lagrangian stochastic (bLs) dispersion model Suitable for point, line or area sources (any shape) Inputs: geometry of source area height and location of sensor, wind speed and direction, atmospheric stability, gas concentrations upwind and downwind Uses a software package called WindTrax to calculate surface fluxes from concentration and micrometeorological data Micromet. Source area wind Point concentration sensor Touchdowns

4 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Point concentration measurements: an example from grazing (315 dairy cows) Ammonia concentrations measured with passive samplers

5 CSIRO. Inferring gas fluxes from point or line-averaged concentrations WindTrax map 2 adjoining pasture bays grazed in 6 sessions, one-third of a bay at a time Sensors located at heights of 1.4 and 2m on 12 masts on the corners of each grazed section Chemical sensors Meteorological Sensors: 2 anemometers Wind vane Atmos. stability Background concentration unknown Grazed sections

6 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Sensor numbers: measuring NH3 emissions after N fertiliser applied to the whole bay 2.66 Average fluxes (μgNH3-N m -2 s -1 ), , using different sensor combinations; wind direction 170 o 24 sensors, 2 to each mast, at 1.4 and 2m 2 sensors, one upwind & one downwind, each at 1.4m If background unknown, need 2 sensors If >2 sensors, problem is over-determined & model returns least-squares, best-fit background and flux

7 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Multiple source areas (using 16 sensors) Average fluxes, , μgNH 3 -N m -2 s -1 Grazed yesterday → Grazed today → Ungrazed → -0.02

8 CSIRO. Inferring gas fluxes from point or line-averaged concentrations An example result: emissions from one grazed section Before grazing: small NH 3 uptake Continuous NH 3 emission during & after grazing Large NH 3 emissions after fertilizing Emissions cease after irrigation

9 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Line-averaged concentrations: laser and Fourier Transform Infrared (FTIR) systems Lasers measure line-averaged gas concentrations up to 1km, FTIR less Lasers: tripod-mounted, stand alone, battery-operated units; FTIR requires mains power Suitable for point, line and small area sources Laser FTIR Reflector Line-average concentration Open-path FTIR (CO 2,CH 4, N 2 O, NH 3 ) Open-path laser (CO 2, CH 4, NH 3 )

10 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Tests: releases and recoveries  CH 4, N 2 O, NH 3 released from cylinders through mass-flow controllers  Tests conducted of recoveries from point source and plane source emissions 40m x 15m grid of permeable pipes Daisy – our virtual cow 40m x 15m grid of permeable pipe

11 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Tests: releases and recoveries_ point sources  Average NH3 concentrations measured by a laser instrument at 1.5m height along a line of 123m, 10m downwind of a point source of ammonia 0.5m above ground.

12 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Tests: releases and recoveries_ areal sources Top: Recovery by laser of NH 3 released from ground level grid, 25m x 25m Laser 2m downwind of grid Path 128m NH 3 released at 5L min Bottom: Recovery by 2 lasers and FTIR of CH 4 released from ground level grid, 40m x 15m Path 140m

13 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Example application of open-path systems: CH 4 emission from a feedlot with 14,000 cattle WindTrax map of feedlot layout Laser paths Micromet. tower

14 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Strengths and weaknesses bLs technique + WindTrax represent a powerful new tool for measuring gas emissions from well-defined source areas Main advantage: fluxes determined from just one concentration measurement and knowledge of the background concentration + turbulence statistics Both closed and open-path measuring systems possible Path lengths of up to 1 km possible, but 100 to 300m seem more reliable Open –path systems: Lasers tuned to individual gases: CO 2, CH 4, NH 3 and H 2 O FTIR units measure many of the gases of interest in the context of landscape-atmosphere exchanges simultaneously: CO 2, CH 4, NH 3, H 2 O, N 2 O and CO The main disadvantage of the bLs technique may be in its parameterisation of turbulent transport, but many tests have shown that with appropriate precautions, gas emissions can be measured with acceptable accuracy (Flesch et al., 2004; McBain and Desjardins, 2005; Laubach et al., 2008).

15 CSIRO. Inferring gas fluxes from point or line-averaged concentrations Acknowledgements Collaborators University of Melbourne: Deli Chen, Debra Turner, Yong Li, Zoe Loh, Julian Hill University of Wollongong: David Griffith, Mei Bai, Glenn Bryant, Travis Naylor DPI Victoria: Kevin Kelly, Frances Phillips Charlton Feedlot Sandalwood Feedlot Funding Australian Greenhouse Office Meat and Livestock Australia

16 Contact Us Phone: or Web: Thank you CSIRO Land and Water and University of Melbourne Tom Denmead Fellow Phone: Web:


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