Transcript Inferring gas fluxes from point or lineaveraged concentrations Tom Denmead Fellow, CSIRO Land and Water & University of Melbourne Ozflux Conference, 4 February.

Inferring gas fluxes from point or lineaveraged concentrations
Tom Denmead
Fellow, CSIRO Land and Water & University of Melbourne
Ozflux Conference, 4 February 2008
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/w0|
C is downwind concentration
Q is the surface flux
N is the number of
trajectories commonly,
50,000
w0 is the vertical velocity of
particles at touchdown
Q = (C-Cbackground) /
(C/Q)sim
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
Micromet.
Source
area
Point
concentration
sensor
wind
Touchdowns
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
Micromet.
Source
area
Point
concentration
sensor
wind
• Uses a software package called
WindTrax to calculate surface
fluxes from concentration and
micrometeorological data
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
Touchdowns
Point concentration measurements: an
example from grazing (315 dairy cows)
Ammonia concentrations
measured with passive
samplers
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
Grazed
sections
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
Meteorological
Sensors:
2 anemometers
Wind vane
Atmos. stability
Background
concentration
unknown
Sensor numbers: measuring NH3 emissions after
N fertiliser applied to the whole bay
Average fluxes (μgNH3-N m-2 s-1), 0900-1800, using
different sensor combinations; wind direction 170o
If background
unknown, need
2 sensors
If >2 sensors,
problem is
over-determined
& model returns
least-squares,
best-fit
background and
flux
2.66
2.33
1.55
2 sensors, one
upwind & one
downwind, each
at 1.4m
2.05
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
24 sensors,
2 to each mast,
at 1.4 and 2m
Multiple source areas (using 16 sensors)
Average fluxes, 0800-1730, μgNH3-N m-2 s-1
Grazed yesterday → 0.14
Grazed today →
0.30
Ungrazed → -0.02
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
An example result: emissions from one grazed
section
• Before grazing:
small NH3 uptake
NH3 fluxes Kyabram 2004 - top Bay 8
5
• Continuous NH3
emission during &
after grazing
ug NH3-N/m2/s
4
3
50 kgN/ha
Urea
2
• Large NH3
emissions after
fertilizing
1
0
26-Mar
-1
28-Mar
30-Mar
1-Apr
3-Apr
time
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
5-Apr
7-Apr
9-Apr
• Emissions cease
after irrigation
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
Line-average concentration
Reflector
Laser
FTIR
Open-path FTIR (CO2,CH4, N2O, NH3) Open-path laser (CO2, CH4, NH3)
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
Tests: releases and recoveries
Daisy – our virtual cow
 CH4, N2O, NH3
40m x 15m
released from
grid of
cylinders through
mass-flow permeable
controllers pipes
40m x 15m
grid of
permeable
pipe
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
 Tests conducted of
recoveries from
point source and
plane source
emissions
Tests: releases and recoveries_ point sources
Release rates 0f NH3 and downwind NH3 concentrations, 29/07/05
Concentration
10
200
8
160
6
120
4
80
2
40
0
NH3 concentration (ppb)
-1
Release rate (L min )
Release rate
0
10521130
11301205
12051242
12421351
13511426
14261500
15001530
15301600
16001630
16301700
 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.
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
Tests: releases and recoveries_ areal sources
Ammonia laser 2m downwind of grid, Aug 2, 2005
Released
Measured
mg NH 3 s-1
80
60
40
20
0
12:00
13:00
14:00
15:00
16:00
Recovery tests for CH 4 over 1 hour, Aug 3, 2005
mg CH4 s-1
80
• Top:
• Recovery by laser
of NH3 released
from ground level
grid, 25m x 25m
• Laser 2m downwind
of grid
• Path 128m
• NH3 released at 5L
min
• Bottom:
60
40
20
0
Cylinder
Laser #1012
Laser #1013
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
FTIR-CH4
• Recovery by 2
lasers and FTIR of
CH4 released from
ground level grid,
40m x 15m
• Path 140m
Example application of open-path systems: CH4
emission from a feedlot with 14,000 cattle
WindTrax map of feedlot
layout
Laser paths
Micromet. tower
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: CO2, CH4, NH3 and H2O
• FTIR units measure many of the gases of interest in the context of
landscape-atmosphere exchanges simultaneously: CO2, CH4, NH3, H2O,
N2O 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).
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
CSIRO. Inferring gas fluxes from point or line-averaged concentrations
CSIRO Land and Water and University of Melbourne
Tom Denmead
Fellow
Phone: +61 2 6246 5568
Email: [email protected]
Web: www.csiro.au
Thank you
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