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Kira Shonkwiler and Jay Ham
Department of Atmospheric Science, Colorado State University
Department of Soil and Crop Sciences, Colorado State University
Estimating Ammonia Emissions
Using Low-cost, Time-averaged
Concentration Measurements
Objectives
• Adapt diffusive NH3
samplers for weather-based
conditional sampling
• Field test at beef feedlots
and dairies
• Estimate pen NH3 emissions
using an inverse model
Radiello Diffusive/Passive
Samplers
• Pros
– Widely used for NH3
(e.g., AMoN network)
– Inexpensive, Simple
• Cons
– Cumulative
– Conc. affected by wind
dir. /speed, stability, …
– No Stationarity over
sample period
www.nescaum.org/documents/mac/
mac-committee...3/rury-amon.pdf/
Conditional Samplers
• Robotic mechanism
exposes samplers when a
given set of user-defined
weather conditions exist
– Min. Wind speed
– Wind Direction Range
– Time of Day, others
• Wireless Sensor Net
– Synoptic sampling
– Xbee
Actuator Control
``
Linear Actcuator
Acrylic Tube
Cap
Spacer
Vertical Adapter
Radiello Diffusive
Sampler
Foam
Acrylic Disc
Hall Effect Sensor
Control Cable
Spacer
Clevis
Clevis
Plunger
Magnet
Hall Effect Cable
Arduino Shield Stacks and
Datalogger Module
Weather-Based Sampling
Under automated control, samplers were exposed to
the air for a total of 3.5 to 4 days during a 14 day
sampling period.
Field Testing: Feedlot
Feedlot
25,000 Head
700 m
North
South
West Lot
Base
Pasture
Prevailing
Wind
Average concentration for each deployment (Oct 2012 – Feb 2013)
Inverse Modeling
Know average concentration, wind
characteristics, and site layout…
 Can infer emissions
Inverse Model
Weather Data
Source Geometry
and Roughness
Concentration
Data Emissions
FIDES
Flux Interpretation by Dispersion Exchange
over Short-range (FIDES)
 Inverse model (inputs: u* and L)
 Solves the advection-dispersion equation
 Uses concentration ( ) from a location (x, z) to estimate
source strength of a different location (xs, zs)
Loubet et al., 2001; 2010
 Cbgd is the constant background concentration
 S is the source strength
 D is a dispersion function
FIDES
Applied successfully in Europe
(Loubet et al., 2001; 2009; 2010)
 NH3 concentrations measured with high-speed
instrumentation
Work at CSU is first attempt at modeling NH3
emissions from time-averaged data (i.e., the
conditional passive samplers)
Model Output – Emissions
Average emissions for each deployment cycle (Oct 2012 – Feb 2013)
 Decrease in volatilization from surface during winter
Average temperature
(above) and wind
speed (below) for each
deployment period.
Temperatures during
the 2012 – 2013 winter
(green line) were much
higher than the 15-year
normal (red line)
Mean wind speeds
varied little over each
period
Model Output – Emissions
Histogram (frequency distribution) of output emissions from FIDES
 Log-normal distribution, 92% of values fall between 20 – 100 g m-2 s-1
Model Output – Emissions
Emissions summary
 Average Emissions: 4.7 g m-2 d-1; 96.5 g head-1 d-1
 Percent of Fed-Nitrogen emitted as NH3 averaged 53%
Conclusions
 Emissions decreased throughout the winter as temperature
decreased
 Predicted emissions have a log-normal distribution
 Average model output was 20 – 100 g m-2 s-1
 Emissions averaged 96.5 g head-1 d-1 and 4.7 g m-2 d-1
 53% of Fed N emitted to air as NH3
Next steps
 Compare to continuous NH3 measurements (e.g., boreal
laser  Todd et al., 2008; mobile sampling)
References
1. Fdfs
Thank
You!

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Estimating Ammonia Emissions from Livestock Operations Using Low-Cost, Time-Averaged Concentration Measurements

  • 1. Kira Shonkwiler and Jay Ham Department of Atmospheric Science, Colorado State University Department of Soil and Crop Sciences, Colorado State University Estimating Ammonia Emissions Using Low-cost, Time-averaged Concentration Measurements
  • 2. Objectives • Adapt diffusive NH3 samplers for weather-based conditional sampling • Field test at beef feedlots and dairies • Estimate pen NH3 emissions using an inverse model
  • 3. Radiello Diffusive/Passive Samplers • Pros – Widely used for NH3 (e.g., AMoN network) – Inexpensive, Simple • Cons – Cumulative – Conc. affected by wind dir. /speed, stability, … – No Stationarity over sample period www.nescaum.org/documents/mac/ mac-committee...3/rury-amon.pdf/
  • 4. Conditional Samplers • Robotic mechanism exposes samplers when a given set of user-defined weather conditions exist – Min. Wind speed – Wind Direction Range – Time of Day, others • Wireless Sensor Net – Synoptic sampling – Xbee
  • 5. Actuator Control `` Linear Actcuator Acrylic Tube Cap Spacer Vertical Adapter Radiello Diffusive Sampler Foam Acrylic Disc Hall Effect Sensor Control Cable Spacer Clevis Clevis Plunger Magnet Hall Effect Cable
  • 6. Arduino Shield Stacks and Datalogger Module
  • 7.
  • 8. Weather-Based Sampling Under automated control, samplers were exposed to the air for a total of 3.5 to 4 days during a 14 day sampling period.
  • 9. Field Testing: Feedlot Feedlot 25,000 Head 700 m North South West Lot Base Pasture Prevailing Wind
  • 10. Average concentration for each deployment (Oct 2012 – Feb 2013)
  • 11. Inverse Modeling Know average concentration, wind characteristics, and site layout…  Can infer emissions Inverse Model Weather Data Source Geometry and Roughness Concentration Data Emissions
  • 12. FIDES Flux Interpretation by Dispersion Exchange over Short-range (FIDES)  Inverse model (inputs: u* and L)  Solves the advection-dispersion equation  Uses concentration ( ) from a location (x, z) to estimate source strength of a different location (xs, zs) Loubet et al., 2001; 2010  Cbgd is the constant background concentration  S is the source strength  D is a dispersion function
  • 13. FIDES Applied successfully in Europe (Loubet et al., 2001; 2009; 2010)  NH3 concentrations measured with high-speed instrumentation Work at CSU is first attempt at modeling NH3 emissions from time-averaged data (i.e., the conditional passive samplers)
  • 14.
  • 15. Model Output – Emissions Average emissions for each deployment cycle (Oct 2012 – Feb 2013)  Decrease in volatilization from surface during winter
  • 16. Average temperature (above) and wind speed (below) for each deployment period. Temperatures during the 2012 – 2013 winter (green line) were much higher than the 15-year normal (red line) Mean wind speeds varied little over each period
  • 17. Model Output – Emissions Histogram (frequency distribution) of output emissions from FIDES  Log-normal distribution, 92% of values fall between 20 – 100 g m-2 s-1
  • 18. Model Output – Emissions Emissions summary  Average Emissions: 4.7 g m-2 d-1; 96.5 g head-1 d-1  Percent of Fed-Nitrogen emitted as NH3 averaged 53%
  • 19. Conclusions  Emissions decreased throughout the winter as temperature decreased  Predicted emissions have a log-normal distribution  Average model output was 20 – 100 g m-2 s-1  Emissions averaged 96.5 g head-1 d-1 and 4.7 g m-2 d-1  53% of Fed N emitted to air as NH3 Next steps  Compare to continuous NH3 measurements (e.g., boreal laser  Todd et al., 2008; mobile sampling)

Editor's Notes

  1. Point out that an inverse model is different from a backwards lagrangian stochastic model
  2. The North site was always higher, the cows were younger in this pen, and the pen was smaller (feed younger cattle more N)
  3. Put the temperature data here, compare to historical average
  4. % Fed N compare well to the work done by Rick Todd and Andy Cole, stocking rate = 20.5 m2, 8.8 % fed N to the bunk (180.4 g/head)We only used a few pens around the samplers to calculate the emissions factorsCrude protein in the diet, ~13%
  5. Saw spatial variability in model output; next steps section – compare to boreal laser (Todd et al, 2008)