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

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Linear Actcuator

Acrylic Tube

Cap

Spacer

Vertical Adapter

Radiello Diffusive Sampler

Foam

Acrylic Disc

Hall Effect Sensor

Control Cable

Spacer

Clevis

Clevis

PlungerMagnet

Hall Effect Cable

Arduino Shield Stacks andDatalogger 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

Feedlot25,000 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 (𝑪𝜶) 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

𝑪𝜶 (𝒙 , 𝒛 )=𝑪𝒃𝒅𝒈+∫𝑺 ( 𝒙𝒔 , 𝒛 𝒔 )𝑫 ( 𝒙 , 𝒛 /𝒙𝒔 , 𝒛 𝒔)𝒅𝒙 𝒔

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 mg 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 mg 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)

References1. Fdfs

Thank You!