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\begin{document}

\title{A Dispersion Model to Estimate CH\textsubscript{4} emissions from Manure
Lagoons in Dairy Farms}



\author[1]{Ranga Rajan Thiruvenkatachari}%
\author[1]{Valerie Carranza}%
\author[1]{Akula Venkatram}%
\author[1]{Faraz Ahangar}%
\author[1]{Francesca Hopkins}%
\affil[1]{University of California Riverside}%


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  \date{June 18, 2024}


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\begin{abstract}
Atmospheric concentrations of CH4 have tripled since the Industrial
Revolution. One culprit of this increase is animal agriculture,
contributing 8 to 10\% of global greenhouse gas emissions primarily in
the form of CH4. According to US Environmental Protection Agency
greenhouse gas inventory estimates, the majority of the manure emissions
are from manure management on dairy farms (53\%). Most of these manure
emissions are generated from liquid manure in anaerobic lagoons. Thus,
accurate estimates of the emissions from these lagoons are essential for
developing management strategies to reduce CH4 emissions. Emissions of
methane from two manure lagoons, one in Southern California and the
other in Central California, were estimated by fitting results from a
state-of-the-art dispersion model to CH4 concentrations measured with a
mobile monitor. The sampling was conducted by stationing the mobile
monitors at several locations (29-42) around the lagoons for time
intervals ranging from 10 to 15 minutes. A sonic anemometer provided
micrometeorological measurements used by the dispersion model. Emissions
were computed by fitting the time-averaged methane concentrations to
model estimates. The 95\% confidence intervals for the emissions were
computed by bootstrapping pseudo observations created by adding
residuals between model estimates and corresponding observations to the
best fit model estimates. The coefficient of determination, r2, between
model and measurements made at the Southern California dairy was over
0.86 and the geometric standard deviation (sg) was 1.1; the steady
westerly wind direction was a major factor for this result. At the
Central California dairy, the winds were light and variable resulting in
an r2 of about 0.9 and a high sg of 1.4. The sensitivity of the emission
estimates to wind direction was determined by running the dispersion
model for different wind sectors. We found that the emission estimates
were within 1.5 times of each other under all wind conditions. The
dispersion model was cross-validated by estimating the emissions using
only half the total receptors and then predicting the concentration at
other receptors using this emission rate. This technique can be used to
improve methane emission estimates in manure management and to assess
the effectiveness of the different strategies to reduce emissions.%
\end{abstract}%



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