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\begin{document}

\title{Drive-Around Surveys for Detection and Quantification of Methane Leaks Estimating Emission Rates from Drive-Around Surveys}



\author[1]{Jeffrey Nivitanont}%
\author[1]{Kristen Pozsonyi}%
\author[2]{Tim Vaughn}%
\author[3]{Arthur Barbaro}%
\author[2]{Santos sr.}%
\author[2]{Stuart Riddick}%
\author[1]{Shane Murphy}%
\author[2]{Daniel Zimmerle}%
\affil[1]{Department of Atmospheric Science, University of Wyoming}%
\affil[2]{Energy Institute, Colorado State University}%
\affil[3]{Affiliation not available}%


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  \date{May 24, 2024}


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\begin{abstract}
Current MMRV solutions have the potential to quickly survey entire
oilfields or detect methane leaks down to the component level, but also
carry high price tags or, indirectly, high implementation costs. The
Stanford/EDF Mobile Monitoring Challenge (MMC) conducted in 2018 was the
first study to systematically evaluate methane mitigation technologies
for incorporation into LDAR programs at the operator level. Three
vehicle-based solutions tested in the MMC utilized a fence-line
screening pattern that encompassed a production site and equipment,
which we refer to as a ``drive-around survey,'' and showed promising
results of greater than or equal to 88\% true positive source
identification rates for controlled releases in the 0-26 kg CH4/hr
range.

In this work, we evaluate a similar on-site drive-around survey as an
alternative methane leak detection method under the EPA's recent update
to the Standards of Performance for New, Reconstructed, and Modified
Sources and Emissions: Oil and Natural Gas Sector (NSPS). We find that a
simple methane enhancement threshold binary classification system
performs well with true positive rates \textgreater{} 0.8, though the
precision of this classifier is inversely related to the magnitude of
the emission rates for each class. We also describe a heuristic approach
to estimating dispersion without source distance information.
Incorporating this information into a linear model of emission rates
regressed on survey data, we improve the model fit to R\^{}2
\textgreater{} 0.9.%
\end{abstract}%



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