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\begin{document}

\title{Near-Automated Estimate of City Nitrogen Oxides Emissions Applied to
South and Southeast Asia}



\author[1]{Gongda Lu}%
\author[1]{Eloise Ann Marais}%
\author[1]{Karn Vohra}%
\author[1]{Rebekah P Horner}%
\author[2]{Dandan Zhang}%
\author[3]{Randall V Martin}%
\author[4]{Sarath K Guttikunda}%
\affil[1]{University College London}%
\affil[2]{Washington University in St Louis}%
\affil[3]{Washington University in St. Louis}%
\affil[4]{Urban Emissions}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
Cities in South and Southeast Asia are developing rapidly without
routine, up-to-date knowledge of air pollutant precursor emissions. This
data deficit can potentially be addressed for nitrogen oxides
(NO\textsubscript{x}) by deriving city NO\textsubscript{x} emissions
from satellite observations of nitrogen dioxide (NO\textsubscript{2})
sampled under windy conditions. NO\textsubscript{2} plumes of isolated
cities are aligned along a consistent wind-rotated direction and a
best-fit Gaussian is applied to estimate emissions. This approach
currently relies on non-standardized selection of the area to sample
around the city centre and Gaussian fits often fail or yield
non-physical parameters. Here, we automate this approach by defining
many (54) sampling areas that we test with TROPOspheric Monitoring
Instrument (TROPOMI) NO\textsubscript{2} observations for 2019 over 19
cities in South and Southeast Asia. Our approach is efficient, adaptable
to many cities, standardizes and eliminates sensitivity of the Gaussian
fit to sampling area choice, and increases success of deriving annual
emissions from 40-60\% with one sampling area to 100\% (all 19 cities)
with 54. The annual emissions we estimate range from 16\selectlanguage{ngerman}±5 mol
s\textsuperscript{-1} for Yangon (Myanmar) and Bangalore (India) to
125±41 mol s\textsuperscript{-1} for Dhaka (Bangladesh). With the
enhanced success of our approach, we find evidence from comparison of
our top-down emissions to past studies and to inventory estimates that
the wind rotation and EMG fit approach may be biased, as it does not
adequately account for spatial and seasonal variability in
NO\textsubscript{x} photochemistry. Further methodological development
is needed to enhance its accuracy and to exploit it to derive sub-annual
emissions.%
\end{abstract}\selectlanguage{ngerman}%



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