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\begin{document}

\title{Air Quality Estimation and Forecasting via Data Fusion with Uncertainty
Quantification: Theoretical Framework and Preliminary Results}



\author[1]{Carl Malings}%
\author[1]{K. Emma Knowland}%
\author[2]{Nathan Pavlovic}%
\author[2]{Justin G. Coughlin}%
\author[3]{Christoph A. Keller}%
\author[4]{Stephen E. Cohn}%
\author[5]{Randall V Martin}%
\affil[1]{Morgan State University}%
\affil[2]{Sonoma Technology, Inc.}%
\affil[3]{Universities Space Research Association}%
\affil[4]{Data Assimilation Office}%
\affil[5]{Washington University in St. Louis}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Integrating air quality information from models, satellites, and in-situ
monitors allows for both better estimation of air quality and better
quantification of uncertainties in this estimation. Uncertainty
quantification is important to appropriately convey confidence in these
estimates and forecasts to users who will base decisions on these.
Uncertainty quantification also allows tracing the value of information
provided by different data sources. This can identify gaps in the
monitoring network where additional data could further reduce
uncertainties. This paper presents a framework for data fusion with
uncertainty quantification, applicable to multiple air-quality-relevant
pollutants. Testing of this framework in the context of nitrogen dioxide
forecasting at sub-city scales shows promising results, with confidence
intervals typically encompassing the expected number of actual
measurements during cross-validation. The framework is now being
implemented into an online tool to support local air quality management
decision-making. Future work will also include the incorporation of
low-cost air sensor data and the quantification of uncertainty at
hyper-local scales.%
\end{abstract}%



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