\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{}



\author[1]{Yin Yu}%
\author[2]{Peiyuan Li}%
\author[1]{Daning Huang}%
\author[2,3,4]{Ashish Sharma}%
\affil[1]{Department of Aerospace Engineering, Pennsylvania State University}%
\affil[2]{Discovery Partners Institute, University of Illinois System}%
\affil[3]{Department of Climate, Meteorology \& Atmospheric Sciences, University of Illinois at Urbana-Champaign}%
\affil[4]{Environmental Science Division, Argonne National Laboratory}%


\vspace{-1em}



  
  \date{May 20, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
**The manuscript has been submitted to Urban Climate for peer review.

Estimating street-level air temperature is a challenging task due to the
highly heterogeneous urban~surfaces, canyon-like street morphology, and
the diverse physical processes in the built environment.{~Though
pioneering studies have embarked on investigations via data-driven
approaches, many questions~}remain to be answered. In this study, we
leveraged an innovative framework and redefined the street-level
temperature estimation problem using Graph Neural Networks (GNN) with
spatial embedding{~techniques. The results showed that GNN models are
more capable and consistent of estimating street-level}~temperature
among tested locations, benefiting from its unique strength in handling
extensive data{~over unstructured graph topology. In addition, we
conducted an in-depth analysis of feature importance}~to enhance the
model interpretability. Among the urban features analyzed in this study,
the time-variant canopy density and meter-level land use data emerge as
crucial factors. Our findings highlight{~GNN's high potential in
capturing the complex dynamics between urban elements and their impacts
on~}microclimate, thus offering valuable insights for comprehensive
urban data collection and urban climate modeling in general.
Collectively, this study also contributes to urban planning and policy
by providing{~avenues to enhance city resilience against climate change,
thereby advancing the agenda for environmental}~stewardship and urban
sustainability.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

