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\begin{document}

\title{Substantial decreases in NO2 emissions from reduced transportation
volumes in US cities during COVID-19 shutdowns reveal health
vulnerabilities of urban populations}



\author[1]{Gabriel Filippelli}%
\author[2]{Asrah Heintzelman}%
\author[3]{Vijay Lulla}%
\affil[1]{Indiana University - Purdue University Indianapolis}%
\affil[2]{Unknown}%
\affil[3]{IUPUI}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
The air pollutant NO is derived largely from transportation sources, and
is known to cause various respiratory diseases. Substantial reduction in
transport and industrial processes around the globe stemming from the
novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp
declines in emissions, including for NO. Additionally, the COVID-19
disease that results from the coronavirus may present in its most severe
form in those who have been exposed to high levels of air pollution and
thus have various co-morbidities. To explore these links, we compared
ground-based NOsensor data from 15 US cities from a one month window in
2019 versus the same window during shutdown in 2020. Levels of NO
declined roughly 20-60\% in 13 of the 15 cities in 2020, linked to
similar declines in traffic volume in those cities. To broaden the
spatial analysis beyond the individual ground-based monitors, satellite
data for tropospheric NO was also analyzed, and was largely consistent
with the ground measurements. Many of the cities studied had a
substantial percentage of the population with various pre-existing
conditions, and a relationship was found between NO levels, respiratory
disease, and COVID-19 case counts. This finding indicates that
substantial improvements in air pollution and health outcomes can be
achieved quickly with local and state policy directives, perhaps leading
to more population-level health resilience in the face of future
pandemics.%
\end{abstract}%



\sloppy


\textbf{Supporting Information}

\textbf{Substantial decreases in NO\textsubscript{2} emissions from
reduced transportation volumes in US cities during COVID-19 shutdowns
reveal health vulnerabilities of urban populations}

Asrah Heintzelman\textsuperscript{1,2}, Vijay O.
Lulla\textsuperscript{3}, and Gabriel M.
Filippelli\textsuperscript{1,2\#}

\textsuperscript{1}Department of Earth Sciences, Indiana
University-Purdue University Indianapolis (IUPUI)

\textsuperscript{2}Environmental Resilience Institute, Indiana
University

\textsuperscript{3}Department of Geography, Indiana University-Purdue
University Indianapolis (IUPUI)

\textsuperscript{\#}Corresponding author: gfilippe@iu.edu\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{VMT\_normalized} \textbf{area}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Pct\_ped} \textbf{asthma}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Pct\_adult asthma}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Pct\_adult asthma}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Pct} \textbf{copd}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
VMT\_normalized\_area\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
pct\_ped asthma\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.335513897\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
pct\_adult asthma\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.424744751\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.209894652\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.209894652\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
pct\_copd\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.051055607\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.616866122\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.616866122\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.3427601\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
pct-lung cancer\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.192682223\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.665499529\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.665499529\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.330156319\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.844530689\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S1. Disease Correlation}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\emph{County} & \emph{City} & \emph{City} & \textbf{Pct adult asthma} &
\textbf{Pct adult asthma} & \textbf{Pct ped asthma}\tabularnewline
\midrule
\endhead
Queens & Queens & NY & NY & 8.17\% & 1.24\%\tabularnewline
Philadelphia & Philadelphia & Philadelphia & Philadelphia & 7.90\% &
2.11\%\tabularnewline
Maricopa & Maricopa & Phoenix & Phoenix & 7.71\% & 1.92\%\tabularnewline
Marion & Marion & Indianapolis & Indianapolis & 7.44\% &
2.14\%\tabularnewline
San Francisco & San Francisco & San Francisco & San Francisco & 7.33\% &
0.83\%\tabularnewline
Mecklenburg & Mecklenburg & Charlotte & Charlotte & 7.27\% &
2.71\%\tabularnewline
Duval & Duval & Jacksonville & Jacksonville & 6.81\% &
1.68\%\tabularnewline
Los Angeles & Los Angeles & LA & LA & 6.66\% & 1.34\%\tabularnewline
San Diego & San Diego & San Diego & San Diego & 6.66\% &
1.33\%\tabularnewline
Santa Clara & Santa Clara & San Jose & San Jose & 6.64\% &
1.35\%\tabularnewline
Travis & Travis & Austin & Austin & 5.81\% & 1.72\%\tabularnewline
Bexar & Bexar & San Antonio & San Antonio & 5.55\% &
2.02\%\tabularnewline
Tarrant & Tarrant & Fort Worth & Fort Worth & 5.51\% &
2.08\%\tabularnewline
Dallas & Dallas & Dallas & Dallas & 5.51\% & 2.07\%\tabularnewline
Harris & Harris & Houston & Houston & 5.47\% & 2.11\%\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S2. Percent Asthma}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Correlation} & \textbf{Pct\_adult asthma}\tabularnewline
\midrule
\endhead
pct\_adult asthma & 1\tabularnewline
death\_rate\_covid & 0.467336406\tabularnewline
cases\_rate\_covid & 0.578234609\tabularnewline
VMT\_jan\_normalized & 0.424744751\tabularnewline
pct\_ped\_asthma & -0.209894652\tabularnewline
pct\_copd & 0.3427601\tabularnewline
pct-lung cancer & 0.330156319\tabularnewline
mean\_NO\textsubscript{2}\_2019 & 0.318820764\tabularnewline
raster\_mar2019 & 0.218847794\tabularnewline
raster\_apr2019 & 0.187058132\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S3. Correlation of percent adult asthma}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Correlations} & \textbf{VMT\_jan\_normalized}\tabularnewline
\midrule
\endhead
VMT\_jan\_normalized & 1\tabularnewline
cases\_rate\_covid & 0.713780978\tabularnewline
raster\_mar2019 & 0.592131794\tabularnewline
raster\_apr2019 & 0.517037472\tabularnewline
pct\_adult asthma & 0.424744751\tabularnewline
death\_rate\_covid & 0.328400892\tabularnewline
pct-lung cancer & 0.192682223\tabularnewline
Mean\_ NO\textsubscript{2}\_2019 & 0.031041247\tabularnewline
pct\_copd & -0.051055607\tabularnewline
pct\_ped asthma & -0.335513897\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S4. Correlation of VMT normalized}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Correlation} & \textbf{Cases\_rate\_covid}\tabularnewline
\midrule
\endhead
cases\_rate\_covid & 1\tabularnewline
raster\_mar2019 & 0.825389115\tabularnewline
death\_rate\_covid & 0.78354049\tabularnewline
VMT\_jan\_normalized & 0.713780978\tabularnewline
raster\_apr2019 & 0.709177717\tabularnewline
pct\_adult asthma & 0.578234609\tabularnewline
pct-lung cancer & 0.379933163\tabularnewline
Mean\_ NO\textsubscript{2}\_2019 & 0.229179331\tabularnewline
pct\_copd & 0.113024761\tabularnewline
pct\_ped asthma & -0.220742254\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S5. Correlation of COVID-19 cases rate}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Correlation & Death\_rate\_covid\tabularnewline
\midrule
\endhead
death\_rate\_covid & 1\tabularnewline
cases\_rate\_covid & 0.78354049\tabularnewline
raster\_mar2019 & 0.69124339\tabularnewline
raster\_apr2019 & 0.518801979\tabularnewline
pct\_adult asthma & 0.467336406\tabularnewline
VMT\_jan\_normalized & 0.328400892\tabularnewline
pct-lungcancer & 0.07930734\tabularnewline
Mean\_NO\textsubscript{2}\_2019 & -0.073176656\tabularnewline
pct\_copd & -0.085953128\tabularnewline
pct\_pedasthma & -0.307275369\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S6. Correlation of COVID-19 death rate}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{CountyName} & \textbf{City} &
\textbf{Cases\_rate\_covid}\tabularnewline
\midrule
\endhead
Queens & NY & 1.90\%\tabularnewline
Philadelphia & Philadelphia & 0.66\%\tabularnewline
Marion & Indianapolis & 0.47\%\tabularnewline
Los Angeles & LA & 0.18\%\tabularnewline
San Francisco & San Francisco & 0.14\%\tabularnewline
Harris & Houston & 0.12\%\tabularnewline
Travis & Austin & 0.11\%\tabularnewline
Dallas & Dallas & 0.10\%\tabularnewline
Mecklenburg & Charlotte & 0.10\%\tabularnewline
Duval & Jacksonville & 0.10\%\tabularnewline
San Diego & San Diego & 0.08\%\tabularnewline
Tarrant & Fort Worth & 0.08\%\tabularnewline
Santa Clara & San Jose & 0.07\%\tabularnewline
Maricopa & Phoenix & 0.07\%\tabularnewline
Bexar & San Antonio & 0.06\%\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S7. COVID-19 cases in 15 cities}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{CountyName} & \textbf{City} &
\textbf{Death\_rate\_covid}\tabularnewline
\midrule
\endhead
Queens & NY & 10.42\%\tabularnewline
Santa Clara & San Jose & 5.92\%\tabularnewline
Marion & Indianapolis & 4.74\%\tabularnewline
Los Angeles & LA & 4.66\%\tabularnewline
Maricopa & Phoenix & 3.89\%\tabularnewline
San Diego & San Diego & 3.88\%\tabularnewline
Bexar & San Antonio & 3.76\%\tabularnewline
Mecklenburg & Charlotte & 3.27\%\tabularnewline
Tarrant & Fort Worth & 2.86\%\tabularnewline
Dallas & Dallas & 2.64\%\tabularnewline
Philadelphia & Philadelphia & 2.61\%\tabularnewline
Travis & Austin & 2.44\%\tabularnewline
San Francisco & San Francisco & 1.79\%\tabularnewline
Duval & Jacksonville & 1.75\%\tabularnewline
Harris & Houston & 1.53\%\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S8. COVID-19 death rate in 15 cities}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{correlations} & \textbf{death\_rate\_covid} &
\textbf{cases\_rate\_covid}\tabularnewline
\midrule
\endhead
pct\_adult asthma & 0.467336406 & 0.578234609\tabularnewline
pct\_ped asthma & -0.307275369 & -0.220742254\tabularnewline
pct\_copd & -0.085953128 & 0.113024761\tabularnewline
pct-lungcancer & 0.07930734 & 0.379933163\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S9. Correlation of diseases and COVID-19 for all 15 cities
studied.}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Column1} & \textbf{Mean\_}
\textbf{NO\textsubscript{2}\_2019}\tabularnewline
\midrule
\endhead
Mean\_ NO\textsubscript{2}\_2019 & 1\tabularnewline
raster\_apr2019 & 0.356824066\tabularnewline
pct\_adult asthma & 0.318820764\tabularnewline
raster\_mar2019 & 0.27820681\tabularnewline
cases\_rate\_covid & 0.229179331\tabularnewline
pct-lungcancer & 0.065654954\tabularnewline
VMT\_jan\_normalized & 0.031041247\tabularnewline
pct\_copd & 0.005648168\tabularnewline
pct\_ped asthma & -0.055242012\tabularnewline
death\_rate\_covid & -0.073176656\tabularnewline
\bottomrule
\end{longtable}

\emph{Table S10. Correlation of NO\textsubscript{2} with disease in all
15 cities measured}

\selectlanguage{english}
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\end{document}

