cff-version: 1.2.0
abstract: "<p>Supplementary data accompanying the article "Transport Patterns of Global Aviation NO<sub>x</sub> and their Short-term O<sub>3</sub> Radiative Forcing – A Machine Learning Approach". </p>
<p><br></p>
<p>This data tracks the global transport of emitted NO<sub>x </sub>throughout a 90-day period since its emission from a representative aircraft cruising altitude of 250 hPa (~10.4 km) in 5 regions (North America, South America, Eurasia, Africa and Australasia) during the first day of January and July of 2014.</p>
<p><br></p>
<p>The short-term NO<sub>x</sub>-induced net O<sub>3</sub> production is also calculated as well as its associated instantaneous radiative forcing impact. The Lagrangian modelling approach adopted in this study allows for the amount of NO<sub>x</sub> emitted and consequent O<sub>3</sub> produced to be accompanied along each point of every air parcel trajectory. Lastly, information regarding the background NO<sub>x</sub> conditions during the times of emission is also included. </p>"
authors:
  - family-names: Maruhashi
    given-names: Jin
    orcid: "https://orcid.org/0000-0003-2667-4161"
  - family-names: Grewe
    given-names: Volker
    orcid: "https://orcid.org/0000-0002-8012-6783"
  - family-names: Frömming
    given-names: Christine
    orcid: "https://orcid.org/0000-0001-5516-7180"
  - family-names: Jöckel
    given-names: Patrick
    orcid: "https://orcid.org/0000-0002-8964-1394"
  - family-names: C Dedoussi
    given-names: Irene
    orcid: "https://orcid.org/0000-0002-8966-9469"
title: "Supplementary Data of &#34;Transport Patterns of Global Aviation NOx and their Short-term O3 Radiative Forcing – A Machine Learning Approach&#34;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/16886977.v1
license: CC BY 4.0
date-released: 2022-12-09