GMD - Air quality modeling intercomparison and multiscale ensemble chain for Latin America

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name="citation_fulltext_world_readable" content=""> <meta name="citation_publisher" content="Copernicus GmbH"/> <meta name="citation_title" content="Air quality modeling intercomparison and multiscale ensemble chain for Latin America"/> <meta name="citation_abstract" content="<p><strong class="journal-contentHeaderColor">Abstract.</strong> A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO<span class="inline-formula"><sub>2</sub></span> exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO<span class="inline-formula"><sub>2</sub></span>, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.</p>"/> <meta name="citation_publication_date" content="2024/10/28"/> <meta name="citation_online_date" content="2024/10/28"/> <meta name="citation_journal_title" content="Geoscientific Model Development"/> <meta name="citation_volume" content="17"/> <meta name="citation_issue" content="20"/> <meta name="citation_issn" content="1991-959X"/> <meta name="citation_doi" content="https://doi.org/10.5194/gmd-17-7467-2024"/> <meta name="citation_firstpage" content="7467"/> <meta name="citation_lastpage" content="7512"/> <meta name="citation_author" content="Pachón, Jorge E."/> <meta name="citation_author_institution" content="Department of Environmental Engineering, Universidad de La Salle, Bogotá, 111711, Colombia"/> <meta name="citation_author" content="Opazo, Mariel A."/> <meta name="citation_author_institution" content="Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile"/> <meta name="citation_author" content="Lichtig, Pablo"/> <meta name="citation_author_institution" content="Comisión Nacional de Energía Atómica – CNEA, Buenos Aires, C1429BNP, Argentina"/> <meta name="citation_author_orcid" content="0000-0003-1896-234X"> <meta name="citation_author" content="Huneeus, Nicolas"/> <meta name="citation_author_institution" content="Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile"/> <meta name="citation_author" content="Bouarar, Idir"/> <meta name="citation_author_institution" content="Max Planck Institute for Meteorology, 20146 Hamburg, Germany"/> <meta name="citation_author" content="Brasseur, Guy"/> <meta name="citation_author_institution" content="Max Planck Institute for Meteorology, 20146 Hamburg, Germany"/> <meta name="citation_author_orcid" content="0000-0001-6794-9497"> <meta name="citation_author_email" content="guy.brasseur@mpimet.mpg.de"> <meta name="citation_author" content="Li, Cathy W. 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A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers."> <meta property="og:url" content="https://gmd.copernicus.org/articles/17/7467/2024/"> <meta property="twitter:image" content="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-avatar-web.png"/> <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:title" content="Air quality modeling intercomparison and multiscale ensemble chain for Latin America"> <meta name="twitter:description" content="Abstract. A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers."> <link rel="icon" href="https://www.geoscientific-model-development.net/favicon.ico" type="image/x-icon"/> <script type="text/javascript" src="https://cdn.copernicus.org/libraries/jquery/1.11.1/ui/jquery-ui.min.js"></script> <script type="text/javascript" src="https://cdn.copernicus.org/libraries/jquery/1.11.1/ui/jquery-ui-slider-pips.js"></script> <script type="text/javascript" src="https://cdn.copernicus.org/libraries/jquery/1.11.1/ui/template_jquery-ui-touch.min.js"></script> <script type="text/javascript" src="https://cdn.copernicus.org/js/respond.js"></script> <script type="text/javascript" src="https://cdn.copernicus.org/libraries/highstock/2.0.4/highstock.js"></script> <script type="text/javascript" src="https://cdn.copernicus.org/apps/htmlgenerator/js/CoPublisher.js"></script> <script 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This work is distributed under <br class="hide-on-mobile hide-on-tablet" />the Creative Commons Attribution 4.0 License.</div> </div> </div> </div> <div class="col-7 d-none d-xl-block"> <div class="text-right articleBackLink"> <a href="https://gmd.copernicus.org/">Articles</a> | <a href="https://gmd.copernicus.org/articles/17/issue20.html">Volume 17, issue 20</a> </div> <div class="tab co-angel-left d-md-none"></div> <div class="tab co-angel-right d-md-none"></div> <div class="mobile-citation"> <ul class="tab-navigation no-styling"> <li class="tab1.articlf active"><nobr><a href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.html">Article</a></nobr></li><li class="tab2.assett"><nobr><a href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-assets.html">Assets</a></nobr></li><li class="tab3.discussioo"><nobr><a href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-discussion.html">Peer review</a></nobr></li><li class="tab450.metrict"><nobr><a href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-metrics.html">Metrics</a></nobr></li><li class="tab500.relationt"><nobr><a href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-relations.html">Related articles</a></nobr></li> </ul> </div> </div> </div> <div class="ms-type row no-gutters d-none d-lg-flex mb-1 mt-0 align-items-center"> <div class="col"> <div class="row no-gutters align-items-center"> <div class="col-auto"> Model evaluation paper </div> <div class="col-auto">  | <strong>Highlight paper</strong> </div> <div class="col">  | <a target="_blank" href="https://creativecommons.org/licenses/by/4.0/" rel="license" class="licence-icon-svg"><img src="https://www.geoscientific-model-development.net/licenceSVG_16.svg"></a> </div> </div> </div> <div class="col-auto text-right">28 Oct 2024</div> </div> <div class="ms-type row no-gutters d-lg-none mb-1 align-items-center"> <div class="col-12"> Model evaluation paper | <strong>Highlight paper</strong> | <a target="_blank" href="https://creativecommons.org/licenses/by/4.0/" rel="license" class="licence-icon-svg "><img src="https://www.geoscientific-model-development.net/licenceSVG_16.svg"></a> | <span>28 Oct 2024</span> </div> </div> <a class="article-avatar hide-on-mobile hide-on-tablet" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-avatar-web.png" target="_blank"> <img border="0" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-avatar-thumb150.png" data-caption="© Author(s). Distributed under the Creative Commons Attribution 4.0 License." data-web="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-avatar-web.png" data-width="600" data-height="466"> </a> <h1>Air quality modeling intercomparison and multiscale ensemble chain for Latin America</h1> <div class="auto-fixed-top-forced article-title"> <div class="grid-container show-on-fixed" style="display: none"> <div class="grid-85 mobile-grid-85 tablet-grid-85 grid-parent"> <span class="d-block hide-on-mobile hide-on-tablet journal-contentHeaderColor">Air quality modeling intercomparison and multiscale ensemble chain for Latin America</span> <span class="d-block hide-on-desktop journal-contentHeaderColor">Air quality modeling intercomparison and multiscale ensemble chain for Latin America</span> <span>Jorge E. Pachón et al.</span> </div> <div class="grid-1 mobile-grid-15 tablet-grid-15 grid-parent text-right"> <a id="scrolltop" class="scrollto" href="https://gmd.copernicus.org/articles/17/7467/2024/#top"><i class="co-home"></i> </a> </div> </div> </div> <div class="mb-3 authors-with-affiliations"> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905422">Jorge E. Pachón</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905423">Mariel A. Opazo</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905424">Pablo Lichtig</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905425">Nicolas Huneeus</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905426">Idir Bouarar</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905427">Guy Brasseur<a href="mailto:guy.brasseur@mpimet.mpg.de"><i class="fal fa-envelope ml-1"></i></a></span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905428">Cathy W. Y. Li</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905429">Johannes Flemming</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905430">Laurent Menut</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905431">Camilo Menares</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905432">Laura Gallardo</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905433">Michael Gauss</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905434">Mikhail Sofiev</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905435">Rostislav Kouznetsov</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905436">Julia Palamarchuk</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905437">Andreas Uppstu</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905438">Laura Dawidowski</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905439">Nestor Y. Rojas</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905440">María de Fátima Andrade</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905441">Mario E. Gavidia-Calderón</span>,</nobr> <nobr><span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905442">Alejandro H. Delgado Peralta</span>,</nobr> <nobr>and <span class="hover-cursor-pointer journal-contentLinkColor hover-underline" data-toggle="modal" data-target=".author905443">Daniel Schuch</span></nobr> </div> <div class="modal fade author905422" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Jorge E. Pachón</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Environmental Engineering, Universidad de La Salle, Bogotá, 111711, Colombia </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905423" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Mariel A. Opazo</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905424" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Pablo Lichtig</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0003-1896-234X" data-title="https://orcid.org/0000-0003-1896-234X"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0003-1896-234X</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Comisión Nacional de Energía Atómica – CNEA, Buenos Aires, C1429BNP, Argentina </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905425" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Nicolas Huneeus</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905426" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Idir Bouarar</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Max Planck Institute for Meteorology, 20146 Hamburg, Germany </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905427" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Guy Brasseur</h3> <div class="row no-gutters"> <div class="col-12">CORRESPONDING AUTHOR</div> <div class="col-12"><a href="mailto:guy.brasseur@mpimet.mpg.de"><i class="fal fa-envelope mr-2"></i>guy.brasseur@mpimet.mpg.de</a></div> </div> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-6794-9497" data-title="https://orcid.org/0000-0001-6794-9497"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-6794-9497</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Max Planck Institute for Meteorology, 20146 Hamburg, Germany </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905428" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Cathy W. 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European Centre for Medium-Range Weather Forecasts – ECMWF, 53175 Bonn, Germany </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905430" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Laurent Menut</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-9776-0812" data-title="https://orcid.org/0000-0001-9776-0812"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-9776-0812</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Laboratoire de Météorologie Dynamique, Palaiseau, 91128, France </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905431" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Camilo Menares</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905432" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Laura Gallardo</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-7605-3721" data-title="https://orcid.org/0000-0001-7605-3721"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-7605-3721</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Geophysics and Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, 8320000, Chile </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905433" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Michael Gauss</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Norwegian Meteorological Institute, Oslo, 0313, Norway </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905434" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Mikhail Sofiev</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-9542-5746" data-title="https://orcid.org/0000-0001-9542-5746"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-9542-5746</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Finnish Meteorological Institute, Helsinki, 00560, Finland </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905435" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Rostislav Kouznetsov</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-5140-0037" data-title="https://orcid.org/0000-0001-5140-0037"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-5140-0037</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Finnish Meteorological Institute, Helsinki, 00560, Finland </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905436" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Julia Palamarchuk</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Finnish Meteorological Institute, Helsinki, 00560, Finland </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905437" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Andreas Uppstu</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Finnish Meteorological Institute, Helsinki, 00560, Finland </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905438" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Laura Dawidowski</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Comisión Nacional de Energía Atómica – CNEA, Buenos Aires, C1429BNP, Argentina </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905439" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Nestor Y. Rojas</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-7804-0449" data-title="https://orcid.org/0000-0001-7804-0449"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-7804-0449</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Department of Chemical and Environmental Engineering, Universidad Nacional de Colombia, Bogotá, 111321, Colombia </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905440" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">María de Fátima Andrade</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0001-5351-8311" data-title="https://orcid.org/0000-0001-5351-8311"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0001-5351-8311</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidad de São Paulo, São Paulo, 05508-09B, Brazil </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905441" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Mario E. Gavidia-Calderón</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0002-7371-1116" data-title="https://orcid.org/0000-0002-7371-1116"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0002-7371-1116</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidad de São Paulo, São Paulo, 05508-09B, Brazil </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905442" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Alejandro H. Delgado Peralta</h3> <div class="row no-gutters"> <div class="col-12"> <a class="orcid-authors-logo" target="_blank" href="https://orcid.org/0000-0002-4413-3732" data-title="https://orcid.org/0000-0002-4413-3732"><svg class="mr-2" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><image xlink:href="https://www.geoscientific-model-development.net/orcid_icon.svg" src="https://www.geoscientific-model-development.net/orcid_icon_128x128.png" width="100%" height="100%"></image></svg>https://orcid.org/0000-0002-4413-3732</a> </div> </div> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidad de São Paulo, São Paulo, 05508-09B, Brazil </div> </div> </div> </div> </div> </div> </div> <div class="modal fade author905443" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <div class="container-fluid p-0"> <h3 class="modal-title">Daniel Schuch</h3> </div> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="container-fluid p-0"> <div class="row"> <div class="col-12 mb-3"> Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidad de São Paulo, São Paulo, 05508-09B, Brazil </div> </div> <div class="row"> <div class="col-12 mb-3"> Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA </div> </div> </div> </div> </div> </div> </div> <div class="abstract sec" id="abstract"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-show="#abstract .co-arrow-open,.abstract-content" data-hide="#abstract .co-arrow-closed,.abstract-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Abstract<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed" style="display:none"></i><i class="co-arrow-open" style="display:inline-block"></i></span></div></span></div> <div class="abstract-content show-no-js"><p id="d2e343">A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO<span class="inline-formula"><sub>2</sub></span> exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO<span class="inline-formula"><sub>2</sub></span>, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.</p></div><span class="abstract-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet" style="display:none"></span></div> <div id="oldMobileDownloadBox" class="widget dark-border hide-on-desktop download-and-links"> <div class="legend journal-contentLinkColor">Download & links</div> <div class="content"> <ul class="additional_info no-bullets no-styling"> <li> <a class="triangle" data-toggle=".box-notice" data-duration="300" title="PDF Version (19817 KB)" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.pdf" > Article (PDF, 19817 KB) </a> </li> </ul> </div> </div> <div id="downloadBoxOneColumn" class="widget dark-border hide-on-desktop download-and-links"> <div class="legend journal-contentLinkColor">Download & links</div> <div class="content"> <ul class="additional_info no-bullets no-styling"> <li><a class="triangle" title="PDF Version (19817 KB)" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.pdf">Article</a> <nobr>(19817 KB)</nobr> </li> <li> <a class="triangle" title="XML Version" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.xml">Full-text XML</a> </li> <li><a class="triangle" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.bib">BibTeX</a></li> <li><a class="triangle" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.ris">EndNote</a></li> </ul> </div> </div> <div id="share" class="oneColumnShareMobileBox widget dark-border hide-on-desktop"> <div class="legend journal-contentLinkColor">Share</div> <div class="content row m-0 py-1"> <div class="col-auto pl-0"> <a class="share-one-line" href="https://www.mendeley.com/import/?url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Mendeley" target="_blank"> <img src="https://www.geoscientific-model-development.net/mendeley.png" alt="Mendeley"/> </a> </div> <div class="col-auto"> <a class="share-one-line" href="https://www.reddit.com/submit?url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Reddit" target="_blank"> <img src="https://www.geoscientific-model-development.net/reddit.png" alt="Reddit"> </a> </div> <div class="col-auto"> <a class="share-one-line last" href="https://twitter.com/intent/tweet?text=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Twitter" target="_blank"> <img src="https://www.geoscientific-model-development.net/twitter.png" alt="Twitter"/> </a> </div> <div class="col-auto"> <a class="share-one-line" href="https://www.facebook.com/share.php?u=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F&t=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America" title="Facebook" target="_blank"> <img src="https://www.geoscientific-model-development.net/facebook.png" alt="Facebook"/> </a> </div> <div class="col-auto pr-0"> <a class="share-one-line last" href="https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F&title=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America" title="LinkedIn" target="_blank"> <img src="https://www.geoscientific-model-development.net/linkedin.png" alt="LinkedIn"> </a> </div> <div class="col pr-0 mobile-native-share"> <a href="#" data-title="Geoscientific Model Development" data-text="*Air quality modeling intercomparison and multiscale ensemble chain for Latin America* Jorge E. Pachón et al." data-url="https://gmd.copernicus.org/articles/17/7467/2024/" class="mobile-native-share share-one-line last"><i class="co-mobile-share display-none"></i></a> </div> </div> </div> <div id="citation-footer" class="sec"> <div class="h1-special journal-contentHeaderColor">How to cite. </div> <div class="citation-footer-content show-no-js"> <p> <div class="citation-footer"> Pachón, J. E., Opazo, M. A., Lichtig, P., Huneeus, N., Bouarar, I., Brasseur, G., Li, C. W. Y., Flemming, J., Menut, L., Menares, C., Gallardo, L., Gauss, M., Sofiev, M., Kouznetsov, R., Palamarchuk, J., Uppstu, A., Dawidowski, L., Rojas, N. Y., Andrade, M. D. F., Gavidia-Calderón, M. E., Delgado Peralta, A. H., and Schuch, D.: Air quality modeling intercomparison and multiscale ensemble chain for Latin America, Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, 2024. </div> </p> </div> </div> <div id="article-dates" class="sec"> <div class="article-dates dates-content my-3"> <nobr>Received: 19 Mar 2024</nobr> – <nobr>Discussion started: 25 Apr 2024</nobr> – <nobr>Revised: 06 Aug 2024</nobr> – <nobr>Accepted: 14 Aug 2024</nobr> – <nobr>Published: 28 Oct 2024</nobr> </div> </div> <div class="sec intro" id="section1"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section1 .co-arrow-open,.section1-content" data-show="#section1 .co-arrow-closed,.section1-mobile-bottom-border"><div id="Ch1.S1" class="h1"><span class="label">1</span> Introduction<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section1-content show-no-js hide-on-mobile-soft"><p id="d2e373">Latin America has some of the most populated urban areas in the world. Notably, Mexico City and São Paulo have populations exceeding 20 million, while Lima, Bogotá, Rio de Janeiro and Buenos Aires have more than 10 million inhabitants each <span class="cit" id="xref_paren.1">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx107" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">United Nations</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx107" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>. These densely populated regions often experience air pollution events due to large<span id="page7468"></span> emission sources and due to atmospheric conditions. Other major cities, such as Santiago and Medellín, with a population of <span class="inline-formula">∼</span> 7 million and <span class="inline-formula">∼</span> 3.5 million, respectively, are also affected by poor air quality. This urban air pollution not only has long-lasting effects on the health of the population but also has a significant negative impact on the environment and possibly the regional climate <span class="cit" id="xref_paren.2">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx15" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Busch et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx15" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx43" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gouveia et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx43" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx72" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Molina et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx72" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx91" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rodríguez-Villamizar et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx91" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx93" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Romieu et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx93" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>. Latin America could greatly benefit from an air quality analysis and forecasting (AQF) system that informs the public about air pollution episodes and supports policy actions.</p><p id="d2e396">To better understand the causes of air pollution events in Latin America, it is important to consider the local emission sources. In addition to the usual urban pollution sources (e.g., industrial facilities, residential heating, energy production and transportation sectors), plumes from biomass burning and long-range dust transport can occasionally reach major cities. In northern South America, increased pollution levels in the dry season have been associated with biomass burning <span class="cit" id="xref_paren.3">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ballesteros-González et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx16" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Casallas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx16" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mendez-Espinosa et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span> and dust from the Sahara <span class="cit" id="xref_paren.4">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx67" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mendez-Espinosa et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx67" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. The latter source also affects the Caribbean and central Mexico in early spring <span class="cit" id="xref_paren.5">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx56" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kramer and Kirtman</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx56" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx88" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ramírez-Romero et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx88" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>)</span>. Also, in the context of climate and land use change, wildfires are a recurrent phenomenon in southern South America <span class="cit" id="xref_paren.6">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx90" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Resquin et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx90" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx24" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">de la Barrera et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx24" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx95" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Sarricolea et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx95" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. The Amazon is the largest forest in the world and a significant source of biogenic volatile organic compounds (BVOCs), precursors of CO, ozone and secondary aerosols <span class="cit" id="xref_paren.7">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx75" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Nascimento et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx75" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx116" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zimmerman et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx116" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1988</a>)</span>.</p><p id="d2e414">Air quality management in Latin America (LAC) has been traditionally focused on surveillance and building emission inventories <span class="cit" id="xref_paren.8">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx34" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Franco et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx34" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. Modeling activities for LAC are less frequent than North America, Europe or Asia, mainly due to limited computing resources and scarce information of emission sources. Of more than 30 regional AQF systems identified worldwide, only one exists in Latin America <span class="cit" id="xref_paren.9">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx115" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhang et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx115" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>. In addition to the restrictions already mentioned, LAC has other challenges: complex terrain where cities are situated in the valleys and canyons of the Andes, varying meteorological conditions due to their proximity to mountains and coastlines, deep convection in the tropics, extensive biomass burning in the Orinoco and Amazon basins, and the presence of densely populated megacities and urban areas, among others. Despite limitations on applying air quality models in LAC, regional models have been successfully implemented since 2000.</p><p id="d2e423">The Coupled Chemistry Aerosol and Tracer Transport model to the Brazilian development of the Regional Atmospheric Modeling System (CCATT–BRAMS) was developed in the region <span class="cit" id="xref_paren.10">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx60" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Longo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx60" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2013</a>)</span> to investigate the impact of the Amazonian wildfires on air quality in major Brazilian cities <span class="cit" id="xref_paren.11">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx81" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pereira et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx81" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2011</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx35" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Freitas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx35" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2011</a>)</span>. The North American Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) meteorological model, has been used in Colombia and Brazil to predict pollutant concentrations and assess reduction strategies <span class="cit" id="xref_paren.12">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Albuquerque et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx26" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">East et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx26" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx82" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pérez-Peña et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx82" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx77" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Nedbor-Gross et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx77" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx79" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pachón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx79" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>. The WRF model coupled with chemistry (WRF–Chem) online has been actively used to study the impact of regional sources on air quality in urban centers across Colombia <span class="cit" id="xref_paren.13">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ballesteros-González et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Casallas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx42" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">González et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx42" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mendez-Espinosa et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>, Chile <span class="cit" id="xref_paren.14">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Saide et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>)</span> and São Paulo <span class="cit" id="xref_paren.15">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx39" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gavidia-Calderón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx39" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. CHIMERE <span class="cit" id="xref_paren.16">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx68" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Menut et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx68" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2013</a>)</span> and MATCH <span class="cit" id="xref_paren.17">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Andersson et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>)</span> models have been applied in Chile to assess pollutant chemical transformation and dispersion as well as emission reduction strategies <span class="cit" id="xref_paren.18">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx36" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gallardo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx36" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2002</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mailler et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>. Additionally, CAMS reanalysis data have been compared against air quality observations, observing well-captured temporal trends for PM<span class="inline-formula"><sub>10</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span> and SO<span class="inline-formula"><sub>2</sub></span> but not for NO<span class="inline-formula"><sub><i>x</i></sub></span> <span class="cit" id="xref_paren.19">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Casallas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p><p id="d2e495">This work conducts the first model intercomparison effort and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) project (<span class="uri"><a href="https://papila-h2020.eu/papila" target="_blank">https://papila-h2020.eu/papila</a></span>, last access: 14 August 2024). The aim of PAPILA was to develop an AQF system for the region with increasing capabilities in major cities. This objective is in line with the Global Air Quality Forecasting and Information System (GAFIS) initiative that supports the implementation of AQF systems, especially in countries and regions where they do not exist, such as Africa and South America <span class="cit" id="xref_paren.20">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx109" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">WMO</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx109" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>)</span>. This article presents a retrospective (hindcast) analysis. Section 2 presents model descriptions, emission inventories utilized in the models and observations employed for model evaluation. In Sect. 3 we analyze the model performance and conduct intercomparisons for each pollutant (NO<span class="inline-formula"><sub>2</sub></span>, O<span class="inline-formula"><sub>3</sub></span>, CO, SO<span class="inline-formula"><sub>2</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span>). We also discuss the season variability of predictions and the analysis of large vs. small urban areas. Finally, Sect. 4 summarizes our findings and outlines directions for future development.</p></div><span class="section1-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="sec" id="section2"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section2 .co-arrow-open,.section2-content" data-show="#section2 .co-arrow-closed,.section2-mobile-bottom-border"><div id="Ch1.S2" class="h1"><span class="label">2</span> Methodology<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section2-content show-no-js hide-on-mobile-soft"><p id="d2e549">The model intercomparison and construction of the ensemble required relevant activities: the execution of global and regional models in a common domain, harmonization of the model output, ensemble construction, collection of air quality observations, analysis of temporal and spatial variability, and model evaluation.</p><span id="page7469"></span><div class="sec"><h2 id="Ch1.S2.SS1"><span class="label">2.1</span> Description of the models and modeling setup</h2> <p id="d2e560">For the model intercomparison, two global models (CAMS and SILAM) and three regional models (CHIMERE, WRF–Chem, EMEP MSC-W) were selected based on the expertise of the research groups working on the PAPILA project (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.T1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1</a>). WRF–Chem was implemented by two different groups, the Max Planck Institute for Meteorology (MPIM) in Germany and the University of São Paulo (USP) in Brazil, with different setups. It is worth noting that the early simulations analyzed hereby do not represent the best performance of each model in the LAC region or over individual urban areas. The different models are briefly described in the following paragraphs.</p> <p id="d2e565">The Copernicus Atmosphere Monitoring Service (CAMS) provides state-of-the-art global atmospheric composition data based on the IFS (Integrated Forecasting System) model of the European Centre for Medium-Range Weather Forecasts (ECMWF) <span class="cit" id="xref_paren.21">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx53" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Inness et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx53" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. The chemical mechanism of the IFS is an extended version of the Carbon Bond 2005 (CB05) and complements the MACC aerosol module <span class="cit" id="xref_paren.22">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx33" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Flemming et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx33" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx74" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Morcrette et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx74" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2009</a>)</span>. The CAMS reanalysis data used for this project are a combination of satellite observations of atmospheric composition and the IFS modeling setup. Anthropogenic emissions from the MACC/CityZen (MACCity) inventory <span class="cit" id="xref_paren.23">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx44" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Granier et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx44" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2011</a>)</span> and biomass-burning emissions from the Global Fire Assimilation System (GFAS) v1.2 <span class="cit" id="xref_paren.24">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx54" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kaiser et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx54" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span> were used in the simulations (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.T1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1</a>). The biogenic emissions were simulated offline by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 <span class="cit" id="xref_paren.25">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Guenther et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span> using an offline emission inventory <span class="cit" id="xref_paren.26">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx28" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">ECCAD</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx28" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>)</span>. CAMS has been extensively evaluated against ozonesondes, aircraft profiles, surface observations and global satellite retrievals <span class="cit" id="xref_paren.27">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx32" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Flemming et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx32" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>)</span>.</p> <span class="tableCitations"></span><div class="table-wrap" id="Ch1.T1"><div class="caption"><p id="d2e595"><strong class="caption-number">Table 1</strong>Description of the models included in the ensemble.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01-web.png" data-width="2067" data-height="1519" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01.xlsx"></a><div class="table-wrap-foot"><p id="d2e598">Abbreviations: FMI – Finnish Meteorological Institute, ECMWF – European Center for Weather and Modeling Forecast, LMD – Laboratoire de Météorologie Dynamique, MPIM – Max Planck Institute for Meteorology, UCL – University of Chile and USP – University of São Paulo, EDGAR – Emissions Database for Global Atmospheric Research, CBMZ – Carbon Bond Mechanism version Z.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t01.xlsx" target="_blank">Download XLSX</a></p></div> <p id="d2e1059">The System for Integrated Modeling of Atmospheric Composition (SILAM, <span class="uri"><a href="http://silam.fmi.fi" target="_blank">http://silam.fmi.fi</a></span>, last access: 14 August 2024) is a chemical transport model for global-to-local simulations of atmospheric composition and air quality that was developed at the Finish Meteorological Institute (FMI) <span class="cit" id="xref_paren.28">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx102" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Sofiev</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx102" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2002</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx55" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kouznetsov and Sofiev</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx55" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx105" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Sofiev et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx105" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2010</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx103" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx106" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>)</span>. Briefly, SILAM employs the Carbon Bond Mechanism IV (CBM-IV) for gas-phase chemistry <span class="cit" id="xref_paren.29">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx40" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gery et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx40" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1989</a>)</span>. For further details on the model characteristics, refer to <span class="cit" id="xref_text.30"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">METEO-FRANCE</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. For this work, the SILAM simulations were driven by the meteorological IFS model of the ECMWF. Anthropogenic emissions were adopted from the CAMS global emission inventory v2.1, whereas biomass-burning emissions were generated by the Integrated Monitoring and Modelling System for Wildland Fires (IS4FIRES) (<span class="uri"><a href="http://is4fires.fmi.fi" target="_blank">http://is4fires.fmi.fi</a></span>, last access: 3 July 2024) <span class="cit" id="xref_paren.31">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx104" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Sofiev et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx104" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2009</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx101" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Soares and Sofiev</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx101" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2014</a>)</span>. The biogenic emissions were simulated offline by MEGAN v2.1 <span class="cit" id="xref_paren.32">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Guenther et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span>, particularly isoprene and monoterpene emissions computed for the year 2010, as found on the MEGAN website (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.T1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1</a>). The model has been extensively evaluated in numerous international retrospective studies <span class="cit" id="xref_paren.33">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx64" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Marécal et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx64" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx57" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kukkonen et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx57" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx10" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Blechschmidt et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx10" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Petersen et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span> and real-time operational applications. SILAM is included in the regional European forecasting system provided by CAMS together with CHIMERE and EMEP MSC-W and eight other models <span class="cit" id="xref_paren.34">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx20" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Colette et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx20" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>.</p> <p id="d2e1092">CHIMERE is a Eulerian chemical transport model (CTM). It is able to perform simulations from urban to hemispheric scale <span class="cit" id="xref_paren.35">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mailler et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx70" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Menut et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx70" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>)</span>. The model can be used online (with WRF only) or offline (with several meteorological models). The model characteristics are published elsewhere <span class="cit" id="xref_paren.36">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">METEO-FRANCE</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. For this study, the meteorological forcing is the IFS global simulation provided by the ECMWF. The biogenic emissions are calculated online using MEGAN v2.1 <span class="cit" id="xref_paren.37">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Guenther et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span> using the 30 s horizontal-resolution database. Fire emissions are those of CAMS <span class="cit" id="xref_paren.38">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx54" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kaiser et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx54" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span> and reformatted for CHIMERE using the dedicated preprocessor <span class="cit" id="xref_paren.39">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx70" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Menut et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx70" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>)</span>. The mineral dust is calculated online using the <span class="cit" id="xref_text.40"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Alfaro and Gomes</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2001</a>)</span> scheme, and the sea salt emissions are also calculated online using the <span class="cit" id="xref_text.41"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx73" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Monahan</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx73" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1986</a>)</span> scheme. NO<span class="inline-formula"><sub><i>x</i></sub></span> values by lightning are calculated using the scheme described in <span class="cit" id="xref_text.42"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx69" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Menut et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx69" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. CHIMERE is used for analysis and forecast in tens of countries around the world and at various spatial scales, including the CAMS forecast. More specifically for Latin America, it was used for several studies about anthropogenic emissions, deposition of black carbon on snow, and indirect effects and impact of megafires on cloud formation <span class="cit" id="xref_paren.43">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx59" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mailler et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx61" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Lapere</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx58" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>. For this exercise, CHIMERE was run for the 31 d of January and July 2015. However due to problems in the output files, 15 d of data were missing (5 d from 14 to 18 January and 10 d from 11 to 19 July and 9 July).</p> <p id="d2e1132"><span id="page7471"></span>The EMEP MSC-W model (“EMEP model” hereafter) is an offline chemical transport model that was developed at the Norwegian Meteorological Institute (MET Norway). It is used to simulate photo-oxidants as well as organic and inorganic aerosols in scales ranging from local to global <span class="cit" id="xref_paren.44">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Simpson et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>. Details regarding the model characteristics can be found in <span class="cit" id="xref_text.45"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">METEO-FRANCE</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx71" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. For this study the model was driven by meteorological data from the IFS model of the ECMWF. Gas-phase chemistry from the “EMEP scheme” comprises 70 species and 140 reactions <span class="cit" id="xref_paren.46">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Andersson-Sköld and Simpson</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1999</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Simpson et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>, inorganics from the MARS equilibrium module <span class="cit" id="xref_paren.47">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Binkowski and Shankar</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1995</a>)</span>, and organics from the CBM-Z mechanism <span class="cit" id="xref_paren.48">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx111" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zaveri and Peters</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx111" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1999</a>)</span>. Emissions from forest and vegetation fires are taken from the Fire INventory from NCAR (FINN v1.0) <span class="cit" id="xref_paren.49">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx108" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Wiedinmyer et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx108" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2011</a>)</span>. Biogenic emissions of isoprene and (if required) monoterpenes are calculated in the model for every grid cell <span class="cit" id="xref_paren.50">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Simpson et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx100" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>. The EMEP model has for several decades been the main tool for underpinning air quality policies under the United Nations Economic Commission for Europe (UNECE) convention on long-range transboundary air pollution. However, it should be noted that the runs for this study were the very first EMEP model simulations ever conducted on a regional scale for LAC and should thus be considered only as a first demonstration of model capabilities. For PAPILA, the EMEP model was run by the modeling team at the University of Chile in Santiago with some support by MET Norway.</p> <p id="d2e1157">WRF–Chem is the Weather Research and Forecasting (WRF) model coupled with chemistry, developed at the National Center for Atmospheric Research (NCAR) with the purpose of simulating urban- to regional-scale fields of trace gases and particulates. The air quality and meteorological components share the same transport and physics scheme, as well as the same horizontal and vertical grids <span class="cit" id="xref_paren.51">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx31" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Fast et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx31" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx45" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Grell et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx45" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2005</a>)</span>. The MPIM WRF–Chem uses version 3.6.1 to simulate meteorology and chemistry simultaneously online in South America at <span class="inline-formula">∼</span> 20 km horizontal resolution and 36 vertical levels extending from the surface to <span class="inline-formula">∼</span> 21 km altitude. The gas-phase chemistry is represented by version 4 of the Model for Ozone and Related Chemical Tracers (MOZART-4) chemical scheme <span class="cit" id="xref_paren.52">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx30" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emmons et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx30" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2010</a>)</span>. The Goddard Chemistry Aerosol Radiation and Transport (GOCART) bulk aerosol module coupled with MOZART is used in this study to consider the aerosol processes <span class="cit" id="xref_paren.53">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx19" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Chin et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx19" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2002</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx41" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ginoux et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx41" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2001</a>)</span>. Boundary and initial conditions for the meteorology were set up from the Global Forecast System (GFS) and for the chemical species concentrations from CAM-Chem. The anthropogenic emissions were from CAMS-GLOB-ANT v4.2, which consists of 0.1° <span class="inline-formula">×</span> 0.1° grid maps of several species, including CO, SO<span class="inline-formula"><sub>2</sub></span>, NO, non-methane volatile organic compounds (NMVOCs), NH<span class="inline-formula"><sub>3</sub></span>, black carbon (BC) and organic carbon (OC). Daily varying emissions of trace species from biomass burning were taken from the FINN v1.5 dataset <span class="cit" id="xref_paren.54">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx108" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Wiedinmyer et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx108" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2011</a>)</span>. Biogenic emissions of trace species from terrestrial ecosystems are calculated online using MEGAN v2.04 <span class="cit" id="xref_paren.55">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Guenther et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx47" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span>. Further details on the MPIM WRF–Chem model settings can be found in <span class="cit" id="xref_text.56"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx11" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Bouarar et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx11" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>.</p> <p id="d2e1218">WRF–Chem run by the USP (version 3.9.1) uses similar characteristics to those previously described with a horizontal resolution <span class="inline-formula">∼</span> 22 km and 35 vertical layers. Some differences from the MPIM configuration are the version of global emissions of CAMS-GLOB-ANT v5.3 <span class="cit" id="xref_paren.57">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx27" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">ECCAD</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx27" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>, the speciation of the chemical boundary condition from the CAM-Chem model <span class="cit" id="xref_paren.58">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx14" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Buchholz et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx14" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx30" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emmons et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx30" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2010</a>)</span> and the speciation of FINN v1.5 emissions, which are suitable for simulation over São Paulo. For this exercise, WRF–Chem did not include Mexico City in the modeling domain.</p> <p id="d2e1234">CHIMERE, IFS, EMEP, WRF–Chem, LOTOS-EUROS and SILAM models are used in an ensemble mode to configure the MarcoPolo–Panda prediction system in Asia <span class="cit" id="xref_paren.59">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx13" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Brasseur et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx13" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Petersen et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. It has been observed that, under specific circumstances, a model ensemble can outperform individual models, demonstrating the potential benefits of this approach. With the desire to replicate the experience in Latin America, the selected models were applied in a common domain, defined by the southeastern corner at 119°54<span class="inline-formula"><sup>′</sup></span> W, 59°54<span class="inline-formula"><sup>′</sup></span> S and the northeastern corner at 28°6<span class="inline-formula"><sup>′</sup></span> W, 29°54<span class="inline-formula"><sup>′</sup></span> N. The models were run at a spatial resolution of <span class="inline-formula">∼</span> 0.2° <span class="inline-formula">×</span> 0.2° (<span class="inline-formula">∼</span> 20 <span class="inline-formula">×</span> 20 km). Input meteorology and emissions were up to the modeling group (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.T1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1</a>). The simulation period covers January (Southern Hemisphere summer) and July (Southern Hemisphere winter) of 2015. The modeling data are available in a public repository <span class="cit" id="xref_paren.60">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx80" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pachón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx80" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> </div><div class="sec"><h2 id="Ch1.S2.SS2"><span class="label">2.2</span> Model evaluation</h2> <p id="d2e1318">The performance of the models was assessed by comparing the simulated concentrations with the average of the observations for each available city, pollutant and considered period. The observation's average was constructed by computing the arithmetic mean of all air quality stations available in the network within the city's polygon. On the other hand, the simulated concentrations for the models were estimated as the average of the models' closest grid point to the location of each station that is within the city's polygon for every city and pollutant considered in this study. This results in a weighted average of the model where the weight is given by the number of stations that measure the pollutant closest to each grid point, resulting in the same geographical sampling for the observations and the models, thus reducing any potential station's sampling bias to the best of our abilities. This approach was chosen with the objective of assessing the model performance in cities rather than for each air quality station separately. It is outside the scope of this work to conduct an intra-urban variability study of the model performance given the chosen resolution of 0.2° <span class="inline-formula">×</span> 0.2°. The model evaluation was focused on nitrogen dioxide (NO<span class="inline-formula"><sub>2</sub></span>), ozone (O<span class="inline-formula"><sub>3</sub></span>), carbon monoxide (CO), sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>), and particulate matter less than 2.5 <span class="inline-formula">µ</span>m (PM<span class="inline-formula"><sub>2.5</sub></span>) and less than 10 <span class="inline-formula">µ</span>m (PM<span class="inline-formula"><sub>10</sub></span>).</p> <p id="d2e1390">For each period, pollutant and city, the model evaluation included the following metrics: model <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M46" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="hide-js svg-formula" dspmath="mathimg" md5hash="2251cbae0b7d78a459605b060cf1ad8c"><image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00001.svg" width="100%" height="14pt" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00001.png"></image></svg></span></span> observations ratio, mean bias (BIAS), modified normalized bias (MNBIAS), root mean square error (RMSE), fractional gross error (FGE) and correlation coefficient (<span class="inline-formula"><i>R</i></span>). The formulas were replicated from the MarcoPolo–Panda project <span class="cit" id="xref_paren.61">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Petersen et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx83" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span> and are presented in Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A1</a> in Appendix A. These evaluation metrics were computed for all models and the ensemble using the Modelling and Observation System and Analysis Tool, MOSPAT <span class="cit" id="xref_paren.62">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx49" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Huneeus and Opazo</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx49" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> </div><div class="sec"><h2 id="Ch1.S2.SS3"><span class="label">2.3</span> Air quality monitoring networks in Latin America</h2> <p id="d2e1424">Several air quality monitoring networks (AQMNs) are available throughout Latin America, especially in major cities. However, worldwide access to the datasets can be difficult<span id="page7472"></span> due to language barriers and the lack of a centralized platform. A comprehensive list of AQMNs in Latin America was assembled for the PAPILA project (<span class="uri"><a href="https://papila-h2020.eu/observations" target="_blank">https://papila-h2020.eu/observations</a></span>, last access: 14 August 2024). For the year 2015, we collected air quality data for 12 cities in Mexico, Colombia, Ecuador, Peru, Chile, Brazil and Uruguay. Only stations with a minimum of 75 % data completeness were considered when calculating the city average of the observations, resulting in eight cities with enough data to use for this study. This data completeness requirement considers a minimum of 75 % of days available for each period, as well as a minimum of 75 % of hourly data to construct their daily average. We focus in this study on the four major cities (from north to south) Mexico City, Bogotá, São Paulo and Santiago (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">1</a>). However, data of all available cities were used in the model evaluation (Tables <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S2.T10" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">B1</a> through <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S2.T17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">B8</a> in Appendix B).</p> <div class="fig" id="Ch1.F1"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f01-web.jpg"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f01" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f01-web.jpg" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f01-thumb.jpg" data-width="2067" data-height="2066"></a><div class="caption"><p id="d2e1438"><strong class="caption-number">Figure 1</strong>Location of air quality stations in major Latin American cities (Santiago, Bogotá, Mexico City, São Paulo) alongside the city's definition for computing the modeled city average. © OpenStreetMap contributors 2024. Distributed under the Open Data Commons Open Database License (ODbL) v1.0.</p></div><p class="downloads"></p></div> </div></div><span class="section2-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="sec" id="section3"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section3 .co-arrow-open,.section3-content" data-show="#section3 .co-arrow-closed,.section3-mobile-bottom-border"><div id="Ch1.S3" class="h1"><span class="label">3</span> Results<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section3-content show-no-js hide-on-mobile-soft"><p id="d2e1456">Simulated concentrations of all pollutants from all models were compared against observations from every city and for both periods (January and July) in 2015. In this section, we present results from the model evaluation, the spatial and temporal variability of simulated fields, and the impact of large versus small urban areas in the model intercomparison.</p><div class="sec"><h2 id="Ch1.S3.SS1"><span class="label">3.1</span> Model evaluation</h2> <p id="d2e1466">The following results are presented for every pollutant: analysis of observations from AQMNs, simulated concentrations by the models, comparison of evaluation metrics and discussion of model performance, including the ensemble and analysis of model variation.</p> <div class="sec"><h3 id="Ch1.S3.SS1.SSS1"><span class="label">3.1.1</span> Nitrogen dioxide – NO<span class="inline-formula"><sub>2</sub></span></h3> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx1">Observations</h3> <p id="d2e1491">The number of stations per city recording NO<span class="inline-formula"><sub>2</sub></span> during January and July 2015 varies between 7 in Bogotá and 24 in Mexico City (Appendix B). The highest daily average concentration of NO<span class="inline-formula"><sub>2</sub></span> is observed in Santiago during winter at around 30 ppb (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2</a>). This can be attributed to adverse meteorological conditions and emissions from transportation and residential combustion in the surrounding municipalities <span class="cit" id="xref_paren.63">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mazzeo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Saide et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>)</span>, whereas in the summer NO<span class="inline-formula"><sub>2</sub></span> levels fall to 11 ppb. The second largest values are shown in Mexico City and São Paulo with daily average NO<span class="inline-formula"><sub>2</sub></span> levels of 27 and 20 ppb, respectively, due to the heavy use of fossil fuels in transportation and power generation. The lowest levels of NO<span class="inline-formula"><sub>2</sub></span> are measured in Bogotá with 16.4 ppb on average.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx2">Model performance</h3> <p id="d2e1552">In Bogotá and Santiago, NO<span class="inline-formula"><sub>2</sub></span> is underestimated by the ensemble members (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2</a>). In Santiago, the mean of the models is 10.3 ppb in summer and 22.1 ppb in winter, lower than the mean of the observations. Similarly, in Bogotá the mean of the modeled values is 6.6 ppb, much lower than observations. In contrast, in São Paulo and Mexico City, the models both over- and underpredict the ambient concentrations, and the averages of the modeled fields (23.6 and 30.3 ppb, respectively) are on the same order of magnitude as the observations.</p> <div class="fig" id="Ch1.F2"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02.png" data-width="2067" data-height="1358"></a><div class="caption"><p id="d2e1568"><strong class="caption-number">Figure 2</strong>Observed (black) and simulated NO<span class="inline-formula"><sub>2</sub></span> daily mean concentrations in Santiago, Bogotá, Mexico City and São Paulo for January (left) and July (right) 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f02.png" target="_blank">Download</a></p></div> <p id="d2e1586">São Paulo and Mexico City exhibit the lowest MNBIAS and FGE for NO<span class="inline-formula"><sub>2</sub></span> (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a>). The correlation between the models and observations hovers around 0.7, which is larger than the goal benchmark proposed for this pollutant (<span class="inline-formula"><i>R</i>≥0.6</span>) <span class="cit" id="xref_paren.64">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> <p id="d2e1615">In Santiago, the MNBIAS is mostly negative during both seasons except in the SILAM and EMEP models, which resulted in a positive bias. The degree to which the models underestimate the observations is notably higher in winter than in summer and with a larger FGE (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a>). The correlation between models and observations in Santiago is larger in summer than in winter, with some models achieving the criteria benchmark (<span class="inline-formula"><i>R</i>>0.5</span>) (<span class="cit" id="xref_altparen.65"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a></span>). In Bogotá, the MNBIAS values are large and consistently negative, and the FGE varies between 50 % and 156 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a>). Despite these lower scores, the correlation between observations and models is moderate, around 0.6 in January, meeting criteria benchmarks and demonstrating that certain models can successfully replicate the temporal variations but not the magnitude of the pollutant.</p> <p id="d2e1638">The adequate performance in São Paulo and Mexico City may be attributed to an accurate portrayal of the temporal and spatial variability that is achieved in large urban areas like these (<span class="inline-formula">>3500</span> km<span class="inline-formula"><sup>2</sup></span>), which encompass at least nine model cells (20 km <span class="inline-formula">×</span> 20 km). The lower simulated NO<span class="inline-formula"><sub>2</sub></span> levels in Bogotá likely stems from an underestimation of emissions. A study by <span class="cit" id="xref_text.66"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rojas et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>)</span> utilized local data to estimate on-road emissions in Colombia and revealed substantial underestimation of NO<span class="inline-formula"><sub>x</sub></span> emissions by global inventories such as EDGAR 6.1, CAMS and the Community Emissions Data System (CEDS). Their findings recommend adjustments to the emission factors used for NO<span class="inline-formula"><sub><i>x</i></sub></span>, particularly for heavy-duty and passenger vehicles, followed by a recalculation of the resulting emissions. The underestimation of NO<span class="inline-formula"><sub>2</sub></span> can also be noted in other cities such as Medellín, Guadalajara, Lima and Quito (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">8</a>). These cities, along with Bogotá, possess urban areas ranging from 235 to 890 km<span class="inline-formula"><sup>2</sup></span> and are confined within one or two cells of the models (20 km <span class="inline-formula">×</span> 20 km). It is possible that the average of observations is heavily influenced by local sources, in which case a finer modeling resolution is required to accurately capture the spatial variability of air pollution.</p> </div> <span id="page7473"></span><div class="sec"><h3 id="Ch1.S3.SS1.SSSx3">Model intercomparison</h3> <p id="d2e1733">For NO<span class="inline-formula"><sub>2</sub></span>, CAMS underestimates the observations in the four cities, whereas SILAM underestimates this pollutant in Bogotá, Mexico City and São Paulo (only in July) and overestimates the observations in Santiago and in São Paulo (in January). CAMS displayed larger MNBIAS and FGE than SILAM. In general, SILAM reproduces at least 80 % of the NO<span class="inline-formula"><sub>2</sub></span> levels, with the exception of Bogotá, where only 30 % of the NO<span class="inline-formula"><sub>2</sub></span> levels are simulated. The correlation coefficient is better for SILAM (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">∼</span> 0.6) than for CAMS (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">∼</span> 0.3).</p> <p id="d2e1792">The results from regional models are very diverse. In general, WRF–MPI, CHIMERE and EMEP have lower values of MNBIAS and FGE for NO<span class="inline-formula"><sub>2</sub></span> in São Paulo and Mexico City (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a>). In São Paulo, except for WRF–USP, regional models tend to overestimate NO<span class="inline-formula"><sub>2</sub></span> with a MNBIAS between 20 % and 70 %. WRF–USP reproduces about 76 % of NO<span class="inline-formula"><sub>2</sub></span> concentrations. In Mexico City, the tendency of regional models is to overestimate the NO<span class="inline-formula"><sub>2</sub></span> levels (MNBIAS: 10 % to 75 %). In Santiago, CHIMERE achieves the lowest MNBIAS (<span class="inline-formula">−2</span> %) in January but not in July (<span class="inline-formula">−119</span> %). In Bogotá, the MNBIAS in regional models remains consistently negative.</p> <p id="d2e1854">In Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2</a>, the model variation is visible. In Santiago in winter the range of NO<span class="inline-formula"><sub>2</sub></span> values is 48 ppb, which corresponds to a coefficient of variation (CV) of 71 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A8</a>); this contrasts with the range in summer of 15 ppb (CV <span class="inline-formula">=</span> 49 %). Other large variations are observed in Mexico City in July (range 54 ppb, CV 57 %) and São Paulo (range 32 ppb, CV<span id="page7474"></span> 46 % to 58 %). It is interesting to note the case of Bogotá, where all models consistently underestimate NO<span class="inline-formula"><sub>2</sub></span>, but the model variation is the lowest (8 ppm with CVs of 39 % and 56 %).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx4">Ensemble performance</h3> <p id="d2e1892">The median ensemble underestimates NO<span class="inline-formula"><sub>2</sub></span> concentrations in Bogotá and to a lesser extent in Santiago. This is consistent with the underestimation trend by most of the models. The ensemble in these two cities has some of the lowest MNBIAS, FGE and <span class="inline-formula"><i>R</i></span>, but they are not always better than individual models (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a>). On the contrary, in Mexico City and São Paulo, the ensemble median outperforms the models for NO<span class="inline-formula"><sub>2</sub></span>. In summer and winter, the ensemble presents the lowest FGE in both cities. The correlation coefficient range is between 0.5 and 0.8 within the criteria benchmark <span class="inline-formula"><i>R</i>>0.5</span> <span class="cit" id="xref_paren.67">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. The MNBIAS values are also the lowest (<span class="inline-formula">−2.9</span> % to 17.7 %).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSS2"><span class="label">3.1.2</span> Ozone – O<span class="inline-formula"><sub>3</sub></span></h3> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx5">Observations</h3> <p id="d2e1972">The number of stations per city recording O<span class="inline-formula"><sub>3</sub></span> during January and July 2015 varies between 9 in Santiago and 29 in Mexico City (Appendix B). The highest observed ozone concentration was in Mexico City in July with an average of 31 ppb. However, this value is significantly lower than the surface ozone concentrations reported in the MAM (March–April–May) season, with values larger than 70 ppb <span class="cit" id="xref_paren.68">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Barrett and Raga</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx99" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Silva-Quiroz et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx99" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. The second largest ozone value occurs in São Paulo during January with daily averages of 24 ppb. This is probably due to an abundance of ozone precursors, in particular volatile organic compounds (VOCs) from the use of biofuels in the transportation sector <span class="cit" id="xref_paren.69">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">de Fatima Andrade et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx39" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gavidia-Calderón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx39" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span> and biogenic VOCs <span class="cit" id="xref_paren.70">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx63" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Martins et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx63" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span>. Santiago experiences a marked seasonal cycle of ozone concentrations with summer values of approximately 22 ppb and winter concentrations around 3.6 ppb. This seasonal difference has been observed in other studies <span class="cit" id="xref_paren.71">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx97" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Seguel et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx97" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. In Bogotá, ozone concentrations are the lowest and below 13 ppb.</p> </div> <span id="page7475"></span><div class="sec"><h3 id="Ch1.S3.SS1.SSSx6">Model performance</h3> <p id="d2e2003">In the four cities, simulations of O<span class="inline-formula"><sub>3</sub></span> are mainly overestimated (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">3</a>). In the summer in São Paulo and Mexico City, simulations can reach up to 100 ppb, which is significantly above the observations. In Santiago in the winter, the mean of models (<span class="inline-formula">∼</span> 20 ppb) is significantly larger than observations, indicating that the models have difficulty reproducing low values of this secondary pollutant. In the summer, ozone estimates are much closer to observations. Similarly, in Bogotá, models estimate an average of 17 ppb, which is on the same order of magnitude as the observations.</p> <div class="fig" id="Ch1.F3"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03.png" data-width="2067" data-height="1373"></a><div class="caption"><p id="d2e2026"><strong class="caption-number">Figure 3</strong>Observed (black) and simulated O<span class="inline-formula"><sub>3</sub></span> daily mean concentrations in Santiago, Bogotá, Mexico City and São Paulo for January (left) and July (right) 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f03.png" target="_blank">Download</a></p></div> <p id="d2e2044">The overestimation of O<span class="inline-formula"><sub>3</sub></span> in Santiago might be related to the underestimation of NO<span class="inline-formula"><sub>2</sub></span> previously described and the inadequate titration of ozone. Ozone formation in Santiago has been found to be VOC-limited <span class="cit" id="xref_paren.72">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx96" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Seguel et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx96" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. This situation is also observed in Bogotá, where most models overestimate O<span class="inline-formula"><sub>3</sub></span> with a MNBIAS between 25 % and 80 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A3</a>). In contrast, in Mexico and São Paulo, the models that overestimate NO<span class="inline-formula"><sub>2</sub></span> also overestimate O<span class="inline-formula"><sub>3</sub></span>. This complex situation is explained by the nonlinearities in the formation of ozone <span class="cit" id="xref_paren.73">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx46" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Grewe</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx46" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2004</a>)</span>. In general, correlation coefficients for O<span class="inline-formula"><sub>3</sub></span> are very low (<span class="inline-formula"><i>R</i><0.3</span>), especially in São Paulo and Mexico City, indicating the challenge to adequately reproduce the spatial and temporal variability of this pollutant. Only in Santiago in January is the criteria benchmark for O<span class="inline-formula"><sub>3</sub></span> (<span class="inline-formula"><i>R</i>>0.5</span>) achieved by some models <span class="cit" id="xref_paren.74">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx7">Model intercomparison</h3> <p id="d2e2153">In the case of global models, CAMS underestimates O<span class="inline-formula"><sub>3</sub></span> in the four cities except in Santiago during winter. Additionally, CAMS tends to have low correlation levels along with a higher bias and error (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A3</a>). SILAM displays a lower bias and error compared to CAMS. However, just like with CAMS, SILAM significantly overestimates O<span class="inline-formula"><sub>3</sub></span> levels in Santiago during the winter. In Bogotá, SILAM underestimates O<span class="inline-formula"><sub>3</sub></span> to a lesser extent than CAMS, with a larger FGE in July (74 %) than in January (22 %).</p> <p id="d2e2185">In São Paulo, daytime concentrations of ozone are generally overestimated by most models (except for CAMS). The largest overprediction of O<span class="inline-formula"><sub>3</sub></span> (MNBIAS from 30 % to 90 %) is associated with overestimation of NO<span class="inline-formula"><sub>2</sub></span>, especially for MPI, EMEP and CHIMERE models. For the models with NO<span class="inline-formula"><sub>2</sub></span> levels in reasonable agreement with observations (SILAM, USP), the ozone overprediction is lower (MNBIAS <span class="inline-formula"><</span> 25 %). Among the regional models, EMEP and WRF–MPI consistently overestimate O<span class="inline-formula"><sub>3</sub></span> levels in all cities, with relatively high MNBIAS and FGE. In contrast, WRF–USP proves particularly suitable for São Paulo, achieving some of the lowest FGE. CHIMERE also performs well in Santiago in the summer, likely owing to local adjustments and parameterizations tailored to these specific cities.</p> <p id="d2e2231">Figure <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">3</a> shows a relatively large model variation for ozone. The largest ozone variability is shown in Mexico City in summertime with a range of 62 ppb and a CV of 72 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A8</a>). This wide variability is caused by the simulation of the EMEP model (71 ppb) and CAMS (9.6 ppb), which represent the extreme cases of over- and underestimation. In a similar manner, in Bogotá, São Paulo and Santiago, the CVs are 61 %, 49 % and 47 %, respectively, explained by the strong underestimation of CAMS and severe overestimation by EMEP and WRF–MPI.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx8">Ensemble performance</h3> <p id="d2e2245">In Santiago in January, the median ensemble showed one of the lowest MNBIAS and FGE, surpassed only by CHIMERE (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A3</a>), and achieved the criteria benchmark for this pollutant (<span class="inline-formula"><i>R</i>>0.5</span>) <span class="cit" id="xref_paren.75">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>. In July, the overestimation of ozone by most models impacts the performance of the ensemble, which also overestimates O<span class="inline-formula"><sub>3</sub></span> concentrations. In Bogotá, the ensemble has some of the best scores for MNBIAS and FGE and represents an intermediate value between all models. In São Paulo, in wintertime, the ensemble has superior metrics (MNBIAS <span class="inline-formula">∼</span> <span class="inline-formula">−3.4</span> %) compared to any individual model, while in the summer the ensemble overestimates the observations as most models do. In Mexico City, the ensemble median performs better than all individual models with a MNBIAS between 4 % (summer) and 13 % (winter) and a FGE less than 32 %. Similar to the individual models, for most of the cases, the correlation coefficient for the ensemble does not meet any of the benchmarks <span class="cit" id="xref_paren.76">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSS3"><span class="label">3.1.3</span> Carbon monoxide – CO</h3> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx9">Observations</h3> <p id="d2e2309">The number of stations per city recording CO during January and July 2015 varies between 7 in Bogotá and 24 in Mexico City (Appendix B). CO levels are generally below 1.0 ppm for all cities (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">4</a>). However, in Santiago during winter some values surpass 1.5 ppm due to a combination of adverse meteorological conditions and emissions from the transportation sector and residential combustion, commonly employed for heating in neighboring municipalities <span class="cit" id="xref_paren.77">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Saide et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx37" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gallardo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx37" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2012</a>)</span>.</p> <p id="d2e2317">There is a slight increase of CO in São Paulo in July with respect to January, due to the atmospheric conditions where lower winds and lower boundary layer increased the primary pollutant concentration during winter. Additionally, biomass burning from wildfires, which begin in July and peak in August and September for the southern part of the Amazon rainforest, can bring more CO <span class="cit" id="xref_paren.78">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx62" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Marlier et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx62" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. Likewise, larger CO concentrations in Bogotá in January are part of the wildfire season in northern South America lasting from the end of December until April <span class="cit" id="xref_paren.79">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mendez-Espinosa et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx66" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>.</p> </div> <span id="page7476"></span><div class="sec"><h3 id="Ch1.S3.SS1.SSSx10">Model performance</h3> <p id="d2e2333">Santiago records the largest simulated value of CO in winter with peak of 5.0 ppm (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">4</a>). The second largest values are observed in Mexico City with values around 3.0 ppm. In both cases, models severely overestimate the observations with some MNBIAS larger than 100 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A4</a>). São Paulo displays intermediate values with an average CO of 0.5 ppm, and Bogotá has the lowest modeled values with an average of 0.27 ppm.</p> <div class="fig" id="Ch1.F4"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04.png" data-width="2067" data-height="1354"></a><div class="caption"><p id="d2e2342"><strong class="caption-number">Figure 4</strong>Observed (black) and simulated CO daily mean concentrations in Santiago, Bogotá, Mexico City and São Paulo for January (left) and July (right) 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f04.png" target="_blank">Download</a></p></div> <p id="d2e2351">CO simulations in Santiago, São Paulo and Mexico City both over- and underpredict observations (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">4</a>). However, in Santiago in winter only the SILAM model overpredicts CO values (MNBIAS 98 %); the other models underpredict the values (MNBIAS between <span class="inline-formula">−152</span> % and <span class="inline-formula">−1</span> %). This situation could be explained by emissions, synoptic conditions or the models' simulation of the boundary layer <span class="cit" id="xref_paren.80">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mazzeo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>. In Bogotá, all models consistently underestimate the CO with a MNBIAS between <span class="inline-formula">−50</span> % and <span class="inline-formula">−131</span> % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A4</a>). In January correlation coefficients for CO hover around 0.6, achieving benchmarks (<span class="inline-formula"><i>R</i>>0.4</span>) <span class="cit" id="xref_paren.81">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. This result demonstrates the model's capability to reproduce the time variability of this pollutant in Bogotá, even if the levels are under- or overestimated. The same situation is observed in Mexico City and São Paulo, where goal (<span class="inline-formula"><i>R</i>>0.6</span>) and criteria (<span class="inline-formula"><i>R</i>>0.4</span>) benchmarks are often achieved <span class="cit" id="xref_paren.82">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> <p id="d2e2445">The underestimation in Bogotá is similar to that observed for NO<span class="inline-formula"><sub>2</sub></span>, which we attributed to a shortfall in emissions. According to the local inventory, CO emissions are predominantly attributed to mobile sources (99 %), with motorcycles contributing to 45 % of these emissions, automobiles accounting for 36 % and the remainder originating from other vehicles (SDA – Secretaría Distrital de Ambiente, 2018). Notably, it has been identified that motorcycle emissions are underestimated in Colombia <span class="cit" id="xref_paren.83">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rojas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>)</span>. The significant rise in the number of motorcycles in the country and their declining condition is not accurately reflected in global emission inventories, such as EDGAR 6.1.</p> <p id="d2e2461">Observed CO mixing ratios are also underestimated in cities such as Medellín, Guadalajara, Quito and Lima (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">8</a>), which might be explained by the coarse resolution of the model not capturing the local characteristics. It is possible that issues with CO emissions in global inventories or excess of OH radicals in photochemistry also contribute to<span id="page7477"></span> this trend. In addition, a major source of atmospheric CO is the oxidation of BVOCs <span class="cit" id="xref_paren.84">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx110" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Worden et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx110" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>, which are significantly underestimated in the Southern Hemisphere <span class="cit" id="xref_paren.85">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx112" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zeng et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx112" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>)</span>.</p> <p id="d2e2472">In São Paulo, five out of six models slightly underestimate CO with a relatively high correlation coefficient. The simulated concentrations for daily values range from 0.1 to 2.0 ppm, similar to that found in other studies <span class="cit" id="xref_paren.86">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx25" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Deroubaix et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx25" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. Nevertheless, concentrations exceeding 1.2 ppm are simulated only for certain days (13 January and 30 July) and are probably due to wood burning (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S3.F9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">C1</a>).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx11">Model intercomparison</h3> <p id="d2e2486">Global models, particularly CAMS, tend to underestimate CO levels in Bogotá, São Paulo and Mexico City with a MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">−50</span> %. In Santiago, CAMS adequately simulates CO levels with a MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">±2.5</span> % and a FGE <span class="inline-formula"><25</span> %. The correlation coefficient achieves the criteria benchmark (<span class="inline-formula"><i>R</i>>0.4</span>) proposed by <span class="cit" id="xref_text.87"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. SILAM underestimates CO in Bogotá (model <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M129" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="hide-js svg-formula" dspmath="mathimg" md5hash="8c00021cbb7b39d8843b374b42423a30"><image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00002.svg" width="100%" height="14pt" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00002.png"></image></svg></span></span> observations of <span class="inline-formula">∼</span> 0.6) and overestimates it in Santiago, while it performs relatively well in São Paulo and Mexico City (MNBIAS <span class="inline-formula"><</span> 22 %). Correlation coefficients meet the criteria and goal benchmarks (<span class="inline-formula"><i>R</i>>0.4</span> and <span class="inline-formula"><i>R</i>>0.6</span>) proposed by <span class="cit" id="xref_text.88"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> <p id="d2e2598">When it comes to regional models, WRF–USP consistently underestimates CO levels with a high bias (MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">−60</span> %) and error (FGE <span class="inline-formula">></span> 60 %). WRF–MPI has better performance, especially in São Paulo and Mexico City (MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">±15</span> %), and correlation coefficients within the goal benchmark <span class="cit" id="xref_paren.89">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. EMEP and CHIMERE largely overestimate observations in Mexico City, while in São Paulo they closely match observations. In Santiago, these models tend to overpredict CO in the summer and underpredict it during the winter.</p> <p id="d2e2646">The largest model variation is observed in Santiago during wintertime with a range of 3.2 ppm and CV of 106 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A8</a>). Mexico City also shows large variation in summer (CV: 72 %) and winter (CV: 56 %). Bogotá and São Paulo present less variation between model results.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx12">Ensemble performance</h3> <p id="d2e2657">In winter in Santiago and Bogotá in both periods, the ensemble follows the underestimation pattern of all models (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A4</a>). In São Paulo there are models with better<span id="page7478"></span> performance than the ensemble, but the ensemble results are reasonable, with a MNBIAS close to <span class="inline-formula">−15</span> % and an <span class="inline-formula"><i>R</i></span> of approximately 0.5. In Mexico City, the overestimation of CO by the EMEP and CHIMERE models (MNBIAS <span class="inline-formula">></span> 60 %) is reduced in the ensemble (MNBIAS <span class="inline-formula">∼</span> 15 %).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSS4"><span class="label">3.1.4</span> Sulfur dioxide – SO<span class="inline-formula"><sub>2</sub></span></h3> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx13">Observations</h3> <p id="d2e2717">The number of stations per city recording SO<span class="inline-formula"><sub>2</sub></span> during January and July 2015 varies between 4 in Santiago and 26 in Mexico City (Appendix B). The largest concentration of SO<span class="inline-formula"><sub>2</sub></span> is observed in Mexico City with values between 3.0 ppb (January) and 4.4 ppb (July) due to volcanic emissions <span class="cit" id="xref_paren.90">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx23" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">de Foy et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx23" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2009</a>)</span> and the heavy consumption of coal in power generation and cement production, especially in the proximity of the “Tula–Vito–Apasco” industrial area <span class="cit" id="xref_paren.91">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx98" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">SEMARNAT and INECC</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx98" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. On the other hand, SO<span class="inline-formula"><sub>2</sub></span> in Bogotá, Santiago and São Paulo is lower, with concentrations ranging from 1.0 to 1.8 ppb (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">5</a>).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx14">Model performance</h3> <p id="d2e2762">The largest simulation is shown in Mexico City, with an average of 45 ppb SO<span class="inline-formula"><sub>2</sub></span>, followed by São Paulo, with a mean concentration of 8.5 ppb. In Santiago, the average SO<span class="inline-formula"><sub>2</sub></span> value is 8.5 ppb. The lowest modeled values are found in Bogotá, with an average of 0.97 ppb (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A5</a>).</p> <div class="fig" id="Ch1.F5"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05.png" data-width="2067" data-height="1357"></a><div class="caption"><p id="d2e2787"><strong class="caption-number">Figure 5</strong>Observed (black) and simulated SO<span class="inline-formula"><sub>2</sub></span> daily mean concentrations in Santiago, Bogotá, Mexico City and São Paulo for January (left) and July (right) 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f05.png" target="_blank">Download</a></p></div> <p id="d2e2805">The models' simulated SO<span class="inline-formula"><sub>2</sub></span> exhibits significant discrepancies when compared to the observations, with severe overestimation in Santiago, Mexico City and São Paulo (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">5</a>), with a MNBIAS reaching up to 190 % and a FGE up to 200 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A5</a>). On the contrary, for Bogotá the predicted SO<span class="inline-formula"><sub>2</sub></span> values are in reasonable alignment with the observations, except for the WRF–Chem USP simulation, which drastically underestimates SO<span class="inline-formula"><sub>2</sub></span> (MNBIAS: <span class="inline-formula">−200</span> %) (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A5</a>).</p> <p id="d2e2852">The overestimation of SO<span class="inline-formula"><sub>2</sub></span> levels could stem from issues within global emission inventories. In fact, an overestimation of SO<span class="inline-formula"><sub>2</sub></span> emissions in CAMS was observed for Buenos Aires and Santiago when compared to the PAPILA inventory <span class="cit" id="xref_paren.92">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Castesana et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>)</span>. These emissions primarily originate from the energy and industrial sectors, where the sulfur content in coal appears to significantly contribute to this overestimation.</p> <p id="d2e2877">The good performance in Bogotá might be related to lower SO<span class="inline-formula"><sub>2</sub></span> emissions apportioned in the city. In fact, the vast majority of SO<span class="inline-formula"><sub>2</sub></span> emissions (<span class="inline-formula">∼</span> 90 %) in Colombia originate from the industrial and energy production sectors <span class="cit" id="xref_paren.93">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx52" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">IDEAM</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx52" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. However, these facilities are typically located outside major urban areas. Bogotá contributes only 1.5 % of the total national SO<span class="inline-formula"><sub>2</sub></span> emissions <span class="cit" id="xref_paren.94">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx21" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">de Ambiente</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx21" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx15">Model intercomparison</h3> <p id="d2e2928">CAMS and SILAM severely overestimate SO<span class="inline-formula"><sub>2</sub></span> in Mexico City, São Paulo and Santiago with a MNBIAS and a FGE larger than 100 %. In Bogotá, both global models underestimate SO<span class="inline-formula"><sub>2</sub></span> concentrations (MNBIAS from <span class="inline-formula">−56</span> % to <span class="inline-formula">−80</span> %) but with lower FGE (<span class="inline-formula"><80</span> %) than CAMS. In January, correlation coefficients in São Paulo met the criteria benchmark (<span class="inline-formula"><i>R</i>>0.35</span>) suggested by <span class="cit" id="xref_text.95"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhai et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx113" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p> <p id="d2e2995">The performance of regional models for SO<span class="inline-formula"><sub>2</sub></span> is quite diverse. WRF–USP severely underestimates SO<span class="inline-formula"><sub>2</sub></span> in all cities (MNBIAS close to <span class="inline-formula">−200</span> %). In Santiago, Mexico City and São Paulo the models overestimate SO<span class="inline-formula"><sub>2</sub></span> in a similar fashion to global models. In Bogotá, EMEP and WRF–MPI show the lowest MNBIAS (<span class="inline-formula"><16</span> %).</p> <p id="d2e3045">The largest model variation for SO<span class="inline-formula"><sub>2</sub></span> is found in Mexico City, where the range of models is 200 ppb, and the CV is larger than 150 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A8</a>). In Santiago and São Paulo, the model variation is close to a CV of 95 %. In Bogotá, the variation is the lowest (CV: <span class="inline-formula">∼</span> 75 %).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx16">Ensemble performance</h3> <p id="d2e3072">In Mexico City, Santiago and São Paulo, SO<span class="inline-formula"><sub>2</sub></span> is overestimated by all models, except the USP. Therefore, the median ensemble also overestimates SO<span class="inline-formula"><sub>2</sub></span> concentration and does not represent any improvement in the evaluation metrics (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A5</a>). In Bogotá, the ensemble tends to underestimate the concentrations (MNBIAS: <span class="inline-formula">∼</span> <span class="inline-formula">−55</span> %) to a lesser extent than individual models.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSS5"><span class="label">3.1.5</span> Fine particulate matter – PM<span class="inline-formula"><sub>2.5</sub></span></h3> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx17">Observations</h3> <p id="d2e3136">The number of stations per city recording PM<span class="inline-formula"><sub>2.5</sub></span> during January and July of 2015 varies between 9 in Bogotá and 16 in Mexico City (Appendix B). The largest PM<span class="inline-formula"><sub>2.5</sub></span> concentrations are found in Santiago during the Southern Hemisphere winter, with daily values around 56 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>. This can be attributed to adverse meteorological conditions and emissions from transportation and residential combustion in the surrounding municipalities <span class="cit" id="xref_paren.96">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mazzeo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Saide et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx94" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>)</span>. The second largest values are shown in Mexico City, with an average of 23 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> due to local emission sources. In São Paulo, PM<span class="inline-formula"><sub>2.5</sub></span> levels are larger in July (19 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) than in January (16 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>), due to the impact of wildfires from the Amazon basin and sugarcane burning <span class="cit" id="xref_paren.97">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">de Fatima Andrade et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>. In Bogotá, PM<span class="inline-formula"><sub>2.5</sub></span> concentrations are the lowest in July (13 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) due to the influence of trade winds <span class="cit" id="xref_paren.98">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx79" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pachón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx79" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span> but with larger values in January (19 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) due to biomass-burning events and frequent thermal inversions <span class="cit" id="xref_paren.99">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx87" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ramírez et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx87" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>)</span>.</p> </div> <span id="page7479"></span><div class="sec"><h3 id="Ch1.S3.SS1.SSSx18">Model performance</h3> <p id="d2e3317">In Santiago in wintertime, the mean of the models is larger than observations, whereas in summer the simulations are mostly below observations. In Mexico City, simulated values are approximately double the observations. In São Paulo, PM<span class="inline-formula"><sub>2.5</sub></span> is under- and overpredicted by the models. In Bogotá, most of the simulations are below the observations (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">6</a>).</p> <div class="fig" id="Ch1.F6"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06.png" data-width="2067" data-height="1348"></a><div class="caption"><p id="d2e3333"><strong class="caption-number">Figure 6</strong>Observed (black) and simulated PM<span class="inline-formula"><sub>2.5</sub></span> daily mean concentrations in Santiago, Bogotá, Mexico City and São Paulo for January (left) and July (right) 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f06.png" target="_blank">Download</a></p></div> <p id="d2e3351">In Santiago, Bogotá and Mexico City, models over- and underpredict PM<span class="inline-formula"><sub>2.5</sub></span> (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A6</a>). In São Paulo, overestimation is observed in all models with the exception of WRF–USP and may be linked to an excess of fire emissions, as suggested by other studies <span class="cit" id="xref_paren.100">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx25" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Deroubaix et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx25" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>. The MNBIAS varies from 35 % to 120 % except for WRF–USP, whose MNBIAS is negative (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A6</a>). The correlation coefficients for PM<span class="inline-formula"><sub>2.5</sub></span> are in some cases larger than the goal (<span class="inline-formula"><i>R</i>>0.7</span>) or criteria (<span class="inline-formula"><i>R</i>>0.4</span>) benchmarks proposed by <span class="cit" id="xref_text.101"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>. It is worth noting the cases of Mexico City in January and São Paulo in July, where most models achieve the goal or criteria metric. In smaller urban areas like Medellín, Lima and Quito (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">8</a>), most models tend to underestimate observations, potentially due to the coarse resolution of the models.</p> <p id="d2e3409">Hourly simulations of PM<span class="inline-formula"><sub>2.5</sub></span> are useful to understand the discrepancies between model and observations. In Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S3.F10" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">C2</a>, we show the hourly data and model outputs. In São Paulo, the highest PM<span class="inline-formula"><sub>2.5</sub></span> concentrations are simulated by SILAM on 13 January (<span class="inline-formula">>320</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>) and 30 July (<span class="inline-formula">>400</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>), which corresponds to days with high simulated CO values as well (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S3.F9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">C1</a> in Appendix C) and may indicate an overestimation of biomass burning by the IS4FIRES module in SILAM. From 15 to 30 January there is also an excess of PM<span class="inline-formula"><sub>2.5</sub></span> from SILAM.</p> <p id="d2e3505">In Mexico City, the highest PM<span class="inline-formula"><sub>2.5</sub></span> concentrations are simulated by the CAMS model with about 250 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> in January and 160 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> in July (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S3.F11" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">C3</a>), which are severely overestimated. The large PM<span class="inline-formula"><sub>2.5</sub></span> values are distributed in the whole period rather than specific days and do not correspond to high CO concentrations to suspect the influence of fires. This situation might indicate a local and continuous source of PM<span class="inline-formula"><sub>2.5</sub></span>.</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx19">Model intercomparison</h3> <p id="d2e3584">Both global models consistently overestimate PM<span class="inline-formula"><sub>2.5</sub></span> in Santiago, São Paulo and Mexico City, but they behave differently<span id="page7480"></span> in Bogotá. In Mexico City, CAMS has a greater overestimation than SILAM, but in São Paulo and Santiago SILAM values are larger (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">6</a>). In Bogotá, CAMS overestimates PM<span class="inline-formula"><sub>2.5</sub></span> (MNBIAS <span class="inline-formula">∼</span> 37 %), whereas SILAM underestimates it (MNBIAS <span class="inline-formula">∼</span> <span class="inline-formula">−85</span> %). The SILAM correlation coefficient meets the criteria benchmark suggested by <span class="cit" id="xref_text.102"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>.</p> <p id="d2e3635">Among the regional models, EMEP shows the largest underestimation (MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">−110</span> %) in all cites, except in São Paulo, where the model overestimates PM<span class="inline-formula"><sub>2.5</sub></span> but is within the criteria benchmark (MNBIAS <span class="inline-formula"><</span> <span class="inline-formula">±60</span> %) <span class="cit" id="xref_paren.103">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx12" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Boylan and Russell</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx12" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span> and with a correlation coefficient (<span class="inline-formula"><i>R</i>>0.4</span>) that meets the criteria benchmark by <span class="cit" id="xref_text.104"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span> in July (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A6</a>). WRF–USP heavily underestimates in Bogotá and Santiago but performs well in São Paulo with the lowest errors. This difference in behavior might be explained by a good adaptation of the model's inputs to the city. The WRF–MPI model meets goal benchmarks for MNBIAS and FGE in Bogotá and Mexico City.</p> <p id="d2e3702">The largest model variation is observed in Mexico City and Santiago during wintertime with a CV greater than 100 % (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A8</a>). Santiago in summer and Bogotá present intermediate values (CV of 70 to 80 %), whereas São Paulo shows the least variability between models (CV <span class="inline-formula"><</span> 56 %).</p> </div> <div class="sec"><h3 id="Ch1.S3.SS1.SSSx20">Ensemble performance</h3> <p id="d2e3720">Considering the large underestimation of most models in Bogotá and Santiago, the ensemble displays lower bias and error than some of the individual models (Table <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A6</a>). In Mexico City, the ensemble outperforms models with a MNBIAS of <span class="inline-formula">−5</span> % in January and <span class="inline-formula">+30</span> % in July, achieving the goal benchmark suggested by <span class="cit" id="xref_text.105"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx12" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Boylan and Russell</a> (<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx12" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span>, as well as the correlation coefficient (<span class="inline-formula"><i>R</i>>0.8</span>) in January. For São Paulo, all models tend to overestimate PM<span class="inline-formula"><sub>2.5</sub></span>, so it follows that the ensemble presents the same behavior as MNBIAS <span class="inline-formula">></span> 61 %. The correlation coefficient meets the criteria benchmark (<span class="inline-formula"><i>R</i>>0.4</span>) in both periods <span class="cit" id="xref_paren.106">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Emery et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx29" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>)</span>.</p> </div> </div><div class="sec"><h2 id="Ch1.S3.SS2"><span class="label">3.2</span> Spatial seasonal variability of predictions</h2> <p id="d2e3801"><span id="page7481"></span>For all pollutants, models and periods, maps of mean concentrations were constructed to visualize the spatial differences (Appendix D). In order to summarize the results, other spatial plots were also prepared: median ensemble (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">7</a>), median absolute deviation (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S5.F22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">E1</a> in Appendix E) and mean standard deviation (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S5.F23" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">E2</a>). In Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">7</a>, pollution hot spots are clearly visible around major urban areas, in particular in São Paulo on the southeastern coast and Mexico City in the northwestern part of the continent. São Paulo and Mexico City each cover a significant area, of approximately 3600 km<span class="inline-formula"><sup>2</sup></span>, spanning at least nine modeling cells (400 km<span class="inline-formula"><sup>2</sup></span> each). This extensive coverage offers some spatial representation of the physical and chemical atmospheric processes. Other regions highlighted on the maps include Lima and Santiago on the Pacific coast, Buenos Aires along the southern shore of the Río de la Plata, and cities in the northern part of South America like Quito, Bogotá, Medellín and Caracas. However, most of these cities are encompassed by six or fewer modeling cells, limiting the potential for significant spatial variation.</p> <div class="fig" id="Ch1.F7"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f07-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f07" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f07-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f07-thumb.png" data-width="2067" data-height="1595"></a><div class="caption"><p id="d2e3833"><strong class="caption-number">Figure 7</strong>Spatial variability of simulated PM<span class="inline-formula"><sub>10</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span>, O<span class="inline-formula"><sub>3</sub></span> and CO in LAC for January and July 2015 (based on the median of the models).</p></div><p class="downloads"></p></div> <p id="d2e3869">The temporal seasonality can also be observed in Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">7</a>. The left and right panels show results for January and July, corresponding to the Southern Hemisphere summer and winter, respectively. For SO<span class="inline-formula"><sub>2</sub></span>, major hot spots appear in Mexico City, São Paulo and the surrounding areas, and the Pacific coast in Chile. The SO<span class="inline-formula"><sub>2</sub></span> concentrations are associated with volcanic emissions and the use of coal in power generation, cement production and copper smelting, which are active in both summer and winter <span class="cit" id="xref_paren.107">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx50" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Huneeus et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx50" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx98" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">SEMARNAT and INECC</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx98" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>. Similarly, NO<span class="inline-formula"><sub>2</sub></span> hot spots are common in major urban areas due to transportation emissions.</p> <p id="d2e3905">In January, the median ensemble shows high concentrations of PM<span class="inline-formula"><sub>10</sub></span> in several areas. In the south of Argentina, the concentrations are primarily due to dust from the Patagonia desert areas <span class="cit" id="xref_paren.108">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx38" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Gassó and Torres</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx38" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. In the north of Brazil and the Guianas, increased PM<span class="inline-formula"><sub>10</sub></span> levels are most likely associated with fires in the Orinoco basin during the dry season <span class="cit" id="xref_paren.109">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx48" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Hernandez et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx48" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2019</a>)</span>. In a similar manner, PM<span class="inline-formula"><sub>2.5</sub></span> concentrations show an increase in the northern part of Brazil due to biomass burning. Large concentrations of PM<span class="inline-formula"><sub>2.5</sub></span> in São Paulo in both January and July are probably caused by overestimation of fires, as previously discussed.</p> <p id="d2e3951">During the austral summer, the southeastern part of Brazil (including São Paulo) displays high concentrations of ozone that were simulated mainly by the regional models WRF–Chem and EMEP and the global SILAM (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S4.F14" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">D3</a> in Appendix D). Several studies have shown the influence of urban plumes of NO<span class="inline-formula"><sub>2</sub></span> on the Amazon rainforest, rich in BVOCs, with the consequent generation of ozone <span class="cit" id="xref_paren.110">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx86" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rafee et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx86" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx75" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Nascimento et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx75" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>)</span>. In January, simulated O<span class="inline-formula"><sub>3</sub></span> concentrations are also high in Mexico City during winter, a situation that has been observed in other studies <span class="cit" id="xref_paren.111">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Barrett and Raga</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>)</span>. There is a maximum of CO in the area between north of Argentina, south of Bolivia, Paraguay and south of Brazil, probably related to fires and the abundance of BVOCs.</p> <p id="d2e3980">In July, during the austral winter, concentrations of CO, PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> are significant in Santiago due to transportation and residential heating emissions under adverse meteorological conditions. PM<span class="inline-formula"><sub>10</sub></span> concentrations are high in the Caribbean and central Mexico, primarily due to the transport of Saharan dust into these urban areas <span class="cit" id="xref_paren.112">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx56" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Kramer and Kirtman</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx56" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx88" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Ramírez-Romero et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx88" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2021</a>)</span>. Similarly, along the Pacific coast between Chile and Peru, increased PM<span class="inline-formula"><sub>10</sub></span> is probably explained by anthropogenic emissions of copper smelters in connection with strong eastern wind events <span class="cit" id="xref_paren.113">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx50" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Huneeus et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx50" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2006</a>)</span>. Large concentrations of O<span class="inline-formula"><sub>3</sub></span> are visible in Mexico City that are associated with clear skies under high-pressure atmospheric conditions <span class="cit" id="xref_paren.114">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Barrett and Raga</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2016</a>)</span>. Elevated O<span class="inline-formula"><sub>3</sub></span> values in the Andes between northern Chile and central Peru might be explained by the abundance of VOCs from metropolitan regions and industrial zones <span class="cit" id="xref_paren.115">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx96" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Seguel et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx96" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>)</span>.</p> <p id="d2e4050">The median absolute deviation maps (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S5.F22" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">E1</a>) and the standard deviation maps (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S5.F23" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">E2</a>) display spatial differences between model simulations. In particular, for particulate matter (PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span>) a notorious dissimilarity is observed in northern Brazil in January, Venezuela in July and the south of Argentina in both periods. The reason for this disagreement is the simulation of the WRF–MPI model, which contributes with significant PM mass in the mentioned zones, probably due to an overestimation of fires in the northern part of the continent and dust in the southern areas. In July, CO showed large differences in the Colombian and Peruvian Amazon, mostly driven by the EMEP model. This situation might be related to an incorrect estimation of BVOC emissions as precursors of CO in forested areas. The inadequate simulation of NO<span class="inline-formula"><sub>2</sub></span> by the CAMS model, explained in Sect. 3.1.1, is the cause of the large standard deviation of model results for this pollutant.</p> </div><div class="sec"><h2 id="Ch1.S3.SS3"><span class="label">3.3</span> Large versus small urban areas</h2> <p id="d2e4092">The coarse resolution used in the modeling systems (0.2° <span class="inline-formula">×</span> 0.2°) poses challenges in adequately representing the intricate topography and diverse meteorological conditions of the different cities in LAC. Capturing these physical phenomena can be very difficult and requires a finer scale with much greater computational demand. In the last few years, emission inventories for LAC at high spatial and temporal resolution have been constructed <span class="cit" id="xref_paren.116">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Castesana et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Álamos et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx84" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Puliafito et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx84" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx85" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rojas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>)</span>, and it is expected they will complement existing global emission inventories at coarse resolution. We observe that, in large urban areas (<span class="inline-formula">>3500</span> km<span class="inline-formula"><sup>2</sup></span>), the models tend in general to have a lower and positive MNBIAS compared to medium-size (600 <span class="inline-formula"><</span> area <span class="inline-formula"><</span> 3600 km<span class="inline-formula"><sup>2</sup></span>) or small-size (area <span class="inline-formula"><</span> 600 km<span class="inline-formula"><sup>2</sup></span>) cities (Fig. <a href="https://gmd.copernicus.org/articles/17/7467/2024/#Ch1.F8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">8</a>). For example, for Mexico City and São Paulo, the two largest cities in LAC, the mean of the models shows the lowest MNBIAS and FGE for CO (<span class="inline-formula">−27</span> % to 29 %) and NO<span class="inline-formula"><sub>2</sub></span> (<span class="inline-formula">−6</span> % to 6 %), while in other cities they display a larger and negative MNBIAS and FGE (Tables <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A2</a> and <a href="https://gmd.copernicus.org/articles/17/7467/2024/#App1.Ch1.S1.T5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">A4</a>). The discrepancies in NO<span class="inline-formula"><sub>2</sub></span> have a corresponding impact in the overestimation of O<span class="inline-formula"><sub>3</sub></span>. For particulate matter, a similar pattern is observed, with a positive MNBIAS for larger urban areas and a negative MNBIAS<span id="page7482"></span> for medium and small cities. High-resolution simulations are necessary to resolve the spatial variation, but unfortunately global models at high performance are scarce in the Southern Hemisphere <span class="cit" id="xref_paren.117">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx114" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Zhang et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx114" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>)</span>.</p> <div class="fig" id="Ch1.F8"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08.png" data-width="2067" data-height="817"></a><div class="caption"><p id="d2e4224"><strong class="caption-number">Figure 8</strong>MNBIAS estimated for large and small urban areas.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f08.png" target="_blank">Download</a></p></div> <p id="d2e4233">Although the size of cities can influence the performance of the models at coarse resolution, other challenging features for models exist. For instance, Bogotá and Santiago have several challenges in terms of topography and meteorology <span class="cit" id="xref_paren.118">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Mazzeo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx65" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2018</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx76" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Nedbor-Gross et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx76" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2017</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx89" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Reboredo et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx89" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2015</a>)</span>, and local emissions are not always accounted for in global inventories <span class="cit" id="xref_paren.119">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Castesana et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx18" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx51" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Huneeus et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx51" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2020</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx78" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Osses et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx78" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2022</a>; <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Rojas et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx92" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2023</a>)</span>. Ideally, we would have access to more cities of various sizes to make this determination with more certainty; unfortunately, local measured data were only available for the cities we considered.</p> </div></div><span class="section3-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="sec conclusions" id="section4"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section4 .co-arrow-open,.section4-content" data-show="#section4 .co-arrow-closed,.section4-mobile-bottom-border"><div id="Ch1.S4" class="h1"><span class="label">4</span> Conclusions and future developments<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section4-content show-no-js hide-on-mobile-soft"><p id="d2e4251">This study performed the first intercomparison and model evaluation effort in Latin America with the idea to develop an AQF system that can inform the public about air pollution episodes and support policy actions. Despite the limitations of air quality and emissions data, as well as computing resources, the scientific community in Latin America, with international support, has achieved significant progress in air quality modeling and in understanding the fate and transport of pollutants in the region. For instance, the impacts of Saharan dust, biomass burning from the Orinoco and the Amazon basins, and biogenic VOCs of the Amazon rainforest are becoming better understood through modeling.</p><p id="d2e4254">Several challenges still exist. In addition to the intricate topography and diverse meteorological conditions, limitations are found in anthropogenic, volcanic and biogenic emissions; in spatial and temporal profiles; in land use and vegetation types; and in other data that are relevant for the calculation of wildfire emissions. This last source is crucial in the region under a climate change scenario, for which adequate parameterization of biomass burning is necessary. The boundary conditions of the models can be improved, which are especially important for long-lived species. The experience of local researchers who have been implementing air quality models for several years can greatly benefit international efforts, such as global emission inventories and the recently launched WMO GAFIS initiative.</p><p id="d2e4257">At this first stage of development, interesting and insightful findings were identified for the region. Despite the fact that some of the models were still in an early phase for regional implementation, most models could adequately reproduce air quality observations with the best performance observed for nitrogen dioxide in Mexico City and São Paulo. These enormous urban areas (<span class="inline-formula">>3500</span> km<span class="inline-formula"><sup>2</sup></span>) outperformed Bogotá and Santiago, which are cities between 500 and 1000 km<span class="inline-formula"><sup>2</sup></span>. This suggests an accurate portrayal of the temporal and spatial variability in large cities with the current model resolution (0.2° <span class="inline-formula">×</span> 0.2°) and the need for a finer model domain in smaller cities that could capture circulation and emission features. At the moment, high-resolution global simulations in the Global South remain rare.</p><p id="d2e4296">The ensemble median was evaluated on its potential to outperform individual models. In certain periods and cities, the ensemble performed better than any individual models, for example, when the errors of the models compensate for each other but not when the errors are recurring in all the models. The results varied per city, pollutant and period. Before defining whether the ensemble is the correct approximation for an AQF system, more research is necessary. This work only looked at 2 months (1 in summer and 1 in winter); a thorough analysis of one entire annual cycle with sufficient spinup time should be conducted. More observations should also be included for model calibration and evaluation. For 2015, only eight cities in LAC had data that complied with quality and completeness criteria. In recent years, more AQ networks have been implemented and data are more publicly available.</p></div><span class="section4-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="app sec" id="section5"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section5 .co-arrow-open,.section5-content" data-show="#section5 .co-arrow-closed,.section5-mobile-bottom-border"><div id="App1.Ch1.S1" class="h1"><span>Appendix A:</span> Evaluation scores<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section5-content show-no-js hide-on-mobile-soft"><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T2"><div class="caption"><p id="d2e4316"><strong class="caption-number">Table A1</strong>Metrics used for model evaluation.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02-web.png" data-width="2067" data-height="1494" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02.xlsx"></a><div class="table-wrap-foot"><p id="d2e4319"><span class="inline-formula"><i>O</i><sub>d</sub></span> and <span class="inline-formula"><i>m</i><sub>d</sub></span> are the observation and modeled value for each day. <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M276" display="inline" overflow="scroll" dspmath="mathml"><mover accent="true"><mi>m</mi><mo mathvariant="normal">‾</mo></mover></math><span><svg xmlns:svg="http://www.w3.org/2000/svg" width="12pt" height="11pt" class="hide-js svg-formula" dspmath="mathimg" md5hash="6da6a1f41b9423b74d788ad6a69eb2dd"><image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00020.svg" width="100%" height="11pt" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00020.png"></image></svg></span></span> is the mean of the models for each month and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M277" display="inline" overflow="scroll" dspmath="mathml"><mover accent="true"><mi>O</mi><mo mathvariant="normal">‾</mo></mover></math><span><svg xmlns:svg="http://www.w3.org/2000/svg" width="10pt" height="13pt" class="hide-js svg-formula" dspmath="mathimg" md5hash="6570382e1e8d8e3a764b40e77b5c71d6"><image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00021.svg" width="100%" height="13pt" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-ie00021.png"></image></svg></span></span> the mean of the observations for each city. <span class="inline-formula"><i>σ</i><sub>m</sub></span> denotes the standard deviation for each model. <span class="inline-formula"><i>N</i></span> is the number of model–observation pairs available for each month.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t02.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T3"><div class="caption"><p id="d2e5155"><strong class="caption-number">Table A2</strong>NO<span class="inline-formula"><sub>2</sub></span> model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03-web.png" data-width="2067" data-height="870" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03.xlsx"></a><div class="table-wrap-foot"><p id="d2e5167"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t03.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T4"><div class="caption"><p id="d2e6777"><strong class="caption-number">Table A3</strong>O<span class="inline-formula"><sub>3</sub></span> model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04-web.png" data-width="2067" data-height="879" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04.xlsx"></a><div class="table-wrap-foot"><p id="d2e6789"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t04.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T5"><div class="caption"><p id="d2e8392"><strong class="caption-number">Table A4</strong>CO model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05-web.png" data-width="2067" data-height="866" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05.xlsx"></a><div class="table-wrap-foot"><p id="d2e8395"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t05.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T6"><div class="caption"><p id="d2e9984"><strong class="caption-number">Table A5</strong>SO<span class="inline-formula"><sub>2</sub></span> model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06-web.png" data-width="2067" data-height="878" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06.xlsx"></a><div class="table-wrap-foot"><p id="d2e9996"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t06.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T7"><div class="caption"><p id="d2e11529"><strong class="caption-number">Table A6</strong>PM<span class="inline-formula"><sub>2.5</sub></span> model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07-web.png" data-width="2067" data-height="876" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07.xlsx"></a><div class="table-wrap-foot"><p id="d2e11541"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t07.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T8"><div class="caption"><p id="d2e13134"><strong class="caption-number">Table A7</strong>PM<span class="inline-formula"><sub>10</sub></span> model evaluation scores (January and July).</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08-web.png" data-width="2067" data-height="865" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08.xlsx"></a><div class="table-wrap-foot"><p id="d2e13146"><span class="inline-formula"><sup>*</sup></span> Ensemble: based on the median value of the models; mean: arithmetic mean of the models; CAMS: Copernicus Atmosphere Monitoring Service's (CAMS); MPI: WRF–Chem executed by MPIM; EMEP: European Monitoring and Evaluation Programme; CHIM: CHIMERE transport model; SILAM: System for Integrated modeling of Atmospheric composition; USP: WRF–Chem executed by University of São Paulo.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t08.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S1.T9"><div class="caption"><p id="d2e14806"><strong class="caption-number">Table A8</strong>Coefficient of variation (CV) per city during January and July.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09-web.png" data-width="2067" data-height="429" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09.xlsx"></a><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t09.xlsx" target="_blank">Download XLSX</a></p></div></div><span class="section5-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="app sec" id="section6"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section6 .co-arrow-open,.section6-content" data-show="#section6 .co-arrow-closed,.section6-mobile-bottom-border"><div id="App1.Ch1.S2" class="h1"><span>Appendix B:</span> Air quality observations<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section6-content show-no-js hide-on-mobile-soft"><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T10"><div class="caption"><p id="d2e15058"><strong class="caption-number">Table B1</strong>Station availability and location for Mexico City.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10-web.png" data-width="2067" data-height="1360" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10.xlsx"></a><div class="table-wrap-foot"><p id="d2e15061">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days and USP missing information for Mexico, given their simulation domain did not include it.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t10.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T11"><div class="caption"><p id="d2e16327"><strong class="caption-number">Table B2</strong>Station availability and location for Bogotá.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11-web.png" data-width="2067" data-height="1353" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11.xlsx"></a><div class="table-wrap-foot"><p id="d2e16330">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t11.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T12"><div class="caption"><p id="d2e17631"><strong class="caption-number">Table B3</strong>Station availability and location for Santiago.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12-web.png" data-width="2067" data-height="1340" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12.xlsx"></a><div class="table-wrap-foot"><p id="d2e17634">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t12.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T13"><div class="caption"><p id="d2e18934"><strong class="caption-number">Table B4</strong>Station availability and location for São Paulo.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13-web.png" data-width="2067" data-height="1335" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13.xlsx"></a><div class="table-wrap-foot"><p id="d2e18937">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t13.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T14"><div class="caption"><p id="d2e20237"><strong class="caption-number">Table B5</strong>Station availability and location for Quito.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14-web.png" data-width="2067" data-height="1354" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14.xlsx"></a><div class="table-wrap-foot"><p id="d2e20240">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t14.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T15"><div class="caption"><p id="d2e21541"><strong class="caption-number">Table B6</strong>Station availability and location for Medellín.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15-web.png" data-width="2067" data-height="1328" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15.xlsx"></a><div class="table-wrap-foot"><p id="d2e21544">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t15.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T16"><div class="caption"><p id="d2e22844"><strong class="caption-number">Table B7</strong>Station availability and location for Lima.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16-web.png" data-width="2067" data-height="1347" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16.xlsx"></a><div class="table-wrap-foot"><p id="d2e22847">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t16.xlsx" target="_blank">Download XLSX</a></p></div><span class="tableCitations"></span><div class="table-wrap" id="App1.Ch1.S2.T17"><div class="caption"><p id="d2e24147"><strong class="caption-number">Table B8</strong>Station availability and location for Guadalajara.</p></div><a class="table-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17.png" target="_blank"><img src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17-thumb.png" target="_blank" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17-web.png" data-width="2067" data-height="1128" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17.png" data-csvversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17.xlsx"></a><div class="table-wrap-foot"><p id="d2e24150">The observation availability refers to the percentage of days in each period when at least one station records enough data to construct their daily average (minimum of 18 h). Additionally, only stations that maintain at least 75 % of daily availability throughout the entire period are considered (at least 23 d with 18 h minimum). The model availability refers to the percentage of days for which we have modeled data, with CHIMERE being the only one with missing days and USP missing information for Guadalajara, given their simulation domain did not include it.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17.png" target="_blank">Download Print Version</a><span class="hide-on-mobile download-separator"> | </span><a class="triangle journal-contentLinkColor table-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-t17.xlsx" target="_blank">Download XLSX</a></p></div></div><span class="section6-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="app sec" id="section7"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section7 .co-arrow-open,.section7-content" data-show="#section7 .co-arrow-closed,.section7-mobile-bottom-border"><div id="App1.Ch1.S3" class="h1"><span>Appendix C:</span> Particular hourly simulations<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section7-content show-no-js hide-on-mobile-soft"><div class="fig" id="App1.Ch1.S3.F9"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09.png" data-width="2067" data-height="428"></a><div class="caption"><p id="d2e25236"><strong class="caption-number">Figure C1</strong>Hourly CO simulations in São Paulo for January and July 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f09.png" target="_blank">Download</a></p></div><div class="fig" id="App1.Ch1.S3.F10"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10.png" data-width="2067" data-height="436"></a><div class="caption"><p id="d2e25249"><strong class="caption-number">Figure C2</strong>Hourly PM<span class="inline-formula"><sub>2.5</sub></span> simulations in São Paulo for January and July 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f10.png" target="_blank">Download</a></p></div><div class="fig" id="App1.Ch1.S3.F11"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11-thumb.png" data-printversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11.png" data-width="2067" data-height="436"></a><div class="caption"><p id="d2e25272"><strong class="caption-number">Figure C3</strong>Hourly PM<span class="inline-formula"><sub>2.5</sub></span> simulations in Mexico City for January and July 2015.</p></div><p class="downloads"><a class="triangle journal-contentLinkColor figure-download" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f11.png" target="_blank">Download</a></p></div></div><span class="section7-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="app sec" id="section8"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section8 .co-arrow-open,.section8-content" data-show="#section8 .co-arrow-closed,.section8-mobile-bottom-border"><div id="App1.Ch1.S4" class="h1"><span>Appendix D:</span> Simulation of all models<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section8-content show-no-js hide-on-mobile-soft"><div class="fig" id="App1.Ch1.S4.F12"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f12-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f12" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f12-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f12-thumb.png" data-width="2067" data-height="1398"></a><div class="caption"><p id="d2e25304"><strong class="caption-number">Figure D1</strong>NO<span class="inline-formula"><sub>2</sub></span> simulations of January 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F13"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f13-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f13" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f13-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f13-thumb.png" data-width="2067" data-height="1410"></a><div class="caption"><p id="d2e25326"><strong class="caption-number">Figure D2</strong>NO<span class="inline-formula"><sub>2</sub></span> simulations of July 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F14"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f14-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f14" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f14-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f14-thumb.png" data-width="2067" data-height="1398"></a><div class="caption"><p id="d2e25350"><strong class="caption-number">Figure D3</strong>O<span class="inline-formula"><sub>3</sub></span> simulations of January 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F15"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f15-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f15" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f15-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f15-thumb.png" data-width="2067" data-height="1410"></a><div class="caption"><p id="d2e25372"><strong class="caption-number">Figure D4</strong>O<span class="inline-formula"><sub>3</sub></span> simulations of July 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F16"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f16-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f16" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f16-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f16-thumb.png" data-width="2067" data-height="1387"></a><div class="caption"><p id="d2e25395"><strong class="caption-number">Figure D5</strong>CO simulations of January 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F17"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f17-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f17" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f17-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f17-thumb.png" data-width="2067" data-height="1387"></a><div class="caption"><p id="d2e25408"><strong class="caption-number">Figure D6</strong>CO simulations of July 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F18"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f18-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f18" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f18-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f18-thumb.png" data-width="2067" data-height="1398"></a><div class="caption"><p id="d2e25423"><strong class="caption-number">Figure D7</strong>SO<span class="inline-formula"><sub>2</sub></span> simulations of January 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F19"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f19-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f19" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f19-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f19-thumb.png" data-width="2067" data-height="1410"></a><div class="caption"><p id="d2e25445"><strong class="caption-number">Figure D8</strong>SO<span class="inline-formula"><sub>2</sub></span> simulations of July 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F20"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f20-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f20" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f20-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f20-thumb.png" data-width="2067" data-height="1415"></a><div class="caption"><p id="d2e25468"><strong class="caption-number">Figure D9</strong>PM<span class="inline-formula"><sub>2.5</sub></span> simulations of January 2015 for all models.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S4.F21"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f21-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f21" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f21-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f21-thumb.png" data-width="2067" data-height="1415"></a><div class="caption"><p id="d2e25490"><strong class="caption-number">Figure D10</strong>PM<span class="inline-formula"><sub>2.5</sub></span> simulations of July 2015 for all models.</p></div><p class="downloads"></p></div></div><span class="section8-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="app sec" id="section9"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section9 .co-arrow-open,.section9-content" data-show="#section9 .co-arrow-closed,.section9-mobile-bottom-border"><div id="App1.Ch1.S5" class="h1"><span>Appendix E:</span> Model deviations<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section9-content show-no-js hide-on-mobile-soft"><div class="fig" id="App1.Ch1.S5.F22"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f22-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f22" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f22-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f22-thumb.png" data-width="2067" data-height="1589"></a><div class="caption"><p id="d2e25522"><strong class="caption-number">Figure E1</strong>Median absolute deviation (MDA) of the models with respect to the ensemble for PM<span class="inline-formula"><sub>10</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span>, O<span class="inline-formula"><sub>3</sub></span>, CO, SO<span class="inline-formula"><sub>2</sub></span> and NO<span class="inline-formula"><sub>2</sub></span> in LAC for January and July 2015.</p></div><p class="downloads"></p></div><div class="fig" id="App1.Ch1.S5.F23"><a target="_blank" class="figure-link" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f23-web.png"><img alt="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f23" data-webversion="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f23-web.png" src="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024-f23-thumb.png" data-width="2067" data-height="1606"></a><div class="caption"><p id="d2e25581"><strong class="caption-number">Figure E2</strong>Standard deviation (SD) of the models with respect to their mean for PM<span class="inline-formula"><sub>10</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span>, O<span class="inline-formula"><sub>3</sub></span>, CO, SO<span class="inline-formula"><sub>2</sub></span> and NO<span class="inline-formula"><sub>2</sub></span> in LAC for January and July 2015.</p></div><p class="downloads"></p></div></div><span class="section9-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section10" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section10 .co-arrow-open,.section10-content" data-show="#section10 .co-arrow-closed,.section10-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Code and data availability<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section10-content show-no-js hide-on-mobile-soft"><p id="d2e25641">All model data analyzed in the intercomparison are archived at <span class="uri"><a href="https://doi.org/10.5281/zenodo.10934489" target="_blank">https://doi.org/10.5281/zenodo.10934489</a></span> (<span class="cit" id="xref_altparen.120"><a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx80" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Pachón et al.</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx80" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a></span>). The tool to create the plots, MOSPAT, can additionally be found on GitHub at <span class="uri"><a href="https://github.com/NeoMOSPAT/NeoMOSPAT_PAPILA.git" target="_blank">https://github.com/NeoMOSPAT/NeoMOSPAT_PAPILA.git</a></span> <span class="cit" id="xref_paren.121">(<a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx49" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Huneeus and Opazo</a>, <a href="https://gmd.copernicus.org/articles/17/7467/2024/#bib1.bibx49" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">2024</a>)</span>.</p></div><span class="section10-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section11" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section11 .co-arrow-open,.section11-content" data-show="#section11 .co-arrow-closed,.section11-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Author contributions<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section11-content show-no-js hide-on-mobile-soft"><p id="d2e25659">JEP and MAO performed the formal analysis of the data; PL, NH, IB, JF, LM, CM, MG, MS, RK, JP, AU, AHDP, MEGC and DS performed the model simulations; JEP, MAO, PL, NH and IB prepared the manuscript with contributions from all co-authors; GB and LG provided the financial support for the project to led to this publication; LD, NYR, NH and MdFA coordinated research activities; CWYL provided technical support; all co-authors reviewed and edited the manuscript.</p></div><span class="section11-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section12" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section12 .co-arrow-open,.section12-content" data-show="#section12 .co-arrow-closed,.section12-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Competing interests<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section12-content show-no-js hide-on-mobile-soft"><p id="d2e25667">The contact author has declared that none of the authors has any competing interests.</p></div><span class="section12-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section13" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section13 .co-arrow-open,.section13-content" data-show="#section13 .co-arrow-closed,.section13-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Disclaimer<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section13-content show-no-js hide-on-mobile-soft"><p id="d2e25673">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.</p></div><span class="section13-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="ack sec" id="section14"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section14 .co-arrow-open,.section14-content" data-show="#section14 .co-arrow-closed,.section14-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Acknowledgements<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section14-content show-no-js hide-on-mobile-soft"><p id="d2e25679">The PAPILA (Prediction of Air Pollutants in Latin America) project was funded by the European Commission under the MSCA action for research and innovation staff exchange (grant agreement ID 777544). Support from the Academy of Finland HEATCOST and ACCC flagship projects (grants nos. 334798 and 337552) and the H2020 project AQ-WATCH (grant<span id="page7507"></span> no. 870301) is acknowledged. Support from the German Climate Computer Center (Deutsches Klimarechenzentrum, DKRZ) is acknowledged. Support from the Center for Climate and Resilience Research (FONDAP/ANID 1523A0002) is acknowledged.</p></div><span class="section14-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section15" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section15 .co-arrow-open,.section15-content" data-show="#section15 .co-arrow-closed,.section15-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Financial support<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section15-content show-no-js hide-on-mobile-soft"><p id="d2e25684">This research has been supported by the EU H2020 Marie Skłodowska-Curie Actions (grant no. 777544), Academy of Finland HEATCOST and ACCC flagship projects (grants nos. 334798 and 337552), EU H2020 project AQ-Watch (grant no. 870301), and the Center for Climate and Resilience Research (grant no. FONDAP/ANID 1523A0002).<br><br>The article processing charges for this open-access publication were covered by the Max Planck Society.</p></div><span class="section15-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div id="section16" class="sec"><div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section16 .co-arrow-open,.section16-content" data-show="#section16 .co-arrow-closed,.section16-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>Review statement<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section16-content show-no-js hide-on-mobile-soft"><p id="d2e25695">This paper was edited by Jason Williams and reviewed by two anonymous referees.</p></div><span class="section16-mobile-bottom-border mobile-bottom-border hide-on-desktop hide-on-tablet"></span></div> <div class="ref-list sec" id="section17"> <div class="grid-container no-margin header-element"><span class="grid-100 mobile-grid-100 tablet-grid-100 grid-parent more-less-mobile" data-hide="#section17 .co-arrow-open,.section17-content" data-show="#section17 .co-arrow-closed,.section17-mobile-bottom-border"><div class="h1"><span class="section-number"> </span>References<span class="hide-on-desktop hide-on-tablet triangleWrapper"> <i class="co-arrow-closed"></i><i class="co-arrow-open" style="display:none"></i></span></div></span></div> <div class="section17-content show-no-js hide-on-mobile-soft"><p class="ref" id="bib1.bibx1"><span class="mixed-citation">Álamos, N., Huneeus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Denier van der Gon, H., Schueftan, A., Reyes, R., and Calvo, R.: High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile, Earth Syst. 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class="co_function_get_navigation menu_level1"> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#abstract" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Abstract</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section1" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Introduction</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section2" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Methodology</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section3" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Results</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section4" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Conclusions and future developments</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section5" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix A:</span> Evaluation scores</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section6" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix B:</span> Air quality observations</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section7" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix C:</span> Particular hourly simulations</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section8" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix D:</span> Simulation of all models</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section9" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix E:</span> Model deviations</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section10" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Code and data availability</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section11" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Author contributions</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section12" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Competing interests</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section13" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Disclaimer</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section14" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Acknowledgements</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section15" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Financial support</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section16" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Review statement</a></li> <li class="menuitem_level1 co_function_get_navigation_is_parent co_function_get_navigation_is_closed" id="co_getnavigation_page_about"> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section17" class="link_level1 scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">References</a></li> </ul> </div> </div> <div id="leftColumnExtras" class="CMSCONTAINER w-sidebar col-auto d-none d-lg-block pt-2"> <div class="widget dark-border"> <div class="legend journal-contentLinkColor">Download</div> <div class="content"> <ul class="additional_info no-bullets no-styling"> <li><a class="triangle" title="PDF Version (19817 KB)" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.pdf">Article</a> <nobr>(19817 KB)</nobr> </li> <li> <a class="triangle" title="XML Version" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.xml">Full-text XML</a> </li> </ul> </div> <div class="content"> <ul class="additional_info no-bullets no-styling"> <li><a class="triangle" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.bib">BibTeX</a></li> <li><a class="triangle" href="https://gmd.copernicus.org/articles/17/7467/2024/gmd-17-7467-2024.ris">EndNote</a></li> </ul> </div> </div> <div class="widget dark-border"> <div class="legend journal-contentLinkColor">Executive editor</div> <div class="content hide-js shortSummaryFullOnHighlihgt">This multi-model inter-comparison presents a state-of-the-art assessment of Latin America for the first time which is a region often ignored in air quality studies. </div> <div style="display: none" class="content show-js shortSummaryShortenOnHighlihgt">This multi-model inter-comparison presents a state-of-the-art assessment of Latin America for...</div> <div class="content"> <a href="#" class="more-less show-js triangle" data-hide=".shortSummaryFullOnHighlihgt" data-show=".shortSummaryShortenOnHighlihgt" data-toggleCaption='Hide'>Read more</a> </div> </div> <div class="widget dark-border"> <div class="legend journal-contentLinkColor">Short summary</div> <div class="content hide-js shortSummaryFull">Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.</div> <div style="display: none" class="content show-js shortSummaryShorten">Latin America (LAC) has some of the most populated urban areas in the world, with high levels of...</div> <div class="content"> <a href="#" class="more-less show-js triangle" data-hide=".shortSummaryFull" data-show=".shortSummaryShorten" data-toggleCaption='Hide'>Read more</a> </div> </div> <div class="widget dark-border hide-on-mobile hide-on-tablet p-0" id="share"> <div class="legend journal-contentLinkColor">Share</div> <div class="row p-0"> <div class="col-auto pl-0"> <a class="share-one-line" href="https://www.mendeley.com/import/?url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Mendeley" target="_blank"> <img src="https://www.geoscientific-model-development.net/mendeley.png" alt="Mendeley"/> </a> </div> <div class="col-auto"> <a class="share-one-line" href="https://www.reddit.com/submit?url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Reddit" target="_blank"> <img src="https://www.geoscientific-model-development.net/reddit.png" alt="Reddit"> </a> </div> <div class="col-auto"> <a class="share-one-line last" href="https://twitter.com/intent/tweet?text=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F" title="Twitter" target="_blank"> <img src="https://www.geoscientific-model-development.net/twitter.png" alt="Twitter"/> </a> </div> <div class="col-auto"> <a class="share-one-line" href="https://www.facebook.com/share.php?u=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F&t=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America" title="Facebook" target="_blank"> <img src="https://www.geoscientific-model-development.net/facebook.png" alt="Facebook"/> </a> </div> <div class="col-auto pr-0"> <a class="share-one-line last" href="https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fgmd.copernicus.org%2Farticles%2F17%2F7467%2F2024%2F&title=Air+quality+modeling+intercomparison+and+multiscale+ensemble+chain+for+Latin+America" title="LinkedIn" target="_blank"> <img src="https://www.geoscientific-model-development.net/linkedin.png" alt="LinkedIn"> </a> </div> <div class="col pr-0 mobile-native-share"> <a href="#" data-title="Geoscientific Model Development" data-text="*Air quality modeling intercomparison and multiscale ensemble chain for Latin America* Jorge E. Pachón et al." data-url="https://gmd.copernicus.org/articles/17/7467/2024/" class="mobile-native-share share-one-line last"><i class="co-mobile-share display-none"></i></a> </div> </div> </div> <div class="ajax-content" data-src="https://editor.copernicus.org/similarArticles.php?article=118885&journal=365&isSecondStage=1&ajax=true"> </div> </div> <div class="auto-fixed-top px-1 mb-3 articleNavigation" data-fixet-top-target="#section1"> <button class="btn btn-success mb-3 btn-block" id="mathjax-turn"><i class="fal fa-function"></i> Turn MathJax on</button> <div class="widget dark-border m-0"> <div class="legend journal-contentLinkColor">Sections</div> <div class="content"> <ul class="toc-styling p-0"> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#abstract" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Abstract</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section1" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Introduction</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section2" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Methodology</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section3" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Results</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section4" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Conclusions and future developments</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section5" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix A:</span> Evaluation scores</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section6" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix B:</span> Air quality observations</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section7" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix C:</span> Particular hourly simulations</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section8" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix D:</span> Simulation of all models</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section9" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title"><span>Appendix E:</span> Model deviations</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section10" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Code and data availability</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section11" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Author contributions</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section12" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Competing interests</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section13" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Disclaimer</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section14" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Acknowledgements</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section15" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Financial support</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section16" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">Review statement</a> </li> <li> <a href="https://gmd.copernicus.org/articles/17/7467/2024/#section17" class="scrollto" data-fixed-element=".auto-fixed-top-forced.article-title">References</a> </li> </ul> </div> </div> </div> </div> </div> </div> </main>  <footer class="d-print-none version-2023"> <div class="footer"> <div class="container"> <div class="row align-items-center mb-3"> <div class="col-12 col-lg-auto text-center text-md-left title-wrapper"> <div id="j-header-footer" class="text-center text-md-left"> <div class="h1 text-center text-md-left"> Geoscientific Model Development </div> <p>An interactive open-access journal of the European Geosciences Union</p> </div> </div> <div class="col-12 col-lg-auto text-center text-md-left pt-lg-2"> <div class="row align-items-center"> <div class="col-12 col-sm col-md-auto text-center text-md-left mb-3 mb-sm-0"> <span class="egu-logo"><a href="http://www.egu.eu/" target="_blank"><img src="https://contentmanager.copernicus.org/319373/365/ssl" alt="" style="width: 410px; 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GMD - Air quality modeling intercomparison and multiscale ensemble chain for Latin America